Combining 16S sequencing and qPCR quantification reveals Staphylococcus aureus driven bacterial overgrowth in the skin of severe atopic dermatitis patients

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

Benchmarking MicrobIEM – a user-friendly tool for decontamination of microbiome sequencing data

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

Decontamination benchmarking

Benchmarking of bioinformatic decontamination tools for microbiome sequencing dat

Microbial fingerprints reveal interaction between museum objects, curators, and visitors

Summary: Microbial communities reside at the interface between humans and their environment. Whether the microbiome can be leveraged to gain information on human interaction with museum objects is unclear. To investigate this, we selected objects from the Museum für Naturkunde and the Pergamonmuseum in Berlin, Germany, varying in material and size. Using swabs, we collected 126 samples from natural and cultural heritage objects, which were analyzed through 16S rRNA sequencing. By comparing the microbial composition of touched and untouched objects, we identified a microbial signature associated with human skin microbes. Applying this signature to cultural heritage objects, we identified areas with varying degrees of exposure to human contact on the Ishtar gate and Sam’al gate lions. Furthermore, we differentiated objects touched by two different individuals. Our findings demonstrate that the microbiome of museum objects provides insights into the level of human contact, crucial for conservation, heritage science, and potentially provenance research