Evolutionary genomics reveals plant origins of acetic acid bacteria in fermented food

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Bret , View ORCID Profile David A. Duchêne , View ORCID Profile Angela M. Oliverio , View ORCID Profile Jonathan Z. Shik , View ORCID Profile Sandra B. Andersen , Robert R. Dunn , View ORCID Profile Juan Antonio Rodríguez , View ORCID Profile Veronica M. Sinotte doi: https://doi.org/10.1101/2025.07.09.663944 Ana Cuesta-Maté 1 Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ana Cuesta-Maté For correspondence: ana.mate{at}sund.ku.dk sinotte{at}food.ku.dk Thibault P. Bret 1 Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site David A. Duchêne 1 Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen , Copenhagen, Denmark 2 Section for Epidemiology, Department of Public Health, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David A. Duchêne Angela M. Oliverio 3 Department of Biology, Syracuse University , Syracuse, New York, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Angela M. Oliverio Jonathan Z. Shik 4 Section for Ecology and Evolution, Department of Biology, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jonathan Z. Shik Sandra B. Andersen 1 Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sandra B. Andersen Robert R. Dunn 5 Department of Applied Ecology, North Carolina State University , Raleigh, North Carolina, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Juan Antonio Rodríguez 1 Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Juan Antonio Rodríguez Veronica M. Sinotte 1 Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen , Copenhagen, Denmark 6 Section for Food Microbiology, Gut Health, and Fermentation, Department of Food Science, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Veronica M. Sinotte For correspondence: ana.mate{at}sund.ku.dk sinotte{at}food.ku.dk Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Humans have historically relied on acetic acid bacteria (AAB) for food fermentation, yet their origins must trace back to free-living species outside of human environments. In nature, plants, fruit flies, and social insects host AAB. However, the evolutionary transitions of AAB from symbiotic hosts to fermented foods remain ambiguous. Here, we conduct a comprehensive phylogenomic analysis of 570 publicly available AAB genomes. We find that the ∼170My evolutionary history of this group is concordant with the rise of angiosperms, corbiculate bees, and the consequential accelerated availability of environmental carbohydrates. Unlike other ferment-associated microbes, ferment-associated AAB have exclusively evolved from clades inhabiting flowers and fruits, but not insect hosts. Genomic features are similar in plant- and ferment-associated AAB, yet markers of early adaptation to ferments are also present. Conversely, social insect-associated AAB have reduced genome sizes, which may have limited their functional capability to disperse into ferments. Plant- and ferment-associated AAB coincide in the ability to metabolise diverse plant carbohydrates, though both have adapted to produce habitat-specific carbohydrate-active enzymes. In contrast, metabolic capacity is reduced in social insect-associated AAB. By tracing the phylogenomics of this clade, we understand how evolution forged AAB capable of performing metabolic work for humans, shaping the history and potential futures of fermentation. Introduction Acetic acid bacteria (AAB) are central to the fermentation of food and drinks. Archaeological evidence indicates that human populations have harnessed the potential of AAB for food fermentation over the last several thousand years ( Mazza & Murooka, 2009 ; Giudici et al., 2015; Pecci et al., 2020 ). In the history of Western science, AAB were among the first microbes characterized, documented by Antonie van Leeuwenhoek’s study of vinegar ( Lane, 2015 ; van Leuwenhoek, 1676). Today, AAB are fundamental to fermentations including vinegar, kombucha (De Roos & De Vuyst, 2018a; Tran et al., 2020 ), select sourdoughs ( Landis et al., 2021 ; Rappaport et al., 2024 ), and vitamin C biosynthesis ( Xu et al., 2004 ; Hattori et al., 2012 ; Gomes et al., 2018 ; La China et al., 2018 ). In ferments, these bacteria metabolize sugar and alcohol into acetic acid. This acid reduces substrate pH, inhibits the growth of other microbes, and thus facilitates food preservation ( Matsushita & Matsutani, 2016 ). However, for perhaps hundreds of millions of years before human use, AAB evolved as symbionts of plants, social insects (i.e., bees and some ants), and non-social insects (i.e., fruit flies) ( Yamada & Yukphan, 2008 ; Chouaia et al., 2014 ; Pedraza, 2016 ; Brown & Wernegreen, 2019 ; Härer et al., 2022 ; Miller, 2023 ; Sannino & Dobson, 2023 ). The evolutionary transitions of these bacteria from symbiotic contexts into human-mediated fermentation remain are understood. In day-to-day fermentation, food microbes can be seeded by tools repeatedly used for fermentation (i.e. barrels) ( Verberg, 2019 ; Dunn et al., 2021 ), spaces (i.e. fermentation rooms, wine/cheese caves) ( Bokulich & Mills, 2013 ), microbial starters (i.e. commercial bacterial cultures) ( De Melo Pereira et al., 2020 ), or previous ferments (i.e. backslopping) ( Landis et al., 2021 ). However, prior to the advent of food fermentation, these microbes must have inhabited and evolved outside of human environments. Such evolutionary origins are now being discovered for a handful of microbes used to produce fermented foods. For example, the brewer’s yeast Saccharomyces cerevisiae depends upon social wasps for dispersal, overwintering, and hybridization ( Stefanini et al., 2012 , 2016 ; Madden et al., 2018 , 2022 ), suggesting wasps may have provided the ancestral yeasts for food fermentation. Similarly, ants have been suggested to be the evolutionary origin of the sourdough lactic acid bacteria, Fructilactobacillus sanfranciscensis ( Sinotte et al., 2024 ; Zheng et al., 2022 ). We hypothesize that coevolutionary histories of AAB with symbiotic hosts provided adaptations that predisposed a subset of AAB to successfully colonise fermented foods. Their ancestral symbioses with social insects that feed on sugar-rich resources ( Anderson et al., 2013 , 2018 ; Brown & Wernegreen, 2016 , 2019 ; Smith & Newton, 2020 ) or with plants that produce sugary fruits or floral nectar ( Pedraza, 2008 , 2016 ) could have prepared AAB to overtake sugar-rich fermented food environments. Fruit flies may have also mediated this AAB transition into fermented foods because they host AAB and frequently visit flowers, fruits, and ferments ( Clymans et al., 2019 ; Madden et al., 2022 ; Woodcock et al., 2014 ). To date, studies of AAB have lacked sufficient interdisciplinarity to connect the dots between the applied uses of AAB in food fermentation ( Guzman & Vilcinskas, 2022 ; Murooka, 2016 ) and the natural history of their insect- ( Anderson et al., 2013 ; Miller et al., 2021 ) and plant-associated AAB ancestors ( Pedraza, 2016 ). Thus, the evolutionary origins of AAB in fermented foods is unresolved. Evolutionary transitions of AAB from symbiotic hosts to fermented foods potentially involved genomic and metabolic changes. We predict that human-mediated fermentation practices, such as the repeated use of tools, spaces or backslopping, have left genomic signatures in AAB akin to vertical transmission. These practices result in recurrent transfer of AAB from an old ferment to a new ferment. This broadly meets the definition of vertical transmission, in which symbionts are inherited across host generations. In symbiotic hosts, vertically transmitted bacteria often undergo genome reduction ( Moran et al., 2008 ) and exhibit adenine-thymine (AT) nucleotide bias ( Van Leuven & McCutcheon, 2012 ; Brown & Wernegreen, 2016 ). We hypothesise that ferment vertical transmission also induced these genomic changes, which have previously been observed in AAB vertically transmitted by ants and bees ( Brown & Wernegreen, 2016 , 2019 ; Comandatore et al., 2021 ). It is possible that the genomic changes associated with transitions to fermentation included, not only, loss of genes and functions, but also metabolic adaptations. The resources and conditions associated with fermentation may differ from ancestral states of AAB. For example, carbohydrate metabolism may be implicated in these genomic adaptations; it is the use of carbohydrates and consequent acid production that allows AAB to outcompete other microbes, and these carbohydrates vary across symbiotic hosts and ferments. Here, we use comparative phylogenomics of AAB associated with ferments, plants, social insects and fruit flies to clarify their evolutionary history. First, we reconstruct the AAB phylogeny to infer the evolutionary origins of AAB used in fermented foods. We additionally link evolutionary transitions in AAB and the evolution of their hosts, including angiosperm (flowering plants) and social bee diversifications ( Condamine et al., 2005 ; Benton et al., 2022 ; Peris & Condamine, 2024 ; Cardinal & Danforth, 2011 , 2013 ; Peled et al., 2025 ). Second, we test whether vertical transmission of fermented food AAB yields evolutionary signatures of genome reduction and AT-bias, like those often seen with AAB-insect symbioses. Third, we compare the metabolic profiles of AAB, focusing on carbohydrate metabolism, to investigate whether AAB show changes in their metabolic ability reconcilable with adaptation to life within ferments. Ultimately, this study underscores the ecological and human relevance of using a holistic framework. Evolutionary genomics provides a lens through which to discern the origins of fermented food AAB and, importantly, clarify how these diverse origins may be implicated in past and future food fermentations. Results Fermented food AAB originate from plant symbionts Phylogenetic inference from acetic acid bacteria (AAB) genomes clarifies their evolutionary host transitions relative to plants, social insects, fruit flies, and ferments. The AAB phylogeny and time-calibration was based on a published set of 71 single-copy core genes from 132 AAB genomes, and 2 genomes from the acidophilic sister group (Supplemental Table 2). The results suggest that host transitions in AAB were linked with major events of adaptation and diversification in host taxa. In this ancient clade (∼170 Mya), the earliest split across all of the AAB considered occured with the uniquely lichen-associated genera Lichenicoccus and Lichenicola ( Noh et al., 2020 ; Pankratov et al., 2020 ). Then dominant clades of AAB arose during the angiosperm terrestrial revolution between 60 to 120 million years ago, a time where sugar sources in flowers and fruits increased in abundance and diversity ( Figure 1 ; Peris & Condamine, 2024 ). Likewise, we estimate that the Bombella clade, largely associated with bumblebees and honeybees, split from plant-associated AAB at a period of accelerated diversification of their modern bee hosts ( Cardinal & Danforth, 2013 ). Last, we find that substantial further diversification of the AAB species within the last 2 to 23 My ( Figure 1 ). Download figure Open in new tab Figure 1. Time-calibrated phylogenomic tree of acetic acid bacteria (AAB) and their habitats. The AAB phylogeny inferred using 134 genomes, with each genome being the most complete and least contaminated for its species. The tree was built on a 71 single-copy core gene alignment ( Hug et al., 2016 ; Lee, 2019 ), and nearly all branches displayed satisfactory site concordance factors (sCF) (>70, considered strong support; Supplemental Figure 1). The boxes distinguishing the clades are based on the taxonomy established in Guzman & Vilcinskas, (2022) . Clade names are coloured by habitat, as indicated by the isolation source for the majority of genomes. Species font colour indicates the isolation source for the specific genome. The pie charts next to the species names represent habitats, according to the different isolation sources of publicly available genomes (570 genomes for all acetic acid bacteria; Supplementary Data 1). The blue circle indicates the branch split that we chose for time-calibration, marking the separation between honeybees and stingless bees (∼80 Mya) according to Cardinal & Danforth, (2011) , which host the distinct AAB lineages. The grey bars on the nodes reference the 95% highest posterior density intervals. Shaded areas delineate the angiosperm terrestrial revolution (green) and corbiculate bee diversification (yellow) according to Morreau et al., (2006) and Cardinal & Danforth, (2011 , 2013 ), respectively. All AAB associated with fermented foods have originated and diversified exclusively from four plant-associated lineages: Komagataeibacter , Acetobacter , Gluconobacter, and Asaia ( Figure 1 ). Plant-associated AAB lineages thus seem predisposed to successful colonization of ferments. One exception to this pattern appears to be the Gluconacetobacter clade, which is found exclusively in plants and not known to be involved in fermentation. Fruit flies are host to a phylogenetically dispersed set of AAB ( Figure 1 ), yet do not have specific clade associated with them. Given that they are also closely associated with multiple AAB habitats (i.e., fruits, flowers, ferments), our data lends evidence to the hypothesis that fruit flies mediate plant-to-ferment or ferment-to-ferment AAB dispersal (Supplemental figure 2). In contrast to plant-associated AAB, no AAB in fermented foods have evolved from social insects. Rather, it appears that once AAB adapted to life within social insects, transitions to other habitats have been hardly feasible ( Figure 1 ). We find two social insect-associated AAB clades: the Commensalibacter clade and the Bombella clade. The Commensalibacter clade has thus far been detected primarily in social insects, such as social bees, although there have also been a handful of strains sampled from cockroaches and butterflies ( Botero et al., 2023 ; Guzman et al., 2021 ). Species of the Bombella clade are confined to social Hymenoptera ( Härer et al., 2022 ), with their earliest-diverging lineage being associated with ants, followed by diversification into stingless bees and wasps, and later honeybees and bumblebees (Supplemental Data 1). Fermented food AAB genomes have early evolutionary markers of vertical transmission In many systems in which bacterial symbionts are associated with and vertically transmitted by hosts, bacteria undergo reduction in genome size and GC nucleotide content. We compared the genome size and nucleotide composition across 570 publicly available AAB genomes and 87 genomes of acidophilic bacteria found free-living in soil and water ( Guzman & Vilcinskas, 2022 , Supplemental Data 1). We found that in the transition from free-living habitats, AAB reduced genome size and GC content, suggesting adaptation to symbiotic lifestyles ( Figure 2A ). Subsequent AAB evolution from plant to ferment habitats caused no overall differences in genome size and GC-content ( Figure 2A ; Genome size: z=2.4268, p =0.0914, GC%: z=-0.2051, p =1.0000). However, when we increase resolution, to focus on the unique split of the plant-associated Gluconacetobacter clade with the ferment-associated Komagataeibacter sister clade, we observed genome reduction and AT-bias ( Figure 2B ; Genome size: W=1982, z=5.98, p <0.0001; GC%: W=2208, z=7.52, p <0.0001). This is congruent with the genomic consequences of vertical transmission. As expected, AAB clades associated with social insects, Commensalibacter and Bombella , have reduced genome size and GC content ( Figure 2A , Supplemental Fig 3; Genome size: all comparisons p <0.0001; GC%: all comparisons p <0.05), consistent with the literature ( Alonso et al., 2019 ; Comandatore et al., 2021 ; Guzman et al., 2021 ; Härer et al., 2022 ; Botero et al., 2023 ). Collectively, the results suggest there are genomic consequences of vertical transmission of select ferment-associated AAB, where genomic changes are more recent than counterparts found in social insects. Download figure Open in new tab Figure 2. Genomic characteristics of acetic acid bacteria found in fermented foods, plants, fruit flies and social insects. ( A-E ) The figure includes 570 AAB genomes, and ( A ) contains an additional 87 acidophilic bacteria genomes (Supplemental Data 1). (A) Scatter plot of genome size and GC content (%), including the free-living sister group, acidophilic bacteria. AAB habitat has a significant effect on genome size (χ 2 (3)=204.52, p <0.0 001), and GC content (χ 2 (3)=104.39, p <0.0001), as well as phylogenetic relatedness (genome size Blomberg’s K =0.417; p <0.001; GC content Blomberg’s K =1.808; p <0.001). (B) Comparison between Gluconacetobacter (plant) and Komagataeibacter (ferment) sister clades, indicating reduction in genome size and GC% in the ferment clade. Grey dots represent the remaining AAB genomes not included in the comparison. (C) Average gene length within AAB genomes. (D) Proportion of the genome composed of coding sequences over the length of the full genome. (E) Number of genes per genome. In panels (C-E) boxplots represent the first and third quartiles, the horizontal line represents the median, whiskers extend 1.5 interquartile ranges, and dots represent each of the data points. The letters represent statistical dissimilarities across groups according to Kruskal-Wallis with a Dunn’s Post Hoc test for pairwise comparisons ( p <0.05). If the boxplots share the same letters, it means no statistical differences between groups. All statistics included in Supplementary Table 3. Bacterial genome reduction found with vertical transmission can be mediated by decreases in gene length, altered proportion of coding sequences, and gene loss. AAB associated with ferments exhibited two of these genome features. First, the average gene length decreased in ferment-associated AAB compared to that of AAB living with plant or insect hosts ( Figure 2C , all comparisons p <0.0001, Supplemental Table 3). Here, shortened genes may thus mediate the genome reduction observed in some fermented food AAB. Second, the overall proportion of coding sequences in ferment genomes was significantly reduced ( Figure 2D , all p <0.0001). Vertical transmission can cause small population sizes and therefore ineffective DNA repair machinery. Consequential accumulated mutations (i.e., Muller’s ratchet) will result in a greater proportion of non-coding regions ( Moran, 1996 ; McCutcheon & Moran, 2012 ; Comandatore et al., 2021 ). These genomic modifications in fermented food AAB resemble early evolutionary consequences of vertical transmission. The accumulation of mutations over long evolutionary timescales of vertical transmission will eventually lead to the loss of those non-coding regions and additional genes mutated ( McCutcheon & Moran, 2012 ; Moran & Bennett, 2014 ); thus, those genomes will be significantly reduced. This is the case for the transition of AAB into social insects, where events of gene loss ( Figure 2E , all p <0.05) appear to have driven the genome reduction. Carbohydrate metabolism evolves with AAB transitions to fermented foods and social insects AAB transitions to fermented food and social insect habitats appear to have modified the diversity of enzymes for carbohydrate metabolism, albeit in different ways. Ferment-associated AAB maintain similar numbers of carbohydrate-active enzymes (CAZymes) to those in their plant-associated counterparts ( Figure 3A ), but their composition was significantly different ( Figure 3B ; Adonis pairwise: F=3.1677, R 2 =0.0307, p =0.03). The differences in CAZyme composition were due, in part, to niche-endemic CAZyme families; 18 CAZyme families were unique to plant-associated AAB and 6 were unique to ferment-associated AAB (Supplementary Figure 4). Conversely, social insect-associated AAB carbohydrate metabolism appears specialised, with a significantly reduced the number of CAZymes compared to plant or ferment-associated AAB ( Figure 3A ; Tukey HSD: p <0.0001), thus generating distinct CAZyme profiles from all other AAB ( Figure 3B ; Adonis pairwise, social insect vs all other habitats: p <0.01; Supplementary table 5). It appears that over 100 million years of evolution and vertical transmission resulted in the reduction of genes, including those related to carbohydrate metabolism. Download figure Open in new tab Figure 3. Carbohydrate metabolic potential of acetic acid bacteria isolated from fermented foods, plants, and insects. (A) Observed richness of carbohydrate-active enzyme (CAZyme) families for each AAB habitat, represented as the absolute number of CAZymes annotated for each genome. 112 CAZymes were found across all genomes (Supplemental Data 4). AAB habitat significantly affected the total number of CAZymes encoded per genome ( Figure 3A , ANOVA: Observed richness: F 3,127 =13,14 Cohen’s F=0.56, p <0.0001). Boxplots represent the first and third quartiles, the horizontal line represents the median, whiskers extend 1.5 interquartile ranges and dots represent each of the data points. The letters represent statistical dissimilarities across groups according to ANOVA with Tukey Post Hoc test for pairwise comparisons ( p <0.0001). All statistics are included in Supplementary Table 5. (B) Non-metric multidimensional scaling (NMDS) ordination based on Bray-Curtis distances of the CAZyme family profiles of each bacterial genome from the respective AAB habitats. The composition of CAZyme families also differed as a function of AAB habitat ( Figure 3B ; PERMANOVA: F 3,127 =10.209, R2=0.194, p <0.0001). (C) Heatmap of the number of CAZymes predicted to metabolise carbohydrate substrates found in fermented foods, plants and social insects. The 134 genomes used to reconstruct the AAB phylogenetic tree ( Figure 1 ) were annotated for the study of metabolic capacity. The carbohydrate metabolism of AAB in different habitats is reconcilable with the evolutionary transitions and natural history of those AAB, particularly with regard to transitions to fermented foods and social insects ( Figure 3C ). Plant- and ferment-associated AAB encode enzymatic machinery for broad, diverse and overlapping use of carbohydrate substrates. These similarities may reflect the relatively recent evolutionary transition of AAB from plants to ferments and be reinforced by the diversity of substrates shared between ancestral plant and ferment habitats. In contrast, AAB in social insects have a highly limited carbohydrate metabolism, aligned with host control in coevolved mutualisms. For example, they lack the ability to metabolise substrates in host-specific tissues (chitin) or host diet (sucrose) and retain genes for a small subset of habitat-specific carbohydrates. Discussion In the context of fermentation, acetic acid bacteria (AAB) are among the most economically and culturally important microorganisms to humans. Yet, their evolutionary origins in our ferments, particularly with regard to their host associations before the advent of food fermentation, have until now been unresolved. We show evidence that AAB in fermented food evolved exclusively from AAB living with and on plants, in particular, angiosperms. We hypothesize that the similarities between ferments and sugar-rich plant parts facilitated these habitat transitions. First, many ferments are made from plants and contain similar carbohydrates, which explains the overlap in carbohydrate metabolism. Second, ferments, floral nectar, and fruits host comparable microbiomes; in both AAB depend upon metabolic exchanges with yeast ( De Roos & De Vuyst, 2018 ; De Vuyst & Leroy, 2020 ; Fleet, 2003 ; Ge et al., 2025 ; Hall et al., 2018 ; Landis et al., 2021 ). This keeps with our observation that plant- and ferment-associated AAB have the ability to metabolize chitin and glycogen, carbohydrates found in yeast and other fungi. Third, the seasonality of flowering and fruiting in angiosperms is akin to the ephemerality of ferment making. We hypothesize that this ephemerality drives extreme resource competition ( Smid & Lacroix, 2013 ; Bauer et al., 2018 ; Toju et al., 2018 ) and fosters a “sit-and-wait” strategy. During substrate scarcity, AAB wait in a low metabolic state ( Walther & Ewald, 2004 ; Sundberg et al., 2014 ; Rayner et al., 2024 ), then when resources become available, these microbes divide quickly and produce acetic acid as a biocide ( Matsushita & Matsutani, 2016 ; Tran et al., 2020 ; Wang et al., 2024 ; Han et al., 2024 ). Traditional balsamic vinegar provides an excellent example of how similarities between plants and ferments may have facilitated AAB habitat transitions. The AAB species used in traditional balsamic vinegar fermentation are closely related to AAB found on the grapes from which it is made (i.e. Komagataeibacter , Novacetimonas , Acetobacter ) ( Gullo et al., 2006 ; Gullo & Giudici, 2008 ; Murooka, 2016 ). Plausibly, these bacteria were able to metabolise the sugar in grapes and evolved in concert with Zygosaccharomyces yeasts, found on both grapes and in balsamic fermentation (Giudici et al., 2015; Rainieri & Zambonelli, 2009 ; Solieri & Giudici, 2009 ). We hypothesize that a “sit-and-wait” strategy to quickly monopolise rotting grapes represented a pre-adaptation that enabled those first AAB to dominate when transferred to balsamic fermentation. The time of that habitat transition, from grapes to balsamic, is not known. However, centuries of use of the same wooden casks in traditional Italian balsamic production (Giudici et al., 2015) likely shaped the AAB evolution into the proficient balsamic AAB embedded within them. The evolution of well-studied lineages of fermentation microbes in response to transitions from hosts to ferments, such as in the case of Saccharomyces yeasts, has now been well demonstrated ( Stefanini et al., 2012 , 2016 ; Madden et al., 2018 ; Di Paola et al., 2023 ). In AAB, we also see evolutionary changes consistent with the vertical transmission mediated by fermentation practices, albeit relatively modest ones. We hypothesize that the significant reduction in proportion of coding sequences in ferment-associated AAB is caused by the mutations accumulated by faulty DNA machinery, consistently found in the small population sizes generated by vertical transmission (i.e. each generation of ferments goes through a population bottleneck) ( McCutcheon & Moran, 2012 ; Moran et al., 2008 ; Moran & Wernegreen, 2000 ). Eventually, the accumulation of mutations can lead to GC reduction and ultimately genome size reduction ( Moran & Plague, 2004 ). We observed this when comparing the ferment clade Komagataeibacter to its plant-associated sister group Gluconacetobacter . These reductions in genome size and GC content are less pronounced than those seen in insect-associated AAB. In that case, tens of millions of years of insect-AAB symbiosis likely drove more drastic genome evolution, compared to the relatively recent, centuries-long presence of AAB in our ferments. The accumulation of ferment-specific carbohydrate enzymes further indicates genomic adaptation, aligned with metabolic changes observed in habitat transitions for fermented foods ( Sinotte et al., 2024 ; Somerville et al., 2024 ). AAB in social insects do not disperse to ferments, contrary to our predictions based on observations from other fermented food microbes ( Stefanini et al., 2012 , 2016 ; Di Paola et al., 2023 ; Sinotte et al., 2024 ). Rather, evolution in social insects has finely attuned AAB to their hosts. The transition of AAB from plants into social insects occurred just twice, around 100 My apart. Subsequent evolution in social insects led to significant genome reduction, consistent with prior findings of adaptive genome reduction in insect symbionts ( Chouaia et al., 2014 ; Moran & Bennett, 2014 ; Latorre & Manzano-Marín, 2017 ; Comandatore et al., 2021 ). The remaining genes preferentially complement the hosts’ needs, favouring cooperation ( McCutcheon & Moran, 2012 ; Wernegreen, 2015 ; Fisher et al., 2017 ). For instance, social insect-associated AAB cannot metabolise sucrose, a sugar prevalent in the mouth and crop of honeybees ( Wang et al., 2021 ); or chitin, a structural component of insects’ exoskeletons ( Chaudhari et al., 2011 ). Once AAB adapted to life with their social insect host, we hypothesize that their diminished metabolic capabilities prevented transitions to any other habitats. The origins and ecologies of fermentative AAB present exciting avenues for fermented food research. From publicly available AAB genomes, we reveal a previously unseen metabolic diversity that may support novel applications in fermentation. Our findings suggest that fruits and flowers are an overlooked and biodiverse reservoir of AAB. First, the broad metabolic potential encoded by these AAB offers an opportunity to select strains that can be implemented in the fermentation of novel foods. For example, food waste products (i.e. sidestreams), such as brewer’s spent grain, contain recalcitrant carbohydrates including lignin and hemicellulose ( Mussatto et al., 2006 ). Here, we identify AAB species with the metabolic machinery to act on these carbohydrates. These AAB may thus allow sidestreams to be integrated in fermentations such as sourdough bread ( Rappaport et al., 2024 ), supporting a circular economy and a sustainable food system ( Fassio & Tecco, 2019 ; Tamasiga et al., 2022 ; Zhang et al., 2022 ). Second, we may consider the adaptations of AAB as they transition from plant to ferment habitats. The genes or enzymes under positive selection in transitions to fermentation potentially have valuable functions for the transformation of food substrates. Upon better understanding the role of these enzymes, we can better design fermented food microbiomes, or implement the enzymes independently in processes such as precision fermentation. Evolutionary ecology allows us to engage with AAB diversity to effectively engineer future ferments and biosolutions, while uncovering how their evolutionary histories have shaped their current functional roles. Data and code availability All genomes were downloaded from the National Center for Biotechnology Information (NCBI) page, and the accession numbers are available in Data S1. This paper does not report any original code. All code used is available at g ithub.com/ThBret/Fermentation-bacteria and will be published with a doi through Zenodo. All additional data gathered is available in Supplemental Data and Tables (Supplemental Data available upon request at: ana.mate{at}sund.ku.dk ). Supplemental information Document S1. Figure S1-S4. Tables S3 and S5. Data S1. Genomes included the genomic characteristic analysis. Data S2. Genomes included in the phylogenomic tree. Data S4. Annotated CAZymes. Author contributions A.C.M., R.R.D. and V.M.S. conceived the paper, A.C.M., T.P.B., D.D., J.A.R. and V.M.S. conceived the analyses, A.C.M. and T.P.B. collected the data, D.D., A.M.O., J.Z.S, S.B.A. contributed data or analyses tools, A.C.M., T.P.B. and D.D. performed the analyses. A.C.M., R.R.D, J.A.R. and V.M.S. wrote the paper, with continuous collaboration with the rest of the authors. Declaration of interest The authors declare no competing interests. Methods Genomic dataset and data curation A dataset of the 675 publicly available genomes of Acetobacteraceae bacteria was curated for phylogenomic analysis. Genomes for all acetic acid bacteria, also known as the acetous group ( Guzman & Vilcinskas, 2022 ; Qiu et al., 2021 ), were downloaded from NCBI Genbank in January 2024 (n=588). Additionally, the reference genomes of the acidophilic group (n=87), sister and basal to the AAB group and also within the Acetobacteraceae family, were downloaded. Genome size, guanine-cytosine content percentage (GC content), completeness, contamination scores, and the isolation source were retrieved for each of the genomes from the NCBI database. We supplemented the NCBI data with the bacterial temperature and pH ranges when available (REF: https://bacdive.dsmz.de/ ). Only genomes that were over 90% complete with <5% contamination (defined as the presence of foreign DNA sequences within a genome assembly, belonging to a different organism) were included in our study, with the exception of 7 genomes: Acetobacter papayae , Acetobacteraceae bacteriumESL0697 , Asia astibilis , Asia platycodi , Gluconacetobacter sacchari , Komagataeibacter kakiaceti , Nguyenibacter vanlangensis ) which were added for taxonomic completeness (Supplementary Data 1). In the end, 570 genomes were included for analysis of acetic acid bacteria genome characteristics. Phylogenomic analyses The most complete and least contaminated genome for each of the species of acetic acid bacteria, under the criteria described above, was included for the construction of phylogenetic trees, leading to a total of 132 genomes. Two outgroups belonging to the basal acidophilic group were included, Roseomonas mucosa and Acidocella aromatica (Supplementary table 1). Anvi’o v.8 ( Eren et al., 2021 ) was used to construct the contigs.db files, using the commands anvi-script-reformat-fasta anvi-gen-contigs-database command. A total of 71 single-copy core genes (SCG) were used for phylogenetic inference, as specified in the Bacteria_71 Hidden Markov Model (HMM) profile ( Lee, 2019 ) embedded in Anvi’o, using anvi-run-hmms over each contigs.db file. Amino acid sequences for SCG were obtained through anvi-get-sequences-for-hmm-hits , with the flag get-aa-sequences . The alignment was constructed from amino acid sequences and was composed of 18,698 columns including 8,661 parsimony-informative sites, i.e. columns that contained at least two different amino acids present in more than two sequences ( Katoh et al., 2002 ; Ranwez et al., 2011 ). The alignment was used to construct a maximum-likelihood tree with IQ-Tree2 v.2.2.6 ( Minh et al., 2025 ). Phylogenetic reconstruction was based on the best-performing LG+F+R5 substitution model, as determined using information-criterion model selection implemented in ModelFinder ( Kalyaanamoorthy et al., 2017 ). To quantify support across branches we focused on the highly intuitive site concordance factors, which represent the portion of informative alignment sites that include an explicit signal supporting each given tree branch (sCF; ( Minh et al., 2020 ). The package ape ( Paradis et al., 2004 ) was used to visualise trees in R (v. 4.3.2). Trait evolution patterns Phylogenetic signal refers to the phylogenetic clustering of a given trait, expected if the trait is highly heritable and arises rarely, as opposed to showing random scatter across the phylogenetic tree ( Revell et al., 2008 ; Kamilar & Cooper, 2013 ). We investigated the phylogenetic signal of genome size and GC content, using Blomberg’s K ( Blomberg et al., 2003 ) as implemented in the phylosig function of the Phytools v2.1.1 R package ( Revell, 2024 ). We then investigated the evolution of the isolation source trait by fitting a Markov-based model for the evolution of discrete traits ( Lewis, 2001 ) using the fitMk function of the Phytools v2.1.1 R package ( Revell, 2024 ). The function was run with an ARD model to obtain evolutionary rate values ( q ) for transitions between each isolation source. Time-dated phylogenetic inference Phylogenetic time-calibration was based on one key split in the phylogeny, between Parasaccharibacter apium and Acetobacteraceae bacterium ESL0697 , according to the divergence time of their hosts. Parassaccharibacter apium is isolated from honeybees Apis mellifera , while Acetobacteraceae bacterium ESL0697 is isolated from stingless bees Melipona lateralis . Exchange of this bacterial symbiont between these two species is highly unlikely after their evolutionary divergence, given their high specificity to their insect host gut microbiomes and the lack of known interactions between distant geographic distributions ( Powell et al., 2016 ; Kwong et al., 2017 ; Mazel et al., 2025 ). Thus, we make the assumption that the timing of their divergence is also the time of the most recent common ancestor between their bacterial microbiomes. The split between these bee taxa is estimated to have occurred between 78 and 95 million years ago ( Cardinal & Danforth, 2011 ), based on fossil records and estimates of the timing of the appearance of eusociality ( Brady et al., 2006 ). Bayesian divergence time inference was performed by forcing the IQ-TREE2 tree topology, under approximate likelihood computation for highly efficient analysis as implemented in the module MCMCTree ( dos Reis, 2022 ) of the PAML package v4.10.7 ( Yang, 1997 ). We used a GTR+Γ model of nucleotide substitution and a relaxed clock with gamma-distributed rates across branches. A birth-death process was used as a tree prior with default parameters. We approximated the posterior distribution via Markov chain Monte Carlo sampling, using 10 7 chain steps after a burn-in phase of 10 5 , with samples taken every 10 3 . Convergence to the stationary distribution was verified by running two independent analyses and comparing parameter estimates and checking that effective samples sizes across all parameters were above 200. Analysis of genomic characteristics Genome size, GC content, completeness and contamination scores were also retrieved for each of the genomes. Further data curation was performed using Prokka v.1.14 ( Seemann, 2014 ) for gene annotation and coding density. Boxplots and scatterplots were generated for several genomic features, and all statistical analyses were performed in R (v. 4.3.2). For the analyses of genomic characteristics such as genome size, GC content and coding sequence information, Shapiro-Wilk tests were used to confirm the normality of our data, using shapiro.test function from the lmtest R package (v.0.9-40; Zeileis & Hothorn, 2002 ). Kruskal-Wallis tests were performed to address the significant impact of our categorical variables in our data, using the function kruskal.test from the stats R package (v.4.3.2; Kruskal & Wallis, 1952 ). Dunn’s Post Hoc multiple testing was used for pairwise comparisons between groups, using the dunnTest function in the R package DesTools (v0.99.54; Dunn, 1964 ). For pairwise comparisons of relevant clades, normality was confirmed as described above; the impact of our variables was addressed using a Wilcoxon rank sums test for two independent groups, using the wilcox.test function in the stats R package (v.4.3.2; Mann & Whitney, 1947 ). Statistical significance and parameters are included in Supplementary Table 3. Carbohydrate metabolic potential of AAB We explored the carbohydrate metabolism capabilities by using the dbcan4 v.4.0 pipeline ( Zheng et al., 2023 ), which scanned our AAB genomes against a pre-annotated CAZyme nucleotide database ( Drula et al., 2022 ). We calculated Observed richness and Shannon diversity on the relative abundance of CAZyme families in each of the AAB habitats, using the estimateR and diversity functions in the vegan R package ( Oksanen et al., 2025 ). The effect of the isolation source was determined in R (v. 4.3.2) using ANOVA ( aov function) and subsequent pairwise testing with ANOVA with Tukey Post Hoc test for pairwise comparisons ( TukeyHSD function), in the stats R package (v.4.3.2.). Effect sizes were determined with cohens_f function in the effectsize package (v.08.8). Compositional differences for CAZyme families between AAB genomes were determined by calculating Bray Curtis dissimilarity differences, and performing non-metric multidimensional scaling (NMDS) analysis, using the metaMDS function in the vegan package (v2.6-6.1, Oksanen, 2013) in R (distance=”bray”). The ordination of these results was visualised using ggplot2 (v.3.5.1). To determine if the isolation source had an effect on AAB CAZyme profiles, we performed a Permutational Multivariate Analysis of Variance (PERMANOVA) using the adonis2 function from vegan v2.6-6.1 on the distance matrix of Bray Curtis dissimilarity measurements. Pairwise comparisons of each timepoint were explored with the pairwise.adonis2 function from the pairwiseAdonis package v.0.4.1. Acknowledgments The authors would like to thank Jazmín Ramos-Madrigal for her theoretical contributions and methodological discussions early in the development of this project. The authors thank Jennifer Wernergreen and Leonie Johanna Jahn for their valuable theoretical contributions in the earlier stages of the project. The authors thank Robert Murphy for his input on the analyses of CAZyme activity. The authors thank Dinah Maran Parker for comments on the first draft of the manuscript. A.C.M., T.P.B., S.B.A. J.A.R. and V.M.S. were supported by the Danish National Research Foundation Centre for Evolutionary Hologenomics DNRF grant nr 143. D.A.D. acknowledges funding from a Novo Nordisk Fonden Emerging Data Science Investigator Award (NNF23OC0084647). A.M.O. was supported by National Science Foundation MCB award #2328528. J.Z.S. was funded by the Villum Foundation (VEX grant no. 50281), and a Semper Ardens: Accelerate grant (CF22-0664) from the Carlsberg Foundation, Denmark ( https://www.carlsbergfondet.dk ). Footnotes ↵ 7 Lead contact References ↵ Alonso , D. P. , Mancini , M. V. , Damiani , C. , Cappelli , A. , Ricci , I. , Alvarez , M. V. 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