Mass spectrometry websites, social media, journal feeds, and other links and items of interest to the mass spectrometry community.
Mass spectrometry : nature.com subject feeds
March 14, 2023
https://www.nature.com/articles/s41467-023-36745-0
ScienceDirect Publication: Journal of Proteomics
Publication date: 30 April 2023
Source: Journal of Proteomics, Volume 278
Author(s): Bismarck Santiago, Welder Baldassini, OtĂĄvio Machado Neto, Luis Artur Chardulo, Rodrigo Torres, Guilherme Pereira, RogĂŠrio Curi, Marcos Roberto Chiaratti, Pedro Padilha, Laura Alessandroni, Mohammed Gagaoua
March 14, 2023
https://www.sciencedirect.com/science/article/pii/S187439192300060X?dgcid=rss_sd_all
Journal of the American Society for Mass Spectrometry: Latest Articles (ACS Publications)
Cameron N. Naylor, Elvin R. Cabrera, and Brian H. Clowers
March 14, 2023
http://dx.doi.org/10.1021/jasms.2c00308
Wiley: Rapid Communications in Mass Spectrometry: Table of Contents
Human breath analysis has been attracting increasing interest in the detection of abused drugs in forensic and clinical applications because of its noninvasive sampling and distinctive molecular information. Mass spectrometry (MS)-based approaches have been proven to be powerful tools for accurately analyzing exhaled abused drugs. The major advantages of MS-based approaches include high sensitivity, high specificity, and versatile couplings with various breath sampling methods.
Recent advances in the methodological development of MS analysis of exhaled abused drugs are discussed. Breath collection and sample pretreatment methods for MS analysis are also introduced.
Recent advances in technical aspects of breath sampling methods are summarized, highlighting active and passive sampling. MS methods for detecting different exhaled abused drugs are reviewed, emphasizing their features, advantages, and limitations. The future trends and challenges in MS-based breath analysis of exhaled abused drugs are also discussed.
The coupling of breath sampling methods with MS approaches has been proven to be a powerful tool for the detection of exhaled abused drugs, offering highly attractive results in forensic investigations. MS-based detection of exhaled abused drugs in exhaled breath is a relatively new field and is still in the early stages of methodological development. New MS technologies promise a substantial benefit for future forensic analysis.
Jianfeng Zhang,
Ying Zhang,
Chunhua Xu,
Zhengxu Huang,
Bin Hu
March 14, 2023
https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/rcm.9503?af=R
Wiley: Rapid Communications in Mass Spectrometry: Table of Contents
Two new CRMs have been prepared providing three steroids certified for stable carbon isotope delta values, δ(13C) â°. These materials have been designed to assist anti-doping laboratories validate their calibration method or employed as calibrant for stable carbon isotope measurements of Boldenone, Boldenone Metabolite 1 and Formestane. These CRMs will allow for accurate and traceable analysis in compliance with WADA Technical Document TD2021IRMS.
Certification was performed using an EA-IRMS primary reference method on the bulk carbon isotope ratios of nominally pure steroid starting materials. EA-IRMS analyses were carried out on a Flash EA Isolink CN coupled via a Conflo IV to a Delta V plus Mass Spectrometer. Confirmation analysis was performed by GC-C-IRMS using a Trace 1310 GC coupled to a Delta V plus Mass Spectrometer via GC Isolink II.
Based on the EA-IRMS analysis the materials were certified with δ(13C) values of -30.38 Ⱐ(Boldenone), -29.71 Ⱐ(Boldenone Metabolite 1) and 30.71 Ⱐ(Formestane). Noting that the assumption of 100% purity in the starting materials has the potential to introduce bias this was investigated by GC-C-IRMS analysis and theoretical modelling based on purity assessment data.
Careful application of this theoretical model was shown to provide reasonable estimates of uncertainty while avoiding the introduction of errors associated with analyte specific fractionation during GC-C-IRMS analysis.
Jeffrey P. Merrick,
FongâHa Liu,
Mark Lewin,
Raluca Iavetz
March 14, 2023
https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/rcm.9502?af=R
Journal of the American Society for Mass Spectrometry: Latest Articles (ACS Publications)
Filip Ottosson, Francesco Russo, Anna Abrahamsson, Nadia MacSween, Julie Courraud, Zaki Krag Nielsen, David M. Hougaard, Arieh S. Cohen, and Madeleine Ernst
March 14, 2023
http://dx.doi.org/10.1021/jasms.2c00358
Mass spectrometry : nature.com subject feeds
March 13, 2023
https://www.nature.com/articles/s41580-023-00595-x
Mass spectrometry : nature.com subject feeds
March 13, 2023
https://www.nature.com/articles/s43587-023-00380-7
Journal of the American Society for Mass Spectrometry: Latest Articles (ACS Publications)
James P. Quinn, Elizabeth C. Ethier, Angelo Novielli, Aygul Malone, Christopher E. Ramirez, Lauren Salloum, Bianca A. Trombetta, Pia KivisaĚkk, Michael Bremang, Stefan Selzer, Marjorie Fournier, Sudeshna Das, Yaoyi Xing, Steven E. Arnold, and Becky C. Carlyle
March 13, 2023
http://dx.doi.org/10.1021/jasms.2c00341
Wiley: PROTEOMICS: Table of Contents
Accurate retention time prediction is important for spectral library-based analysis in data-independent acquisition mass spectrometry-based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state-of-the-art performance in many fields such as natural language processing, computer vision and biology. We assess the performance of the transformer architecture for retention time prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state-of-the-art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field.
This article is protected by copyright. All rights reserved
Thang V Pham,
Vinh V Nguyen,
Duong Vu,
Alex A Henneman,
Robin A Richardson,
Sander R Piersma,
Connie R Jimenez
March 13, 2023
https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.202200041?af=R
bioRxiv Subject Collection: Systems Biology
This feed contains articles for bioRxiv Subject Collection "Systems Biology"
Colorectal cancer exhibits dynamic cellular and genetic heterogeneity during progression from precursor lesions toward malignancy. Leveraging spatial molecular information to construct a phylogeographic map of tumor evolution can reveal individualized growth trajectories with diagnostic and therapeutic potential. Integrative analysis of spatial multi-omic data from 31 colorectal specimens revealed simultaneous microenvironmental and clonal alterations as a function of progression. Copy number variation served to re-stratify microsatellite stable and unstable tumors into chromosomally unstable (CIN+) and hypermutated (HM) classes. Phylogeographical maps classified tumors by their evolutionary dynamics, and clonal regions were placed along a global pseudotemporal progression trajectory. Cell-state discovery from a single-cell cohort revealed recurring epithelial gene signatures and infiltrating immune states in spatial transcriptomics. Charting these states along progression pseudotime, we observed a transition to immune exclusion in CIN+ tumors as characterized by a novel gene expression signature comprised of DDR1, TGFBI, PAK4, and DPEP1. We demonstrated how these genes and their protein products are key regulators of extracellular matrix components, are associated with lower cytotoxic immune infiltration, and show prog- nostic value in external cohorts. Through high-dimensional data integration, this atlas provides insights into co-evolution of tumors and their microenvironments, serving as a resource for stratification and targeted treatment of CRC.
Heiser, C. N., Simmons, A. J., Revetta, F., McKinley, E. T., Ramirez-Solano, M. A., Wang, J., Shao, J., Ayers, G. D., Wang, Y., Glass, S. E., Kaur, H., Rolong, A., Chen, B., Vega, P. N., Drewes, J. L., Saleh, N., Vandekar, S., Jones, A. L., Washington, M. K., Roland, J. T., Sears, C. L., Liu, Q., Shrubsole, M. J., Coffey, R. J., Lau, K. S.
March 13, 2023
http://biorxiv.org/cgi/content/short/2023.03.09.530832v1?rss=1
bioRxiv Subject Collection: Systems Biology
This feed contains articles for bioRxiv Subject Collection "Systems Biology"
The declining capacity of cells to maintain a functional proteome is a major driver of cellular dysfunction and decreased fitness in aging. Here we assess the impact of aging on multiple proteome dimensions, which are reflective of function, across the replicative lifespan of Saccharomyces cerevisiae. We quantified protein abundance, protein turnover, protein thermal stability, and protein phosphorylation in mother yeast cells and their derived progeny at different ages. We find progressive and cumulative proteomic alterations that are reflective of dysregulation of complex assemblies, mitochondrial remodeling, post-translational activation of the AMPK/Snf1 energy sensor in mother cells, and an overall shift from biosynthetic to energy-metabolic processes. Our multidimensional proteomic study systematically corroborates previous findings of asymmetric segregation and daughter cell rejuvenation, and extends these concepts to protein complexes, protein phosphorylation, and activation of signaling pathways. Lastly, profiling age-dependent proteome changes in a caloric restriction model of yeast provided mechanistic insights into longevity, revealing minimal remodeling of energy-metabolic pathways, improved mitochondrial maintenance, ameliorated protein biogenesis, and decreased stress responses. Taken together, our study provides thousands of age-dependent molecular events that can be used to gain a holistic understanding of mechanisms of aging.
Leutert, M., Armstrong, J. O., Ollodart, A. R., Hess, K. N., Muir, M., Rodriguez-Mias, R. A., Kaeberlein, M., Dunham, M. J., Villen, J.
March 13, 2023
http://biorxiv.org/cgi/content/short/2023.03.09.531951v1?rss=1
bioRxiv Subject Collection: Systems Biology
This feed contains articles for bioRxiv Subject Collection "Systems Biology"
The pancreas plays a critical role in maintaining glucose homeostasis through the secretion of hormones from the islets of Langerhans. Glucose-stimulated insulin secretion (GSIS) by the pancreatic beta-cell is the main mechanism for reducing elevated plasma glucose. Here we present a systematic modeling workflow for the development of kinetic pathway models using the Systems Biology Markup Language (SBML). Steps include retrieval of information from databases, curation of experimental and clinical data for model calibration and validation, integration of heterogeneous data including absolute and relative measurements, unit normalization, data normalization, and model annotation. An important factor was the reproducibility and exchangeability of the model, which allowed the use of various existing tools. The workflow was applied to construct the first consensus model of GSIS in the pancreatic beta-cell based on experimental and clinical data from 39 studies spanning 50 years of pancreatic, islet, and beta-cell research in humans, rats, mice, and cell lines. The model consists of detailed glycolysis and equations for insulin secretion coupled to cellular energy state (ATP/ADP ratio). Key findings of our work are that in GSIS there is a glucose-dependent increase in almost all intermediates of glycolysis. This increase in glycolytic metabolites is accompanied by an increase in energy metabolites, especially ATP and NADH. One of the few decreasing metabolites is ADP, which, in combination with the increase in ATP, results in a large increase in ATP/ADP ratios in the beta-cell with increasing glucose. Insulin secretion is dependent on ATP/ADP, resulting in glucose-stimulated insulin secretion. The observed glucose-dependent increase in glycolytic intermediates and the resulting change in ATP/ADP ratios and insulin secretion is a robust phenomenon observed across data sets, experimental systems and species. Model predictions of the glucose-dependent response of glycolytic intermediates and insulin secretion are in good agreement with experimental measurements. Our model predicts that factors affecting ATP consumption, ATP formation, hexokinase, phosphofructokinase, and ATP/ADP-dependent insulin secretion have a major effect on GSIS. In conclusion, we have developed and applied a systematic modeling workflow for pathway models that allowed us to gain insight into key mechanisms in GSIS in the pancreatic beta-cell.
Maheshvare, D., Raha, S., Konig, M., Pal, D.
March 13, 2023
http://biorxiv.org/cgi/content/short/2023.03.10.532028v1?rss=1
bioRxiv Subject Collection: Systems Biology
This feed contains articles for bioRxiv Subject Collection "Systems Biology"
RNA structural switches are key regulators of gene expression in bacteria, yet their characterization in Metazoa remains limited. Here we present SwitchSeeker, a comprehensive computational and experimental approach for systematic identification of functional RNA structural switches. We applied SwitchSeeker to the human transcriptome and identified 245 putative RNA switches. To validate our approach, we characterized a previously unknown RNA switch in the 3 UTR of the RORC transcript. In vivo DMS-MaPseq, coupled with cryogenic electron microscopy, confirmed its existence as two alternative structural conformations. Furthermore, we used genome-scale CRISPR screens to identify trans factors that regulate gene expression through this RNA structural switch. We found that nonsense-mediated mRNA decay acts on this element in a conformation-specific manner. SwitchSeeker provides an unbiased, experimentally-driven method for discovering RNA structural switches that shape the eukaryotic gene expression landscape.
Khoroshkin, M., Asarnow, D., Navickas, A., Winters, A., Yu, J., Zhou, S. K., Zhou, S., Palka, C., Fish, L., Ansel, K. M., Cheng, Y., Gilbert, L. A., Goodarzi, H.
March 13, 2023
http://biorxiv.org/cgi/content/short/2023.03.11.532161v1?rss=1
bioRxiv Subject Collection: Systems Biology
This feed contains articles for bioRxiv Subject Collection "Systems Biology"
Finding the best knockout strategy for coupling biomass growth and production of a target metabolite using a metabolic model is a challenge in biotechnology. In this research, a three-step method named OptEnvelope is developed based on finding minimal active reactions for a target point in the feasible solution space using a mixed-integer linear programming formula. The method initially finds the reduced desirable solution space (envelope) in the product versus biomass plot by removing all inactive reactions, and then, with reinsertion of the deleted reactions, OptEnvelope attempts to reduce the number of knockouts so that the production envelope is preserved. Additionally, OptEnvelope searches for envelopes with higher minimum product yields or fewer knockouts for different target points within the desired solution space. A number of knockouts can be set and evaluated using OptEnvelope in order to determine how it affects the intended envelope. The method was implemented on metabolic models of E. coli and S. cerevisiae to benchmark the capability of these industrial microbes for overproduction of acetate and glycerol under aerobic conditions and succinate and ethanol under anaerobic conditions. The results indicate that E. coli is more appropriate to produce acetate and succinate while S. cerevisiae is a better host for glycerol. The positive effect of deleting some genes responsible for the proposed reactions for knocking out was previously confirmed by reported experimental data. Both organisms are suitable for ethanol production, however, more knockouts for the adaptation of E. coli are required. OptEnvelope is available at https://github.com/lv-csbg/optEnvelope.
Motamedian, E., Berzins, K., Muiznieks, R., Stalidzans, E.
March 13, 2023
http://biorxiv.org/cgi/content/short/2023.03.10.532079v1?rss=1
ScienceDirect Publication: International Journal of Mass Spectrometry
Publication date: June 2023
Source: International Journal of Mass Spectrometry, Volume 488
Author(s): Yanhua Liu, Yuxin Cai, Xueying Bai, Xinyu Zhao, Xianyue Meng, Xin Zhang, Zhaoying Wang, Zhi Zhou, Yanhua Chen, Zhonghua Wang, Zeper Abliz
March 12, 2023
https://www.sciencedirect.com/science/article/pii/S1387380623000295?dgcid=rss_sd_all
Wiley: Rapid Communications in Mass Spectrometry: Table of Contents
Shiga toxin-producing Escherichia coli (STEC) are an on-going threat to public health and agriculture. Our laboratory has developed a rapid method for identification of Shiga toxin (Stx), bacteriophage and host proteins produced from STEC. We demonstrate this technique on two genomically sequenced STEC O145:H28 strains linked to two major outbreaks of foodborne illness occurring in 2007 (Belgium) and 2010 (Arizona).
Our approach is to induce expression of stx, prophage and host genes by antibiotic exposure, chemically reduce samples and identify protein biomarkers from unfractionated samples using MALDI-TOF-TOF, tandem mass spectrometry (MS/MS) and post-source decay (PSD). The protein mass and prominent fragment ions were used to identify protein sequences using top-down proteomic software developed in-house. Prominent fragment ions are the result of polypeptide backbone cleavage (PBC) resulting from the aspartic acid effect fragmentation mechanism.
The B-subunit of Stx and acid-stress proteins HdeA and HdeB were identified in both STEC strains in their intramolecular disulfide bond-intact and reduced states. In addition, two cysteine-containing phage tail proteins were detected and identified from the Arizona strain but only under reducing conditions which suggests that bacteriophage complexes are bound by intermolecular disulfide bonds. An acyl carrier protein (ACP) and a phosphocarrier protein (HPr) were also identified from the Belgium strain. ACP was post-translationally modified with attachment of a phosphopantetheine linker at residue S36. The abundance of ACP (plus linker) was significantly increased upon chemical reduction suggesting the release of fatty acids bound to the ACP + linker at a thioester bond. MS/MS-PSD revealed dissociative loss of the linker from the precursor ion as well as fragment ions with and without the attached linker consistent with its attachment at S36.
This study demonstrates the advantages of chemical reduction in facilitating the detection and top-down identification of protein biomarkers of pathogenic bacteria.
Clifton K. Fagerquist,
Yanlin Shi,
Claire E. Dodd
March 12, 2023
https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/rcm.9505?af=R
bioRxiv Subject Collection: Systems Biology
This feed contains articles for bioRxiv Subject Collection "Systems Biology"
Colorectal cancer (CRC) shows high incidence and mortality, partly due to the tumor microenvironment, which is viewed as an active promoter of disease progression. Macrophages are among the most abundant cells in the tumor microenvironment. These immune cells are generally categorized as M1, with inflammatory and anti-cancer properties, or M2, which promote tumor proliferation and survival. Although the M1/M2 subclassification scheme is strongly influenced by metabolism, the metabolic divergence between the subtypes remains poorly understood. Therefore, we generated a suite of computational models that characterize the M1- and M2-specific metabolic states. Our models show key differences between the M1 and M2 metabolic networks and capabilities. We leverage the models to identify metabolic perturbations that cause the metabolic state of M2 macrophages to more closely resemble M1 cells. Overall, this work increases understanding of macrophage metabolism in CRC and elucidates strategies to promote the metabolic state of anti-tumor macrophages.
Gelbach, P. E., Finley, S. D.
March 12, 2023
http://biorxiv.org/cgi/content/short/2023.03.09.532000v1?rss=1
bioRxiv Subject Collection: Systems Biology
This feed contains articles for bioRxiv Subject Collection "Systems Biology"
A reduced capacity for butyrate production by the early infant gut microbiota is associated with negative health effects, such as inflammation and the development of allergies. Here we develop new hypotheses on the effect of the prebiotic galacto-oligosaccharides (GOS) or 2′-fucosyllactose (2′-FL) on butyrate production by the infant gut microbiota using a multiscale, spatiotemporal mathematical model of the infant gut. The model simulates a community of cross-feeding gut bacteria at metabolic detail. It represents the gut microbiome as a grid of bacterial populations that exchange intermediary metabolites, using 20 different subspecies-specific metabolic networks taken from the AGORA database. The simulations predict that both GOS and 2′-FL promote the growth of Bifidobacterium, whereas butyrate producing bacteria are only consistently abundant in the presence of propane-1,2-diol, a product of 2′-FL metabolism. The results suggest that in absence of prebiotics or in presence of only GOS, bacterial species, including Cutibacterium acnes and Bacteroides vulgatus, outcompete butyrate producers by feeding on intermediary metabolites. In presence of 2′-FL, however, production of propane-1,2-diol specifically supports butyrate producers.
Versluis, D. M., Schoemaker, R., Looijesteijn, E., Geurts, J. M. W., Merks, R. M. H.
March 12, 2023
http://biorxiv.org/cgi/content/short/2023.03.10.532059v1?rss=1
Wiley: PROTEOMICS: Table of Contents
The evolution of omics and computational competency has accelerated discoveries of the underlying biological processes in an unprecedented way. High throughput methodologies, such as flow cytometry, can reveal deeper insights into cell processes, thereby allowing opportunities for scientific discoveries related to health and diseases. However, working with cytometry data often imposes complex computational challenges due to high-dimensionality, large size, and nonlinearity of the data structure. In addition, cytometry data frequently exhibit diverse patterns across biomarkers and suffer from substantial class imbalances which can further complicate the problem. The existing methods of cytometry data analysis either predict cell population or perform feature selection. Through this study, we propose a âwisdom of the crowdâ approach to simultaneously predict rare cell populations and perform feature selection by integrating a pool of modern machine learning (ML) algorithms. Given that our approach integrates superior performing ML models across different normalization techniques based on entropy and rank, our method can detect diverse patterns existing across the model features. Furthermore, the method identifies a dynamic biomarker structure that divides the features into persistently selected, unselected, and fluctuating assemblies indicating the role of each biomarker in rare cell prediction, which can subsequently aid in studies of disease progression.
Sreejata Dutta,
Andrew C. Box,
Yanming Li,
Mihaela E. Sardiu
March 11, 2023
https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.202200290?af=R