Tag: analytical methods

Immune cell proteomes of Long COVID patients have functional changes similar to those in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Abstract:

Of those infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), ~ 10% develop the chronic post-viral debilitating condition, Long COVID (LC). Although LC is a heterogeneous condition, about half of cases have a typical post-viral fatigue condition with onset and symptoms that are very similar to Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). A key question is whether these conditions are closely related.

ME/CFS is a post-stressor fatigue condition that arises from multiple triggers. To investigate the pathophysiology of LC, a pilot study of patients and healthy controls has used quantitative proteomics to discover changes in peripheral blood mononuclear cell (PBMC) proteins. A principal component analysis separated all Long COVID patients from healthy controls.

Analysis of 3131 proteins identified 162 proteins differentially regulated, of which 37 were related to immune functions, and 21 to mitochondrial functions. Markov cluster analysis identified clusters involved in immune system processes, and two aspects of gene expression-spliceosome and transcription. These results were compared with an earlier dataset of 346 differentially regulated proteins in PBMC’s from ME/CFS patients analysed by the same methodology.

There were overlapping protein clusters and enriched molecular pathways particularly in immune functions, suggesting the two conditions have similar immune pathophysiology as a prominent feature, and mitochondrial functions involved in energy production were affected in both conditions.

Source: Katie Peppercorn, Christina D. Edgar, Torsten Kleffmann, Warren. P Tate. Immune cell proteomes of Long COVID patients have functional changes similar to those in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Research Square preprint https://doi.org/10.21203/rs.3.rs-3335919/v1 https://www.researchsquare.com/article/rs-3335919/v1 (Full text) https://www.nature.com/articles/s41598-023-49402-9 (Final full text)

Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts

Abstract:

Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power.

We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84-197 subjects in each study with 562-888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as “prior information” into the next study, thereby improving the overall assessment of the likelihood of finding specific differences between plasma metabolite levels.

Using classic statistics and Benjamini-Hochberg FDR-corrections, Study 1 detected 18 metabolic differences and Study 2 detected no differences. Using Bayesian statistics on the same data, we found a high likelihood that 97 compounds were altered in concentration in Study 2, after using the results of Study 1 as the prior distributions. These findings included lower levels of peroxisome-produced ether-lipids, higher levels of long-chain unsaturated triacylglycerides, and the presence of exposome compounds that are explained by the difference in diet and medication between healthy subjects and ME/CFS patients.

Although Study 3 reported only 92 compounds in common with the other two studies, these major differences were confirmed. We also found that prostaglandin F2alpha, a lipid mediator of physiological relevance, was reduced in ME/CFS patients across all three studies. The use of Bayesian statistics led to biological conclusions from metabolomic data that were not found through frequentist approaches. We propose that Bayesian statistics is highly useful for studies with similar research designs if similar metabolomic assays are used.

Source: Brydges C, Che X, Lipkin WI, Fiehn O. Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts. Metabolites. 2023 Aug 31;13(9):984. doi: 10.3390/metabo13090984. PMID: 37755264; PMCID: PMC10535181. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535181/ (Full text)

Analysis of clinical, epidemiologic, and laboratory data on chronic fatigue syndrome

Abstract:

Much of the research conducted on chronic fatigue syndrome (CFS) is exploratory. The researchers’ overall goal is to use clinical, epidemiologic, and laboratory data to provide clues about the etiology of this syndrome. In preparation for this symposium, a review of numerous publications on CFS has indicated that the literature generally does not reflect the application of optimal statistical methods for exploration of data.

Whenever the researchers’ aim is to generate hypotheses, modern methods designed specifically for exploratory data analysis are likely to provide greater insights into any patterns of data than are the traditional approaches to hypothesis testing. In addition, the use of formal methods of data synthesis for ongoing and future research on CFS is a means of strengthening collaborative efforts and of improving the ability of researchers to interpret the evidence available that relates to specific etiologic factors. The inclusion on the research team of experienced biostatisticians, who would oversee the statistical methods and the development of innovative analyses, is recommended.

 

Source: Redmond CK. Analysis of clinical, epidemiologic, and laboratory data on chronic fatigue syndrome. Rev Infect Dis. 1991 Jan-Feb;13 Suppl 1:S90-3. http://www.ncbi.nlm.nih.gov/pubmed/1826967