The human microbiome contributes significantly to the genetic content of the human body. Genetic and environmental factors help shape the microbiome, and as such, the microbiome can be unique to an individual. Previous studies have demonstrated the potential to use microbiome profiling for forensic applications; however, a method has yet to identify stable features of skin microbiomes that produce high classification accuracies for samples collected over reasonably long time intervals. A novel approach is described here to classify skin microbiomes to their donors by comparing two feature types: pangenome presence/absence features and nucleotide diversities of stable clade-specific markers. Supervised learning was used to attribute skin microbiomes from 14 skin body sites from 12 healthy individuals sampled at three time points over a >2.5-year period with accuracies of up to 100% for three body sites. Feature selection identified a reduced subset of markers from each body site that are highly individualizing, identifying 187 markers from 12 clades. Classification accuracies were compared in a formal model testing framework, and the results of this analysis indicate that learners trained on nucleotide diversity perform significantly better than those trained on presence/absence encodings. This study used supervised learning to identify individuals with high accuracy and associated stable features from skin microbiomes over a period of up to almost 3 years. These selected features provide a preliminary marker panel for future development of a robust and reproducible method for skin microbiome profiling for forensic human identification. A novel approach is described to attribute skin microbiomes, collected over a period of >2.5 years, to their individual hosts with a high degree of accuracy. Nucleotide diversities of stable clade-specific markers with supervised learning were used to classify skin microbiomes from a particular individual with up to 100% classification accuracy for three body sites. Attribute selection was used to identify 187 genetic markers from 12 clades which provide the greatest differentiation of individual skin microbiomes from 14 skin sites. This study performs skin microbiome profiling from a supervised learning approach and obtains high classification accuracy for samples collected from individuals over a relatively long time period for potential application to forensic human identification.
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http://dx.doi.org/10.1128/AEM.01672-17 | DOI Listing |
J Pharm Biomed Anal
March 2025
Vice President's Office, Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou 550005, China. Electronic address:
The focus of this study is to explore the impact of gut microbiota in different states on the blood components of couplet medications (dried toad skin and radix clematidis) and to identify drug metabolites associated with the gut microbiota. By constructing a pseudo-sterile rat model and combining non-targeted metabolomics with plasma pharmacology, we found that the plasma metabolites of couplet medications underwent significant changes in different gut microbiome environments. The GABA and PGE1 levels in the model group and the model+TCM (traditional chinese medicine) group were both significantly lower than those in the normal+TCM group.
View Article and Find Full Text PDFFront Microbiol
February 2025
Centre of Biosciences of the Slovak Academy of Sciences, Institute of Animal Physiology, Košice, Slovakia.
Introduction: Human and animal skin is colonized by a complex microbial population. An imbalance of these microorganisms is often associated with dermatological diseases.
Methods: The aim of this work was to describe the skin bacterial microbiota composition of healthy dogs and dogs with inflammatory skin lesions.
Front Allergy
February 2025
Department of Paediatrics, Ruijin Hospital Affiliated with Shanghai Jiaotong University School of Medicine, Shanghai, China.
Background: A reduction in biodiversity and alterations in the microbiota composition are relevant to allergic diseases. However, combined analyses of the skin, nasal and gut microbiotas are lacking in the literature. In addition, in previous studies, microbiota were detected mainly by V3-V4 sequencing, but other sequences might be missed with this technique.
View Article and Find Full Text PDFInt J Mol Sci
February 2025
Givaudan Active Beauty, R&D, 31400 Toulouse, France.
Cranberry oil is known for nutritional benefits, and this work is aimed at studying its soothing properties and potential as an intimate care ingredient. The antioxidant, anti-inflammatory, and anti-irritation properties of cranberry oil were evaluated on epithelial cells and tissues, including the vaginal epithelium. The impact of the oil on vaginal microbiota was assessed in vitro.
View Article and Find Full Text PDFInt J Mol Sci
February 2025
School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China.
Triple-negative breast cancer (TNBC) poses a major clinical challenge due to its aggressive progression and limited treatment options, making early diagnosis and prognosis critical. MicroRNAs (miRNAs) are crucial post-transcriptional regulators that influence gene expression. In this study, we unveil novel miRNA-mRNA interactions and introduce a prognostic model based on miRNA-target interaction (MTI), integrating miRNA-mRNA regulatory correlation inference and the machine learning method to effectively predict the survival outcomes in TNBC cohorts.
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