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Ardern-Jones, M. R.

Publications and source records attributed to Ardern-Jones, M. R..

3 recordsLinked to original sources

Methods for the analysis of skin microbiomes: a comparison of sampling processes and 16S rRNA hypervariable regions.

BackgroundThe skin microbiome is increasingly recognised as a critical component of the innate skin immune response and is central to the pathogenesis of many inflammatory skin disorders including atopic dermatitis. Previous studies have not looked in detail at the impact of changing both sampling methodology and hypervariable region sequencing for skin microbiome analysis. ObjectivesWe set out to undertake a detailed analysis comparing microbe population diversity and resolution at the single species level by swab, tape, scrape and scrape then swab sampling assayed by hypervariable region sequencing. MethodsIn triplicate samples from the antecubital fossa were taken from healthy volunteers for 16s RNA analysis by primer amplification for hypervariable regions 1-3, and 3-4. ResultsAlpha (phylogenetic) diversity was the greatest with tape sampling and V1-3 analysis, whereas for V4, tape and swab were equivalent. Scrape sampling showed lower alpha diversity at both V1-3 and V4. All measures of beta diversity showed the scrape methodology yielded a lesser diversity than the others. Phyla composition was similar across all sampling methodologies. Minor differences in composition were noted between V1-3 and V4 sequencing, but V1-3 was optimal for identification of firmicutes (including staphylococci). ConclusionsIn the methodological planning of skin microbiome analysis skin scientists need to consider the microbes of interest to choose the optimal hypervariable region to sequence, and harmonisation of methodological approaches would be beneficial to the field. For detection of staphylococci on flexural skin, we would recommend tape sampling with analysis of V1-3. Whats already known about this topic?O_LIPrevious microbiome studies have utilised many different methodologies, but a detailed comparison of their utility in skin research has not been undertaken C_LIO_LIMethodological choices in skin microbiome analysis affect both sensitivity to detect diversity and sensitivity to identify at a species level C_LIO_LIOptimisation to detect diversity and species identification can require different approaches C_LI What does this study add?O_LIOur study compared a matrix the four most widely utilised skin sampling methodologies and the two principle means for hypervariable gene analysis. C_LIO_LIWe show that alpha diversity is optimal with tape sampling C_LIO_LIAdditionally, in contrast to gut microbiome analyses, in the skin hypervariable region V1-3 analysis is superior to V4 when the species of interest are staphylococci. C_LI

microbiology↗

Rank Selection Genetic Algorithm optimises robust parameter estimation for systems biology models

The ability to reliably predict and infer cellular responses to environmental exposures would offer a major advance in the investigation of immune regulation in health and disease. One possible approach is the use of in silico modelling. Design of such a mathematical kinetic model would be based on existing knowledge of a biological system and utilise a partial data set to parameterise. However, the process of parameter estimation, key for the accuracy of the model, is difficult to conduct by hand, and thus a computational alternative is necessary. We report the utility of Genetic Algorithm with Rank Selection (GARS) as a parameter estimation tool on multiple biological models, including heat shock, signal transduction via ERK, circadian rhythm and NF{kappa}B systems, where it showed strong accuracy and superiority to the Extended Kalman Filter method, Algebraic Difference Equations, and MATLAB fminsearch approaches. GARS parameter estimation is a valuable tool for biological data because it reliably infers system behaviour from partial data sets, allowing for the prediction of cellular responses to environmental exposures.

systems biology↗

Genomic programming of antigen cross-presentation in IRF4-expressing human Langerhans cells

Langerhans cells (LCs) in the epidermis present MHC I and MHC II-restricted antigens thereby priming either CD8 or CD4 T cell immune responses. The genomic programs and transcription factors regulating antigen presentation in LCs remain to be elucidated. We show human LCs are highly efficient in MHC I-antigen cross-presentation but lack the transcription factor IRF8 that is critical in dendritic cells. LC migration from the epidermis enhances their ability to cross-present antigens and is accompanied by the induction of the transcription factor IRF4, whose expression is correlated by scRNA-seq with genes involved in ubiquitin-dependent protein degradation. Chromatin profiling reveals enrichment of EICE and AICE composite DNA binding motifs in regulatory regions of antigen-presentation genes, which can be recognized by IRF4 in conjunction with PU.1 or BATF3 expressed in LCs. Thus, the genomic programming of human LCs including inducible expression of IRF4 with enhanced cross-presentation distinguishes them from conventional dendritic cells.

immunology↗