91 results found
Correia GDS, Takis PG, Sands CJ, et al., 2022, 1H NMR Signals from urine excreted protein are a source of bias in probabilistic quotient normalization, Analytical Chemistry, ISSN: 0003-2700
Normalization to account for variation in urinary dilution is crucial for interpretation of urine metabolic profiles. Probabilistic quotient normalization (PQN) is used routinely in metabolomics but is sensitive to systematic variation shared across a large proportion of the spectral profile (>50%). Where 1H nuclear magnetic resonance (NMR) spectroscopy is employed, the presence of urinary protein can elevate the spectral baseline and substantially impact the resulting profile. Using 1H NMR profile measurements of spot urine samples collected from hospitalized COVID-19 patients in the ISARIC 4C study, we determined that PQN coefficients are significantly correlated with observed protein levels (r2 = 0.423, p < 2.2 × 10–16). This correlation was significantly reduced (r2 = 0.163, p < 2.2 × 10–16) when using a computational method for suppression of macromolecular signals known as small molecule enhancement spectroscopy (SMolESY) for proteinic baseline removal prior to PQN. These results highlight proteinuria as a common yet overlooked source of bias in 1H NMR metabolic profiling studies which can be effectively mitigated using SMolESY or other macromolecular signal suppression methods before estimation of normalization coefficients.
Inglese P, Huang HX, Wu V, et al., 2022, Mass recalibration for desorption electrospray ionization mass spectrometry imaging using endogenous reference ions, BMC Bioinformatics, Vol: 23, Pages: 1-17, ISSN: 1471-2105
BackgroundMass spectrometry imaging (MSI) data often consist of tens of thousands of mass spectra collected from a sample surface. During the time necessary to perform a single acquisition, it is likely that uncontrollable factors alter the validity of the initial mass calibration of the instrument, resulting in mass errors of magnitude significantly larger than their theoretical values. This phenomenon has a two-fold detrimental effect: (a) it reduces the ability to interpret the results based on the observed signals, (b) it can affect the quality of the observed signal spatial distributions.ResultsWe present a post-acquisition computational method capable of reducing the observed mass drift by up to 60 ppm in biological samples, exploiting the presence of typical molecules with a known mass-to-charge ratio. The procedure, tested on time-of-flight and Orbitrap mass spectrometry analyzers interfaced to a desorption electrospray ionization (DESI) source, improves the molecular annotation quality and the spatial distributions of the detected ions.ConclusionThe presented method represents a robust and accurate tool for performing post-acquisition mass recalibration of DESI-MSI datasets and can help to increase the reliability of the molecular assignment and the data quality.
Climaco Pinto R, Karaman I, Lewis MR, et al., 2022, Finding correspondence between metabolomic features in untargeted liquid chromatography-mass spectrometry metabolomics datasets., Analytical Chemistry, Vol: 94, Pages: 5493-5503, ISSN: 0003-2700
Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.
Lippa KA, Aristizabal-Henao JJ, Beger RD, et al., 2022, Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC), METABOLOMICS, Vol: 18, ISSN: 1573-3882
Sliz E, Shin J, Ahmad S, et al., 2022, Circulating Metabolome and White Matter Hyperintensities in Women and Men, CIRCULATION, Vol: 145, Pages: 1040-1052, ISSN: 0009-7322
Närhi F, Moonesinghe SR, Shenkin SD, et al., 2022, Implementation of corticosteroids in treatment of COVID-19 in the ISARIC WHO Clinical Characterisation Protocol UK: prospective, cohort study., The Lancet Digital Health, Vol: 4, Pages: e220-e234, ISSN: 2589-7500
BACKGROUND: Dexamethasone was the first intervention proven to reduce mortality in patients with COVID-19 being treated in hospital. We aimed to evaluate the adoption of corticosteroids in the treatment of COVID-19 in the UK after the RECOVERY trial publication on June 16, 2020, and to identify discrepancies in care. METHODS: We did an audit of clinical implementation of corticosteroids in a prospective, observational, cohort study in 237 UK acute care hospitals between March 16, 2020, and April 14, 2021, restricted to patients aged 18 years or older with proven or high likelihood of COVID-19, who received supplementary oxygen. The primary outcome was administration of dexamethasone, prednisolone, hydrocortisone, or methylprednisolone. This study is registered with ISRCTN, ISRCTN66726260. FINDINGS: Between June 17, 2020, and April 14, 2021, 47 795 (75·2%) of 63 525 of patients on supplementary oxygen received corticosteroids, higher among patients requiring critical care than in those who received ward care (11 185 [86·6%] of 12 909 vs 36 415 [72·4%] of 50 278). Patients 50 years or older were significantly less likely to receive corticosteroids than those younger than 50 years (adjusted odds ratio 0·79 [95% CI 0·70-0·89], p=0·0001, for 70-79 years; 0·52 [0·46-0·58], p<0·0001, for >80 years), independent of patient demographics and illness severity. 84 (54·2%) of 155 pregnant women received corticosteroids. Rates of corticosteroid administration increased from 27·5% in the week before June 16, 2020, to 75-80% in January, 2021. INTERPRETATION: Implementation of corticosteroids into clinical practice in the UK for patients with COVID-19 has been successful, but not universal. Patients older than 70 years, independent of illness severity, chronic neurological disease, and dementia, were less likely to receive corticosteroids than those who were younger, as were pregnant wom
Gadgil MD, Sarkar M, Sands C, et al., 2022, Associations of NAFLD with circulating ceramides and impaired glycemia., Diabetes Res Clin Pract, Vol: 186
AIM: Determine the association of circulating ceramides with NAFLD and glycemic impairment. METHODS: Sample: 669 participants in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) cohort aged 40-84 years without cardiovascular disease, cirrhosis, or significant alcohol intake. CLINICAL MEASURES: Computed tomography scans at baseline for hepatic attenuation. Fasting serum specimens at baseline and after 5 years. Lipidomics: LC-MS-based analysis of 19 known ceramide signals. STATISTICAL ANALYSIS: Linear and logistic regression models of log-transformed ceramides, hepatic attenuation and glucose adjusted for age, sex, calories, study site, BMI, exercise, diet quality, alcohol, saturated fat, lipid-lowering medications and fasting glucose. RESULTS: Average age was 55 years, 44% were women, mean BMI was 25.9 kg/m2, and 8% had NAFLD. In adjusted models, Cer(d16:1/20:0) and Cer(d18:1/18:0) were associated with lower mean hepatic attenuation (increased liver fat) (β -4.29; 95% CI [-5.98, -2.59]) and (β -3.40; 95% CI [-5.11, -1.70]), and LacCer(d18:1/16:0) with higher attenuation (β 4.44; 95% CI [2.15, 6.73]). All three ceramides partially mediated the relationship between hepatic attenuation and fasting glucose by 16%, 11% and 5%, respectively, after 5-years. CONCLUSIONS: Three circulating ceramides were strongly associated with NAFLD and fasting glucose after 5 years, and partially mediated this association.
Mehta R, Chekmeneva E, Jackson H, et al., 2022, Antiviral metabolite 3’-Deoxy-3’,4’-didehydro-cytidine is detectable in serum and identifies acute viral infections including COVID-19, Med, Vol: 3, Pages: 204-215.e6, ISSN: 2666-6340
Background:There is a critical need for rapid viral infection diagnostics to enable prompt case identification in pandemic settings and support targeted antimicrobial prescribing.Methods:Using untargeted high-resolution liquid chromatography coupled with mass spectrometry, we compared the admission serum metabolome of emergency department patients with viral infections including COVID-19, bacterial infections, inflammatory conditions, and healthy controls. Sera from an independent cohort of emergency department patients admitted with viral or bacterial infections underwent profiling to validate findings. Associations between whole-blood gene expression and the identified metabolite of interest were examined.Findings:3'-Deoxy-3',4'-didehydro-cytidine (ddhC), a free base of the only known human antiviral small molecule ddhC-triphosphate (ddhCTP), was detected for the first time in serum. When comparing 60 viral to 101 non-viral cases in the discovery cohort, ddhC was the most differentially abundant metabolite, generating an area under the receiver operating characteristic curve (AUC) of 0.954 (95% CI: 0.923-0.986). In the validation cohort, ddhC was again the most significantly differentially abundant metabolite when comparing 40 viral to 40 bacterial cases, generating an AUC of 0.81 (95% CI 0.708-0.915). Transcripts of viperin and CMPK2, enzymes responsible for ddhCTP synthesis, were amongst the five genes most highly correlated to ddhC abundance.Conclusions:The antiviral precursor molecule ddhC is detectable in serum and an accurate marker for acute viral infection. Interferon-inducible genes viperin and CMPK2 are implicated in ddhC production in vivo. These findings highlight a future diagnostic role for ddhC in viral diagnosis, pandemic preparedness, and acute infection management.
Gritti F, David M, Brothy P, et al., 2022, Model of retention time and density of gradient peak capacity for improved LC-MS method optimization: Application to metabolomics, ANALYTICA CHIMICA ACTA, Vol: 1197, ISSN: 0003-2670
Graca G, Cai Y, Lau C-H, et al., 2022, Automated annotation of untargeted all-ion fragmentation LC-MS metabolomics data with MetaboAnnotatoR, Analytical Chemistry, Vol: 94, Pages: 3446-3455, ISSN: 0003-2700
Untargeted metabolomics and lipidomics LC-MS experiments produce complex datasets, usually containing tens of thousands of features from thousands of metabolites whose annotation requires additional MS/MS experiments and expert knowledge. All-ion fragmentation (AIF) LC-MS/MS acquisition provides fragmentation data at no additional experimental time cost. However, analysis of such datasets requires reconstruction of parent fragment relationships and annotation of the resulting pseudo-MS/MS spectra. Here we propose a novel approach for automated annotation of isotopologues, adducts and in-source fragments from AIF LC-MS datasets by combining correlation-based parent-fragment linking with molecular fragment matching. Our workflow focuses on a subset of features rather than trying to annotate the full dataset, saving time and simplifying the process. We demonstrate the workflow in three human serum datasets containing 599 features manually annotated by experts. Precision and recall values of 82- 92% and 82-85% respectively, were obtained for features found in the highest-rank scores (1-5). These results equal or outperform those obtained using MS-DIAL software, the current state-of-the-art for AIF data annotation. Further validation for other biological matrices and different instrument types showed variable precision (60-89%) and recall (10-88%) particularly for datasets dominated by non-lipid metabolites. The workflow is freely available as an open-source R package, MetaboAnnotatoR, together with the fragment libraries from Github (https://github.com/gggraca/MetaboAnnotatoR).
Mehta R, Chekmeneva E, Jackson H, et al., 2022, Antiviral metabolite 3'-deoxy-3',4'-didehydro-cytidine is detectable in serum and identifies acute viral infections including COVID-19., Med (New York, N.Y.), Vol: 3, Pages: 204-215.e6, ISSN: 2666-6359
<h4>Background</h4>There is a critical need for rapid viral infection diagnostics to enable prompt case identification in pandemic settings and support targeted antimicrobial prescribing.<h4>Methods</h4>Using untargeted high-resolution liquid chromatography coupled with mass spectrometry, we compared the admission serum metabolome of emergency department patients with viral infections (including COVID-19), bacterial infections, inflammatory conditions, and healthy controls. Sera from an independent cohort of emergency department patients admitted with viral or bacterial infections underwent profiling to validate findings. Associations between whole-blood gene expression and the identified metabolite of interest were examined.<h4>Findings</h4>3'-Deoxy-3',4'-didehydro-cytidine (ddhC), a free base of the only known human antiviral small molecule ddhC-triphosphate (ddhCTP), was detected for the first time in serum. When comparing 60 viral with 101 non-viral cases in the discovery cohort, ddhC was the most significantly differentially abundant metabolite, generating an area under the receiver operating characteristic curve (AUC) of 0.954 (95% CI: 0.923-0.986). In the validation cohort, ddhC was again the most significantly differentially abundant metabolite when comparing 40 viral with 40 bacterial cases, generating an AUC of 0.81 (95% CI 0.708-0.915). Transcripts of viperin and <i>CMPK2</i>, enzymes responsible for ddhCTP synthesis, were among the five genes most highly correlated with ddhC abundance.<h4>Conclusions</h4>The antiviral precursor molecule ddhC is detectable in serum and an accurate marker for acute viral infection. Interferon-inducible genes viperin and <i>CMPK2</i> are implicated in ddhC production <i>in vivo</i>. These findings highlight a future diagnostic role for ddhC in viral diagnosis, pandemic preparedness, and acute infection management.<h4>Funding</h
Harshfield EL, Sands CJ, Tuladhar AM, et al., 2022, Metabolomic profiling in small vessel disease identifies multiple associations with disease severity., Brain
Cerebral small vessel disease is a major cause of vascular cognitive impairment and dementia. There are few treatments, largely reflecting limited understanding of the underlying pathophysiology. Metabolomics can be used to identify novel risk factors in order to better understand pathogenesis and to predict disease progression and severity. We analysed data from 624 patients with symptomatic cerebral small vessel disease from two prospective cohort studies. Serum samples were collected at baseline and patients underwent MRI scans and cognitive testing at regular intervals with up to 14 years of follow-up. Using ultra-performance liquid chromatography mass spectrometry and nuclear magnetic resonance spectroscopy, we obtained metabolic and lipidomic profiles from 369 annotated metabolites and 54,764 unannotated features and examined their association with respect to disease severity, assessed using MRI small vessel disease markers, cognition, and future risk of all-cause dementia. Our analysis identified 28 metabolites that were significantly associated with small vessel disease imaging markers and cognition. Decreased levels of multiple glycerophospholipids and sphingolipids were associated with increased small vessel disease load as evidenced by higher white matter hyperintensities volume, lower mean diffusivity normalised peak height, greater brain atrophy, and impaired cognition. Higher levels of creatine, FA(18:2(OH)), and SM(d18:2/24:1) were associated with increased lacune count, higher white matter hyperintensities volume, and impaired cognition. Lower baseline levels of carnitines and creatinine were associated with higher annualised change in peak width of skeletonised mean diffusivity, and 25 metabolites, including lipoprotein subclasses, amino acids, and xenobiotics, were associated with future dementia incidence. Our results show multiple distinct metabolic signatures that are associated with imaging markers of small vessel disease, cognition, and conver
Jones B, Sands C, Alexiadou K, et al., 2022, The metabolomic effects of tripeptide gut hormone infusion compared to Roux-en-Y gastric bypass and caloric restriction, Journal of Clinical Endocrinology and Metabolism, Vol: 107, Pages: e767-e782, ISSN: 0021-972X
Context: The gut-derived peptide hormones glucagon-like peptide-1 (GLP-1), oxyntomodulin (OXM), and peptide YY (PYY) are regulators of energy intake and glucose homeostasis, and are thought to contribute to the glucose-lowering effects of bariatric surgery. Objective: To establish the metabolomic effects of a combined infusion of GLP-1, OXM and PYY (tripeptide “GOP”) in comparison to a placebo infusion, Roux-en-Y gastric bypass (RYGB) surgery, and a very low-calorie diet (VLCD). Design and setting: Sub-analysis of a single-blind, randomised, placebo-controlled study of GOP infusion (ClinicalTrials.gov NCT01945840), including VLCD and RYGB comparator groups. Patients and interventions: 25 obese patients with type 2 diabetes or prediabetes were randomly allocated to receive a 4-week subcutaneous infusion of GOP (n=14) or 0.9% saline control (SAL; n=11). An additional 22 patients followed a VLCD, and 21 underwent RYGB surgery. Main outcome measures: Plasma and urine samples collected at baseline and 4 weeks into each intervention were subjected to cross-platform metabolomic analysis, followed by unsupervised and supervised modelling approaches to identify similarities and differences between the effects of each intervention. Results: Aside from glucose, very few metabolites were affected by GOP, contrasting with major metabolomic changes seen with VLCD and RYGB. Conclusions: Treatment with GOP provides a powerful glucose-lowering effect but does not replicate the broader metabolomic changes seen with VLCD and RYGB. The contribution of these metabolomic changes to the clinical benefits of RYGB remains to be elucidated.
Albreht A, Hussain H, Jimenez B, et al., 2022, Structure elucidation and mitigation of endogenous interferences in LC-MS-based metabolic profiling of urine, Analytical Chemistry, Vol: 94, ISSN: 0003-2700
Liquid chromatography mass spectrometry (LC-MS) is the main workhorse of metabolomics owing to its high degree of analytical sensitivity and specificity when measuring diverse chemistry in complex biological samples. LC-MS-based metabolic profiling of human urine, a biofluid of primary interest for clinical and biobank studies, is not widely considered to be compromised by the presence of endogenous interferences and is often accomplished using a simple “dilute-and-shoot” approach. Yet, it is our experience that broad obscuring signals are routinely observed in LC-MS metabolic profiles and represent interferences which lack consideration in the relevant metabolomics literature. In this work we chromatographically isolated the interfering metabolites from human urine and unambiguously identified them via de novo structure elucidation as two separate proline-containing dipeptides: N,N,N-trimethyl-L-alanine-L-proline betaine (L,L-TMAP) and N,N-dimethyl-L-proline-L-proline betaine (L,L-DMPP), the latter reported here for the first time. Offline LC-MS/MS, MRMS, and NMR spectroscopy were essential components of this workflow for the full chemical and spectroscopic characterization of these metabolites and for establishing the co-existence of cis and trans isomers of both dipeptides in solution. Analysis of these definitive structures highlighted intramolecular ionic interactions as responsible for slow interconversion between these isomeric forms resulting in their unusually broad elution profiles. Proposed mitigation strategies, aimed at increasing the quality of LC-MS-based urine metabolomics data, include modification of the column temperature and mobile phase pH to reduce the chromatographic footprint of these dipeptides, thereby reducing their interfering effect on the underlying metabolic profiles. Alternatively, sample dilution and internal standardization methods may be employed to reduce or account for the observed effects of ionization suppression on
Yeung KTD, Penney N, Whiley L, et al., 2022, The impact of bariatric surgery on serum tryptophan-kynurenine pathway metabolites, Scientific Reports, Vol: 12, ISSN: 2045-2322
Objectives: This study aims to explore the immediate effects of bariatric surgery on serum tryptophan – kynurenine pathway metabolites in individuals with type 2 diabetes and BMI >30. With the goal of providing insight into the link between tryptophan pathway metabolites, type 2 diabetes, and chronic obesity-induced inflammation. Methods: This longitudinal study included 20 participants. Half were diagnosed with type 2 diabetes. 11 and 9 underwent RYGB and SG respectively. Blood samples were obtained at pre-operative and three months post-operative timepoints. Tryptophan and downstream metabolites of the kynurenine pathway were quantified with an ultrahigh-performance liquid chromatography tandem mass spectrometry with electrospray ionisation method. Results: At 3 months post-operation, RYGB led to significant reductions in tryptophan, kynurenic acid and xanthurenic acid levels when compared to baseline. Significant reductions of the same metabolites after surgery were also observed in individuals with T2D irrespective of surgical procedure. These metabolites were significantly correlated with serum HbA1c levels and BMI. Conclusions: Bariatric surgery, in particular RYGB reduces serum levels of tryptophan and its downstream kynurenine metabolites. These metabolites are associated with T2D and thought to be potentially mechanistic in the systemic processes of obesity induced inflammation leading to insulin resistance. Its reduction after surgery is associated with an improvement in glycaemic control (HbA1c).
Inglese P, Huang X, Wu V, et al., 2021, Mass recalibration for desorption electrospray ionization mass spectrometry imaging using endogenous reference ions, Publisher: Cold Spring Harbor Laboratory
BackgroundMass spectrometry imaging (MSI) data often consist of tens of thousands of mass spectra collected from a sample surface. During the time necessary to perform a single acquisition, it is likely that uncontrollable factors alter the validity of the initial mass calibration of the instrument, resulting in mass errors of magnitude significantly larger than their theoretical values. This phenomenon has a two-fold detrimental effect: a) it reduces the ability to interpret the results based on the observed signals, b) it can affect the quality of the observed signal spatial distributions. ResultsWe present a post-acquisition computational method capable of reducing the observed mass drift by up to 60 ppm in biological samples, exploiting the presence of typical molecules with a known mass-to-charge ratio. The procedure, tested on time-of-flight (TOF) and Orbitrap mass spectrometry analyzers interfaced to a desorption electrospray ionization (DESI) source, improves the molecular annotation quality and the spatial distributions of the detected ions.ConclusionThe presented method represents a robust and accurate tool for performing post-acquisition mass recalibration of DESI-MSI datasets and can help to increase the reliability of the molecular assignment and the data quality.
Ferreira MR, Sands CJ, Li J, et al., 2021, Impact of pelvic radiation therapy for prostate cancer on global metabolic profiles and microbiota-driven gastrointestinal late side effects: a longitudinal observational study, International Journal of Radiation: Oncology - Biology - Physics, Vol: 111, Pages: 1204-1213, ISSN: 0360-3016
PurposeRadiation therapy to the prostate and pelvic lymph nodes (PLNRT) is part of the curative treatment of high-risk prostate cancer. Yet, the broader influence of radiation therapy on patient physiology is poorly understood. We conducted comprehensive global metabolomic profiling of urine, plasma, and stools sampled from patients undergoing PLNRT for high-risk prostate cancer.Methods and MaterialsSamples were taken from 32 patients at 6 timepoints: baseline, 2 to 3 and 4 to 5 weeks of PLNRT; and 3, 6, and 12 months after PLNRT. We characterized the global metabolome of urine and plasma using 1H nuclear magnetic resonance spectroscopy and ultraperformance liquid chromatography-mass spectrometry, and of stools with nuclear magnetic resonance. Linear mixed-effects modeling was used to investigate metabolic changes between timepoints for each biofluid and assay and determine metabolites of interest.ResultsMetabolites in urine, plasma and stools changed significantly after PLNRT initiation. Metabolic profiles did not return to baseline up to 1 year post-PLNRT in any biofluid. Molecules associated with cardiovascular risk were increased in plasma. Pre-PLNRT fecal butyrate levels directly associated with increasing gastrointestinal side effects, as did a sharper fall in those levels during and up to 1 year postradiation therapy, mirroring our previous results with metataxonomics.ConclusionsWe showed for the first time that an overall metabolic effect is observed in patients undergoing PLNRT up to 1 year posttreatment. These metabolic changes may effect on long-term morbidity after treatment, which warrants further investigation.
Takis P, Lewis M, 2021, PROCESSING 1H-NMR SPECTRAL DATA
Wickenhagen A, Sugrue E, Lytras S, et al., 2021, A prenylated dsRNA sensor protects against severe COVID-19, Science, Vol: 374, Pages: 1-18, ISSN: 0036-8075
Johnson H, Piggin M, Maslen L, et al., 2021, Insight Report: Online public involvement session on the use of molecular phenomics to improve health and care
Drake TM, Riad AM, Fairfield CJ, et al., 2021, Characterisation of in-hospital complications associated with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol UK: a prospective, multicentre cohort study, The Lancet, Vol: 398, Pages: 223-237, ISSN: 0140-6736
BACKGROUND: COVID-19 is a multisystem disease and patients who survive might have in-hospital complications. These complications are likely to have important short-term and long-term consequences for patients, health-care utilisation, health-care system preparedness, and society amidst the ongoing COVID-19 pandemic. Our aim was to characterise the extent and effect of COVID-19 complications, particularly in those who survive, using the International Severe Acute Respiratory and Emerging Infections Consortium WHO Clinical Characterisation Protocol UK. METHODS: We did a prospective, multicentre cohort study in 302 UK health-care facilities. Adult patients aged 19 years or older, with confirmed or highly suspected SARS-CoV-2 infection leading to COVID-19 were included in the study. The primary outcome of this study was the incidence of in-hospital complications, defined as organ-specific diagnoses occurring alone or in addition to any hallmarks of COVID-19 illness. We used multilevel logistic regression and survival models to explore associations between these outcomes and in-hospital complications, age, and pre-existing comorbidities. FINDINGS: Between Jan 17 and Aug 4, 2020, 80 388 patients were included in the study. Of the patients admitted to hospital for management of COVID-19, 49·7% (36 367 of 73 197) had at least one complication. The mean age of our cohort was 71·1 years (SD 18·7), with 56·0% (41 025 of 73 197) being male and 81·0% (59 289 of 73 197) having at least one comorbidity. Males and those aged older than 60 years were most likely to have a complication (aged ≥60 years: 54·5% [16 579 of 30 416] in males and 48·2% [11 707 of 24 288] in females; aged <60 years: 48·8% [5179 of 10 609] in males and 36·6% [2814 of 7689] in females). Renal (24·3%, 17 752 of 73 197), complex respiratory (18·4%, 13 486 of 73 197), and systemic (16·3%, 11 895 of 73 197) complications were
COVID-19 Host Genetics Initiative, 2021, Mapping the human genetic architecture of COVID-19, Nature, Vol: 600, Pages: 472-477, ISSN: 0028-0836
The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3-7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.
Sharma R, Lu H, George J, et al., 2021, Discriminatory changes in circulating lipid and small molecule metabolites in patients with MAFLD associated hepatocellular cancer, Publisher: ELSEVIER, Pages: S490-S490, ISSN: 0168-8278
Wolfer AM, Correia GDS, Sands CJ, et al., 2021, peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC-MS profiling datasets, BIOINFORMATICS, Vol: 37, Pages: 4886-4888, ISSN: 1367-4803
Li J, 2021, Roux-en-Y Gastric bypass-induced bacterial perturbation contributes to altered host-bacterial co-metabolic phenotype, Microbiome, Vol: 9, ISSN: 2049-2618
BACKGROUND: Bariatric surgery, used to achieve effective weight loss in individuals with severe obesity, modifies the gut microbiota and systemic metabolism in both humans and animal models. The aim of the current study was to understand better the metabolic functions of the altered gut microbiome by conducting deep phenotyping of bariatric surgery patients and bacterial culturing to investigate causality of the metabolic observations. METHODS: Three bariatric cohorts (n = 84, n = 14 and n = 9) with patients who had undergone Roux-en-Y gastric bypass (RYGB), sleeve gastrectomy (SG) or laparoscopic gastric banding (LGB), respectively, were enrolled. Metabolic and 16S rRNA bacterial profiles were compared between pre- and post-surgery. Faeces from RYGB patients and bacterial isolates were cultured to experimentally associate the observed metabolic changes in biofluids with the altered gut microbiome. RESULTS: Compared to SG and LGB, RYGB induced the greatest weight loss and most profound metabolic and bacterial changes. RYGB patients showed increased aromatic amino acids-based host-bacterial co-metabolism, resulting in increased urinary excretion of 4-hydroxyphenylacetate, phenylacetylglutamine, 4-cresyl sulphate and indoxyl sulphate, and increased faecal excretion of tyramine and phenylacetate. Bacterial degradation of choline was increased as evidenced by altered urinary trimethylamine-N-oxide and dimethylamine excretion and faecal concentrations of dimethylamine. RYGB patients' bacteria had a greater capacity to produce tyramine from tyrosine, phenylalanine to phenylacetate and tryptophan to indole and tryptamine, compared to the microbiota from non-surgery, normal weight individuals. 3-Hydroxydicarboxylic acid metabolism and urinary excretion of primary bile acids, serum BCAAs and dimethyl sulfone were also perturbed following bariatric surgery. CONCLUSION: Altered bacterial composition and metabolism contribute to metabolic observations in biofluid
Maciejewski M, Sands C, Nair N, et al., 2021, Prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics, Scientific Reports, Vol: 11, ISSN: 2045-2322
Methotrexate (MTX) is a common first-line treatment for new-onset rheumatoid arthritis (RA). However, MTX is ineffective for 30-40% of patients and there is no way to know which patients might benefit. Here, we built statistical models based on serum lipid levels measured at two time-points (pre-treatment and following 4 weeks on-drug) to investigate if MTX response (by 6 months) could be predicted. Patients about to commence MTX treatment for the first time were selected from the Rheumatoid Arthritis Medication Study (RAMS). Patients were categorised as good or non-responders following 6 months on-drug using EULAR response criteria. Serum lipids were measured using ultra-performance liquid chromatography-mass spectrometry and supervised machine learning methods (including regularized regression, support vector machine and random forest) were used to predict EULAR response. Models including lipid levels were compared to models including clinical covariates alone. The best performing classifier including lipid levels (assessed at 4 weeks) was constructed using regularized regression (ROC AUC 0.61 ± 0.02). However, the clinical covariate based model outperformed the classifier including lipid levels when either pre- or on-treatment time-points were investigated (ROC AUC 0.68 ± 0.02). Pre- or early-treatment serum lipid profiles are unlikely to inform classification of MTX response by 6 months with performance adequate for use in RA clinical management.
Takis PG, Jiménez B, Al-Saffar NMS, et al., 2021, A computationally lightweight algorithm for deriving reliable metabolite panel measurements from 1D 1H NMR., Analytical Chemistry, Vol: 93, Pages: 4995-5000, ISSN: 0003-2700
Small Molecule Enhancement SpectroscopY (SMolESY) was employed to develop a unique and fully automated computational solution for the assignment and integration of 1H nuclear magnetic resonance (NMR) signals from metabolites in challenging matrices containing macromolecules (herein blood products). Sensitive and reliable quantitation is provided by instant signal deconvolution and straightforward integration bolstered by spectral resolution enhancement and macromolecular signal suppression. The approach is highly efficient, requiring only standard one-dimensional 1H NMR spectra and avoiding the need for sample preprocessing, complex deconvolution, and spectral baseline fitting. The performance of the algorithm, developed using >4000 NMR serum and plasma spectra, was evaluated using an additional >8800 spectra, yielding an assignment accuracy greater than 99.5% for all 22 metabolites targeted. Further validation of its quantitation capabilities illustrated a reliable performance among challenging phenotypes. The simplicity and complete automation of the approach support the application of NMR-based metabolite panel measurements in clinical and population screening applications.
Sands CJ, Gómez-Romero M, Correia G, et al., 2021, Representing the metabolome with high fidelity: range and response as quality control factors in LC-MS-based global profiling., Analytical Chemistry, Vol: 93, Pages: 1924-1933, ISSN: 0003-2700
Liquid chromatography-mass spectrometry (LC-MS) is a powerful and widely used technique for measuring the abundance of chemical species in living systems. Its sensitivity, analytical specificity, and direct applicability to biofluids and tissue extracts impart great promise for the discovery and mechanistic characterization of biomarker panels for disease detection, health monitoring, patient stratification, and treatment personalization. Global metabolic profiling applications yield complex data sets consisting of multiple feature measurements for each chemical species observed. While this multiplicity can be useful in deriving enhanced analytical specificity and chemical identities from LC-MS data, data set inflation and quantitative imprecision among related features is problematic for statistical analyses and interpretation. This Perspective provides a critical evaluation of global profiling data fidelity with respect to measurement linearity and the quantitative response variation observed among components of the spectra. These elements of data quality are widely overlooked in untargeted metabolomics yet essential for the generation of data that accurately reflect the metabolome. Advanced feature filtering informed by linear range estimation and analyte response factor assessment is advocated as an attainable means of controlling LC-MS data quality in global profiling studies and exemplified herein at both the feature and data set level.
de Haan LR, Verheij J, van Golen RF, et al., 2021, Unaltered Liver Regeneration in Post-Cholestatic Rats Treated with the FXR Agonist Obeticholic Acid, BIOMOLECULES, Vol: 11
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