bims-mascan 2022-12-11 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2022‒12‒11
seventeen papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh

Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools.
Shotgun Lipidomics of Rodent Tissues.
Metabolite Extraction from RPE Cells and Retinas Related to Retinitis Pigmentosa.
In-depth analysis of the Sirtuin 5-regulated mouse brain malonylome and succinylome using library-free data-independent acquisitions.
Clinical proteomics towards multiomics in cancer.
High-quality and robust protein quantification in large clinical/pharmaceutical cohorts with IonStar proteomics investigation.
Integrating ion mobility into comprehensive multidimensional metabolomics workflows: critical considerations.
Glutamine metabolism and radiosensitivity: Beyond the Warburg effect.
Problems, principles and progress in computational annotation of NMR metabolomics data.
Fatty acid transport proteins (FATPs) in cancer.
NAD precursors cycle between host tissues and the gut microbiome.
An optimized approach and inflation media for obtaining complimentary mass spectrometry-based omics data from human lung tissue.
Lipid dysregulation associated with progression of silica-induced pulmonary fibrosis.
Improving Ion Mobility Mass Spectrometry of Proteins through Tristate Gating and Optimization of Multiplexing Parameters.
CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics.
pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level.
HIV-1 replication and latency are balanced by mTOR-driven cell metabolism.

Metabolomics. 2022 Dec 05. 18(12): 103

Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools.

Niek F de Jonge, Kevin Mildau, David Meijer, Joris J R Louwen, Christoph Bueschl, Florian Huber, Justin J J van der Hooft.

  BACKGROUND: Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.AIM OF REVIEW: We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools.
KEY SCIENTIFIC CONCEPTS OF REVIEW: This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks.

Keywords:  Benchmarking; Machine learning; Mass fragmentation spectra; Mass spectrometry; Metabolite annotation and identification; Untargeted metabolomics

DOI:  https://doi.org/10.1007/s11306-022-01963-y
J Vis Exp. 2022 11 18.

Shotgun Lipidomics of Rodent Tissues.

Oksana Lavrynenko, Sophie Dijon, Björn Titz, Nikolai V Ivanov.

Lipids play a vital role as essential components of all prokaryotic and eukaryotic cells. Constant technological improvements in mass spectrometry have made lipidomics a powerful analytical tool for monitoring tissue lipidome compositions in homeostatic as well as disease states. This paper presents a step-by-step protocol for a shotgun lipid analysis method that supports the simultaneous detection and quantification of a few hundred molecular lipid species in different tissue and biofluid samples at high throughput. This method leverages automated nano-flow direct injection of a total lipid extract spiked with labeled internal standards without chromatographic separation into a high-resolution mass spectrometry instrument. Starting from sub-microgram amounts of rodent tissue, the MS analysis takes 10 min per sample and covers up to 400 lipids from 14 lipid classes in mouse lung tissue. The method presented here is well suited for studying disease mechanisms and identifying and quantifying biomarkers that indicate early toxicity or beneficial effects within rodent tissues.

DOI: https://doi.org/10.3791/63726
Methods Mol Biol. 2023 ;2560 257-265

Metabolite Extraction from RPE Cells and Retinas Related to Retinitis Pigmentosa.

Xuan Cui, Ya-Ju Chang, Laura A Jenny, Sarah R Levi, Jianhai Du, Stephen H Tsang.

  The application of metabolomics in ophthalmology helps to identify new biomarkers and elucidate disease mechanisms in different eye diseases, as well as aiding in the development of potential treatment options. Extracting metabolites successfully is essential for potential further analysis using mass spectrometry. In this chapter, we describe how to extract metabolites from a variety of sources including (1) cells on a dish, (2) cell culture medium, and (3) tissues in vivo with and without stable isotope tracers. Samples prepared using this protocol are suitable for a range of downstream mass spectrometry analyses and are stable in solvent for weeks at -80 °C.

Keywords:  Gas or liquid chromatography with mass spectrometric detection (GC-MS and LC-MS); Metabolites extraction; Metabolomics; RPE cells; Retina dissection

DOI:  https://doi.org/10.1007/978-1-0716-2651-1_24
Proteomics. 2022 Dec 07. e2100371

In-depth analysis of the Sirtuin 5-regulated mouse brain malonylome and succinylome using library-free data-independent acquisitions.

Joanna Bons, Jacob Rose, Ran Zhang, Jordan B Burton, Christopher Carrico, Eric Verdin, Birgit Schilling.

  Post-translational modifications (PTMs) dynamically regulate proteins and biological pathways, typically through the combined effects of multiple PTMs. Lysine residues are targeted for various PTMs, including malonylation and succinylation. However, PTMs offer specific challenges to mass spectrometry-based proteomics during data acquisition and processing. Thus, novel and innovative workflows using data-independent acquisition (DIA) ensure confident PTM identification, precise site localization, and accurate and robust label-free quantification. In this study, we present a powerful approach that combines antibody-based enrichment with comprehensive DIA acquisitions and spectral library-free data processing using directDIA (Spectronaut). Identical DIA data can be used to generate spectral libraries and comprehensively identify and quantify PTMs, reducing the amount of enriched sample and acquisition time needed, while offering a fully automated workflow. We analyzed brains from wild-type and Sirtuin 5 (SIRT5)-knock-out mice, and discovered and quantified 466 malonylated and 2,211 succinylated peptides. SIRT5 regulation remodeled the acylomes by targeting 164 malonylated and 578 succinylated sites. Affected pathways included carbohydrate and lipid metabolisms, synaptic vesicle cycle, and neurodegenerative diseases. We found 48 common SIRT5-regulated malonylation and succinylation sites, suggesting potential PTM crosstalk. This innovative and efficient workflow offers deeper insights into the mouse brain lysine malonylome and succinylome. This article is protected by copyright. All rights reserved.

Keywords:  Acylome; Brain; Data-independent acquisition; Post-translational modifications; Sirtuin 5

DOI:  https://doi.org/10.1002/pmic.202100371
Mass Spectrom Rev. 2022 Dec 10. e21827

Clinical proteomics towards multiomics in cancer.

Chad J Creighton.

  Recent technological advancements in mass spectrometry (MS)-based proteomics technologies have accelerated its application to study greater and greater numbers of human tumor specimens. Over the last several years, the Clinical Proteomic Tumor Analysis Consortium, the International Cancer Proteogenome Consortium, and others have generated MS-based proteomic profiling data combined with corresponding multiomics data on thousands of human tumors to date. Proteomic data sets in the public domain can be re-examined by other researchers with different questions in mind from what the original studies explored. In this review, we examine the increasing role of proteomics in studying cancer, along with the potential for previous studies and their associated data sets to contribute to improving the diagnosis and treatment of cancer in the clinical setting. We also explore publicly available proteomics and multi-omics data from cancer cell line models to show how such data may aid in identifying therapeutic strategies for cancer subsets.

Keywords:  cancer; multiomics; proteogenomics; proteomics

DOI:  https://doi.org/10.1002/mas.21827
Nat Protoc. 2022 Dec 09.

High-quality and robust protein quantification in large clinical/pharmaceutical cohorts with IonStar proteomics investigation.

Shichen Shen, Xue Wang, Xiaoyu Zhu, Sailee Rasam, Min Ma, Shihan Huo, Shuo Qian, Ming Zhang, Miao Qu, Chenqi Hu, Liang Jin, Yu Tian, Sanjay Sethi, David Poulsen, Jianmin Wang, Chengjian Tu, Jun Qu.

Robust, reliable quantification of large sample cohorts is often essential for meaningful clinical or pharmaceutical proteomics investigations, but it is technically challenging. When analyzing very large numbers of samples, isotope labeling approaches may suffer from substantial batch effects, and even with label-free methods, it becomes evident that low-abundance proteins are not reliably measured owing to unsufficient reproducibility for quantification. The MS1-based quantitative proteomics pipeline IonStar was designed to address these challenges. IonStar is a label-free approach that takes advantage of the high sensitivity/selectivity attainable by ultrahigh-resolution (UHR)-MS1 acquisition (e.g., 120-240k full width at half maximum at m/z = 200) which is now widely available on ultrahigh-field Orbitrap instruments. By selectively and accurately procuring quantitative features of peptides within precisely defined, very narrow m/z windows corresponding to the UHR-MS1 resolution, the method minimizes co-eluted interferences and substantially enhances signal-to-noise ratio of low-abundance species by decreasing noise level. This feature results in high sensitivity, selectivity, accuracy and precision for quantification of low-abundance proteins, as well as fewer missing data and fewer false positives. This protocol also emphasizes the importance of well-controlled, robust experimental procedures to achieve high-quality quantification across a large cohort. It includes a surfactant cocktail-aided sample preparation procedure that achieves high/reproducible protein/peptide recoveries among many samples, and a trapping nano-liquid chromatography-mass spectrometry strategy for sensitive and reproducible acquisition of UHR-MS1 peptide signal robustly across a large cohort. Data processing and quality evaluation are illustrated using an example dataset ( http://proteomecentral.proteomexchange.org ), and example results from pharmaceutical project and one clinical project (patients with acute respiratory distress syndrome) are shown. The complete IonStar pipeline takes ~1-2 weeks for a sample cohort containing ~50-100 samples.

DOI: https://doi.org/10.1038/s41596-022-00780-w
Metabolomics. 2022 Dec 06. 18(12): 104

Integrating ion mobility into comprehensive multidimensional metabolomics workflows: critical considerations.

Jody C May, John A McLean.

  BACKGROUND: Ion mobility (IM) separation capabilities are now widely available to researchers through several commercial vendors and are now being adopted into many metabolomics workflows. The added peak capacity that ion mobility offers with minimal compromise to other analytical figures-of-merit has provided real benefits to sensitivity and structural selectivity and have allowed more specific metabolite annotations to be assigned in untargeted workflows. One of the greatest promises of contemporary IM-enabled instrumentation is the capability of operating multiple analytical dimensions inline with minimal sample volumes, which has the potential to address many grand challenges currently faced in the omics fields. However, comprehensive operation of multidimensional mass spectrometry comes with its own inherent challenges that, beyond operational complexity, may not be immediately obvious to practitioners of these techniques.AIM OF REVIEW: In this review, we outline the strengths and considerations for incorporating IM analysis in metabolomics workflows and provide a critical but forward-looking perspective on the contemporary challenges and prospects associated with interpreting IM data into chemical knowledge.
KEY SCIENTIFIC CONCEPTS OF REVIEW: We outline a strategy for unifying IM-derived collision cross section (CCS) measurements obtained from different IM techniques and discuss the emerging field of high resolution ion mobility (HRIM) that is poised to address many of the contemporary challenges associated with ion mobility metabolomics. Whereas the LC step limits the throughput of comprehensive LC-IM-MS, the higher peak capacity of HRIM can allow fast LC gradients or rapid sample cleanup via solid-phase extraction (SPE) to be utilized, significantly improving the sample throughput.

Keywords:  4-dimensional separations; Collision cross section alignment and unification; Compound identifications; Metabolite stereoisomers and charge isomers; RT-CCS-m/z triplet features

DOI:  https://doi.org/10.1007/s11306-022-01961-0
Front Oncol. 2022 ;12 1070514

Glutamine metabolism and radiosensitivity: Beyond the Warburg effect.

Ryan S Alden, Mohammad Zahid Kamran, Bassel A Bashjawish, Brittany A Simone.

  Mounting data suggest that cancer cell metabolism can be utilized therapeutically to halt cell proliferation, metastasis and disease progression. Radiation therapy is a critical component of cancer treatment in curative and palliative settings. The use of metabolism-based therapeutics has become increasingly popular in combination with radiotherapy to overcome radioresistance. Over the past year, a focus on glutamine metabolism in the setting of cancer therapy has emerged. In this mini-review, we discuss several important ways (DNA damage repair, oxidative stress, epigenetic modification and immune modulation) glutamine metabolism drives cancer growth and progression, and present data that inhibition of glutamine utilization can lead to radiosensitization in preclinical models. Future research is needed in the clinical realm to determine whether glutamine antagonism is a feasible synergistic therapy that can be combined with radiotherapy.

Keywords:  cancer; glutamine (Gln); immunotherapy; metabolism; radiation; radiosensitivity; sirpiglenastat; telaglenastat

DOI:  https://doi.org/10.3389/fonc.2022.1070514
Metabolomics. 2022 Dec 05. 18(12): 102

Problems, principles and progress in computational annotation of NMR metabolomics data.

Michael T Judge, Timothy M D Ebbels.

  BACKGROUND: Compound identification remains a critical bottleneck in the process of exploiting Nuclear Magnetic Resonance (NMR) metabolomics data, especially for 1H 1-dimensional (1H 1D) data. As databases of reference compound spectra have grown, workflows have evolved to rely heavily on their search functions to facilitate this process by generating lists of potential metabolites found in complex mixture data, facilitating annotation and identification. However, approaches for validating and communicating annotations are most often guided by expert knowledge, and therefore are highly variable despite repeated efforts to align practices and define community standards.AIM OF REVIEW: This review is aimed at broadening the application of automated annotation tools by discussing the key ideas of spectral matching and beginning to describe a set of terms to classify this information, thus advancing standards for communicating annotation confidence. Additionally, we hope that this review will facilitate the growing collaboration between chemical data scientists, software developers and the NMR metabolomics community aiding development of long-term software solutions.
KEY SCIENTIFIC CONCEPTS OF REVIEW: We begin with a brief discussion of the typical untargeted NMR identification workflow. We differentiate between annotation (hypothesis generation, filtering), and identification (hypothesis testing, verification), and note the utility of different NMR data features for annotation. We then touch on three parts of annotation: (1) generation of queries, (2) matching queries to reference data, and (3) scoring and confidence estimation of potential matches for verification. In doing so, we highlight existing approaches to automated and semi-automated annotation from the perspective of the structural information they utilize, as well as how this information can be represented computationally.

Keywords:  Computational annotation; Feature; Metabolite identification; NMR metabolomics; Reference database matching; Spectral comparison

DOI:  https://doi.org/10.1007/s11306-022-01962-z
Chem Phys Lipids. 2022 Nov 30. pii: S0009-3084(22)00097-4. [Epub ahead of print]250 105269

Fatty acid transport proteins (FATPs) in cancer.

Ranjitha Acharya, Shilpa S Shetty, Suchetha Kumari N.

  Lipids play pivotal roles in cancer biology. Lipids have a wide range of biological roles, especially in cell membrane synthesis, serve as energetic molecules in regulating energy-demanding processes; and they play a significant role as signalling molecules and modulators of numerous cellular functions. Lipids may participate in the development of cancer through the fatty acid signalling pathway. Lipids consumed in the diet act as a key source of extracellular pools of fatty acids transported into the cellular system. Increased availability of lipids to cancer cells is due to increased uptake of fatty acids from adipose tissues. Lipids serve as a source of energy for rapidly dividing cancerous cells. Surviving requires the swift synthesis of biomass and membrane matrix to perform exclusive functions such as cell proliferation, growth, invasion, and angiogenesis. FATPs (fatty acid transport proteins) are a group of proteins involved in fatty acid uptake, mainly localized within cells and the cellular membrane, and have a key role in long-chain fatty acid transport. FATPs are composed of six isoforms that are tissue-specific and encoded by a specific gene. Previous studies have reported that FATPs can alter fatty acid metabolism, cell growth, and cell proliferation and are involved in the development of various cancers. They have shown increased expression in most cancers, such as melanoma, breast cancer, prostate cancer, renal cell carcinoma, hepatocellular carcinoma, bladder cancer, and lung cancer. This review introduces a variety of FATP isoforms and summarises their functions and their possible roles in the development of cancer.

Keywords:  Cancer; Fatty acid; Fatty acid transport proteins; Long-chain fatty acid; Solute carrier family 27 members (SLC27A)

DOI:  https://doi.org/10.1016/j.chemphyslip.2022.105269
Cell Metab. 2022 Dec 06. pii: S1550-4131(22)00495-8. [Epub ahead of print]34(12): 1947-1959.e5

NAD precursors cycle between host tissues and the gut microbiome.

Karthikeyani Chellappa, Melanie R McReynolds, Wenyun Lu, Xianfeng Zeng, Mikhail Makarov, Faisal Hayat, Sarmistha Mukherjee, Yashaswini R Bhat, Siddharth R Lingala, Rafaella T Shima, Hélène C Descamps, Timothy Cox, Lixin Ji, Connor Jankowski, Qingwei Chu, Shawn M Davidson, Christoph A Thaiss, Marie E Migaud, Joshua D Rabinowitz, Joseph A Baur.

  Nicotinamide adenine dinucleotide (NAD) is an essential redox cofactor in mammals and microbes. Here we use isotope tracing to investigate the precursors supporting NAD synthesis in the gut microbiome of mice. We find that dietary NAD precursors are absorbed in the proximal part of the gastrointestinal tract and not available to microbes in the distal gut. Instead, circulating host nicotinamide enters the gut lumen and supports microbial NAD synthesis. The microbiome converts host-derived nicotinamide into nicotinic acid, which is used for NAD synthesis in host tissues and maintains circulating nicotinic acid levels even in the absence of dietary consumption. Moreover, the main route from oral nicotinamide riboside, a widely used nutraceutical, to host NAD is via conversion into nicotinic acid by the gut microbiome. Thus, we establish the capacity for circulating host micronutrients to feed the gut microbiome, and in turn be transformed in a manner that enhances host metabolic flexibility.

Keywords:  NAD; flux; gastrointestinal; microbe; microbiome; mononucleotide; niacin; nicotinamide; nicotinic acid; riboside

DOI:  https://doi.org/10.1016/j.cmet.2022.11.004
Front Mol Biosci. 2022 ;9 1022775

An optimized approach and inflation media for obtaining complimentary mass spectrometry-based omics data from human lung tissue.

Jessica K Lukowski, Heather Olson, Marija Velickovic, Juan Wang, Jennifer E Kyle, Young-Mo Kim, Sarah M Williams, Ying Zhu, Heidi L Huyck, Matthew D McGraw, Cory Poole, Lisa Rogers, Ravi Misra, Theodore Alexandrov, Charles Ansong, Gloria S Pryhuber, Geremy Clair, Joshua N Adkins, James P Carson, Christopher R Anderton.

  Human disease states are biomolecularly multifaceted and can span across phenotypic states, therefore it is important to understand diseases on all levels, across cell types, and within and across microanatomical tissue compartments. To obtain an accurate and representative view of the molecular landscape within human lungs, this fragile tissue must be inflated and embedded to maintain spatial fidelity of the location of molecules and minimize molecular degradation for molecular imaging experiments. Here, we evaluated agarose inflation and carboxymethyl cellulose embedding media and determined effective tissue preparation protocols for performing bulk and spatial mass spectrometry-based omics measurements. Mass spectrometry imaging methods were optimized to boost the number of annotatable molecules in agarose inflated lung samples. This optimized protocol permitted the observation of unique lipid distributions within several airway regions in the lung tissue block. Laser capture microdissection of these airway regions followed by high-resolution proteomic analysis allowed us to begin linking the lipidome with the proteome in a spatially resolved manner, where we observed proteins with high abundance specifically localized to the airway regions. We also compared our mass spectrometry results to lung tissue samples preserved using two other inflation/embedding media, but we identified several pitfalls with the sample preparation steps using this preservation method. Overall, we demonstrated the versatility of the inflation method, and we can start to reveal how the metabolome, lipidome, and proteome are connected spatially in human lungs and across disease states through a variety of different experiments.

Keywords:  MALDI; laser capture microdissection; lipids; mass spectrometry imaging; metabolites; molecular pulmonology; proteins

DOI:  https://doi.org/10.3389/fmolb.2022.1022775
Toxicol Sci. 2022 Dec 07. pii: kfac124. [Epub ahead of print]

Lipid dysregulation associated with progression of silica-induced pulmonary fibrosis.

Ruimin Ma, Yali Fan, Xiaoxi Huang, Jingwei Wang, Shuang Li, Yuanying Wang, Qiao Ye.

  Silicosis is an irreversible, progressive, fibrotic lung disease caused by long-term exposure to dust-containing silica particles at the workplace. Despite the precautions enforced, the rising incidence of silicosis continues to occur globally, particularly in developing countries. A better understanding of the disease progression and potential metabolic reprogramming of silicosis is warranted. The low or high-dose silica-induced pulmonary fibrosis in mice was constructed to mimic chronic or accelerated silicosis. Silica-induced mice lung fibrosis was analyzed by histology, lung function, and CT scans. Non-targeted metabolomics of the lung tissues was conducted by ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) to show the temporal metabolic trajectory. The low-dose silica-induced silicosis characterized inflammation for up to 42 days, with the onset of cellular silicon nodules. Conversely, the high-dose silica-induced silicosis characterized inflammation for up to 14 days, after which the disease developed rapidly, with a large volume of collagen deposition, presenting progressive massive fibrosis. Both low and high silica-induced fibrosis had aberrant lipid metabolism. Combined with the RNA-Seq data, this multi-omics study demonstrated alterations in the enzymes involved in sphingolipid metabolism. Time-dependent metabolic reprogramming revealing abnormal glycerophospholipid metabolism was intimately associated with the process of inflammation, whereas sphingolipid metabolism was crucial during lung fibrosis. These findings suggest that lipid dysregulation, especially sphingolipid metabolism, was involved in the process of silicosis.

Keywords:  accelerated silicosis; chronic silicosis; metabolomics; silica; sphingolipid metabolism

DOI:  https://doi.org/10.1093/toxsci/kfac124
J Am Soc Mass Spectrom. 2022 Dec 05.

Improving Ion Mobility Mass Spectrometry of Proteins through Tristate Gating and Optimization of Multiplexing Parameters.

Jamie P Butalewicz, James D Sanders, Brian H Clowers, Jennifer S Brodbelt.

Coupling drift tube ion mobility (IM) to Fourier transform mass spectrometry (FT-MS) affords the opportunity for gas-phase separation of ions based on size and conformation with high-resolution mass analysis. However, combining IM and FT-MS is challenging because ions exit the drift tube on a much faster time scale than the rate of mass analysis. Fourier transform (FT) and Hadamard transform multiplexing methods have been implemented to overcome the duty-cycle mismatch, offering new avenues for obtaining high-resolution, high-mass-accuracy analysis of mobility-selected ions. The gating methods used to integrate the drift tube with the FT mass analyzer discriminate against the transmission of large, low-mobility ions owing to the well-known gate depletion effect. Tristate gating strategies have been shown to increase ion transmission for drift tube IM-FT-MS systems through implementation of dual ion gating, controlling the quantity and timing of ions through the drift tube to reduce losses of slow-moving ions. Here we present an optimized set of multiplexing parameters for tristate gating ion mobility of several proteins on an Orbitrap mass spectrometer and further report parameters for increased ion transmission and mobility resolution as well as decreased experimental times from 15 min down to 30 s. On average, peak intensities in the arrival time distributions (ATDs) for ubiquitin increased 2.1× on average, while those of myoglobin increased by 1.5× with a resolving power increase on average of 11%.

DOI: https://doi.org/10.1021/jasms.2c00274
Anal Chem. 2022 Dec 06.

CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics.

Markace A Rainey, Chandler A Watson, Carter K Asef, Makayla R Foster, Erin S Baker, Facundo M Fernández.

Metabolite annotation continues to be the widely accepted bottleneck in nontargeted metabolomics workflows. Annotation of metabolites typically relies on a combination of high-resolution mass spectrometry (MS) with parent and tandem measurements, isotope cluster evaluations, and Kendrick mass defect (KMD) analysis. Chromatographic retention time matching with standards is often used at the later stages of the process, which can also be followed by metabolite isolation and structure confirmation utilizing nuclear magnetic resonance (NMR) spectroscopy. The measurement of gas-phase collision cross-section (CCS) values by ion mobility (IM) spectrometry also adds an important dimension to this workflow by generating an additional molecular parameter that can be used for filtering unlikely structures. The millisecond timescale of IM spectrometry allows the rapid measurement of CCS values and allows easy pairing with existing MS workflows. Here, we report on a highly accurate machine learning algorithm (CCSP 2.0) in an open-source Jupyter Notebook format to predict CCS values based on linear support vector regression models. This tool allows customization of the training set to the needs of the user, enabling the production of models for new adducts or previously unexplored molecular classes. CCSP produces predictions with accuracy equal to or greater than existing machine learning approaches such as CCSbase, DeepCCS, and AllCCS, while being better aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Another unique aspect of CCSP 2.0 is its inclusion of a large library of 1613 molecular descriptors via the Mordred Python package, further encoding the fine aspects of isomeric molecular structures. CCS prediction accuracy was tested using CCS values in the McLean CCS Compendium with median relative errors of 1.25, 1.73, and 1.87% for the 170 [M - H]-, 155 [M + H]+, and 138 [M + Na]+ adducts tested. For superclass-matched data sets, CCS predictions via CCSP allowed filtering of 36.1% of incorrect structures while retaining a total of 100% of the correct annotations using a ΔCCS threshold of 2.8% and a mass error of 10 ppm.

DOI: https://doi.org/10.1021/acs.analchem.2c03491
Nat Commun. 2022 Dec 07. 13(1): 7539

pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level.

Siyuan Kong, Pengyun Gong, Wen-Feng Zeng, Biyun Jiang, Xinhang Hou, Yang Zhang, Huanhuan Zhao, Mingqi Liu, Guoquan Yan, Xinwen Zhou, Xihua Qiao, Mengxi Wu, Pengyuan Yang, Chao Liu, Weiqian Cao.

Large-scale intact glycopeptide identification has been advanced by software tools. However, tools for quantitative analysis remain lagging behind, which hinders exploring the differential site-specific glycosylation. Here, we report pGlycoQuant, a generic tool for both primary and tandem mass spectrometry-based intact glycopeptide quantitation. pGlycoQuant advances in glycopeptide matching through applying a deep learning model that reduces missing values by 19-89% compared with Byologic, MSFragger-Glyco, Skyline, and Proteome Discoverer, as well as a Match In Run algorithm for more glycopeptide coverage, greatly expanding the quantitative function of several widely used search engines, including pGlyco 2.0, pGlyco3, Byonic and MSFragger-Glyco. Further application of pGlycoQuant to the N-glycoproteomic study in three different metastatic HCC cell lines quantifies 6435 intact N-glycopeptides and, together with in vitro molecular biology experiments, illustrates site 979-core fucosylation of L1CAM as a potential regulator of HCC metastasis. We expected further applications of the freely available pGlycoQuant in glycoproteomic studies.

DOI: https://doi.org/10.1038/s41467-022-35172-x
Front Cell Infect Microbiol. 2022 ;12 1068436

HIV-1 replication and latency are balanced by mTOR-driven cell metabolism.

Jacqueline M Crater, Douglas F Nixon, Robert L Furler O'Brien.

  Human Immunodeficiency virus type 1 (HIV-1) relies on host cell metabolism for all aspects of viral replication. Efficient HIV-1 entry, reverse transcription, and integration occurs in activated T cells because HIV-1 proteins co-opt host metabolic pathways to fuel the anabolic requirements of virion production. The HIV-1 viral life cycle is especially dependent on mTOR, which drives signaling and metabolic pathways required for viral entry, replication, and latency. As a central regulator of host cell metabolism, mTOR and its downstream effectors help to regulate the expression of enzymes within the glycolytic and pentose phosphate pathways along with other metabolic pathways regulating amino acid uptake, lipid metabolism, and autophagy. In HIV-1 pathogenesis, mTOR, in addition to HIF-1α and Myc signaling pathways, alter host cell metabolism to create an optimal environment for viral replication. Increased glycolysis and pentose phosphate pathway activity are required in the early stages of the viral life cycle, such as providing sufficient dNTPs for reverse transcription. In later stages, fatty acid synthesis is required for creating cholesterol and membrane lipids required for viral budding. Epigenetics of the provirus fueled by metabolism and mTOR signaling likewise controls active and latent infection. Acetyl-CoA and methyl group abundance, supplied by the TCA cycle and amino acid uptake respectively, may regulate latent infection and reactivation. Thus, understanding and exploring new connections between cellular metabolism and HIV-1 pathogenesis may yield new insights into the latent viral reservoirs and fuel novel treatments and cure strategies.

Keywords:  CD4 + T cell; HIV-1; glycolysis; immunometabolism; mTOR

DOI:  https://doi.org/10.3389/fcimb.2022.1068436