Mobiluncus and Peptoniphilus

Mobiluncus is one of bacteria reducing trimethylamine oxide to trimethylamine. It was also found to be associated with halitosis and bacterial vaginosis. We documented this bacterium in the gut and vaginal samples of several participants of our microbiome study. A new paper found Mobiluncus in umbilical dirt of the high odor score group. 

Since odor scores did not show a normal distribution, samples were divided into two groups, one with an odor score ≥2.0 and one <2. Well-known resident bacteria of skin, such as Cutibacterium, Staphylococcus, and Corynebacterium, were not detected, whereas some anaerobic bacteria, including Mobiluncus (q-value=2.1E-33), Arcanobacterium (q-value=4.5E-22), and Peptoniphilus (q-value=4.3E-17), were highly abundant in umbilical dirt samples with high odor scores. The same genera were detected when samples were divided into two groups with an odor score ≥1.5 as the criterion.

By a predictive metagenome analysis using Picrust2, the authors identified genes that appeared to be specific to umbilical dirt with high odor scores. Metabolic pathways common to the extracted gene groups were analyzed by GSEA (Gene Set Enrichment Analysis). Anaerobic metabolic pathways, such as methane metabolism and glycolysis/gluconeogenesis, were more abundant in the high odor score group, and secondary metabolite production pathways, such as the biosynthesis of secondary metabolites and quorum sensing, were also identified.

While, Mobiluncus is associated with halitosis and bacterial vaginosis, Peptinophilus contributes to underarm odor by producing chemicals such as butyric acid. Acetobacter is one of species that could be counteracting the undesirable odors in this context. 

REFERENCES

Yano T, Okajima T, Tsuchiya S, Tsujimura H. Microbiota in Umbilical Dirt and Its Relationship with Odor. Microbes Environ. 2023;38(3). doi: 10.1264/jsme2.ME23007. PMID: 37407492.

Valerie M, Milaine T, Aicha N, Roger A, Patrick MJ, Ibrahima D, Nehemie D, Laure N, Angeline B. Survey on Intravaginal Practices among Women of Reproductive Age at the Gynaeco-Obstetric and Pediatric Hospital of Yaounde: Association with Bacterial Vaginosis Caused by Gardnerella Vaginalis and Mobiluncus. International Journal Of Medical Science And Clinical Research Studies. 2023 Jan 30;3(1):121-6.

Gabashvili IS Cutaneous Bacteria in the Gut Microbiome as Biomarkers of Systemic Malodor and People Are Allergic to Me (PATM) Conditions: Insights From a Virtually Conducted Clinical Trial. JMIR Dermatol 2020;3(1):e10508  doi: 10.2196/10508

Zhang L, Hong Q, Yu C, Wang R, Li C, Liu S. Acetobacter sp. improves the undesirable odors of fermented noni (Morinda citrifolia L.) juice. Food Chemistry. 2023 Feb 1;401:134126. 

Digital Forensics and Sensory Forecasting through VOC Analysis

Everyone leaves a trace, whether it's a tangible object, invisible DNA, or even an odor. 

In a recent study, a team of scientists achieved a remarkable 96% accuracy in determining human sex using a machine learning model guided by human expertise. Researchers collected hand odor samples from 60 individuals and analyzed them using Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS). The results revealed distinct VOC signatures that allowed for the classification and prediction of gender. Various dimensional reduction techniques were employed to interpret the data, such as Partial Least Squares-Discriminant Analysis (PLS-DA), Orthogonal-Projections to Latent Structures Discriminant Analysis (OPLS-DA), and Linear Discriminant Analysis (LDA). The highest discrimination and classification of subject gender were observed with OPLS-DA and LDA as confidence level ellipses of both models were not seen to intersect. 

In another study, a combination of deep learning, chemometrics, and sensory evaluation proved effective in distinguishing between various methods of roasting food. The researchers employed E-nose and E-tongue devices, quantitative descriptive analysis (QDA), HS-GC-IMS, and HS-SPME-GC–MS to differentiate lamb shashliks prepared through traditional charcoal grilling and four alternative methods. The results showed that these techniques effectively identified the characteristic flavors and volatile organic compounds (VOCs) associated with each roasting method. The clustering heat maps were generated using TBtools and Python was used to run SVM, RF, XGBoost, DNN 5-layer, CNN-SVM, and t-SNE. The CNN-SVM model outperformed other models in predicting VOC content and identifying the specific roasting methods. 

REFERENCES

Chantrell J. G. Frazier ,Vidia A. Gokool ,Howard K. Holness,DeEtta K. Mills,Kenneth G. Furton. Multivariate regression modelling for gender prediction using volatile organic compounds from hand odor profiles via HS-SPME-GC-MS Published: July 5, 2023

https://doi.org/10.1371/journal.pone.0286452

Shen C, Cai Y, Ding M, Wu X, Cai G, Wang B, Gai S, Liu D. Predicting VOCs content and roasting methods of lamb shashliks using deep learning combined with chemometrics and sensory evaluation. Food Chem X. 2023 Jun 14;19:100755. doi: 10.1016/j.fochx.2023.100755. PMID: 37389322; PMCID: PMC10300318.