From castaways to discoveries: unveiling treasures in skin RNAseq using a novel multidimensional data processing workflow including infection-host dynamics.

Abstract

Defining physiology and methods to measure biological mechanisms is essential. Extensive datasets such as RNA sequencing are used with little analysis of the knowledge gained from the various methodologies. Within this work, we have processed publicly available NCBI RNAseq datasets using a combination of bioinformatics tools for the largest physiological organ, the skin. In many datasets, we identify the quality of the sample, human transcript mapping, the sex of each sample, foreign RNA from bacteria/viruses/protists, and the presence of B/T-cell immune repertoire. Processing 8,274 samples from 132 different experiments for skin samples identifies common flora of skin with elevation of protists (such as Leishmania), bacteria (Staphylococcus, Cutibacterium acnes), and viruses [Human alphaherpesvirus (HSV), Human papillomavirus (HPV)] that may be involved in physiological differences. We observed samples with the Heilongjiang tick virus, human T-cell leukemia virus type I, and equine infectious anemia virus that likely play pathological roles in physiology. Integrating the various biomarkers identified five ideal datasets for skin pathologies that elucidated a novel correlation between the normal skin flora bacterium Bacillus megaterium with major histocompatibility complex (MHC) regulation and the immune repertoire clonal expansion, particularly in patients with hidradenitis suppurativa. Finally, we show that in multiple independent experiments, biological sex is associated with multiple sex chromosome gene differences, highlighting the importance of future work in studying sex differences in skin. Data integrations and multidimensional data mapping are critical for physiological omics advancements, and this work highlights the exciting ability to apply these tools to skin physiology.NEW & NOTEWORTHY Complex bioinformatics mapping to skin RNA sequencing datasets can simultaneously map biological sex, skin-specific genes, bacteria, viruses, protists, and the acquired immune response. The integration of these datasets elucidated bacterial signatures from common skin flora while identifying novel insights on Bacillus megaterium in the acquired immune response and novel viral signatures for Heilongjiang tick virus and equine infectious anemia virus.