Tag Archives: 2022

HomeMass Analytica –  2022

Analytical and computational workflow for in-depth analysis of oxidized complex lipids in blood plasma

Analytical and computational workflow for in-depth analysis of oxidized complex lipids in blood plasma

November 2022

Angela Criscuolo, Palina Nepachalovich, Diego Fernando Garcia-Del Rio, Mike Lange, Zhixu Ni, Massimo Baroni, Gabriele Cruciani, Laura Goracci, Matthias Blüher , Maria Fedorova

 

Abstract

Lipids are a structurally diverse class of biomolecules which can undergo a variety of chemical modifications. Among them, lipid (per)oxidation attracts most of the attention due to its significance in the regulation of inflammation, cell proliferation and death programs. Despite their apparent regulatory significance, the molecular repertoire of oxidized lipids remains largely elusive as accurate annotation of lipid modifications is complicated by their low abundance and often unknown, biological context-dependent structural diversity. Here, we provide a workflow based on the combination of bioinformatics and LC-MS/MS technologies to support identification and relative quantification of oxidized complex lipids in a modification type- and position-specific manner. The developed methodology is used to identify epilipidomics signatures of lean and obese individuals with and without type 2 diabetes. The characteristic signature of lipid modifications in lean individuals, dominated by the presence of modified octadecanoid acyl chains in phospho- and neutral lipids, is drastically shifted towards lipid peroxidation-driven accumulation of oxidized eicosanoids, suggesting significant alteration of endocrine signalling by oxidized lipids in metabolic disorders.

 

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Retinoic acid-induced 1 gene haploinsufficiency alters lipid metabolism and causes autophagy defects in Smith-Magenis syndrome

Retinoic acid-induced 1 gene haploinsufficiency alters lipid metabolism and causes autophagy defects in Smith-Magenis syndrome

November 2022

Elisa Maria Turco, Angela Maria Giada Giovenale, Laura Sireno, Martina Mazzoni, Alessandra Cammareri, Caterina Marchioretti, Laura Goracci, Alessandra Di Veroli, Elena Marchesan, Daniel D’Andrea, Antonella Falconieri, Barbara Torres, Laura Bernardini, Maria Chiara Magnifico, Alessio Paone, Serena Rinaldo, Matteo Della Monica, Stefano D’Arrigo, Diana Postorivo, Anna Maria Nardone, Giuseppe Zampino, Roberta Onesimo, Chiara Leoni, Federico Caicci, Domenico Raimondo, Elena Binda, Laura Trobiani, Antonella De Jaco, Ada Maria Tata, Daniela Ferrari, Francesca Cutruzzolà, Gianluigi Mazzoccoli, Elena Ziviani, Maria Pennuto, Angelo Luigi Vescovi, Jessica Rosati 

 

Abstract

Smith-Magenis syndrome (SMS) is a neurodevelopmental disorder characterized by cognitive and behavioral symptoms, obesity, and sleep disturbance, and no therapy has been developed to alleviate its symptoms or delay disease onset. SMS occurs due to haploinsufficiency of the retinoic acid-induced-1 (RAI1) gene caused by either chromosomal deletion (SMS-del) or RAI1 missense/nonsense mutation. The molecular mechanisms underlying SMS are unknown. Here, we generated and characterized primary cells derived from four SMS patients (two with SMS-del and two carrying RAI1 point mutations) and four control subjects to investigate the pathogenetic processes underlying SMS. By combining transcriptomic and lipidomic analyses, we found altered expression of lipid and lysosomal genes, deregulation of lipid metabolism, accumulation of lipid droplets, and blocked autophagic flux. We also found that SMS cells exhibited increased cell death associated with the mitochondrial pathology and the production of reactive oxygen species. Treatment with N-acetylcysteine reduced cell death and lipid accumulation, which suggests a causative link between metabolic dyshomeostasis and cell viability. Our results highlight the pathological processes in human SMS cells involving lipid metabolism, autophagy defects and mitochondrial dysfunction and suggest new potential therapeutic targets for patient treatment.

 

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Automated identification of potential pesticides residues in fruit samples using HRMS data

Automated identification of potential pesticides residues in fruit samples using HRMS data

70th ASMS Conference on Mass Spectrometry. June 2022

Elisabeth Ortega1; Ismael Zamora1; Pol Giménez1; Luca Morettoni1; Roberto Romero-gonzalez2; Rosalía Lopez-Ruiz2; Antonia Garrido Frenich2

1Lead Molecular Design, S.L., Sant Cugat del Valles, Spain; 2Research Group ‘Analytical Chemistry of Contaminants’, Department of Chemistry and Physics, Research Centre for Agricultural and Food Biotechnology (BITAL), Agrifood Campus of International Excellence, University of Almeria, Almeria, Spain

Abstract

Introduction

In food safety and related fields, High Resolution Mass Spectrometry techniques applied for multiresidue analysis had become an alternative to the historical routine procedures involving triple quadrupole instruments. This evolution was mainly driven by the possibility to interrogate hundreds or thousands of compounds without a prior individual study of all of them. However, due to the big amount of information that can be generated during the data acquisition, the later data processing and data analysis steps can be quite time demanding. In this presentation we will show how this late step could be automized using Chemical Monitoring workflow included in MassChemSite 3.1.

Methods

For chromatographic analysis, Thermo Fisher Scientific Vanquish Flex Quaternary LC (Thermo Scientific Transcend™, Thermo Fisher Scientific, San Jose, CA, USA) was used. The chromatographic system is coupled to a hybrid mass spectrometer Q-Exactive Orbitrap Thermo Fisher Scientific (ExactiveTM, Thermo Fisher Scientific, Bremen, Germany) using an electrospray interface (ESI) (HESI-II, Thermo Fisher Scientific, San Jose, CA, USA) in positive-negative mode. ESI parameters were as follows: spray voltage, 4 kV; sheath gas (N2, 95%), 35 (adimensional); auxiliary gas (N2, 95%), 10 (adimensional); S-lens RF level, 50 (adimensional); heater temperature, 305 °C; and capillary temperature, 300 °C.

Data processing has been done using MassChemSite 3.1 (Molecular Discovery, Ltd. Borehamwood, UK). Data analysis was performed in ONIRO server (Molecular Discovery, Ltd. Borehamwood, UK).

Preliminary data

Strawberry, white grape and orange samples providing from Almeria (Spain) greenhouses were acquired in the University of Almería and processed using the Chemical Monitoring data workflow included in MassChemSite 3.1. Data was interrogated against an in-house pesticide database generated by literature search including up to 1500 different pesticides. From the total, up to 10 different pesticides were detected in all the samples in less than five minutes of data processing.

The identification step was performed using the MS and MSMS information: MS was used to detect the pesticide in the sample, while fragmentation information was used to finally elucidate the structure of the detected pesticide, by means of a computational fragmentation of the detected pesticide and a later assignation to the MSMS data provided by the instrument. The fitting among computed and experimental fragments is reported as “score” which can be used to discriminate among other structural isobaric compounds associated to the same chromatographic peak.

Data analysis and reporting were done in ONIRO server after an automatic uploading of the raw data. Later filtering steps were applied and tracked by the application for further inspection. Additionally, a final report was generated automatically once the experiment was reviewed. Data generated during the acquisition remained on the server for later use or further re-analysis.

 

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Oligonucleotides: degradation studies

Oligonucleotides: degradation studies

70th ASMS Conference on Mass Spectrometry. June 2022

Ismael Zamora1; Fabien Fontaine1; Tatiana Radchenko1; Bridget Becker2; Robert Greene2

1Lead Molecular Design, S.L., Sant Cugat del Valles, Spain; 2LabCorp Madison WI USA, Madison, WI

 

Abstract

Introduction

The study of the biotransformation products for therapeutic oligonucleotides using LC-MS is challenging issue due to the high molecular weight of this class of compounds. Since these compounds are composed of multiple monomers that can undergo metabolic reactions such as phosphodiester hydrolysis, this makes building the virtual set of all potential metabolites time and computational resource intensive. In addition, due to the high number of cleavable bonds, the fragmentation analysis requires even more time and computing power. Finally, there is a third challenge related to the depiction of the parent and the metabolites as atoms/bonds in a manner suitable for an algorithm to predict where in the molecule the metabolic reaction occurs.

Methods

New algorithms that address the challenges in highly modified therapeutic oligonucleotide structure elucidation have been developed. The peak detection algorithm was improved to use the Most Abundant Isotope for parent and potential metabolites. A new algorithm is producing all virtual metabolites applying the library of chemical reactions to each monomer while maintain the connection to the atoms. A third improvement it is the fragmentation algorithm that has two layers of analysis at monomer levels and the other at the bond levels. Finally, we will also show the results of the implemented algorithm for the analysis results visualization.

Preliminary data

The developed algorithm has been applied to a collection of experimental data for a set of oligonucleotides where the data was collected on a Q-Exactive instrument in negative ionization mode. The oligonucleotides have a molecular weight in the range of 7500 Daltons and contain 20 monomers that were a mixed of natural and non-natural nucleotides. Each construct was incubated in the presence of human or monkey liver homogenate for up to 72 hours to generate the metabolites for this project.

More than 10 biotransformation products were detected for each analyte with high mass accuracy (< 5 ppm). The majority of the metabolites resulted from one metabolic modification, but also compounds resulting from two biotransformations were detected by the new algorithm. Each of these compounds showed a suitable fragmentation pattern that could be compared to the parent by the fragmentation algorithm to assign the shifted and non-shifted fragments. For example, the most abundant metabolite accounted for 11.1 % of the total chromatogram area in one of the incubations, had an 0.16 ppm mass accuracy and had more than 120 fragments that could be used to identify its structure with a MassMetaSite score higher than 3322. The assigned structure corresponds to cleavage of the terminal nucleotide from the parent. The second most abundant metabolite had a mass accuracy of 0.60 ppm, a charge of minus 9, and over 100 assigned fragments yielding a MassMetaSite score of almost 5000.

 

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AI (ARTIFICIAL INTELLIGENCE) applied to enhance the DMPK experimental efficacy

AI (ARTIFICIAL INTELLIGENCE) applied to enhance the DMPK experimental efficacy

70th ASMS Conference on Mass Spectrometry. June 2022

Ismael Zamora; Ramon Adàlia; Tatiana Radchenko

Lead Molecular Design, S.L., Sant Cugat del Valles, Spain

Abstract

Introduction

The goal of this presentation is to show multiple ways to increase DMPK experimental efficacy. An enhancement of efficacy in this context is understood as how to get more knowledge out of the data that is produced. This process is translated on how to decrease the time from data to compound design and/or how to increase the knowledge learned from the data. To achieve this objective automation of data processing, maximizing the extraction of information out of the data and the translation the newly acquired information into practical design is necessary.

Methods

To increase the automation a new methodology will be shown where the input experimental files acquired on a Thermo Q-Exactive instrument for a clearance workflow are processed without human intervention to obtain the clearance and metabolite identification results. The procedure will be exemplified in a collection of 20 PROTACS structures where the Clearance measurement in Hepatocytes has been performed. Clearance end point data computation will be shown together with the automatic fragment assignment for each of the metabolites.

Preliminary data

For example, the fragmentation observed and assigned for a particular compound was used to develop a model to predict the compound fragmentation. In this context several published spectra interpretation from the MoNA database have been used to determine a Machine Learning model to predict the probability of a bond to be broken under certain Mass Spectrometry condition. That publicly available information is used to derive a model that was validated and monitored by using the own experimental data generated in the automatic procedure mentioned before. Considering the result from the comparison between the experimental and the prediction values the new data was incorporated into an automatically learning process to improve model quality. In this way a self-learning system has been developed for the improvement of the virtual fragmentation of a compound.

The above self learning procedure was also applied for other Mass Spectrometry derived property like for example the Cross Collision section (CCS) value to assess the reliability of a structural assignment to a certain peak. In this case a regression model with a regression coefficient between the observed and the calculated CCS values of 0.98 was obtained after developing a model based on published data and enriched with our own dataset.

Finally, an example of how the metabolite identification information extracted from the previously described automation system is used to design compound with a reduction in compound clearance will be shown.

 

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