Fermentation: Teaming up to make kombucha
From cheese to salami, to beer or miso soup, chances are that your favorite delicacy owes its unique flavors to humble communities of microorganisms which ferment sugars into substances that preserve and improve food (Bourdichon et al., 2021). Humans have been enthusiastically brewing or pickling since the Bronze Age, yet surprisingly little is known about the intricacies of the fermentation process (Farag et al., 2019; Yang et al., 2014).
Fermenting food requires dozens if not hundreds of microbial species which work closely together, each producing substances which the others take up, use and transform into new chemicals important for other species in the community (Tamang et al., 2016). These complex interactions make it challenging to disentangle how individual actors contribute to the overall process, and to identify the ones essential for the final product. Now, in eLife, Xiaoning Huang, Yongping Xin and Ting Lu report having methodically reduced the complex microbial system which creates the tangy drink known as kombucha tea, down to a single pair of species (Huang et al., 2022).
Kombucha is created by a thriving community of yeast and bacteria which work together to ferment sugary tea. Huang et al. first focused on the features that this culture must have to produce the famous concoction. Three key characteristics emerged: both yeast and bacteria should be present; a characteristic jelly-like film or ‘pellicle’ should form at the surface; and the culture should consume sucrose while accumulating acetate, ethanol, and small amounts of sucrose constituents such as glucose. Preserving these features ensured that a core community of microbes would capture the essential metabolism of the native culture found in kombucha (Figure 1; Step 1).
Next, the team (who are based at the University of Illinois Urbana-Champaign and the China Agricultural University) isolated five yeast and five bacterial species, examining each of them individually or as yeast-bacteria pairs. Some bacteria completely depended on yeast to break down sucrose into glucose and into other essential molecules required for their survival (Figure 1; Step 2). Although all yeast species could survive on their own, the distinctive properties of kombucha (such as its pellicle, high acidity and acetate production) occurred only in co-cultures, indicating that bacteria did contribute to these community functions.
To understand how the community worked at an even finer scale, Huang et al. focused on a single yeast-bacteria pair which could create all three features characteristic of native kombucha. This co-culture was remarkably stable: no matter the ratio of yeast to bacteria at the start of the process, the final communities had roughly equal numbers of each species once stable. They also all produced concoctions which closely resembled traditional kombucha, with similar levels of acidity, sugars, ethanol, and acetate.
Next, these two species were individually cultured on diverse nutrient sources to closely monitor which compounds they could consume and produce (Figure 1; Step 3). The manipulation revealed that only the yeast could make glucose and ethanol; this likely involves the cells secreting an enzyme that processes sucrose into glucose, which is then available for ‘public use’ (Tran et al., 2020; Smith and Schuster, 2019). In turn, the bacteria could only create a pellicle when they consumed glucose and ethanol at the same time. This experiment helped to finally piece together how the two species interact: yeast feed and stimulate bacteria with glucose and ethanol, while bacteria wrap the community in a film that may shield it from the environment (Yin et al., 2019).
If two species alone can thrive and produce kombucha-like tea, then why does this process normally involve many more microorganisms? This taxonomic diversity may improve adaptability (Willi et al., 2006), or it may just emerge through random processes (Sloan et al., 2006); it could even be an artefact due to sampling at an inadequately large scale (Fierer and Lennon, 2011). Further studies are needed to investigate these possibilities.
The reductionist approach developed by Huang et al. allows scientists to pinpoint the core subgroups of microbes which perform the primary functions of a wider community, and to disentangle the role of individual species. This framework is useful to understand the metabolic processes responsible for the signature look, taste and smell of fermented foods. The next steps would potentially involve finetuning the method to study microbial communities which are harder to define, such as those that interact with host organisms or the wider environment.
References
-
The forgotten role of food culturesFEMS Microbiology Letters 368:fnab085.https://doi.org/10.1093/femsle/fnab085
-
The generation and maintenance of diversity in microbial communitiesAmerican Journal of Botany 98:439–448.https://doi.org/10.3732/ajb.1000498
-
Quantifying the roles of immigration and chance in shaping prokaryote community structureEnvironmental Microbiology 8:732–740.https://doi.org/10.1111/j.1462-2920.2005.00956.x
-
Public goods and cheating in microbesCurrent Biology 29:R442–R447.https://doi.org/10.1016/j.cub.2019.03.001
-
Review: Diversity of microorganisms in global fermented foods and beveragesFrontiers in Microbiology 7:377.https://doi.org/10.3389/fmicb.2016.00377
-
Limits to the adaptive potential of small populationsAnnual Review of Ecology, Evolution, and Systematics 37:433–458.https://doi.org/10.1146/annurev.ecolsys.37.091305.110145
-
Proteomics evidence for kefir dairy in early Bronze Age ChinaJournal of Archaeological Science 45:178–186.https://doi.org/10.1016/j.jas.2014.02.005
-
Biofilms: the microbial “protective clothing” in extreme environmentsInternational Journal of Molecular Sciences 20:E3423.https://doi.org/10.3390/ijms20143423
Article and author information
Author details
Publication history
- Version of Record published: August 11, 2022 (version 1)
Copyright
© 2022, Ponomarova
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,943
- Page views
-
- 149
- Downloads
-
- 0
- Citations
Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Chromosomes and Gene Expression
- Computational and Systems Biology
We herein introduce voyAGEr, an online graphical interface to explore age-related gene expression alterations in 49 human tissues. voyAGEr offers a visualisation and statistical toolkit for the finding and functional exploration of sex- and tissue-specific transcriptomic changes with age. In its conception, we developed a novel bioinformatics pipeline leveraging RNA sequencing data, from the GTEx project, encompassing more than 900 individuals. voyAGEr reveals transcriptomic signatures of the known asynchronous ageing between tissues, allowing the observation of tissue-specific age periods of major transcriptional changes, associated with alterations in different biological pathways, cellular composition, and disease conditions. Notably, voyAGEr was created to assist researchers with no expertise in bioinformatics, providing a supportive framework for elaborating, testing and refining their hypotheses on the molecular nature of human ageing and its association with pathologies, thereby also aiding in the discovery of novel therapeutic targets. voyAGEr is freely available at https://compbio.imm.medicina.ulisboa.pt/app/voyAGEr.
-
- Cancer Biology
- Computational and Systems Biology
Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.