Back

Co-Occurrence Network Analysis Reveals The Alterations Of The Skin Microbiome And Metabolome In Atopic Dermatitis Patients

Portal Gomes, P. W.; Mannochio-Russo, H.; Mao, J.; Zhao, H. N.; Tipton, C. D.; Ancira, J.; Dorrestein, P. C.; Li, M.

2023-08-18 microbiology
10.1101/2023.08.17.553735 bioRxiv
Show abstract

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 better treatments and identify specific microbial or molecular biomarkers. This study investigates the skin microbiome and metabolome of healthy 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. Staphylococcus species, especially S. aureus, were significantly increased in the ADL group. Metabolomic profiles were also different between the groups. Dipeptide-derived are more abundant in ADL, which may be related to skin inflammation. Co-occurrence network analysis was applied to integrate the microbiome and metabolomics data and revealed higher co-occurrence of metabolites and bacteria in healthy and ADNL compared to ADL. S. aureus co-occurred with dipeptide-derived in ADL, while phytosphingosine-derived compounds showed co-occurrences with commensal bacteria, e.g. 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. 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.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.