Abstract
Koumiss is a traditional fermented raw mare’s milk product. It contains high nutritional value and is well-known for its health-promoting effect as an alimentary supplement. This study aimed to investigate the bacterial diversity, especially lactic acid bacteria (LAB), in koumiss and raw mare’s milk. Forty-two samples, including koumiss and raw mare’s milk, were collected from the pastoral area in Yili, Kazakh Autonomous Prefecture, Xinjiang Uygur Autonomous Region in China. This work applied PacBio single-molecule real-time (SMRT) sequencing to profile full-length 16S rRNA genes, which was a powerful technology enabling bacterial taxonomic assignment to the species precision. The SMRT sequencing identified 12 phyla, 124 genera, and 227 species across 29 koumiss samples. Eighteen phyla, 286 genera, and 491 species were found across 13 raw mare’s milk samples. The bacterial microbiota diversity of the raw mare’s milk was more complex and diverse than the koumiss. Raw mare’s milk was rich in LAB, such as Lactobacillus (L.) helveticus, L. plantarum, Lactococcus (Lc.) lactis, and L. kefiranofaciens. In addition, raw mare’s milk also contained sequences representing pathogenic bacteria, such as Staphylococcus succinus, Acinetobacter lwoffii, Klebsiella (K.) oxytoca, and K. pneumoniae. The koumiss microbiota mainly comprised LAB, and sequences representing pathogenic bacteria were not detected. Meanwhile, the koumiss was enriched with secondary metabolic pathways that were potentially beneficial for health. Using a Random Forest model, the two kinds of samples could be distinguished with a high accuracy 95.2% [area under the curve (AUC) = 0.98] based on 42 species and functions. Comprehensive depiction of the microbiota in raw mare’s milk and koumiss might help elucidate evolutionary and functional relationships among the bacterial communities in these dairy products. The current work suffered from the limitation of a low sample size, so further work would be required to verify our findings.
Highlights
Koumiss or kumis is a traditional and highly nutritious fermented milk beverage, which is widely consumed by nomads in Central Asia (Wszolek et al, 2007; Mulyawati et al, 2019)
The number of operational taxonomic units (OTUs) curves did not level off (Figure 1A), but the ShannonWiener diversity curves (Figure 1B), and the rank-abundance curves (Figure 1C) of all samples reached plateau, suggesting that the sequencing depth was adequate to capture most bacterial diversity new phylotypes could still be found with increasing sequencing
Performance improvement was minimal once the top 42 most discriminatory species and functions were included (Supplementary Table S2 and Figure 9A), and samples from the koumiss could be distinguished from raw mare’s milk samples with 95.2% accuracy [ten-fold cross validation area under the curve (AUC) = 0.98, Figure 9B]
Summary
Koumiss or kumis ( named arrag, chige, and airag in Mongolian language) is a traditional and highly nutritious fermented milk beverage, which is widely consumed by nomads in Central Asia (Wszolek et al, 2007; Mulyawati et al, 2019). Koumiss is made from raw mare’s milk. Artisanal koumiss is made from raw milk by back-slopping method. Previous studies have reported associations between koumiss fermentative microbes and metabolites with the unique flavor properties and potential therapeutic components (Menghe et al, 2004; Tang et al, 2020). The rich nutrition, potential therapeutic properties, and high microbial diversity of koumiss make it an interesting product to study. Some previous studies have described koumiss microbiota by culture-dependent and/or culture-independent methods, scarce data are currently available due to the difficulty in accessing the remote areas of sampling of koumiss. No study has yet compared the microbiota structure and composition between raw mare’s milk, the raw material of koumiss, and its fermented product. Culture-independent methods are more sensitive and accurate in providing ample biological information (Menghe et al, 2004; Gesudu et al, 2016; Mulyawati et al, 2019; Guo et al, 2020)
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