cecile frampas image

Miss Cecile Frampas

Postgraduate Research Student

Academic and research departments

Department of Chemistry.


Holly-May Lewis, Yufan Liu, Cecile F. Frampas, Katie Longman, Matt Spick, Alexander Stewart, Emma Sinclair, Nora Kasar, Danni Greener, Anthony D. Whetton, Perdita E. Barran, Tao Chen, Deborah Dunn-Walters, Debra J. Skene, Melanie J. Bailey (2022)Metabolomics Markers of COVID-19 Are Dependent on Collection Wave, In: Metabolites12(8)713 MDPI AG

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.

Matt Spick, Holly-May Lewis, Cecile F. Frampas, Katie Longman, Catia Costa, Alexander Stewart, Deborah Dunn-Walters, Danni Greener, George Evetts, Michael J. Wilde, Eleanor Sinclair, Perdita E. Barran, Debra J. Skene, Melanie J. Bailey (2022)An integrated analysis and comparison of serum, saliva and sebum for COVID-19 metabolomics, In: Scientific reports1211867 Nature Portfolio

Abstract 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.

Catia Costa, Cecile Frampas, Katherine A. Longman, Vladimir Palitsin, Mahado Ismail, Patrick Sears, Ramin Nilforooshan, Melanie J. Bailey (2019)Paper spray screening and LC-MS confirmation for medication adherence testing: a two-step process, In: Rapid Communications in Mass Spectrometry Wiley

RATIONALE: Paper spray offers a rapid screening test without the need for sample preparation. The incomplete extraction of paper spray allows for further testing using more robust, selective and sensitive techniques such as liquid chromatography mass spectrometry (LC-MS). Here we develop a two-step process of paper spray followed by LC-MS to (1) rapidly screen a large number of samples and (2) confirm any disputed results. This demonstrates the applicability for testing medication adherence from a fingerprint. METHODS: Following paper spray analysis, drugs of abuse samples were analysed using LC-MS. All analyses were completed using a Q Exactive™ Plus Orbitrap™ mass spectrometer. This two-step procedure was applied to fingerprints collected from patients on a maintained dose of the antipsychotic drug quetiapine. RESULTS: The extraction efficiency of paper spray for two drugs of abuse and metabolites was found to be between 15-35% (analyte dependent). For short acquisition times, the extraction efficiency was found to vary between replicates by less than 30%, enabling subsequent analysis by LC-MS. This two-step process was then applied to fingerprints collected from two patients taking the antipsychotic drug quetiapine, which demonstrates how a negative screening result from paper spray can be resolved using LC-MS. CONCLUSIONS: We have shown for the first time the sequential analysis of the same sample using paper spray and LC-MS, as well as the detection of an antipsychotic drug from a fingerprint. We propose that this workflow may also be applied to any type of sample compatible with paper spray, and will be especially convenient where only one sample is available for analysis.