Radical ring-opening polymerization is a clever strategy to incorporate cleavable linkages into otherwise non-degradable vinyl polymers. But conventional systems suffer from slow copolymerization, harsh non-selective degradation conditions, and limited application potential because the degradation products (often oligomers or polymers themselves) have properties like the intact species. This work presents fast selective degradation accompanied by a drastic change in a key property, aqueous solubility.The thionolactone dibenzo[c,e]oxepane-5-thione was found to copolymerise radically with a range of primary, secondary, and tertiary neutral and zwitterionic acrylamides with rapid incorporation of degradable biphenyl thiocarboxylate repeat units. Intact copolymers displayed temperature-responsive (LCST or UCST-type) aqueous solubility behaviour, tuneable through the molar composition and (exploiting the non-azeotropic copolymerization behaviour) comonomer sequence. Various conditions led to selective and complete degradation of the backbone thioesters through hydrolysis, aminolysis, transthioesterification (including under physiological conditions), and oxidative hydrolysis which drastically increased aqueous solubility. Polymers containing as little as 8 mol-% of thioester repeat units underwent a temperature-independent insoluble-soluble transition upon degradation with cysteine or potassium persulfate. Insoluble polymers were used to block syringe filters which allowed flow of degradant solutions only, relevant relevant to lab-on-a-chip, sensing, and embolic biomedical applications.

The radical ring-opening polymerization (RROP) of thionolactones provides access to thioester backbone-functional copolymers but has, to date, only been demonstrated on acrylic copolymers. Herein, the thionolactone dibenzo[c,e]oxepane-5-thione (DOT) was subjected to AIBN-initiated free radical homopolymerization which produced a thioester-functional homopolymer with a glass transition temperature of 95 °C and the ability to degrade exclusively into predetermined small molecules. However, the homopolymerization was impractically slow and precluded the introduction of functionality. Conversely, the RAFT-mediated copolymerization of DOT with Nmethylmaleimide (MeMI), N-phenylmaleimide (PhMI), and N-2,3,4,5,6pentafluorophenylmaleimide (PFPMI) rapidly produced well-defined copolymers with the tendency to form alternating sequences increasing in the order MeMI ≪ PhMI < PFPMI, with estimated reactivity ratios of r DOT = 0.198 and r PFPMI = 0.0078 for the latter system. Interestingly, defects in the alternating structure were more likely caused by (degradable) DOT-DOT sequences rather than (non-degradable) MI-MI sequences, which was confirmed through paper spray mass spectrometric analysis of the products from aminolytic degradation. Upon the aminolysis of backbone thioesters, maleimide repeating units were ring-opened, forming bisamide structures. Conversely, copolymer degradation through a thiolate did not result in imide substitution but nucleophilic para-fluoro substitution on PFPMI comonomer units, indicating the ability of DOT-MI copolymers to degrade under different conditions and to form differently functional products. The RROP of thionolactones has distinct advantages over RROP of cyclic ketene acetals and is anticipated to find use in the development of well-defined degradable polymer materials.

The effect of COVID-19 infection on the human metabolome has been widely reported, but to date all such studies have focused on a single wave of infection. COVID-19 has generated numerous waves of disease with different clinical presentations, and therefore it is pertinent to explore whether metabolic disturbance changes accordingly, to gain a better understanding of its impact on host metabolism and enable better treatments. This work used a targeted metabolomics platform (Biocrates Life Sciences) to analyze the serum of 164 hospitalized patients, 123 with confirmed positive COVID-19 RT-PCR tests and 41 providing negative tests, across two waves of infection. Seven COVID-19-positive patients also provided longitudinal samples 2–7 months after infection. Changes to metabolites and lipids between positive and negative patients were found to be dependent on collection wave. A machine learning model identified six metabolites that were robust in diagnosing positive patients across both waves of infection: TG (22:1_32:5), TG (18:0_36:3), glutamic acid (Glu), glycolithocholic acid (GLCA), aspartic acid (Asp) and methionine sulfoxide (Met-SO), with an accuracy of 91%. Although some metabolites (TG (18:0_36:3) and Asp) returned to normal after infection, glutamic acid was still dysregulated in the longitudinal samples. This work demonstrates, for the first time, that metabolic dysregulation has partially changed over the course of the pandemic, reflecting changes in variants, clinical presentation and treatment regimes. It also shows that some metabolic changes are robust across waves, and these can differentiate COVID-19-positive individuals from controls in a hospital setting. This research also supports the hypothesis that some metabolic pathways are disrupted several months after COVID-19 infection.

Background The COVID-19 pandemic has led to an unprecedented demand for testing - for diagnosis and prognosis - as well as for investigation into the impact of the disease on the host metabolism. Sebum sampling has the potential to support both needs by looking at what the virus does to us, rather than looking for the virus itself. Methods In this pilot study, sebum samples were collected from 67 hospitalised patients (30 COVID-19 positive and 37 COVID-19 negative) by gauze swab. Lipidomics analysis was carried out using liquid chromatography mass spectrometry, identifying 998 reproducible features. Univariate and multivariate statistical analyses were applied to the resulting feature set. Findings Lipid levels were depressed in COVID-19 positive participants, indicative of dyslipidemia; p -values of 0·022 and 0·015 were obtained for triglycerides and ceramides respectively, with effect sizes of 0·44 and 0·57. Partial Least Squares-Discriminant Analysis showed separation of COVID-19 positive and negative participants with sensitivity of 57% and specificity of 68%, improving to 79% and 83% respectively when controlled for confounding comorbidities. Interpretation COVID-19 dysregulates many areas of metabolism; in this work we show that the skin lipidome can be added to the list. Given that samples can be provided quickly and painlessly, we conclude that sebum is worthy of future consideration for clinical sampling. Funding The authors acknowledge funding from the EPSRC Impact Acceleration Account for sample collection and processing, as well as EPSRC Fellowship Funding EP/R031118/1, the University of Surrey and BBSRC BB/T002212/1. Mass Spectrometry was funded under EP/P001440/1 .

Background The global COVID-19 pandemic has led to extensive development in many fields, including the diagnosis of COVID-19 infection by mass spectrometry. The aim of this systematic review and meta-analysis was to assess the accuracy of mass spectrometry diagnostic tests developed so far, across a wide range of biological matrices, and additionally to assess risks of bias and applicability in studies published to date. Method 23 retrospective observational cohort studies were included in the systematic review using the PRISMA-DTA framework, with a total of 2858 COVID-19 positive participants and 2544 controls. Risks of bias and applicability were assessed via a QUADAS-2 questionnaire. A meta-analysis was also performed focusing on sensitivity, specificity, diagnostic accuracy and Youden's Index, in addition to assessing heterogeneity. Findings. Sensitivity averaged 0.87 in the studies reviewed herein (interquartile range 0.81–0.96) and specificity 0.88 (interquartile range 0.82–0.98), with an area under the receiver operating characteristic summary curve of 0.93. By subgroup, the best diagnostic results were achieved by viral proteomic analyses of nasopharyngeal swabs and metabolomic analyses of plasma and serum. The performance of other sampling matrices (breath, sebum, saliva) was less good, indicating that these protocols are currently insufficiently mature for clinical application. Conclusions This systematic review and meta-analysis demonstrates the potential for mass spectrometry and ‘omics in achieving accurate test results for COVID-19 diagnosis, but also highlights the need for further work to optimize and harmonize practice across laboratories before these methods can be translated to clinical applications.

Treatments for COVID-19 infections have improved dramatically since the beginning of the pandemic, and glucocorticoids have been a key tool in improving mortality rates. The UK’s National Institute for Health and Care Excellence guidance is for treatment to be targeted only at those requiring oxygen supplementation, however, and the interactions between glucocorticoids and COVID-19 are not completely understood. In this work, a multi-omic analysis of 98 inpatient-recruited participants was performed by quantitative metabolomics (using targeted liquid chromatography-mass spectrometry) and data-independent acquisition proteomics. Both ‘omics datasets were analysed for statistically significant features and pathways differentiating participants whose treatment regimens did or did not include glucocorticoids. Metabolomic differences in glucocorticoid-treated patients included the modulation of cortisol and bile acid concentrations in serum, but no alleviation of serum dyslipidemia or increased amino acid concentrations (including tyrosine and arginine) in the glucocorticoid-treated cohort relative to the untreated cohort. Proteomic pathway analysis indicated neutrophil and platelet degranulation as influenced by glucocorticoid treatment. These results are in keeping with the key role of platelet-associated pathways and neutrophils in COVID-19 pathogenesis and provide opportunity for further understanding of glucocorticoid action. The findings also, however, highlight that glucocorticoids are not fully effective across the wide range of ‘omics dysregulation caused by COVID-19 infections.

The rapid emergence of antibiotic resistant bacterial pathogens constitutes a critical problem in healthcare and requires the development of novel treatments. Potential strategies include the exploitation of microbial social interactions based on public goods, which are produced at a fitness cost by cooperative microorganisms, but can be exploited by cheaters that do not produce these goods. Cheater invasion has been proposed as a 'Trojan horse' approach to infiltrate pathogen populations with strains deploying built-in weaknesses (e.g. sensitiveness to antibiotics). However, previous attempts have been often unsuccessful because population invasion by cheaters was prevented by various mechanisms including the presence of spatial structure (e.g. growth in biofilms), which limits the diffusion and exploitation of public goods.Here we followed an alternative approach and examined whether the manipulation of public good uptake and not its production could result in potential 'Trojan horses' suitable for population invasion. We focused on the siderophore pyoverdine produced by the human pathogen Pseudomonas aeruginosa MPAO1 and manipulated its uptake by deleting and/or overexpressing the pyoverdine primary (FpvA) and secondary (FpvB) receptors. We found that receptor synthesis feeds back on pyoverdine production and uptake rates, which led to strains with altered pyoverdine-associated costs and benefits. Moreover, we found that the receptor FpvB was advantageous under iron-limited conditions but revealed hidden costs in the presence of an antibiotic stressor (gentamicin). As a consequence, FpvB mutants became the fittest strain under gentamicin exposure, displacing the wildtype in liquid cultures, and in biofilms and during infections of the wax moth larvae Galleria mellonella, which both represent structured environments. Our findings reveal that an evolutionary trade-off associated with the costs and benefits of a versatile pyoverdine uptake strategy can be harnessed for devising a Trojan horse candidate for medical interventions.

The majority of metabolomics studies to date have utilised blood serum or plasma, biofluids that do not necessarily address the full range of patient pathologies. Here, correlations between serum metabolites, salivary metabolites and sebum lipids are studied for the first time. 83 COVID-19 positive and negative hospitalised participants provided blood serum alongside saliva and sebum samples for analysis by liquid chromatography mass spectrometry. Widespread alterations to serum-sebum lipid relationships were observed in COVID-19 positive participants versus negative controls. There was also a marked correlation between sebum lipids and the immunostimulatory hormone dehydroepiandrosterone sulphate in the COVID-19 positive cohort. The biofluids analysed herein were also compared in terms of their ability to differentiate COVID-19 positive participants from controls; serum performed best by multivariate analysis (sensitivity and specificity of 0.97), with the dominant changes in triglyceride and bile acid levels, concordant with other studies identifying dyslipidemia as a hallmark of COVID-19 infection. Sebum performed well (sensitivity 0.92; specificity 0.84), with saliva performing worst (sensitivity 0.78; specificity 0.83). These findings show that alterations to skin lipid profiles coincide with dyslipidaemia in serum. The work also signposts the potential for integrated biofluid analyses to provide insight into the whole-body atlas of pathophysiological conditions.