Background: Atopic eczema (atopic dermatitis, AD) is characterized by disrupted skin barrier associated with elevated skin pH and skin microbiome dysbiosis, due to high Staphylococcus aureus loads, especially during flares. Since S aureus shows optimal growth at neutral pH, we investigated the longitudinal interplay between these factors and AD severity in a pilot study.Method: Emollient (with either basic pH 8.5 or pH 5.5) was applied double‐blinded twice daily to 6 AD patients and 6 healthy (HE) controls for 8 weeks. Weekly, skin swabs for microbiome analysis (deep sequencing) were taken, AD severity was assessed, and skin physiology (pH, hydration, transepidermal water loss) was measured.Results: Physiological, microbiome, and clinical results were not robustly related to the pH of applied emollient. In contrast to longitudinally stable microbiome in HE, S aureus frequency significantly increased in AD over 8 weeks. High S aureus abundance was associated with skin pH 5.7‐6.2. High baseline S aureus frequency predicted both increase in S aureus and in AD severity (EASI and local SCORAD) after 8 weeks.Conclusion: Skin pH is tightly regulated by intrinsic factors and limits the abundance of S aureus. High baseline S aureus abundance in turn predicts an increase in AD severity over the study period. This underlines the importance and potential of sustained intervention regarding the skin pH and urges for larger studies linking skin pH and skin S aureus abundance to understand driving factors of disease progression.

Atopic eczema (AE) is an inflammatory skin disease with involvement of genetic, immunological and environmental factors. One hallmark of AE is a skin barrier disruption on multiple, highly interconnected levels: filaggrin mutations, increased skin pH and a microbiome dysbiosis towards Staphylococcus aureus overgrowth are observed in addition to an abnormal type 2 immune response. Extrinsic factors seem to play a major role in the development of AE. As AE is a first step in the atopic march, its prevention and appropriate treatment are essential. Although standard therapy remains topical treatment, powerful systemic treatment options emerged in the last years. However, thorough endotyping of the individual patients is still required for ideal precision medicine approaches in future. Therefore, novel microbial and immunological biomarkers were described recently for the prediction of disease development and treatment response. This review summarizes the current state of the art in AE research.

Atopic dermatitis (AD) is an inflammatory skin disease with a microbiome dysbiosis towards a high relative abundance of Staphylococcus aureus. However, information is missing on the actual bacterial load on AD skin, which may affect the cell number driven release of pathogenic factors. Here, we combined the relative abundance results obtained by next-generation sequencing (NGS, 16S V1-V3) with bacterial quantification by targeted qPCR (total bacterial load = 16S, S. aureus = nuc gene). Skin swabs were sampled cross-sectionally (n = 135 AD patients; n = 20 healthy) and longitudinally (n = 6 AD patients; n = 6 healthy). NGS and qPCR yielded highly inter-correlated S. aureus relative abundances and S. aureus cell numbers. Additionally, intra-individual differences between body sides, skin status, and consecutive timepoints were also observed. Interestingly, a significantly higher total bacterial load, in addition to higher S. aureus relative abundance and cell numbers, was observed in AD patients in both lesional and non-lesional skin, as compared to healthy controls. Moreover, in the lesional skin of AD patients, higher S. aureus cell numbers significantly correlated with the higher total bacterial load. Furthermore, significantly more severe AD patients presented with higher S. aureus cell number and total bacterial load compared to patients with mild or moderate AD. Our results indicate that severe AD patients exhibit S. aureus driven increased bacterial skin colonization. Overall, bacterial quantification gives important insights in addition to microbiome composition by sequencing.

Background: Microbiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls. Results: We benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (10⁸–10³ cells, 0.4–80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the two staggered mocks, particularly in low-biomass samples (≤ 10⁶ cells). We show that a correct decontamination benchmarking requires realistic staggered mock communities and unbiased evaluation measures such as Youden’s index. In the skin dataset, the Decontam prevalence filter and MicrobIEM’s ratio filter effectively reduced common contaminants while keeping skin-associated genera. Conclusions: MicrobIEM’s ratio filter for decontamination performs better or as good as established bioinformatic decontamination tools. In contrast to established tools, MicrobIEM additionally provides interactive plots and supports selecting appropriate filtering parameters via a user-friendly graphical user interface. Therefore, MicrobIEM is the first quality control tool for microbiome experts without coding experience.

Introduction: Microbiome amplicon sequencing data are distorted by multiple protocol-dependent biases from bacterial DNA extraction, contamination, sequence errors, and chimeras, hindering clinical microbiome applications. In particular, extraction bias is a major confounder in sequencing-based microbiome analyses, with no correction method available to date. Here, we suggest using mock community controls to computationally correct extraction bias based on bacterial morphological properties. Methods: We compared dilution series of 3 cell mock communities with an even or staggered composition. DNA of these mock, and additional skin microbiome samples, was extracted with 8 different extraction protocols (2 buffers, 2 extraction kits, 2 lysis conditions). Extracted DNA was sequenced (V1–V3 16S rRNA gene) together with corresponding DNA mocks. Results: Microbiome composition was significantly different between extraction kits and lysis conditions, but not between buffers. Independent of the extraction protocol, chimera formation increased with higher input cell numbers. Contaminants originated mostly from buffers, and considerable cross-contamination was observed in low-input samples. Comparing the microbiome composition of the cell mocks to corresponding DNA mocks revealed taxon-specific protocol-dependent extraction bias. Strikingly, this extraction bias per species was predictable by bacterial cell morphology. Morphology-based computational correction of extraction bias significantly improved resulting microbial compositions when applied to different mock samples, even with different taxa. Equivalent correction of the skin samples showed a substantial impact on microbiome compositions. Conclusions: Our results indicate that higher DNA density increases chimera formation during PCR amplification. Furthermore, we show that computational correction of extraction bias based on bacterial cell morphology would be feasible using appropriate positive controls, thus constituting an important step toward overcoming protocol biases in microbiome analysis.