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Article ; Online: Quantitative LC-MS study of compounds found predictive of COVID-19 severity and outcome.

Roberts, Ivayla / Wright Muelas, Marina / Taylor, Joseph M / Davison, Andrew S / Winder, Catherine L / Goodacre, Royston / Kell, Douglas B

Metabolomics : Official journal of the Metabolomic Society

2023 Volume 19, Issue 11, Page(s) 87

Abstract: Introduction: Since the beginning of the SARS-CoV-2 pandemic in December 2019 multiple metabolomics studies have proposed predictive biomarkers of infection severity and outcome. Whilst some trends have emerged, the findings remain intangible and ... ...

Abstract	Introduction: Since the beginning of the SARS-CoV-2 pandemic in December 2019 multiple metabolomics studies have proposed predictive biomarkers of infection severity and outcome. Whilst some trends have emerged, the findings remain intangible and uninformative when it comes to new patients. Objectives: In this study, we accurately quantitate a subset of compounds in patient serum that were found predictive of severity and outcome. Methods: A targeted LC-MS method was used in 46 control and 95 acute COVID-19 patient samples to quantitate the selected metabolites. These compounds included tryptophan and its degradation products kynurenine and kynurenic acid (reflective of immune response), butyrylcarnitine and its isomer (reflective of energy metabolism) and finally 3',4'-didehydro-3'-deoxycytidine, a deoxycytidine analogue, (reflective of host viral defence response). We subsequently examine changes in those markers by disease severity and outcome relative to those of control patients' levels. Results & conclusion: Finally, we demonstrate the added value of the kynurenic acid/tryptophan ratio for severity and outcome prediction and highlight the viral detection potential of ddhC.
MeSH term(s)	Humans ; Tryptophan/metabolism ; Kynurenic Acid ; Chromatography, Liquid/methods ; Tandem Mass Spectrometry/methods ; COVID-19 ; SARS-CoV-2/metabolism ; Metabolomics
Chemical Substances	Tryptophan (8DUH1N11BX) ; Kynurenic Acid (H030S2S85J)
Language	English
Publishing date	2023-10-18
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2250617-2
ISSN	1573-3890 ; 1573-3882
ISSN (online)	1573-3890
ISSN	1573-3882
DOI	10.1007/s11306-023-02048-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra.

Shrivastava, Aditya Divyakant / Swainston, Neil / Samanta, Soumitra / Roberts, Ivayla / Wright Muelas, Marina / Kell, Douglas B

Biomolecules

2021 Volume 11, Issue 12

Abstract: The 'inverse problem' of mass spectrometric molecular identification ('given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came') is largely unsolved, and is especially acute in metabolomics where many small molecules ... ...

Abstract	The 'inverse problem' of mass spectrometric molecular identification ('given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came') is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem ('calculate a small molecule's likely fragmentation and hence at least some of its mass spectrum from its structure alone') is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the 'translation' a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the 'true' molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are 'similar' to the top hit. In addition to using the 'top hits' directly, we can produce a rank order of these by 'round-tripping' candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower, including those in the last CASMI challenge (for which the results are known), getting 49/93 (53%) precisely correct. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. It seems to act as a Las Vegas algorithm, in that it either gives the correct answer or simply states that it cannot find one. The ability to create and to 'learn' millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.
MeSH term(s)	Algorithms ; Deep Learning ; Mass Spectrometry ; Metabolomics/methods ; Molecular Structure ; Small Molecule Libraries/analysis
Chemical Substances	Small Molecule Libraries
Language	English
Publishing date	2021-11-30
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2701262-1
ISSN	2218-273X ; 2218-273X
ISSN (online)	2218-273X
ISSN	2218-273X
DOI	10.3390/biom11121793
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Quantitative LC-MS study of compounds found predictive of COVID-19 severity and outcome

Roberts, Ivayla / Wright Muelas, Marina / Taylor, Joseph M. / Davison, Andrew S. / Winder, Catherine L. / Goodacre, Royston / Kell, Douglas B.

medRxiv

Abstract: INTRODUCTION Since the beginning of the SARS-CoV-2 pandemic in December 2019 multiple metabolomics studies have proposed predictive biomarkers of infection severity and outcome. Whilst some trends have emerged, the findings remain intangible and ... ...

Abstract	INTRODUCTION Since the beginning of the SARS-CoV-2 pandemic in December 2019 multiple metabolomics studies have proposed predictive biomarkers of infection severity and outcome. Whilst some trends have emerged, the findings remain intangible and uninformative when it comes to new patients. OBJECTIVES In this study, we accurately quantitate a subset of compounds in patient serum that were found predictive of severity and outcome. METHODS A targeted LC-MS method was used in 46 control and 95 acute COVID-19 patient samples to quantitate the selected metabolites. These compounds included tryptophan and its degradation products kynurenine and kynurenic acid (reflective of immune response), butyrylcarnitine and its isomer (reflective of energy metabolism) and finally 3,4-didehydro-3-deoxycytidine, a deoxycytidine analogue, (reflective of host viral defence response). We subsequently examine changes in those markers by disease severity and outcome relative to those of control patients levels. RESULTS & CONCLUSION Finally, we demonstrate the added value of the kynurenic acid / tryptophan ratio for severity and outcome prediction and highlight the viral detection potential of ddhC.
Keywords	covid19
Language	English
Publishing date	2023-03-17
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2023.03.17.23287401
Database	COVID19

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Article ; Online: An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines.

Wright Muelas, Marina / Roberts, Ivayla / Mughal, Farah / O'Hagan, Steve / Day, Philip J / Kell, Douglas B

Metabolomics : Official journal of the Metabolomic Society

2020 Volume 16, Issue 10, Page(s) 107

Abstract: Introduction: It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and that drugs cross ... ...

Abstract	Introduction: It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and that drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar. Untargeted metabolomics can provide a method for determining the differential uptake of such metabolites. Objectives: Blood serum contains many thousands of molecules and provides a convenient source of biologically relevant metabolites. Our objective was to detect and identify metabolites present in serum, but to also establish a method capable of measure their uptake and secretion by different cell lines. Methods: We develop an untargeted LC-MS/MS method to detect a broad range of compounds present in human serum. We apply this to the analysis of the time course of the uptake and secretion of metabolites in serum by several human cell lines, by analysing changes in the serum that represents the extracellular phase (the 'exometabolome' or metabolic footprint). Results: Our method measures some 4000-5000 metabolic features in both positive and negative electrospray ionisation modes. We show that the metabolic footprints of different cell lines differ greatly from each other. Conclusion: Our new, 15-min untargeted metabolome method allows for the robust and convenient measurement of differences in the uptake of serum compounds by cell lines following incubation in serum. This will enable future research to study these differences in multiple cell lines that will relate this to transporter expression, thereby advancing our knowledge of transporter substrates, both natural and xenobiotic compounds.
MeSH term(s)	Animals ; Cell Line/metabolism ; Cell Line, Tumor/metabolism ; Cell Membrane/metabolism ; Chromatography, Liquid/methods ; Drug Carriers/metabolism ; Drug Delivery Systems/methods ; Humans ; Mammals/metabolism ; Membrane Proteins/metabolism ; Metabolome ; Metabolomics/methods ; Phospholipids/metabolism ; Plasma/chemistry ; Tandem Mass Spectrometry/methods
Chemical Substances	Drug Carriers ; Membrane Proteins ; Phospholipids
Language	English
Publishing date	2020-10-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2250617-2
ISSN	1573-3890 ; 1573-3882
ISSN (online)	1573-3890
ISSN	1573-3882
DOI	10.1007/s11306-020-01725-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome.

Roberts, Ivayla / Wright Muelas, Marina / Taylor, Joseph M / Davison, Andrew S / Xu, Yun / Grixti, Justine M / Gotts, Nigel / Sorokin, Anatolii / Goodacre, Royston / Kell, Douglas B

Metabolomics : Official journal of the Metabolomic Society

2021 Volume 18, Issue 1, Page(s) 6

Abstract: Introduction: The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to ... ...

Abstract	Introduction: The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model. Objectives: Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient's infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased). Methods: High resolution untargeted UHPLC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model was created. Results: The predictors were selected for their relevant biological function and include deoxycytidine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74-0.91) and 0.76 (CI 0.67-0.86). Conclusion: Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.
MeSH term(s)	Aged ; Biomarkers/blood ; COVID-19/blood ; Chromatography, Liquid/methods ; Female ; Humans ; Male ; Metabolomics/methods ; Middle Aged ; Prognosis ; SARS-CoV-2 ; Severity of Illness Index ; Tandem Mass Spectrometry/methods
Chemical Substances	Biomarkers
Language	English
Publishing date	2021-12-20
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2250617-2
ISSN	1573-3890 ; 1573-3882
ISSN (online)	1573-3890
ISSN	1573-3882
DOI	10.1007/s11306-021-01859-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Challenges in Screening and Recruitment for a Neuroimaging Study in Cognitively Impaired Geriatric Inpatients.

Apostolova, Ivayla / Lange, Catharina / Roberts, Anna / Igel, Hans Joachim / Mäurer, Anja / Liese, Stephanie / Estrella, Melanie / Prasad, Vikas / Stechl, Elisabeth / Lämmler, Gernot / Steinhagen-Thiessen, Elisabeth / Buchert, Ralph

Journal of Alzheimer's disease : JAD

2017 Volume 56, Issue 1, Page(s) 197–204

Abstract: Background: Neuroimaging-based biomarkers have the potential to improve etiological diagnosis of cognitive impairment in elderly inpatients. However, there is a relative lack of studies on neuroimaging-based biomarkers in hospitalized geriatric patients, ...

Abstract	Background: Neuroimaging-based biomarkers have the potential to improve etiological diagnosis of cognitive impairment in elderly inpatients. However, there is a relative lack of studies on neuroimaging-based biomarkers in hospitalized geriatric patients, as the vast majority of neuroimaging studies in dementia have focused on memory clinic outpatients. An important aspect of study planning is a priori estimation of the rate of screen failures. Objective: To report on the rate and causes of screen failures in a prospective study on the utility of neuroimaging (PET, MRI) for the etiological diagnosis of newly manifested cognitive impairment in acutely hospitalized geriatric patients. Methods: Ten acute care geriatrics clinics with 802 beds participated in the study. The potential recruitment rate had been estimated to 5 patients/100 beds/week. Results: Seventeen months of pre-screening resulted in 322 potential participants. 109 of these patients were enrolled, i.e., the screen failure rate was 66%. 58% of the screen failures were due to refusal of participation by the patient, most often due to lack of interest in clarifying the cause of the cognitive impairment or due to reluctance to engage in additional diagnostic procedures associated with physical stress. 42% of pre-screened patients were excluded because of violation of the eligibility criteria. Conclusion: Enrollment for neuroimaging studies presents considerable additional challenges in acutely hospitalized geriatric patients compared to outpatient settings. Low rate of approaching potential candidates by attending geriatricians and a high rate of screen failures have to be anticipated in the study design.
Language	English
Publishing date	2017
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1440127-7
ISSN	1875-8908 ; 1387-2877
ISSN (online)	1875-8908
ISSN	1387-2877
DOI	10.3233/JAD-160797
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Article ; Online: Combination of Structural MRI and FDG-PET of the Brain Improves Diagnostic Accuracy in Newly Manifested Cognitive Impairment in Geriatric Inpatients.

Journal of Alzheimer's disease : JAD

2016 Volume 54, Issue 4, Page(s) 1319–1331

Abstract: Background: The cause of cognitive impairment in acutely hospitalized geriatric patients is often unclear. The diagnostic process is challenging but important in order to treat potentially life-threatening etiologies or identify underlying ... ...

Abstract	Background: The cause of cognitive impairment in acutely hospitalized geriatric patients is often unclear. The diagnostic process is challenging but important in order to treat potentially life-threatening etiologies or identify underlying neurodegenerative disease. Objective: To evaluate the add-on diagnostic value of structural and metabolic neuroimaging in newly manifested cognitive impairment in elderly geriatric inpatients. Methods: Eighty-one inpatients (55 females, 81.6±5.5 y) without history of cognitive complaints prior to hospitalization were recruited in 10 acute geriatrics clinics. Primary inclusion criterion was a clinical hypothesis of Alzheimer's disease (AD), cerebrovascular disease (CVD), or mixed AD+CVD etiology (MD), which remained uncertain after standard diagnostic workup. Additional procedures performed after enrollment included detailed neuropsychological testing and structural MRI and FDG-PET of the brain. An interdisciplinary expert team established the most probable etiologic diagnosis (non-neurodegenerative, AD, CVD, or MD) integrating all available data. Automatic multimodal classification based on Random Undersampling Boosting was used for rater-independent assessment of the complementary contribution of the additional diagnostic procedures to the etiologic diagnosis. Results: Automatic 4-class classification based on all diagnostic routine standard procedures combined reproduced the etiologic expert diagnosis in 31% of the patients (p = 0.100, chance level 25%). Highest accuracy by a single modality was achieved by MRI or FDG-PET (both 45%, p≤0.001). Integration of all modalities resulted in 76% accuracy (p≤0.001). Conclusion: These results indicate substantial improvement of diagnostic accuracy in uncertain de novo cognitive impairment in acutely hospitalized geriatric patients with the integration of structural MRI and brain FDG-PET into the diagnostic process.
Language	English
Publishing date	2016-10-18
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1440127-7
ISSN	1875-8908 ; 1387-2877
ISSN (online)	1875-8908
ISSN	1387-2877
DOI	10.3233/JAD-160380
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Article: Combination of structural MRI and FDG-PET of the brain improves diagnostic accuracy in newly manifested cognitive impairment in geriatric inpatients

Journal of Alzheimer's Disease

2016 Volume 54, Issue 4, Page(s) 1319–1331

Abstract	Background: The cause of cognitive impairment in acutely hospitalized geriatric patients is often unclear. The diagnostic process is challenging but important in order to treat potentially life-threatening etiologies or identify underlying neurodegenerative disease. Objective: To evaluate the add-on diagnostic value of structural and metabolic neuroimaging in newly manifested cognitive impairment in elderly geriatric inpatients. Methods: Eighty-one inpatients (55 females, 81.6 ± 5.5 y) without history of cognitive complaints prior to hospitalization were recruited in 10 acute geriatrics clinics. Primary inclusion criterion was a clinical hypothesis of Alzheimer's disease (AD), cerebrovascular disease (CVD), or mixed AD+CVD etiology (MD), which remained uncertain after standard diagnostic workup. Additional procedures performed after enrollment included detailed neuropsychological testing and structural MRI and FDG-PET of the brain. An interdisciplinary expert team established the most probable etiologic diagnosis (non-neurodegenerative, AD, CVD, or MD) integrating all available data. Automatic multimodal classification based on Random Undersampling Boosting was used for rater-independent assessment of the complementary contribution of the additional diagnostic procedures to the etiologic diagnosis. Results: Automatic 4-class classification based on all diagnostic routine standard procedures combined reproduced the etiologic expert diagnosis in 31% of the patients (p = 0.100, chance level 25%). Highest accuracy by a single modality was achieved by MRI or FDG-PET (both 45%, p <= 0.001). Integration of all modalities resulted in 76% accuracy (p <= 0.001). Conclusion: These results indicate substantial improvement of diagnostic accuracy in uncertain de novo cognitive impairment in acutely hospitalized geriatric patients with the integration of structural MRI and brain FDG-PET into the diagnostic process.
Keywords	Bildgebende Verfahren ; Cognitive Impairment ; Diagnosis ; Diagnostik ; Geriatrics ; Geriatrie ; Kognitive Beeinträchtigung ; Magnetic Resonance Imaging ; Magnetresonanztomographie ; Neuroimaging ; Positron Emission Tomography ; Positronenemissionstomographie
Language	English
Document type	Article
ZDB-ID	1440127-7
ISSN	1875-8908 ; 1387-2877
ISSN (online)	1875-8908
ISSN	1387-2877
Database	PSYNDEX

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