Biology subjects

Frost, F.

Publications and source records attributed to Frost, F..

3 recordsLinked to original sources

bioRxiv2024

Sputum Proteomics reveals unique signatures linked to key outcomes in cystic fibrosis trials.

RationaleLung function (FEV1) and quality of life (QoL) are key outcomes in most interventional clinical trials conducted in people living with cystic fibrosis. However, no robust pre-clinical surrogates for FEV1 and QoL exist. The precise physiological mechanisms leading to treatment-related improvements in these outcomes are incompletely understood. In this post-hoc analysis we explored the relationship between changes in the sputum proteome and these outcomes with the aim of identifying translational biomarkers. MethodsPaired sputum samples collected during the AZTEC-CF study (NCT02894684) pre and post 14 days of antibiotic treatment for an acute pulmonary exacerbation were included. Samples were analysed using in vitro Mesoscale Discovery (MSD) assays and by nano LC-MS/MS. Peptide identification and quantification was performed and the log-fold change for individual proteins and relationships between protein change and changes in FEV1 and QoL were evaluated. ResultsDistinct patterns were found between proteins that correlated with FEV1 and those that correlated with QoL improvements. FEV1 improvement was characterised by increases in bacterially-derived proteins accompanied by decreases in proteins relating to neutrophil degranulation. Conversely, changes in QoL were associated with increases in antiprotease and antioxidant proteins. MSD analysis revealed changes in some neutrophil-associated markers significantly correlated with FEV1 improvements, but no markers significantly correlated with QoL improvements. ConclusionsThese results suggest changes in two key CF clinical trial outcomes (FEV1 and QoL) may be underpinned by different physiological mechanisms. Understanding these divergent mechanisms is vital to fortify optimal clinical trial design in CF and panels of biomarkers may be needed to improve translational confidence.

immunology↗

bioRxiv2023

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling

Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type 2 diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

bioinformatics↗

bioRxiv2020

Genetics of human gut microbiome composition

To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 out of 410 genera were detected in more than 95% samples. A genome-wide association study (GWAS) of host genetic variation in relation to microbial taxa identified 31 loci affecting microbiome at a genome-wide significant (P<5x10-8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (GWAS signal P=1.28x10-20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95x10-10<P<5x10-8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome has causal effects in ulcerative colitis and rheumatoid arthritis.

genetics↗