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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 (10 8 –10 3 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 (≤ 10 6 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.

Atopic dermatitis (AD) is a prevalent inflammatory skin disorder characterized by chronic inflammation, skin barrier dysfunction, and microbial dysbiosis, with Staphylococcus aureus playing a significant role in its pathogenesis. This paper explores the strain diversity and microevolution of S. aureus within AD patients, emphasizing how specific strains adapt to the altered skin environment, exacerbating the condition. The review emphasizes the significance of variation in specific functional genes among S. aureus strains, which enhances their ability to adapt to different microenvironments and shapes their pathogenic potential. It also discusses how mobile genetic elements, particularly prophages, contribute to genetic diversity and drive the virulence and antibiotic resistance of S. aureus in AD, highlighting the clinical challenges posed by these strain-specific factors in managing the disease. The paper advocates for the integration of advanced genomic tools such as whole-genome sequencing and machine learning to develop targeted therapies. By focusing on the genetic adaptability of S. aureus and its impact on AD, this review underscores the need for strain-specific diagnostics and personalized treatment strategies to improve patient outcomes.

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. 8Pu8vpw8btyxkt5dD_-7SG Video Abstract

The microbiome and the phage meta-genome within the human gut are influenced by antibiotic treatments. Identifying a novel mechanism, here we demonstrate that bacteria use the universal communication molecule AI-2 to induce virulence genes and transfer them via phage release. High concentrations (i.e. 100 μM) of AI-2 promote dispersal of bacteria from already established biofilms, and is associated with release of phages capable of infecting other bacteria. Enterococcus faecalis V583ΔABC harbours 7 prophages in its genome, and a mutant deficient in one of these prophages (i.e. prophage 5) showed a greatly reduced dispersal of biofilm. Infection of a probiotic E. faecalis strain without lytic prophages with prophage 5 resulted in increased biofilm formation and also in biofilm dispersal upon induction with AI-2. Infection of the probiotic E. faecalis strain with phage-containing supernatants released through AI-2 from E. faecalis V583ΔABC resulted in a strong increase in pathogenicity of this strain. The polylysogenic probiotic strain was also more virulent in a mouse sepsis model and a rat endocarditis model. Both AI-2 and ciprofloxacin lead to phage release, indicating that conditions in the gastrointestinal tract of hospitalized patients treated with antibiotics might lead to distribution of virulence genes to apathogenic enterococci and possibly also to other commensals or even to beneficial probiotic strains.

In Escherichia coli, detoxification of methylglyoxal (MG) requires glyoxalases I and II. Glyoxalase I (gloA/GlxI) isomerizes the hemithioacetal, formed spontaneously from MG and glutathione (GSH) to S-lactoylglutathione (SLG), which is hydrolyzed by glyoxalase II (gloB/GlxII) to lactate and GSH. YcbL from Salmonella enterica serovar Typhimurium is an unusual type II glyoxalase whose role in MG detoxification has remained enigmatic. Here we show that YcbL (gloC/GlxII-2) acts as an accessory type II glyoxylase in E. coli. The two isoenzymes have additive effects and ensure maximal MG degradation. GlxII-2 (YcbL) is an accesory type II glyoxalase.

Phages are bacteria targeting viruses and represent the most abundant biological entities on earth. Marine environments are exceptionally rich in bacteriophages, harboring a total of 4x10(30) viruses. Nevertheless, marine phages remain poorly characterized. Here we describe the identification of intact prophage sequences in the genome of the marine γ-proteobacterium Vibrio campbellii ATCC BAA-1116 (formerly known as V. harveyi ATCC BAA-1116), which presumably belong to the family of Myoviridae. One prophage was found on chromosome I and shows significant similarities to the previously identified phage ΦHAP-1. The second prophage region is located on chromosome II and is related to Vibrio phage kappa. Exposure of V. campbellii to mitomycin C induced the lytic cycle of two morphologically distinct phages and, as expected, extracellular DNA from induced cultures was found to be specifically enriched for the sequences previously identified as prophage regions. Heat stress (50°C, 30 min) was also found to induce phage release in V. campbellii. Notably, promoter activity of two representative phage genes indicated heterogeneous phage induction within the population.

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.

Quorum sensing regulates cell density-dependent phenotypes and involves the synthesis, excretion and detection of so-called autoinducers. Vibrio harveyi strain ATCC BAA-1116 (recently reclassified as Vibrio campbellii), one of the best-characterized model organisms for the study of quorum sensing, produces and responds to three autoinducers. HAI-1, AI-2 and CAI-1 are recognized by different receptors, but all information is channeled into the same signaling cascade, which controls a specific set of genes. Here we examine temporal variations of availability and concentration of the three autoinducers in V. harveyi, and monitor the phenotypes they regulate, from the early exponential to the stationary growth phase in liquid culture. Specifically, the exponential growth phase is characterized by an increase in AI-2 and the induction of bioluminescence, while HAI-1 and CAI-1 are undetectable prior to the late exponential growth phase. CAI-1 activity reaches its maximum upon entry into stationary phase, while molar concentrations of AI-2 and HAI-1 become approximately equal. Similarly, autoinducer-dependent exoproteolytic activity increases at the transition into stationary phase. These findings are reflected in temporal alterations in expression of the luxR gene that encodes the master regulator LuxR, and of four autoinducer-regulated genes during growth. Moreover, in vitro phosphorylation assays reveal a tight correlation between the HAI-1/AI-2 ratio as input and levels of receptor-mediated phosphorylation of LuxU as output. Our study supports a model in which the combinations of autoinducers available, rather than cell density per se, determine the timing of various processes in V. harveyi populations.

Our skin influences our physical and mental health, and its chemical composition can reflect environmental and disease conditions. Therefore, through sampling the skin metabolome, we can provide a promising window into the mechanisms of the body. However, the broad application of skin metabolomics has recently been hampered by a lack of easy and widely applicable sampling methods. Here, we present a novel rapid, simple, and, most importantly, painless and non-invasive sampling technique suitable for clinical studies of fragile or weakened skin. The method is called WET PREP and is simply a lavage of the skin which focuses on capturing the metabolome. We systematically evaluate WET PREPs in comparison with the non-invasive method of choice in skin metabolomics, swab collection, using ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS2) on two complementary chromatographic columns (C18 reversed phase and hydrophilic interaction chromatography). We also integrate targeted analyses of key metabolites of skin relevance. Overall, WET PREP provides a strikingly more stable shared metabolome across sampled individuals, while also being able to capture unique individual metabolites with a high consistency in intra-individual reproducibility. With the exception of (phospho-)lipidomic studies, we recommend WET PREPs as the preferred skin metabolome sampling technique due to the quick preparation time, low cost, and gentleness for the patient.

Atopic eczema (AE) is an inflammatory skin disorder affecting approximately 20% of children worldwide and early onset can lead to asthma and allergies. Currently, the mechanisms of the disease are not fully understood. Metabolomics, the analysis of small molecules in the skin produced by the host and microbes, opens a window to observe the mechanisms of the disease which then may lead to new drug targets for AE treatment. Here, we review the latest advances in AE metabolomics, highlighting both the lipid and non-lipid molecules, along with reviewing the metabolites currently known to reside in the skin.