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.

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.

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. 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). High resolution untargeted LC-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. The predictors were selected for their relevant biological function and include cytosine 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). Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

Background: Accurately recognizing that a person may be dying is central to improving their experience of care at the end-of-life. However, predicting dying is frequently inaccurate and often occurs only hours or a few days before death. Methods: We performed urinary metabolomics analysis on patients with lung cancer to create a metabolite model to predict dying over the last 30 days of life. Results: Here we show a model, using only 7 metabolites, has excellent accuracy in the Training cohort n = 112 (AUC = 0·85, 0·85, 0·88 and 0·86 on days 5, 10, 20 and 30) and Validation cohort n = 49 (AUC = 0·86, 0·83, 0·90, 0·86 on days 5, 10, 20 and 30). These results are more accurate than existing validated prognostic tools, and uniquely give accurate predictions over a range of time points in the last 30 days of life. Additionally, we present changes in 125 metabolites during the final four weeks of life, with the majority exhibiting statistically significant changes within the last week before death. Conclusions: These metabolites identified offer insights into previously undocumented pathways involved in or affected by the dying process. They not only imply cancer’s influence on the body but also illustrate the dying process. Given the similar dying trajectory observed in individuals with cancer, our findings likely apply to other cancer types. Prognostic tests, based on the metabolites we identified, could aid clinicians in the early recognition of people who may be dying and thereby influence clinical practice and improve the care of dying patients.

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.

AmyloGram is a computer program that uses n-gram encoding and a random forest classifier to produce a numerical score between 0 and 1 for the predicted amyloidogenicity of a given protein sequence. In a variety of recent studies, we have used AmyloGram to obtain an overall amyloidogenicity score for members of the human proteome. Of 83,567 full-length canonical human polypeptides, 79.2% had a score exceeding 0.7 (the median was 0.813), consistent with the view that most natural protein sequences contain elements that are in fact potentially amyloidogenic. Here, we first asked whether this operational threshold is supported by orthogonal predictors and curated amyloid proteins, and then whether similarly high scores are also observed in evolutionarily ancient proteomes. For the human proteome, PASTA2 values correlated positively with AmyloGram scores (r2 = 0.374 for minimum free energy and 0.321 for average free energy), and proteins with AmyloGram scores ≥0.7 were significantly enriched for strongly negative PASTA2 values (for average free energy <−10 PEU: 4724/66190 versus 75/17,377; χ2P = 2.4 × 10−250). In AmyPro, 117 curated amyloid proteins had substantially higher median AmyloGram scores than did the ~8 curated non-amyloids, although the imbalance of this dataset demands caution. AMYPred-FRL showed only a weak and partly discordant relationship with AmyloGram in the archaeal test proteome examined. We then computed AmyloGram score distributions for the proteomes of 130 other organisms, including archaea, Gram-negative bacteria, Gram-positive bacteria and viruses, representing 475,999 proteins in total. The corresponding organism-level median AmyloGram scores by domain were 0.822, 0.853, 0.851, and 0.825, respectively, while the overall median across all 130 organism-level medians was 0.828. By contrast, 1000 random 100-mer sequences generated with equal amino-acid probabilities had a median AmyloGram score of 0.740. Weighting according to known residue distributions did not change this. However, a weak dependence of AmyloGram score on sequence length within an organism was observed, and the median length of human proteins (∼375 residues) gave a median AmyloGram score of 0.837. Taken together, these findings are consistent with, though they cannot prove, an early evolutionary origin of widespread amyloidogenic potential in natural proteins, and most likely reflect the biophysical properties of the amino acid residues that tend to induce amyloidogenesis.