Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome

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.

An Analysis of Mechanisms for Cellular Uptake of miRNAs to Enhance Drug Delivery and Efficacy in Cancer Chemoresistance

Up until recently, it was believed that pharmaceutical drugs and their metabolites enter into the cell to gain access to their targets via simple diffusion across the hydrophobic lipid cellular membrane, at a rate which is based on their lipophilicity. An increasing amount of evidence indicates that the phospholipid bilayer-mediated drug diffusion is in fact negligible, and that drugs pass through cell membranes via proteinaceous membrane transporters or carriers which are normally used for the transportation of nutrients and intermediate metabolites. Drugs can be targeted to specific cells and tissues which express the relevant transporters, leading to the design of safe and efficacious treatments. Furthermore, transporter expression levels can be manipulated, systematically and in a high-throughput manner, allowing for considerable progress in determining which transporters are used by specific drugs. The ever-expanding field of miRNA therapeutics is not without its challenges, with the most notable one being the safe and effective delivery of the miRNA mimic/antagonist safely to the target cell cytoplasm for attaining the desired clinical outcome, particularly in miRNA-based cancer therapeutics, due to the poor efficiency of neo-vascular systems revolting around the tumour site, brought about by tumour-induced angiogenesis. This acquisition of resistance to several types of anticancer drugs can be as a result of an upregulation of efflux transporters expression, which eject drugs from cells, hence lowering drug efficacy, resulting in multidrug resistance. In this article, the latest available data on human microRNAs has been reviewed, together with the most recently described mechanisms for miRNA uptake in cells, for future therapeutic enhancements against cancer chemoresistance.

Long noncoding RNAs and their link to cancer

The central dogma of molecular biology, developed from the study of simple organisms such as Escherichia coli, has up until recently been that RNA functions mainly as an information intermediate between a DNA sequence (gene), localized in the cell nucleus, serving as a template for the transcription of messenger RNAs, which in turn translocate into the cytoplasm and act as blueprints for the translation of their encoded proteins. There are a number of classes of non-protein coding RNAs (ncRNAs) which are essential for gene expression to function. The specific number of ncRNAs within the human genome is unknown. ncRNAs are classified on the basis of their size. Transcripts shorter than 200 nucleotides, referred to as ncRNAs, which group includes miRNAs, siRNAs, piRNAs, etc, have been extensively studied. Whilst transcripts with a length ranging between 200 nt up to 100 kilobases, referred to as lncRNAs, make up the second group, and are recently receiving growing concerns. LncRNAs play important roles in a variety of biological processes, regulating physiological functions of organisms, including epigenetic control of gene regulation, transcription and post-transcription, affecting various aspects of cellular homeostasis, including proliferation, survival, migration and genomic stability. LncRNAs are also capable of tuning gene expression and impact cellular signalling cascades, play crucial roles in promoter-specific gene regulation, and X-chromosome inactivation. Furthermore, it has been reported that lncRNAs interact with DNA, RNA, and/or protein molecules, and regulate chromatin organisation, transcriptional and post-transcriptional regulation. Consequently, they are differentially expressed in tumours, and they are directly linked to the transformation of healthy cells into tumour cells. As a result of their key functions in a wide range of biological processes, lncRNAs are becoming rising stars in biology and medicine, possessing potential active roles in various oncologic diseases, representing a gold mine of potential new biomarkers and drug targets.

Enhancing Drug Efficacy and Therapeutic Index through Cheminformatics-Based Selection of Small Molecule Binary Weapons That Improve Transporter-Mediated Targeting: A Cytotoxicity System Based on Gemcitabine

The transport of drug molecules is mainly determined by the distribution of influx and efflux transporters for which they are substrates. To enable tissue targeting, we sought to develop the idea that we might affect the transporter-mediated disposition of small-molecule drugs via the addition of a second small molecule that of itself had no inhibitory pharmacological effect but that influenced the expression of transporters for the primary drug. We refer to this as a “binary weapon” strategy. The experimental system tested the ability of a molecule that on its own had no cytotoxic effect to increase the toxicity of the nucleoside analog gemcitabine to Panc1 pancreatic cancer cells. An initial phenotypic screen of a 500-member polar drug (fragment) library yielded three “hits.” The structures of 20 of the other 2,000 members of this library suite had a Tanimoto similarity greater than 0.7 to those of the initial hits, and each was itself a hit (the cheminformatics thus providing for a massive enrichment). We chose the top six representatives for further study. They fell into three clusters whose members bore reasonable structural similarities to each other (two were in fact isomers), lending strength to the self-consistency of both our conceptual and experimental strategies. Existing literature had suggested that indole-3-carbinol might play a similar role to that of our fragments, but in our hands it was without effect; nor was it structurally similar to any of our hits. As there was no evidence that the fragments could affect toxicity directly, we looked for effects on transporter transcript levels. In our hands, only the ENT1-3 uptake and ABCC2,3,4,5, and 10 efflux transporters displayed measurable transcripts in Panc1 cultures, along with a ribonucleoside reductase RRM1 known to affect gemcitabine toxicity. Very strikingly, the addition of gemcitabine alone increased the expression of the transcript for ABCC2 (MRP2) by more than 12-fold, and that of RRM1 by more than fourfold, and each of the fragment “hits” served to reverse this. However, an inhibitor of ABCC2 was without significant effect, implying that RRM1 was possibly the more significant player. These effects were somewhat selective for Panc cells. It seems, therefore, that while the effects we measured were here mediated more by efflux than influx transporters, and potentially by other means, the binary weapon idea is hereby fully confirmed: it is indeed possible to find molecules that manipulate the expression of transporters that are involved in the bioactivity of a pharmaceutical drug. This opens up an entirely new area, that of chemical genomics-based drug targeting.