The focus of this week is the recent publications related to the bio-medical applications. Another highlight of this summary, is the paper which is co-authored by multiple organizations, including researchers from two of our Nodes: McGill University Node and University of Victoria Node.
Drs. Christoph Borchers, Jun Han and David Schibli co-authored an open access publication in “Cancer epidemiology, biomarkers & prevention” journal. “Circulating Isovalerylcarnitine and Lung Cancer Risk: Evidence from Mendelian Randomization and Pre-Diagnostic Blood Measurements” a cancer biomarker research work. The authors screened 207 metabolites for their role in lung cancer predisposition. The scan of 207 metabolites identified that blood isovalerylcarnitine (IVC) was associated with a decreased odds of lung cancer. The results show the compelling evidence in favor of a protective role for a circulating metabolite, IVC, in lung cancer aetiology. This circulating metabolite is modifiable through a restricted protein diet or glycine and L-carnatine supplementation.
Another publication with our University of Victoria researcher Dr. Jun Han “On-tissue pyrene-1-boronic acid labelling assisted MALDI imaging of catecholamines in porcine adrenal gland”. It is published in “Journal of Chromatography A” late July. This study presents on tissue in-situ labelling strategy using a commercially available agent. This approach is robust, easy-to-use and low-cost method of catecholamines imaging. The visualization method of the distribution of three catecholamine compounds (dopamine, epinephrine and norepinephrine) in porcine adrenal gland is developed and employed
And the most recent open access publication is “Early prediction of COVID-19 patient survival by targeted plasma multi-omics and machine learning”. This paper by Christoph Borchers’ team utilizes multi-omics results and machine learning (ML) to predict patients’ survival on the first day of hospitalization. While hundreds of proteins and metabolites were quantified from plasma samples from 120 COVID-19 patients, just 10 proteins and 5 metabolites are sufficient to predict the survival. The importance of this study is in the implementation of targeted MS, where analyte concentration is determined with high precision. Clinical assays require actual biomarker concentrations in order to readily access whether a patient falls within or outside a determined reference range for a specific assay. To achieve this Dr. Borchers’ Node built a machine learning multi-omic model that considered concentrations of relevant proteins and metabolites. The prediction accuracy was 92% on the day of hospitalization.
Dr. Christoph Borchers’ Node at McGill University offers the Development and implementation of the custom targeted clinical LC-MS assays for drugs, hormones and metabolites. The assay development includes
- Streamline LC-MS assay development and optimization
- Quantitative analyte determination using isotopically labeled internal standards
- Assay validation according to CLSI guidelines
- Multiplex analyses of panels of substances
- Multiple analyte classes (drugs, hormones, vitamins, metabolites, etc.)
- Multiple specimen types (blood, plasma, urine, saliva, tissue biopsies)
For a non-clinical development, Dr. Borchers’ Node offers a comprehensive identification of drug metabolites (MetID – metabolites identification). His lab is interested in multi-omics work (combining proteomics and metabolomics), and provides an integration of machine learning to data analysis. Contact us if you want to connect to our McGill Node and Dr. Christoph Borchers.
The summary is prepared by Dr. S. Sapelnikova
