Co-occurrence network analysis reveals the alterations of the skin microbiome and metabolome in adults with mild to moderate atopic dermatitis.

Skin microbiome can be altered in patients with atopic dermatitis (AD). An understanding of the changes from healthy to atopic skin can help develop new targets for treatment by identifying microbial and molecular biomarkers. This study investigates the skin microbiome and metabolome of healthy adult subjects and lesion (ADL) and non-lesion (ADNL) of AD patients by 16S rRNA gene sequencing and mass spectrometry, respectively. Samples from AD patients showed alterations in the diversity and composition of the skin microbiome, with ADL skin having the greatest divergence. Staphylococcus species, especially S. aureus, were significantly increased in AD patients. Metabolomic profiles were also different between the groups. Dipeptide derivatives are more abundant in ADL, which may be related to skin inflammation. Co-occurrence network analysis of the microbiome and metabolomics data revealed higher co-occurrence of metabolites and bacteria in healthy ADNL compared to ADL. S. aureus co-occurred with dipeptide derivatives in ADL, while phytosphingosine-derived compounds showed co-occurrences with commensal bacteria, for example, Paracoccus sp., Pseudomonas sp., Prevotella bivia, Lactobacillus iners, Anaerococcus sp., Micrococcus sp., Corynebacterium ureicelerivorans, Corynebacterium massiliense, Streptococcus thermophilus, and Roseomonas mucosa, in healthy and ADNL groups. Therefore, these findings provide valuable insights into how AD affects the human skin metabolome and microbiome.IMPORTANCEThis study provides valuable insight into changes in the skin microbiome and associated metabolomic profiles in an adult population with mild to moderate atopic dermatitis. It also identifies new therapeutic targets that may be useful for developing personalized treatments for individuals with atopic dermatitis based on their unique skin microbiome and metabolic profiles.

Urine microbes and predictive metagenomic profiles associate with abnormalities in sperm parameters: implications for male subfertility.

OBJECTIVE: To explore the taxonomic and predicted functional relationship between the urine microbiome and alterations of semen analysis (SA) parameters. DESIGN: Cross-sectional study. SETTING: Academic medical center. PATIENT(S): Men presenting for fertility evaluation or men presenting for vasectomy consultation with proven biological paternity were recruited and stratified on the basis of alterations, or lack thereof, in SA parameters. MAIN OUTCOME MEASURE: Changes in the functional and taxonomic urine microbiome profiles of participants with or without alterations in SA parameters. RESULTS: Seventy-three participants were included in our study. Men with abnormal sperm motility (N = 27) showed a nearly 50-fold higher abundance of Dialister micraerophilus compared with those with normal sperm motility (N = 46). This relationship persisted on canonical correlational analysis (r = 0.439). Men with abnormal sperm concentration (N = 20) showed a lower abundance of Enterococcus faecalis and Staphylococcus aureus, compared with those with normal sperm concentration (N = 53). The urine of participants with impaired sperm motility demonstrated dramatic differences in predictive functional profiles in pathways involved in oxidation-reduction balance and cell longevity. CONCLUSIONS: Our findings underscore differences in the urinary microbiome and abnormalities in semen parameters, especially sperm motility. By incorporating predictive functional profiling, we also highlight possible mechanisms that may drive the observed differences in sperm parameters.

Semen microbiota are dramatically altered in men with abnormal sperm parameters.

There has recently been an explosion of studies implicating the human microbiome in playing a critical role in many disease and wellness states. The etiology of abnormal semen analysis (SA) parameters is not identified in 30% of cases; investigations involving the semen microbiome may bridge this gap. Here, we explore the relationship between the semen microbiome and alterations of sperm parameters. We recruited men presenting for fertility evaluation or vasectomy consultation with proven biological paternity. SA and next generation sequencing was performed. Differential abundance testing using Analysis of composition of Microbiota with Bias Correction (ANCOM-BC) was performed along with canonical correlational analysis for microbial community profiling. Men with abnormal (N = 27) sperm motility showed a higher abundance of Lactobacillus iners compared to those with normal (N = 46) sperm motility (mean proportion 9.4% versus 2.6%, p = 0.046). This relationship persisted on canonical correlational analysis (r = 0.392, p = 0.011). Men with abnormal sperm concentration (N = 20) showed a higher abundance of Pseudomonas stutzeri (2.1% versus 1.0%, p = 0.024) and Pseudomonas fluorescens (0.9% versus 0.7%, p = 0.010), but a lower abundance of Pseudomonas putida (0.5% versus 0.8%, p = 0.020), compared to those with normal sperm concentration (N = 53). Major limitations are related to study design (cross-sectional, observational). Our results suggest that a small group of microorganisms may play a critical role in observed perturbations of SA parameters. Some of these microbes, most notably Lactobacillus iners, have been described extensively within other, fertility-related, contexts, whereas for others, this is the first report where they have potentially been implicated. Advances in our understanding of the semen microbiome may contribute to potentially new therapeutic avenues for correcting impairments in sperm parameters and improving male fertility.

Characterizing the biofilm of artificial urinary sphincters (AUS).

Background: There is a paucity of data regarding the bacterial colonization on artificial urinary sphincter (AUS) devices following revision surgery. We aim to evaluate the microbial compositions of explanted AUS devices identified on standard culture at our institution. Methods: Twenty-three AUS devices explanted were included in this study. During revision surgery, aerobic and anaerobic culture swabs are taken from the implant, capsule, fluid surrounding the device, and biofilm, if present. Culture specimens are sent to the hospital laboratory for routine culture evaluation immediately upon case completion. Differences in number of microorganism species detected across samples (richness) against demographic variables were determined through backwards selection of all variables using analysis of variance (ANOVA). We assessed the prevalence (how many times each species occurred) of microbial culture species. Statistical analyses were performed using the statistical package in R (version 4.2.1). Results: Cultures reported positive results in 20 (87%) cases. Coagulase-negative staphylococci were the most commonly identified bacteria among explanted AUS devices (n=16, 80%). Among two of the four infected/eroded implants, more virulent organisms such as Escherichia coli and fungal species such as Candida albicans were identified. The mean number of species identified amongst culture positive devices was 2.15±0.49. The number of unique bacteria identified per sample was not significantly associated with demographic variables including race, ethnicity, age at revision, smoking history, duration of implantation, etiology for explantation, and concomitant medical comorbidities. Conclusions: The majority of AUS devices removed for non-infectious reasons harbor organisms on traditional culture at the time of explantation. The most commonly identified bacteria in this setting is coagulase-negative staphylococci, which may be a result of bacterial colonization introduced at the time of implant. Conversely, infected implants may harbor microorganisms with higher virulence including fungal elements. Bacterial colonization or biofilm formation on implants may not necessarily equate to clinically infected devices. Future studies with more sophisticated technology, such as next-generation sequencing or extended cultures, may evaluate microbial compositions of biofilm at a more granular level to understand its role in device infections.