Genomics and synthetic community experiments uncover the key
metabolic roles of acetic acid bacteria in sourdough starter
microbiomes
Acetic acid bacteria (AAB) are an important member of many fermented food
microbiomes. They are part of the Alphaproteobacteria class, Rhodospirillales order, and Acetobacteraceae family, with over 100 species across 19 genera now described . This group is
well-recognized for the fermentation of vinegar [ Acetobacter
pasteurianus ], kombucha
[ Komagataeibacter spp. ], lambic beer [ Acetobacter lambici ], water kefir [ Acetobacter
sicerae ], and cocoa
[ A. pasteurianus commercially and Acetobacter
ghanensis/senegalensis spontaneously ], among others. Broadly, AAB acidify lambic beer and vinegar,
contribute to flavor, discourage germination in cocoa beans, and produce the
cellulose component of a symbiotic culture of bacteria and yeast (i.e. SCOBY) in
kombucha . AAB also release a variety of
metabolic products that have been applied in food, cosmetics, medicine, and other
industries . These products, including
acetic acid (sour flavor and antimicrobial), DHA (dihydroxyacetone; a common
sunscreen ingredient), and acetoin (butter-like flavor), result from oxidative
fermentation of sugars and alcohols . AAB are also found in insect guts,
including fruit flies and bees , as well
as in flowers and fruits , all of which are
sugar-rich environments . Adaptation to
these sugar-rich environments may have facilitated success in fermented foods and
beverages . Despite their well-recognized importance, there is a limited understanding of the
ecology of AAB. One system where AAB are likely important but have largely been
overlooked is sourdough starters, which we refer to as sourdough, for brevity. The
bulk of sourdough microbiome research has focused on yeast and lactic acid bacteria
(LAB). These two functional groups are sufficient to make a sourdough starter , although starter microbiomes are often
more diverse, ranging from 3 to 10 total species, and include other microbes beyond
LAB and yeast . The
“back slopping” method involved in the maturation and maintenance of
sourdough starters allows for low-abundance species to become more dominant and for
new species to colonize, but factors that allow additional groups to persist are
unclear . For example, recent work has
indicated that AAB are commonly found in sourdough , but the
importance and function of AAB within sourdough remain uncertain as well. A few studies have begun to investigate the functional roles of AAB in sourdough, but
genomic and metabolic characterizations of AAB have been limited to other
environments including insect guts  and
vinegar . Common garden experiments with
wild sourdough starters suggest that AAB may affect dough rise and aroma , but there is limited controlled
experimental validation. A strain of Acetobacter tropicalis is
known to influence the resultant properties of Chinese steamed bread .
Li et al.  found that starters with AAB
added had the lowest pH, highest viscosity and elasticity, and had a greater variety
of flavor compounds than bread with yeast and LAB or yeast alone. Likewise, the
addition of extracted exopolysaccharides (levans and fructans) from AAB to dough
resulted in bread that was softer and had more volume, further indicating that the
compounds released by AAB can impact emergent traits of sourdough . However, insights from the vast majority
of ecologically and functionally diverse AAB remain limited. In addition to their economic and cultural significance, AAB in sourdough also
present an opportunity to study the ecological consequences of genomic and strain
variation within microbiomes. While it is clear that intraspecies diversity exists
across many microbiomes from comparative genomics and metagenomics studies , there are still
surprisingly few studies that have experimentally manipulated strain diversity to
understand impacts on microbiome composition and function. Past studies
investigating the role of variation at the intraspecies level have found that
strain-level differences are associated with microbial community composition and
functional traits , and
multiple strains can coexist in an environment due to multiple-niche polymorphism at
a small scale . As members of microbiomes across a wide range of
environments from sourdough to insects, AAB may be generalists, but environmental
selection pressures may have resulted in intraspecies diversification. It is also
unclear how much strain diversity exists within AAB species in sourdough and other
environments and how this diversity contributes to variation in emergent functions
of microbiomes. Understanding the functional significance of species and strain
diversity within AAB may help reveal novel approaches for managing the functions of
fermentations and other AAB-dominated microbiomes. To shed light on the ecological and functional roles of AAB at multiple levels of
genetic similarity in sourdough starter microbiomes, we isolated dominant AAB taxa
from a diverse collection of 500 sourdough starters contributed by community
scientists from around the world . This
collection represents 21 strains across 11 species spanning two genera . We obtained high-quality draft genomes
of all isolates and also obtained eight metagenome-assembled genomes (MAGs) to
characterize metabolic pathways and differences in gene content. We assessed if
particular functions were enriched in sourdough AAB genomes vs those from other
environments broadly across all AAB and within species clusters. We also used our
sourdough AAB isolate collection to experimentally determine the function of diverse
AAB within the sourdough starter microbiome. Constructing synthetic starter
communities with and without various AAB strains, we measured key emergent
properties such as acidification and metabolite production. We expect that all AAB
will lower the pH of starters due to well-characterized acetic acid production
 and predict that pH will vary by
strain based on corresponding variations in acetic acid production and growth
requirements . Past research has demonstrated that AAB abundance is
strongly correlated with sourdough volatile compound variation , but the extent to which particular
metabolites differ across AAB strains and species remains unclear. Our work
experimentally determines the consequences of an overlooked but functionally
important group of microbes in sourdough starters, the acetic acid bacteria, on
emergent microbiome function. 

Acetic acid bacteria are phylogenetically diverse and abundant in sourdough
starter microbiomes globally To determine the ecological distribution and diversity of AAB in sourdough
starters, we leveraged a sample collection of 500 sourdough starters collected
from a global network of community scientists . First, we did a detailed investigation of AAB across all
starters ( N = 500) using a previously sequenced 16S rRNA
amplicon data set . Then, we obtained
AAB genomes from the same starter collection by sequencing isolates and
reconstructing microbial genomes from metagenomes. Across the 500 sourdough
starters, AAB were present in 29.4% of samples (at ≥1% relative
abundance). The mean relative abundance of AAB was 23.3% of the overall
bacterial community on average, with up to a maximum relative abundance of 79.5%
in a single starter. We detected 26 AAB ASVs (amplicon sequence variants) that
span three genera ( Acetobacter , Gluconobacter ,
and Komagataeibacter ; ; ). In samples where AAB were present, 1.5 AAB ASVs
were detected on average. We next investigated if AAB exhibited consistent patterns of co-occurrence with
LAB, yeast, or other AAB, as this might help explain the variable distribution
patterns of AAB across starters. We found that LAB including Schleiferilactobacillus harbinensis , Lentilactobacillus kefiri , and Furfurilactobacillus
rossiae (Table S2) were enriched (Mann-Whitney, FDR P < 0.001 for all) in samples with a high relative
abundance of AAB (samples where AAB comprised at least 25% of the overall
bacterial community). S. harbinensis and L.
kefiri are common members of water kefir, an acidic beverage that
is often populated by AAB, suggesting shared acid tolerance . However, the most dominant LAB in sourdough
( Levilactobacillus brevis , Lactiplantibacillus
plantarum , Pediococcus parvulus , and Fructilactobacillus sanfranciscensis ) are found similarly
with and without AAB. Pichia mandshurica was the only yeast
significantly associated with AAB-dominant samples. This species is associated
with wine spoilage and produces acetic acid as well , likely explaining its success in AAB-dominant
sourdough samples with higher acidity. Saccharomyces cerevisiae made up about 75% of the yeast in both AAB-dominant (76.8%) and non-AAB-dominant
samples (75.8%), supporting that AAB can likely survive in most sourdough
microbiomes. Within AAB, Komagataeibacter and A.
pasteurianus were frequently detected in the same subset of samples
(Spearman’s rho = 0.32, P < 0.001), indicating
that multiple AAB can co-persist in starters (Table S3). Leveraging our 16S amplicon data to guide culturing efforts, we isolated 21 AAB
strains from 20 sourdough samples. The isolates we cultured span most of the
geographic diversity of AAB from our sourdough starter collection . AAB were found in starters from 4
continents and 14 countries including the USA, Belgium, China, Thailand, New
Zealand, Norway, and India. Reflecting our broader collection, AAB isolates lack
representation from South America and Africa and are overrepresented in North
America, where we collected the most samples overall. We also added eight MAGs
of AAB in sourdough from a set of 40 existing metagenomes ( ; Table S4). These MAGs represent more dominant AAB, as
the large portion of wheat genome reads impact sensitivity to detect
low-abundance AAB. We use both cultured representatives and MAGs for analyses.
We did not find any published AAB genomes isolated from sourdough starters based
on our search of publicly available databases, so our AAB genomes likely
represent a novel collection from sourdough starters. Notably, three of our
recovered isolates likely represent putative novel AAB species based on
<95% average nucleotide identity (ANI) match to described AAB species,
distinct phylogenetic placement, and reported RED (relative evolutionary
divergence) values from GTDB-Tk ( , ; Table S4 ). We anticipate the number of novel AAB lineages to
continue to increase as AAB are further studied in sourdough and other
previously overlooked fermentation environments. The AAB genomes we obtained represent most of the known phylogenetic diversity of
AAB in sourdough starters . We
sought to span the diversity of dominant AAB in sourdough in our targeted
culturing efforts. Across the 500 starters, ASVs were assigned to 20 major
species clusters as the highest possible resolution. Notably, genomes with as
low as 90% ANI were assigned to the same ASV despite representing multiple
species, highlighting the limited ability of short-read amplicon sequencing to
resolve the genetic diversity of AAB. Through the sequencing and assembly of
genomes from both isolates and metagenomes, we obtained representatives from 8
of the 10 most dominant clusters, as well as two representatives from less
abundant clusters . The most
abundant clusters in the 500 sourdough starters were Acetobacter
malorum / cerevisiae and Acetobacter
oryzoeni / oryzifermentans / pasteurianus at around 6.5% mean relative abundance. Our isolates reflect the dominant
sourdough strains, including A. malorum , A.
pasteurianus , Acetobacter orientalis , and Gluconobacter oxydans, among others ( ; Fig. S2). Although we cultured most of the abundant
groups, our efforts did not yield Komagataeibacter isolates. There was high correspondence in the taxonomic identity of isolates obtained from
our culturing efforts relative to expected AAB from amplicon sequencing , indicating that amplicon-informed
targeted culturing is a tractable approach in this system and likely other
fermented food systems where most members are readily culturable . The sourdough AAB genomes we obtained
span much of the broader AAB diversity across environments as well , although a couple of clades are
notable as they were not detected across our 500 starters. For example, the
clade that includes species, such as Acetobacter aceti , Acetobacter nitrogenifigens , and Acetobacter
sacchari , does not contain any members detected in ASVs or isolated
from sourdough. There is extensive variability in gene content and metabolic traits among
sourdough AAB To profile variation in gene content and metabolic traits of AAB genomes, we
compiled a set of AAB genomes from sourdough and other environments. In addition
to our 21 isolate genomes and 8 MAGs, we included 40 high-quality AAB genomes
from NCBI isolated from fermented beverages (beer, kombucha, and cider), fruit,
fruit flies, and a range of other sources (mud, tree bark, and sewage) resulting
in a collection of 61 AAB genomes (Table S4; Fig. S3). The NCBI genomes included
three species that we recovered from sourdough ( A. malorum , A. orientalis , and G. oxydans ) isolated
from other sources and other species of AAB ( Acetobacter
fallax , Acetobacter lovaniensis , A.
aceti , and Komagataeibacter xylinus ). Across AAB,
differences in ANI ranged from 70% (across genera), a noted ANI gap of
90%–95% observed in closely related species clusters, and 96%–98%
within species (Table S5). Across all genomes ( N = 61), we
detected 11,596 unique gene clusters. Of these, 909 represented core gene
clusters that were present in >95% of genomes (7.8%). The other 10,687
gene clusters were only detected in a subset of genomes and represent both
species-specific clusters and other accessory genes that were variably detected
( ; Table S6). We detected notable variation in AAB metabolism within and between species ( ; Table S7) and source environment
(Table S8). For example, genes for cellulose production were detected in several
fermented beverage genomes within A. aceti and A.
nitrogenifigens . In contrast, cellulose production was detected in
only one sourdough genome, A. orientalis ( ; Table S7). In kombucha, cellulose production is a
favored trait of A. nitrogenifigens to form the pellicle used
to inoculate future batches . In contrast, cellulose production may
be a disfavored trait in sourdough due to interference with structural
properties. Arabinan utilization genes were detected in four out of five A. oryzifermentans isolated from sourdough. Arabinan is a
plant polysaccharide, and the ability to break it down likely has functional
consequences within the sourdough starter microbiome, which is fed by wheat
flour. For all gene and pathway data presented in , including acetate, nitrogen, short-chain fatty acid, and
alcohol metabolism, see Table S7. Intraspecies variation may favor the persistence of AAB with key traits
related to resource utilization in the sourdough environment To determine if any genes were enriched in sourdough relative to other
environments at the strain and species cluster levels (where % ANI amongst
members ranged from 90% to 99%), we next focused our analyses on a subset of
three species for which we had both sourdough and non-sourdough genome
representatives: A. malorum / malorum A, A. orientalis , and G. oxydans/potus . We
note that both A. malorum A and G. potus were
recently reclassified as distinct species from A. malorum and G. oxydans , respectively. We first performed pangenomic
analyses on nine genomes within each of the three species clusters. The total
gene clusters ranged from 3,998 ( A. orientalis ) to 6,207
( A. malorum/malorum A; ; Table S6; Fig. S4). On average, species core genomes comprised
~40% of gene clusters, whereas the other 60% of gene clusters were
accessory. There was no clustering of overall genome content by either isolation source or
geographic location within these species . This could be because many of the AAB in sourdough starter
microbiomes are successful generalists adapted to a wide range of environments
rather than one specific niche. They may have a nomadic lifestyle similar to
some species of LAB such as L. plantarum  and the AAB
species A. senegalensis . Alternatively, the inclusion of more AAB genomes from sourdough
and other environments may reveal signatures of adaptation that are environment
specific. Despite an overall lack of clustering by geography or isolation
source, we found potential gene functions that were enriched in sourdough across
multiple strains. GH13 alpha-amylase, involved in starch cleavage, was enriched
in sourdough with the presence of 45% of strains relative to 28% of strains not
isolated from sourdough (Kruskal-Wallis FDR-corrected P < 0.01; ; Table S9). Starch
is a major component of wheat, so this could confer a competitive advantage to
AAB in sourdough vs other environments . Although limited by
the availability of high-quality AAB genomes from other environments, our
analyses start to shed light on the metabolic traits that may favor the
persistence of AAB in the sourdough environment. While gene clusters of unknown function were much more frequent in accessory
genomes, functionally annotated accessory genes largely fell into categories
that may have relevance to success in sourdough. On average, 61.0% of genes were
annotated with a known function (COG database) in accessory vs 87.3% in core
(Table S6). Although expected, this underscores the difficulty in determining
the functional or ecological significance of gene content variation. Mobilome
genes, related to transposons and prophages, made up the largest percentage of
accessory genes ( ; Fig. S5; Table
S10). A. pasteurianus has been known to have a high level of
genetic variability particularly linked to the mobilome, with 9% of genes
encoding transposases . Twenty-two
carbohydrate-active enzymes (CAZy) that may be relevant to success in sourdough
were found in plasmids and carried by prophages in the genomes we recovered from
sourdough, including the GH13 alpha-amylase significantly associated with
sourdough genomes (Fig. S6). Notably, an average of 24% of genes related to
carbohydrate utilization were detected in accessory genomes across the three
species clusters, underscoring the variability in microbial traits related to
nutrient acquisition. Accessory carbohydrate utilization genes included
5-carboxyvanillate decarboxylase LigW, involved in lignin degradation, and
alpha-galactosidase. The high intraspecies variation in resource utilization
genes may impact the establishment success and persistence of the AAB strain
within the sourdough microbiome. Synthetic common garden experiments reveal distinct species and strain level
effects of acetic acid bacteria on overall microbiome function To understand the consequences of acetic acid bacteria on the assembly and
emergent function of sourdough starter microbiomes, we constructed replicate
synthetic sourdough starter communities, varying only the AAB strain across
treatments . We proxy microbiome
function by pH and volatile organic compounds (VOCs) and also measure the
abundance of community members. We selected 10 AAB strains that represented the
breadth of phylogenetic diversity observed and included intraspecies diversity
within the A. malorum cluster. We qualitatively observed
morphological differences in size and color across the 10 strains (Fig. S7). For
example, Acetobacter sp. nov . 517 and G. potus colonies were smaller, whereas A. malorum and A.
malorum A were the largest. This could be due to differences in
starting concentrations, nutritional requirements, or growth rates. Most
colonies were white to pink in color, but A. fabarum had yellow
colonies (Fig. S7). A. orientalis was observed to produce a
biofilm, likely bacterial cellulose, or another exopolysaccharide. Cellulose is
produced by many AAB including those isolated from kombucha , but amorphous colonies due to cellulose production were not
commonly observed in the AAB strains isolated from sourdough. For our synthetic experiments, we selected a LAB ( L. brevis ) and
yeast ( S. cerevisiae ) as the base community, isolated from wild
starter S_129 (resulting in “SynCom129”) varying only the AAB
strain added. We also included a media-only blank {all synthetic communities
were grown in cereal-based fermentation medium [CBFM;  ], a liquid-based cereal fermentation media} and a YL-only
control with only yeast and LAB present. This design resulted in 12 distinct
treatments. For the number of replicates run for each assay (i.e., pH, VOC
production), see Table S11. After incubating for 48 hours, transfer to fresh
media, and incubation for another 48 hours (4 days total), we measured the
resultant community structure and emergent functions across treatments. We
assessed the persistence of all starting members and emergent functions of the
resultant starter including acidification (pH) and VOC profiles. We selected a
second background community of L. brevis and S.
cerevisiae (SynCom361) isolated from a second wild sourdough
(S_361) to confirm the trends we observed with pH and persistence analyses. In both synthetic communities, all 10 AAB strains persisted across replicates.
SynCom129 was isolated from a starter without AAB, suggesting that AAB are
likely able to establish and persist in a range of starters. While yeast and LAB
colony-forming units (CFUs) counts stayed fairly consistent, AAB CFU counts
varied greatly by strain, indicating the potential differential success of
strains in the synthetic communities. Higher AAB CFU counts were significantly
correlated with lower pH (Spearman’s rho = −0.83, P = 0.002), while LAB and yeast CFU counts were not
correlated ( ; Table S14). The pH was
also correlated with AAB ANI ( P = 0.055; ; Table S14). These results suggest that AAB density
and differences in genome content may play a role in modulating starter
acidification. The yeast and LAB persisted in every condition in SynCom129, but
in SynCom361, the yeast sometimes failed to persist. This could be due to a
mismatch of nutritional requirements but also may reflect inhibition by the AAB.
We observed yeast dropout in one condition where yeast appeared glued in EPS
(exopolysaccharides) from A. orientalis (Fig. S8). All AAB treatments acidified the starter environment, and the extent was
strain-specific (Fig. S9). In SynCom129, in comparison to the YL-only control,
AAB overall decreased the pH of the sourdough microbiome by 18.5% in a 4-day
period. The starting pH on day 0 of CBFM was around 6.26. By day 2, communities
had begun to acidify the starter environment. The average pH of communities with
AAB was 4.20 ( N = 30, SD = 0.30), and the average pH for
YL-only was 5.64 ( N = 3, SD = 0.03). On day 4, all communities
with AAB had significantly lower pH from YL-only and CBFM blank
( P < 0.0001; ; Table S14). The average pH of communities with AAB on day 4 was
3.94 ( N = 50, SD = 0.23), while the average for YL-only was
4.84 ( N = 5, SD = 0.05). Significant acidification was also
observed in SynCom361 on day 4 between AAB and non-AAB communities
( P < 0.0001) despite yeast dropout in some
replicates (Fig. S9). This is consistent with studies from other fermented foods
that have shown that AAB lower the pH of their environment .
The release of acetic acid and a corresponding decrease in pH may inhibit the
growth of other species and aid in AAB persistence . While pH was lowered in all AAB treatments, there were
significant differences between species, including between strains of closely
related species. A. oryzifermentans (average 4.36, SD = 0.02)
and G. potus (average 3.56, SD = 0.02) were significantly
different from all other AAB strains. The two A. malorum A
strains did not significantly differ from each other, while the strain of A. malorum differed from only A. malorum A
490. This finding underscores the importance of species and strain-level
diversification on emergent function. Sourdough VOCs significantly differed by starter community members. We analyzed
the VOCs from seven different communities frozen on day 4, including six AAB
treatments and a yeast-LAB-only control (Table S12). VOCs in the YL community
significantly differed from all AAB communities ( P = 0.008;
Table S14), and the AAB communities formed three clusters (A4 alone, A2/A6/A7,
and A8/A10; ) with distinct VOC
profiles ( P = 0.01; ; Table S14). Clusters were not associated with phylogeny. Notably,
despite 97.2% similarity in ANI, A. malorum A 507 and A. malorum A 490 were in different clusters. A variety of
compounds differed between the clusters (Table S13; ). Stearic acid ( P = 0.02;
AAB-associated clusters) and palmitic acid ( P = 0.02; higher in
the no-AAB controls and cluster A4) have both been previously detected in
ferments including Tarhana [a Turkish wheat and yogurt-based fermented food
]. We also detected compounds
previously linked to flavor formation in sourdough including decanal, which is
associated with vinegar and a sweet, citrusy flavor , and L-arabitol, which is associated with sweet flavor
. Both were more abundant in
samples with AAB, although not significantly (Table S13). Our results suggest
that strain-level variation in AAB members can impact the sensory properties of
the resulting community. Conclusions Our study represents a genomic and ecological characterization of AAB isolated
from sourdough starters and demonstrates the utility of synthetic consortia to
determine the functional consequences of specific microbiome members. Our
genomic analyses begin to uncover the diversity in AAB genome content and
suggest that sourdough AAB may be enriched in specific gene functions that
facilitate success in the sourdough environment. These analyses also suggest
that community-wide emergent functions can be affected by differences in gene
content resulting from intraspecies diversification. Our comparative genomic
analyses were limited, however, by the number of high-quality and publicly
available AAB genomes from other environments. Increasing genomic representation
from this group of economically and culturally significant bacteria will
continue to yield new insights on their metabolic diversity and the ways in
which they may be adapted to diverse sugar-rich environments. Likewise,
obtaining closed genomes using approaches such as long-read and hybrid
sequencing will facilitate a more resolved understanding of the location of key
regions and genes of interest and the opportunity to study population-scale
dynamics. Our synthetic experiments demonstrate that phylogenetically and metabolically
diverse AAB can persist in common sourdough microbiomes. We also determine the
impact of AAB species and strain-level diversity on emergent sourdough functions
including acidification and volatile profiles. Notably, we found that
intraspecies variation in A. malorum resulted in significantly
different community VOC profiles of synthetic starters. These findings have
direct relevance to industrial and home-bakers considering flavor profiles and
other features of sourdough bread. A clear next step is to extend these results
and assess how AAB may impact functional consequences in baked sourdough bread.
Similarly, our synthetic experiments were performed using an S.
cerevisae and L. brevis background community.
Although this pairing represents one of the most dominant sourdough LAB and
yeast combinations, it will be important to determine if AAB have similar
functional consequences in other common sourdough types such as F.
sanfranciscensis and Kazachstania humilis . An
exciting avenue for future research is using tools such as interaction screens,
RNA-seq, and metabolomics to shed light on what molecular-level mechanisms
underlie the observed community-wide functional shifts. 

To determine the ecological distribution and diversity of AAB in sourdough
starters, we leveraged a sample collection of 500 sourdough starters collected
from a global network of community scientists . First, we did a detailed investigation of AAB across all
starters ( N = 500) using a previously sequenced 16S rRNA
amplicon data set . Then, we obtained
AAB genomes from the same starter collection by sequencing isolates and
reconstructing microbial genomes from metagenomes. Across the 500 sourdough
starters, AAB were present in 29.4% of samples (at ≥1% relative
abundance). The mean relative abundance of AAB was 23.3% of the overall
bacterial community on average, with up to a maximum relative abundance of 79.5%
in a single starter. We detected 26 AAB ASVs (amplicon sequence variants) that
span three genera ( Acetobacter , Gluconobacter ,
and Komagataeibacter ; ; ). In samples where AAB were present, 1.5 AAB ASVs
were detected on average. We next investigated if AAB exhibited consistent patterns of co-occurrence with
LAB, yeast, or other AAB, as this might help explain the variable distribution
patterns of AAB across starters. We found that LAB including Schleiferilactobacillus harbinensis , Lentilactobacillus kefiri , and Furfurilactobacillus
rossiae (Table S2) were enriched (Mann-Whitney, FDR P < 0.001 for all) in samples with a high relative
abundance of AAB (samples where AAB comprised at least 25% of the overall
bacterial community). S. harbinensis and L.
kefiri are common members of water kefir, an acidic beverage that
is often populated by AAB, suggesting shared acid tolerance . However, the most dominant LAB in sourdough
( Levilactobacillus brevis , Lactiplantibacillus
plantarum , Pediococcus parvulus , and Fructilactobacillus sanfranciscensis ) are found similarly
with and without AAB. Pichia mandshurica was the only yeast
significantly associated with AAB-dominant samples. This species is associated
with wine spoilage and produces acetic acid as well , likely explaining its success in AAB-dominant
sourdough samples with higher acidity. Saccharomyces cerevisiae made up about 75% of the yeast in both AAB-dominant (76.8%) and non-AAB-dominant
samples (75.8%), supporting that AAB can likely survive in most sourdough
microbiomes. Within AAB, Komagataeibacter and A.
pasteurianus were frequently detected in the same subset of samples
(Spearman’s rho = 0.32, P < 0.001), indicating
that multiple AAB can co-persist in starters (Table S3). Leveraging our 16S amplicon data to guide culturing efforts, we isolated 21 AAB
strains from 20 sourdough samples. The isolates we cultured span most of the
geographic diversity of AAB from our sourdough starter collection . AAB were found in starters from 4
continents and 14 countries including the USA, Belgium, China, Thailand, New
Zealand, Norway, and India. Reflecting our broader collection, AAB isolates lack
representation from South America and Africa and are overrepresented in North
America, where we collected the most samples overall. We also added eight MAGs
of AAB in sourdough from a set of 40 existing metagenomes ( ; Table S4). These MAGs represent more dominant AAB, as
the large portion of wheat genome reads impact sensitivity to detect
low-abundance AAB. We use both cultured representatives and MAGs for analyses.
We did not find any published AAB genomes isolated from sourdough starters based
on our search of publicly available databases, so our AAB genomes likely
represent a novel collection from sourdough starters. Notably, three of our
recovered isolates likely represent putative novel AAB species based on
<95% average nucleotide identity (ANI) match to described AAB species,
distinct phylogenetic placement, and reported RED (relative evolutionary
divergence) values from GTDB-Tk ( , ; Table S4 ). We anticipate the number of novel AAB lineages to
continue to increase as AAB are further studied in sourdough and other
previously overlooked fermentation environments. The AAB genomes we obtained represent most of the known phylogenetic diversity of
AAB in sourdough starters . We
sought to span the diversity of dominant AAB in sourdough in our targeted
culturing efforts. Across the 500 starters, ASVs were assigned to 20 major
species clusters as the highest possible resolution. Notably, genomes with as
low as 90% ANI were assigned to the same ASV despite representing multiple
species, highlighting the limited ability of short-read amplicon sequencing to
resolve the genetic diversity of AAB. Through the sequencing and assembly of
genomes from both isolates and metagenomes, we obtained representatives from 8
of the 10 most dominant clusters, as well as two representatives from less
abundant clusters . The most
abundant clusters in the 500 sourdough starters were Acetobacter
malorum / cerevisiae and Acetobacter
oryzoeni / oryzifermentans / pasteurianus at around 6.5% mean relative abundance. Our isolates reflect the dominant
sourdough strains, including A. malorum , A.
pasteurianus , Acetobacter orientalis , and Gluconobacter oxydans, among others ( ; Fig. S2). Although we cultured most of the abundant
groups, our efforts did not yield Komagataeibacter isolates. There was high correspondence in the taxonomic identity of isolates obtained from
our culturing efforts relative to expected AAB from amplicon sequencing , indicating that amplicon-informed
targeted culturing is a tractable approach in this system and likely other
fermented food systems where most members are readily culturable . The sourdough AAB genomes we obtained
span much of the broader AAB diversity across environments as well , although a couple of clades are
notable as they were not detected across our 500 starters. For example, the
clade that includes species, such as Acetobacter aceti , Acetobacter nitrogenifigens , and Acetobacter
sacchari , does not contain any members detected in ASVs or isolated
from sourdough. 

To profile variation in gene content and metabolic traits of AAB genomes, we
compiled a set of AAB genomes from sourdough and other environments. In addition
to our 21 isolate genomes and 8 MAGs, we included 40 high-quality AAB genomes
from NCBI isolated from fermented beverages (beer, kombucha, and cider), fruit,
fruit flies, and a range of other sources (mud, tree bark, and sewage) resulting
in a collection of 61 AAB genomes (Table S4; Fig. S3). The NCBI genomes included
three species that we recovered from sourdough ( A. malorum , A. orientalis , and G. oxydans ) isolated
from other sources and other species of AAB ( Acetobacter
fallax , Acetobacter lovaniensis , A.
aceti , and Komagataeibacter xylinus ). Across AAB,
differences in ANI ranged from 70% (across genera), a noted ANI gap of
90%–95% observed in closely related species clusters, and 96%–98%
within species (Table S5). Across all genomes ( N = 61), we
detected 11,596 unique gene clusters. Of these, 909 represented core gene
clusters that were present in >95% of genomes (7.8%). The other 10,687
gene clusters were only detected in a subset of genomes and represent both
species-specific clusters and other accessory genes that were variably detected
( ; Table S6). We detected notable variation in AAB metabolism within and between species ( ; Table S7) and source environment
(Table S8). For example, genes for cellulose production were detected in several
fermented beverage genomes within A. aceti and A.
nitrogenifigens . In contrast, cellulose production was detected in
only one sourdough genome, A. orientalis ( ; Table S7). In kombucha, cellulose production is a
favored trait of A. nitrogenifigens to form the pellicle used
to inoculate future batches . In contrast, cellulose production may
be a disfavored trait in sourdough due to interference with structural
properties. Arabinan utilization genes were detected in four out of five A. oryzifermentans isolated from sourdough. Arabinan is a
plant polysaccharide, and the ability to break it down likely has functional
consequences within the sourdough starter microbiome, which is fed by wheat
flour. For all gene and pathway data presented in , including acetate, nitrogen, short-chain fatty acid, and
alcohol metabolism, see Table S7. 

To determine if any genes were enriched in sourdough relative to other
environments at the strain and species cluster levels (where % ANI amongst
members ranged from 90% to 99%), we next focused our analyses on a subset of
three species for which we had both sourdough and non-sourdough genome
representatives: A. malorum / malorum A, A. orientalis , and G. oxydans/potus . We
note that both A. malorum A and G. potus were
recently reclassified as distinct species from A. malorum and G. oxydans , respectively. We first performed pangenomic
analyses on nine genomes within each of the three species clusters. The total
gene clusters ranged from 3,998 ( A. orientalis ) to 6,207
( A. malorum/malorum A; ; Table S6; Fig. S4). On average, species core genomes comprised
~40% of gene clusters, whereas the other 60% of gene clusters were
accessory. There was no clustering of overall genome content by either isolation source or
geographic location within these species . This could be because many of the AAB in sourdough starter
microbiomes are successful generalists adapted to a wide range of environments
rather than one specific niche. They may have a nomadic lifestyle similar to
some species of LAB such as L. plantarum  and the AAB
species A. senegalensis . Alternatively, the inclusion of more AAB genomes from sourdough
and other environments may reveal signatures of adaptation that are environment
specific. Despite an overall lack of clustering by geography or isolation
source, we found potential gene functions that were enriched in sourdough across
multiple strains. GH13 alpha-amylase, involved in starch cleavage, was enriched
in sourdough with the presence of 45% of strains relative to 28% of strains not
isolated from sourdough (Kruskal-Wallis FDR-corrected P < 0.01; ; Table S9). Starch
is a major component of wheat, so this could confer a competitive advantage to
AAB in sourdough vs other environments . Although limited by
the availability of high-quality AAB genomes from other environments, our
analyses start to shed light on the metabolic traits that may favor the
persistence of AAB in the sourdough environment. While gene clusters of unknown function were much more frequent in accessory
genomes, functionally annotated accessory genes largely fell into categories
that may have relevance to success in sourdough. On average, 61.0% of genes were
annotated with a known function (COG database) in accessory vs 87.3% in core
(Table S6). Although expected, this underscores the difficulty in determining
the functional or ecological significance of gene content variation. Mobilome
genes, related to transposons and prophages, made up the largest percentage of
accessory genes ( ; Fig. S5; Table
S10). A. pasteurianus has been known to have a high level of
genetic variability particularly linked to the mobilome, with 9% of genes
encoding transposases . Twenty-two
carbohydrate-active enzymes (CAZy) that may be relevant to success in sourdough
were found in plasmids and carried by prophages in the genomes we recovered from
sourdough, including the GH13 alpha-amylase significantly associated with
sourdough genomes (Fig. S6). Notably, an average of 24% of genes related to
carbohydrate utilization were detected in accessory genomes across the three
species clusters, underscoring the variability in microbial traits related to
nutrient acquisition. Accessory carbohydrate utilization genes included
5-carboxyvanillate decarboxylase LigW, involved in lignin degradation, and
alpha-galactosidase. The high intraspecies variation in resource utilization
genes may impact the establishment success and persistence of the AAB strain
within the sourdough microbiome. 

To understand the consequences of acetic acid bacteria on the assembly and
emergent function of sourdough starter microbiomes, we constructed replicate
synthetic sourdough starter communities, varying only the AAB strain across
treatments . We proxy microbiome
function by pH and volatile organic compounds (VOCs) and also measure the
abundance of community members. We selected 10 AAB strains that represented the
breadth of phylogenetic diversity observed and included intraspecies diversity
within the A. malorum cluster. We qualitatively observed
morphological differences in size and color across the 10 strains (Fig. S7). For
example, Acetobacter sp. nov . 517 and G. potus colonies were smaller, whereas A. malorum and A.
malorum A were the largest. This could be due to differences in
starting concentrations, nutritional requirements, or growth rates. Most
colonies were white to pink in color, but A. fabarum had yellow
colonies (Fig. S7). A. orientalis was observed to produce a
biofilm, likely bacterial cellulose, or another exopolysaccharide. Cellulose is
produced by many AAB including those isolated from kombucha , but amorphous colonies due to cellulose production were not
commonly observed in the AAB strains isolated from sourdough. For our synthetic experiments, we selected a LAB ( L. brevis ) and
yeast ( S. cerevisiae ) as the base community, isolated from wild
starter S_129 (resulting in “SynCom129”) varying only the AAB
strain added. We also included a media-only blank {all synthetic communities
were grown in cereal-based fermentation medium [CBFM;  ], a liquid-based cereal fermentation media} and a YL-only
control with only yeast and LAB present. This design resulted in 12 distinct
treatments. For the number of replicates run for each assay (i.e., pH, VOC
production), see Table S11. After incubating for 48 hours, transfer to fresh
media, and incubation for another 48 hours (4 days total), we measured the
resultant community structure and emergent functions across treatments. We
assessed the persistence of all starting members and emergent functions of the
resultant starter including acidification (pH) and VOC profiles. We selected a
second background community of L. brevis and S.
cerevisiae (SynCom361) isolated from a second wild sourdough
(S_361) to confirm the trends we observed with pH and persistence analyses. In both synthetic communities, all 10 AAB strains persisted across replicates.
SynCom129 was isolated from a starter without AAB, suggesting that AAB are
likely able to establish and persist in a range of starters. While yeast and LAB
colony-forming units (CFUs) counts stayed fairly consistent, AAB CFU counts
varied greatly by strain, indicating the potential differential success of
strains in the synthetic communities. Higher AAB CFU counts were significantly
correlated with lower pH (Spearman’s rho = −0.83, P = 0.002), while LAB and yeast CFU counts were not
correlated ( ; Table S14). The pH was
also correlated with AAB ANI ( P = 0.055; ; Table S14). These results suggest that AAB density
and differences in genome content may play a role in modulating starter
acidification. The yeast and LAB persisted in every condition in SynCom129, but
in SynCom361, the yeast sometimes failed to persist. This could be due to a
mismatch of nutritional requirements but also may reflect inhibition by the AAB.
We observed yeast dropout in one condition where yeast appeared glued in EPS
(exopolysaccharides) from A. orientalis (Fig. S8). All AAB treatments acidified the starter environment, and the extent was
strain-specific (Fig. S9). In SynCom129, in comparison to the YL-only control,
AAB overall decreased the pH of the sourdough microbiome by 18.5% in a 4-day
period. The starting pH on day 0 of CBFM was around 6.26. By day 2, communities
had begun to acidify the starter environment. The average pH of communities with
AAB was 4.20 ( N = 30, SD = 0.30), and the average pH for
YL-only was 5.64 ( N = 3, SD = 0.03). On day 4, all communities
with AAB had significantly lower pH from YL-only and CBFM blank
( P < 0.0001; ; Table S14). The average pH of communities with AAB on day 4 was
3.94 ( N = 50, SD = 0.23), while the average for YL-only was
4.84 ( N = 5, SD = 0.05). Significant acidification was also
observed in SynCom361 on day 4 between AAB and non-AAB communities
( P < 0.0001) despite yeast dropout in some
replicates (Fig. S9). This is consistent with studies from other fermented foods
that have shown that AAB lower the pH of their environment .
The release of acetic acid and a corresponding decrease in pH may inhibit the
growth of other species and aid in AAB persistence . While pH was lowered in all AAB treatments, there were
significant differences between species, including between strains of closely
related species. A. oryzifermentans (average 4.36, SD = 0.02)
and G. potus (average 3.56, SD = 0.02) were significantly
different from all other AAB strains. The two A. malorum A
strains did not significantly differ from each other, while the strain of A. malorum differed from only A. malorum A
490. This finding underscores the importance of species and strain-level
diversification on emergent function. Sourdough VOCs significantly differed by starter community members. We analyzed
the VOCs from seven different communities frozen on day 4, including six AAB
treatments and a yeast-LAB-only control (Table S12). VOCs in the YL community
significantly differed from all AAB communities ( P = 0.008;
Table S14), and the AAB communities formed three clusters (A4 alone, A2/A6/A7,
and A8/A10; ) with distinct VOC
profiles ( P = 0.01; ; Table S14). Clusters were not associated with phylogeny. Notably,
despite 97.2% similarity in ANI, A. malorum A 507 and A. malorum A 490 were in different clusters. A variety of
compounds differed between the clusters (Table S13; ). Stearic acid ( P = 0.02;
AAB-associated clusters) and palmitic acid ( P = 0.02; higher in
the no-AAB controls and cluster A4) have both been previously detected in
ferments including Tarhana [a Turkish wheat and yogurt-based fermented food
]. We also detected compounds
previously linked to flavor formation in sourdough including decanal, which is
associated with vinegar and a sweet, citrusy flavor , and L-arabitol, which is associated with sweet flavor
. Both were more abundant in
samples with AAB, although not significantly (Table S13). Our results suggest
that strain-level variation in AAB members can impact the sensory properties of
the resulting community. 

Our study represents a genomic and ecological characterization of AAB isolated
from sourdough starters and demonstrates the utility of synthetic consortia to
determine the functional consequences of specific microbiome members. Our
genomic analyses begin to uncover the diversity in AAB genome content and
suggest that sourdough AAB may be enriched in specific gene functions that
facilitate success in the sourdough environment. These analyses also suggest
that community-wide emergent functions can be affected by differences in gene
content resulting from intraspecies diversification. Our comparative genomic
analyses were limited, however, by the number of high-quality and publicly
available AAB genomes from other environments. Increasing genomic representation
from this group of economically and culturally significant bacteria will
continue to yield new insights on their metabolic diversity and the ways in
which they may be adapted to diverse sugar-rich environments. Likewise,
obtaining closed genomes using approaches such as long-read and hybrid
sequencing will facilitate a more resolved understanding of the location of key
regions and genes of interest and the opportunity to study population-scale
dynamics. Our synthetic experiments demonstrate that phylogenetically and metabolically
diverse AAB can persist in common sourdough microbiomes. We also determine the
impact of AAB species and strain-level diversity on emergent sourdough functions
including acidification and volatile profiles. Notably, we found that
intraspecies variation in A. malorum resulted in significantly
different community VOC profiles of synthetic starters. These findings have
direct relevance to industrial and home-bakers considering flavor profiles and
other features of sourdough bread. A clear next step is to extend these results
and assess how AAB may impact functional consequences in baked sourdough bread.
Similarly, our synthetic experiments were performed using an S.
cerevisae and L. brevis background community.
Although this pairing represents one of the most dominant sourdough LAB and
yeast combinations, it will be important to determine if AAB have similar
functional consequences in other common sourdough types such as F.
sanfranciscensis and Kazachstania humilis . An
exciting avenue for future research is using tools such as interaction screens,
RNA-seq, and metabolomics to shed light on what molecular-level mechanisms
underlie the observed community-wide functional shifts. 

16S rRNA amplicon analysis of AAB in sourdough For our amplicon-based analyses of the distribution of AAB across 500 starters
and their associations with LAB and yeasts, we used previously sequenced 16S and
ITS rRNA gene sequence data from reference . In brief, DNA was extracted from 2 mL starter subsamples with
the Qiagen PowerSoil DNA extraction kit and sequenced with Illumina MiSeq at the
University of Colorado Next-Generation Sequencing Facility. Sequences were
processed with the DADA2 pipeline, resulting in taxonomic assignments and ASV
tables of relative abundances. We calculated the mean relative abundance of AAB
in starter samples and also assessed co-occurrence of AAB with other starter
members. First, we used Spearman’s correlations to test if any AAB ASVs
significantly co-occurred with any LAB, yeast, or other AAB ASVs. We only
included ASVs that had at least a mean relative abundance of 0.1% (Table S3). We
also tested whether particular LAB or yeast tended to be enriched in AAB
dominant samples using Mann-Whitney tests (Table S2) with FDR-adjusted P -values. We defined AAB dominant as samples where the AAB
comprised at least 25% of the overall bacterial community. Isolation and sequencing of acetic acid bacteria strains We leveraged the previously sequenced 16S rRNA gene amplicon data of 500
sourdough starters described above 
to selectively target starter samples that had a high relative abundance of AAB
and to capture a broad range of phylogenetically diverse AAB in our culturing
efforts. We obtained isolates of 21 acetic acid bacteria, most of which were
abundant in samples based on the amplicon data and were isolated from frozen
(−80°C) sourdough starter samples. Subsamples were plated onto
selective GYCA medium (per liter, 30 g glucose, 5 g yeast extract, 3 g peptone,
and 15 g agar) with 10 g CaCO 3 supplemented with 25 mL natamycin
(21.6 mg/L), and subsamples that had amplicon hits to Gluconobacter were plated onto Carr medium supplemented
with natamycin (21.6 mg/L) . To
construct synthetic starter communities, we also obtained isolates of L.
brevis (LAB) which was plated onto Lactobacilli MRS agar
(Criterion) with natamycin (21.6 mg/L) and of S. cerevisiae which was plated on yeast potato dextrose medium with chloramphenicol
(50 mg/L). Distinct morphologies were cultured from each sample, and we
used Sanger sequencing to determine general taxonomic identity with the 16S rRNA
primer sets 27F/1492R for bacteria and ITS1F/ITS4R for yeast. Isolate cultures
were stored in 15% glycerol at −80°C. To sequence isolate genomes, DNA was extracted from grown colonies using
ZymoBIOMICS DNA extraction kits, with bead bashing for cell lysis (Seqcenter,
PA). Following extraction, sample libraries were prepped with the Illumina DNA
prep kit with 10 bp dual indices (IDT) and sequenced on an Illumina NovaSeq
6,000 resulting in 151 bp, paired-end reads. We targeted 200 Mbp per sample
(e.g., isolate). After sequencing reads were demultiplexed and adapters were
trimmed using bcl-convert v4.1.5, we obtained an average of 5,676,503.62
paired-end reads per sample (ranging from 3,703,604 to 10,182,868) and an
average % bp >Q30 of 91.096%. To assemble high-quality draft genomes from raw reads for each isolate in KBase
, reads were trimmed to remove
low-quality bases at both ends with BBTools v38.22 , and low-complexity reads were filtered out with
PRINSEQ . Read quality was assessed
with FastQC v0.11.0 prior to assembly with SPAdes v3.15.3. For a few genomes
( N = 6), the metaSPAdes assembly algorithm  resulted in a higher-quality assembly.
All genomes obtained were high quality, with ≥93.53% complete and
<3.23% contaminated, and the median number of contigs was 29. After
assessing the genome quality of isolates with CheckM v1.0.18 , genomes were taxonomically classified
with GTDB-Tk v1.7.0 . We also used GTDB-Tk to assess the
taxonomic novelty of isolates based on reported ANI and RED values . Genomes that did not fall within 95% ANI of existing reference
genomes are considered putatively novel species. We also placed all genomes
phylogenetically using KBase SpeciesTree v2.2.0 , which constructs species trees from a set of 49 universal core
genes defined by COG gene families to determine relatedness. Assemblies were
annotated with Prokka v1.14.5  and
re-annotated in anvi’o [; see
comparative genomics section below] with anvi-run-hmms and anvi-run-ncbi-cogs
. Inclusion of additional genomes from sourdough metagenomes and NCBI To complement the AAB genomes that we obtained by culturing from sourdough
starter samples, we also assembled genomes from 40 shotgun metagenomic samples,
a subset of the 500-starter collection , deposited in NCBI under Bioproject PRJNA589612 . After quality filtering with
BBDuk  and filtering reads that
aligned to the bread wheat genome (IWGSC CS RefSeq v2.1) using bowtie2 , we used metaSPAdes v. 3.15.5  to assemble contigs. Next, contigs were
placed into genome bins with both VAMB  and Metabat v. 2.15 ,
and then DasTool v. 1.1.5  was used
to select the best bins for each sample. CheckM v. 1.2.2  was used to score the bins. Next, bins from all samples
were collected together, and dRep v. 3.4  was used to make a dereplicated set with the highest-quality
representatives at 95% ANI cutoff to represent species-level genomes (parameters
that deviated from defaults included: completeness, contamination, and coverage,
which were respectively set to 50.0, 10.0, and 0.3). GTDB-tk v. 2.1.1  was used to taxonomically classify the
full set of dereplicated bins. We then filtered the bins based on taxonomy to
only include AAB bins ( N = 10 out of 30). We also built a
genome tree of isolates and MAGs to identify any redundancy between genomes and
MAGs. Where overlap was detected, the isolate was selected for downstream
analysis, and the MAG was excluded. This resulted in a final set of eight
MAGs. To contextualize our AAB sourdough genomes within the broader context of AAB
genomic diversity and to assess if any gene content was enriched in sourdough
environments, we also obtained publicly available AAB genomes from NCBI to
include in our analyses. We found no publicly available genomes from Acetobacter , Gluconobacter , or Komagataeibacter isolated from sourdough on the JGI GOLD
genomes database. For an analysis of strain diversity, we added genomes from
NCBI to a set of genomes we obtained from sourdough from A.
malorum (recently split with A. malorum A, ~90%
ANI) , A. orientalis , and G. oxydans (recently split with G. potus , ~94% ANI) to compile a set of
nine genomes within each species (19 genomes total from NCBI with at least 89%
completion and less than 6.88% contamination). These three species were chosen
based on the availability of multiple genomes from a variety of sources, as most
AAB species have only one to a few genomes sequenced, and A.
malorum genomes were targeted as part of the most common cluster of
AAB genomes from sourdough. We were limited to nine genomes per species cluster
by the poor quality of publicly available genomes as well, even after setting a
reasonable cutoff for percent completion and contamination. We sought to include
a representative set of genomes from across the tree of Acetobacter . We constructed a genome tree of all species of Acetobacter listed on NCBI Taxonomy using SpeciesTree -
v2.2.0 with a reference genome from each species  and downloaded 12 additional Acetobacter genomes from across the tree that we did not recover from sourdough, along with
one species from an additional genus, K. xylinus , as an
outgroup. We targeted genomes that were at least 90% complete with <5%
contamination and only included genomes that had available source and location
information. When source data did not include latitude and longitude, the center
point of the country or city was chosen using latlong.net. To assess how well the genomes we recovered in this study represent the known
diversity of AAB in sourdough, we leveraged our 16S rRNA gene sequencing of 500
sourdough starters. First, we assigned ASV taxonomy using a phylogeny-guided
approach. We extracted full or partial 16S rRNA gene sequences from isolate,
MAG, and NCBI genomes using ContEst16S  and aligned the sequences along with our partial 16S ASV
amplicon sequences (resulting in a total of 197 AAB sequences included in the
phylogeny) using the SINA aligner .
After gap trimming (gap threshold = 0.2), we constructed a phylogenetic tree
using RAxML-HPC2 on XSEDE . ASV taxonomic assignments were then made to the nearest
genome representative. When an ASV was close to genomes from multiple species
due to limited phylogenetic resolution, we assigned a cluster identity (such as
the A. malorum—cerevisiae cluster). We note that this
phylogeny is based on short-read data and is a useful way to improve taxonomic
assignments but is not intended to accurately represent phylogenetic
relationships. Comparative genomic analyses In total, we included 61 AAB genomes in our cross-environment comparative
analysis (21 isolates, 8 MAGs, and 32 from NCBI). All 61 genomes were annotated
with DRAM  to additionally profile a
suite of microbial traits related to categories including short-chain fatty acid
and alcohol conversions, nitrogen metabolism, and CAZy. We display a subset of
these functions that are differential across AAB that were annotated and
distilled using DRAM ( ; ; Table S7). Detailed annotation files
from DRAM are available on Figshare DOI 10.6084 under 10.6084
/m9.figshare.25403599. We also constructed a pangenome of all 61 genomes in
anvi’o (minbit 0.5, mcl-inflation 6, and min-occurrence 2)  and computed ANI using pyANI . We annotated genomes by source (sourdough or other) and tested
for functional enrichment by source and species using the command
“anvi-compute-functional-enrichment-in-pan” , a tool that finds functions that are enriched in the
specified group and relative to genomes outside the group. We used the same
approach to build the three species-level pangenomes for A.
malorum/malorum A, A. orientalis , and G.
oxydans/potus with corresponding ANI and functional enrichment
analyses, using mcl-inflation 10 and no min occurrence. We also identified
viruses and plasmids within the genomes recovered from sourdough using geNomad
and ran DRAMv on viral regions and DRAM on plasmids to annotate genes in those
regions (Fig. S6; Table S10). Construction of synthetic starter communities For synthetic sourdough starter experiments, we used a CBFM that approximates the
dough environment, as described in reference . Briefly, to make the media, whole wheat and all-purpose flour
(Bob’s Red Mill) were mixed with water in a 1:1:9 ratio, centrifuged
(3,000 rpm) to pellet flour particles, and then filtered through a 0.20
µm filter to remove microbial cells from the filtrate. To measure the
effects of AAB on starter microbiome composition and function, we constructed
two LAB and yeast communities: SynCom129, LAB L. brevis strain
129 plus yeast S. cerevisiae strain 129, and Syncom361, L. brevis strain 361 1A plus S. cerevisiae strain 361. These two species are dominant in sourdough starters and are
frequently found together in the same starter . Additionally, this LAB/yeast pairing is commonly found in
starters with AAB (the case for strains 361) and also in starters without AAB
(the case for strains 129). We then selected a subset of 10 AAB isolates
corresponding to 10 treatment groups that spanned the breadth of phylogenetic
diversity we obtained: two genera ( Acetobacter and Gluconobacter ), eight species ( G. potus , A. malorum , A. sp . 517, A.
orientalis , A. senegalensis , A
fabarum , A. ghanensis , and A.
oryzifermentans ), and three strains within A.
malorum/malorum A. We also included a “no AAB”
treatment control group (only LAB/yeast) and a blank with just CBFM. In 96-well plates, approximately 5 µL of 4,000 CFUs/μL were
inoculated into 180 µL of CBFM and 5 µL PBS resulting in a total
volume of 200 µL (10 µL PBS was added for no AAB control, and 20
µL PBS was added for blank). For each condition (10 AAB strains + 1 no
AAB + 1 CBFM blank), we constructed eight replicate communities, resulting in 96
synthetic communities in total for each SynCom. Communities were incubated
aerobically at room temperature on the bench, and 10 µL of the resultant
culture was transferred 48 h after initial inoculation into 190 µL of
fresh CBFM. At this point, wells were tested for presence/absence, and pH was
collected. After the cultures grew for another 48 h (4 days in total), we
harvested the experiment: three wells from each condition of the SynCom129 plate
(180 µL/ well) were immediately frozen at −80°C for
metabolite analyses, and 100 µL from one well of each condition was
subsampled to determine total abundance of each member (AAB, LAB, and yeast) by
serial dilution and plating on selective media at 10 −4 and
10 −5 to count CFUs. Additionally, pH was assessed in situ from the remaining unfrozen communities in
SynCom129 and the corresponding communities in SynCom361, and one well of each
condition was streaked for presence/absence (see below). Assessment of metabolites and acidification All statistical tests were executed in the R environment (version 2023.06.0 +
421, R Core Team). To measure the overall acidification of sourdough starter
communities, the pH of each synthetic sourdough community ( N =
216, excluding those that were sampled for metabolomics to avoid contamination)
was taken with a pHenomenal pH meter (VWR) with an MI-410 Combination microprobe
(Microelectrodes, Inc.) at the conclusion of the experiment. For our AAB
synthetic community experiments, we used analyses of variance to determine the
effects of AAB on community pH, with AAB strain identity as the independent
variable. We included a no AAB control (i.e., L. brevis and S. cerevisiae only) that we refer to as the
“background community” and also a CBFM-only blank. We then used
Tukey’s post hoc significance testing to assess the significance of
pairwise differences . To assess
if pH was correlated with the density of AAB, LAB, or yeast, we used
Spearman’s correlations .
To determine if either the observed pH values or CFU counts were correlated with
AAB phylogenetic distance, we used mantel tests (method = spearman) to correlate
the pairwise distances (pH distance metric used was euclidean), with 9,999
permutations . To prepare samples for GC/MS analysis, 180 µL aliquots were harvested from
the final communities excluding the control and immediately frozen at
−80°C. Samples were shipped to Creative Proteomics (Shirley, NY),
and upon arrival were thawed on ice, and 0.1 mL of each of the 21 samples was
transferred to a tube with 0.3 mL 80% methanol solution and 5 µL ribitol
(5 mg/mL). The tubes were vortexed for 60 seconds and ground for 180 seconds at
60 Hz, sonicated for 30 minutes at 4°C, and centrifuged at 12,000 rpm and
4°C for 10 minutes (Eppendorf). Two hundred microliter of supernatant was
transferred into a new tube, dried under nitrogen, and then added to 35 ul
O-Methylhydroxylame solution (Merck), vortexed for 30 seconds, and incubated at
37°C for 90 minutes. The extractives were derived with 35 µL
BSTFA, reacted at 70°C for 60 minutes, and kept at room temperature for
30 minutes until analysis. Samples were analyzed on a DB-5 column (60 m ×
0.25 mm × 0.25 µm, Agilent). The detector and injector
temperatures were 280°C. The oven temperature was held at 70°C for
5 minutes then raised to 200°C at a rate of 10°C per minute, then
raised to 280°C at 5°C per minute and held for 10 minutes. Helium
was used for column carrier gas at a constant flow rate of 1 mL/min, and split
(20:1) injection mode was used. The temperature of the ion source was
230°C, the electron impact mass spectra were recorded at 70 eV ionization
energy, and the GC-MS (gas chromatography–mass spectrometry) analysis was
carried out in 30–550 mass range scanning mode (Thermo Scientific ISQ
7000). To analyze the variation of VOCs across the samples sent for GC/MS analysis, any
duplicated compound reads were removed, only keeping the highest similarity
matches of each compound. Compounds related to plastic components/degradation,
human metabolites, or low-similarity compounds were also removed as background
or contamination. Then, distance matrices were calculated and ordinated of the
VOCs and samples . Four outlier
samples were removed prior to downstream analyses (YL_C, A2_C, A6_B, and A7_C).
To assess the impact of AAB treatment on overall community VOC profiles, we ran
PERMANOVA models with treatment (AAB strain) and AAB presence (vs yeast-LAB
only) as predictors. We identified four clusters of samples using hierarchical
clustering (method Ward D2) and plotted the resulting dendrogram . We used Kruskal-Wallis tests (with
FDR corrections for multiple comparisons) to assess if any VOCs were
significantly enriched by AAB presence, treatment, or cluster. 

For our amplicon-based analyses of the distribution of AAB across 500 starters
and their associations with LAB and yeasts, we used previously sequenced 16S and
ITS rRNA gene sequence data from reference . In brief, DNA was extracted from 2 mL starter subsamples with
the Qiagen PowerSoil DNA extraction kit and sequenced with Illumina MiSeq at the
University of Colorado Next-Generation Sequencing Facility. Sequences were
processed with the DADA2 pipeline, resulting in taxonomic assignments and ASV
tables of relative abundances. We calculated the mean relative abundance of AAB
in starter samples and also assessed co-occurrence of AAB with other starter
members. First, we used Spearman’s correlations to test if any AAB ASVs
significantly co-occurred with any LAB, yeast, or other AAB ASVs. We only
included ASVs that had at least a mean relative abundance of 0.1% (Table S3). We
also tested whether particular LAB or yeast tended to be enriched in AAB
dominant samples using Mann-Whitney tests (Table S2) with FDR-adjusted P -values. We defined AAB dominant as samples where the AAB
comprised at least 25% of the overall bacterial community. 

We leveraged the previously sequenced 16S rRNA gene amplicon data of 500
sourdough starters described above 
to selectively target starter samples that had a high relative abundance of AAB
and to capture a broad range of phylogenetically diverse AAB in our culturing
efforts. We obtained isolates of 21 acetic acid bacteria, most of which were
abundant in samples based on the amplicon data and were isolated from frozen
(−80°C) sourdough starter samples. Subsamples were plated onto
selective GYCA medium (per liter, 30 g glucose, 5 g yeast extract, 3 g peptone,
and 15 g agar) with 10 g CaCO 3 supplemented with 25 mL natamycin
(21.6 mg/L), and subsamples that had amplicon hits to Gluconobacter were plated onto Carr medium supplemented
with natamycin (21.6 mg/L) . To
construct synthetic starter communities, we also obtained isolates of L.
brevis (LAB) which was plated onto Lactobacilli MRS agar
(Criterion) with natamycin (21.6 mg/L) and of S. cerevisiae which was plated on yeast potato dextrose medium with chloramphenicol
(50 mg/L). Distinct morphologies were cultured from each sample, and we
used Sanger sequencing to determine general taxonomic identity with the 16S rRNA
primer sets 27F/1492R for bacteria and ITS1F/ITS4R for yeast. Isolate cultures
were stored in 15% glycerol at −80°C. To sequence isolate genomes, DNA was extracted from grown colonies using
ZymoBIOMICS DNA extraction kits, with bead bashing for cell lysis (Seqcenter,
PA). Following extraction, sample libraries were prepped with the Illumina DNA
prep kit with 10 bp dual indices (IDT) and sequenced on an Illumina NovaSeq
6,000 resulting in 151 bp, paired-end reads. We targeted 200 Mbp per sample
(e.g., isolate). After sequencing reads were demultiplexed and adapters were
trimmed using bcl-convert v4.1.5, we obtained an average of 5,676,503.62
paired-end reads per sample (ranging from 3,703,604 to 10,182,868) and an
average % bp >Q30 of 91.096%. To assemble high-quality draft genomes from raw reads for each isolate in KBase
, reads were trimmed to remove
low-quality bases at both ends with BBTools v38.22 , and low-complexity reads were filtered out with
PRINSEQ . Read quality was assessed
with FastQC v0.11.0 prior to assembly with SPAdes v3.15.3. For a few genomes
( N = 6), the metaSPAdes assembly algorithm  resulted in a higher-quality assembly.
All genomes obtained were high quality, with ≥93.53% complete and
<3.23% contaminated, and the median number of contigs was 29. After
assessing the genome quality of isolates with CheckM v1.0.18 , genomes were taxonomically classified
with GTDB-Tk v1.7.0 . We also used GTDB-Tk to assess the
taxonomic novelty of isolates based on reported ANI and RED values . Genomes that did not fall within 95% ANI of existing reference
genomes are considered putatively novel species. We also placed all genomes
phylogenetically using KBase SpeciesTree v2.2.0 , which constructs species trees from a set of 49 universal core
genes defined by COG gene families to determine relatedness. Assemblies were
annotated with Prokka v1.14.5  and
re-annotated in anvi’o [; see
comparative genomics section below] with anvi-run-hmms and anvi-run-ncbi-cogs
. 

To complement the AAB genomes that we obtained by culturing from sourdough
starter samples, we also assembled genomes from 40 shotgun metagenomic samples,
a subset of the 500-starter collection , deposited in NCBI under Bioproject PRJNA589612 . After quality filtering with
BBDuk  and filtering reads that
aligned to the bread wheat genome (IWGSC CS RefSeq v2.1) using bowtie2 , we used metaSPAdes v. 3.15.5  to assemble contigs. Next, contigs were
placed into genome bins with both VAMB  and Metabat v. 2.15 ,
and then DasTool v. 1.1.5  was used
to select the best bins for each sample. CheckM v. 1.2.2  was used to score the bins. Next, bins from all samples
were collected together, and dRep v. 3.4  was used to make a dereplicated set with the highest-quality
representatives at 95% ANI cutoff to represent species-level genomes (parameters
that deviated from defaults included: completeness, contamination, and coverage,
which were respectively set to 50.0, 10.0, and 0.3). GTDB-tk v. 2.1.1  was used to taxonomically classify the
full set of dereplicated bins. We then filtered the bins based on taxonomy to
only include AAB bins ( N = 10 out of 30). We also built a
genome tree of isolates and MAGs to identify any redundancy between genomes and
MAGs. Where overlap was detected, the isolate was selected for downstream
analysis, and the MAG was excluded. This resulted in a final set of eight
MAGs. To contextualize our AAB sourdough genomes within the broader context of AAB
genomic diversity and to assess if any gene content was enriched in sourdough
environments, we also obtained publicly available AAB genomes from NCBI to
include in our analyses. We found no publicly available genomes from Acetobacter , Gluconobacter , or Komagataeibacter isolated from sourdough on the JGI GOLD
genomes database. For an analysis of strain diversity, we added genomes from
NCBI to a set of genomes we obtained from sourdough from A.
malorum (recently split with A. malorum A, ~90%
ANI) , A. orientalis , and G. oxydans (recently split with G. potus , ~94% ANI) to compile a set of
nine genomes within each species (19 genomes total from NCBI with at least 89%
completion and less than 6.88% contamination). These three species were chosen
based on the availability of multiple genomes from a variety of sources, as most
AAB species have only one to a few genomes sequenced, and A.
malorum genomes were targeted as part of the most common cluster of
AAB genomes from sourdough. We were limited to nine genomes per species cluster
by the poor quality of publicly available genomes as well, even after setting a
reasonable cutoff for percent completion and contamination. We sought to include
a representative set of genomes from across the tree of Acetobacter . We constructed a genome tree of all species of Acetobacter listed on NCBI Taxonomy using SpeciesTree -
v2.2.0 with a reference genome from each species  and downloaded 12 additional Acetobacter genomes from across the tree that we did not recover from sourdough, along with
one species from an additional genus, K. xylinus , as an
outgroup. We targeted genomes that were at least 90% complete with <5%
contamination and only included genomes that had available source and location
information. When source data did not include latitude and longitude, the center
point of the country or city was chosen using latlong.net. To assess how well the genomes we recovered in this study represent the known
diversity of AAB in sourdough, we leveraged our 16S rRNA gene sequencing of 500
sourdough starters. First, we assigned ASV taxonomy using a phylogeny-guided
approach. We extracted full or partial 16S rRNA gene sequences from isolate,
MAG, and NCBI genomes using ContEst16S  and aligned the sequences along with our partial 16S ASV
amplicon sequences (resulting in a total of 197 AAB sequences included in the
phylogeny) using the SINA aligner .
After gap trimming (gap threshold = 0.2), we constructed a phylogenetic tree
using RAxML-HPC2 on XSEDE . ASV taxonomic assignments were then made to the nearest
genome representative. When an ASV was close to genomes from multiple species
due to limited phylogenetic resolution, we assigned a cluster identity (such as
the A. malorum—cerevisiae cluster). We note that this
phylogeny is based on short-read data and is a useful way to improve taxonomic
assignments but is not intended to accurately represent phylogenetic
relationships. 

In total, we included 61 AAB genomes in our cross-environment comparative
analysis (21 isolates, 8 MAGs, and 32 from NCBI). All 61 genomes were annotated
with DRAM  to additionally profile a
suite of microbial traits related to categories including short-chain fatty acid
and alcohol conversions, nitrogen metabolism, and CAZy. We display a subset of
these functions that are differential across AAB that were annotated and
distilled using DRAM ( ; ; Table S7). Detailed annotation files
from DRAM are available on Figshare DOI 10.6084 under 10.6084
/m9.figshare.25403599. We also constructed a pangenome of all 61 genomes in
anvi’o (minbit 0.5, mcl-inflation 6, and min-occurrence 2)  and computed ANI using pyANI . We annotated genomes by source (sourdough or other) and tested
for functional enrichment by source and species using the command
“anvi-compute-functional-enrichment-in-pan” , a tool that finds functions that are enriched in the
specified group and relative to genomes outside the group. We used the same
approach to build the three species-level pangenomes for A.
malorum/malorum A, A. orientalis , and G.
oxydans/potus with corresponding ANI and functional enrichment
analyses, using mcl-inflation 10 and no min occurrence. We also identified
viruses and plasmids within the genomes recovered from sourdough using geNomad
and ran DRAMv on viral regions and DRAM on plasmids to annotate genes in those
regions (Fig. S6; Table S10). 

For synthetic sourdough starter experiments, we used a CBFM that approximates the
dough environment, as described in reference . Briefly, to make the media, whole wheat and all-purpose flour
(Bob’s Red Mill) were mixed with water in a 1:1:9 ratio, centrifuged
(3,000 rpm) to pellet flour particles, and then filtered through a 0.20
µm filter to remove microbial cells from the filtrate. To measure the
effects of AAB on starter microbiome composition and function, we constructed
two LAB and yeast communities: SynCom129, LAB L. brevis strain
129 plus yeast S. cerevisiae strain 129, and Syncom361, L. brevis strain 361 1A plus S. cerevisiae strain 361. These two species are dominant in sourdough starters and are
frequently found together in the same starter . Additionally, this LAB/yeast pairing is commonly found in
starters with AAB (the case for strains 361) and also in starters without AAB
(the case for strains 129). We then selected a subset of 10 AAB isolates
corresponding to 10 treatment groups that spanned the breadth of phylogenetic
diversity we obtained: two genera ( Acetobacter and Gluconobacter ), eight species ( G. potus , A. malorum , A. sp . 517, A.
orientalis , A. senegalensis , A
fabarum , A. ghanensis , and A.
oryzifermentans ), and three strains within A.
malorum/malorum A. We also included a “no AAB”
treatment control group (only LAB/yeast) and a blank with just CBFM. In 96-well plates, approximately 5 µL of 4,000 CFUs/μL were
inoculated into 180 µL of CBFM and 5 µL PBS resulting in a total
volume of 200 µL (10 µL PBS was added for no AAB control, and 20
µL PBS was added for blank). For each condition (10 AAB strains + 1 no
AAB + 1 CBFM blank), we constructed eight replicate communities, resulting in 96
synthetic communities in total for each SynCom. Communities were incubated
aerobically at room temperature on the bench, and 10 µL of the resultant
culture was transferred 48 h after initial inoculation into 190 µL of
fresh CBFM. At this point, wells were tested for presence/absence, and pH was
collected. After the cultures grew for another 48 h (4 days in total), we
harvested the experiment: three wells from each condition of the SynCom129 plate
(180 µL/ well) were immediately frozen at −80°C for
metabolite analyses, and 100 µL from one well of each condition was
subsampled to determine total abundance of each member (AAB, LAB, and yeast) by
serial dilution and plating on selective media at 10 −4 and
10 −5 to count CFUs. Additionally, pH was assessed in situ from the remaining unfrozen communities in
SynCom129 and the corresponding communities in SynCom361, and one well of each
condition was streaked for presence/absence (see below). 

All statistical tests were executed in the R environment (version 2023.06.0 +
421, R Core Team). To measure the overall acidification of sourdough starter
communities, the pH of each synthetic sourdough community ( N =
216, excluding those that were sampled for metabolomics to avoid contamination)
was taken with a pHenomenal pH meter (VWR) with an MI-410 Combination microprobe
(Microelectrodes, Inc.) at the conclusion of the experiment. For our AAB
synthetic community experiments, we used analyses of variance to determine the
effects of AAB on community pH, with AAB strain identity as the independent
variable. We included a no AAB control (i.e., L. brevis and S. cerevisiae only) that we refer to as the
“background community” and also a CBFM-only blank. We then used
Tukey’s post hoc significance testing to assess the significance of
pairwise differences . To assess
if pH was correlated with the density of AAB, LAB, or yeast, we used
Spearman’s correlations .
To determine if either the observed pH values or CFU counts were correlated with
AAB phylogenetic distance, we used mantel tests (method = spearman) to correlate
the pairwise distances (pH distance metric used was euclidean), with 9,999
permutations . To prepare samples for GC/MS analysis, 180 µL aliquots were harvested from
the final communities excluding the control and immediately frozen at
−80°C. Samples were shipped to Creative Proteomics (Shirley, NY),
and upon arrival were thawed on ice, and 0.1 mL of each of the 21 samples was
transferred to a tube with 0.3 mL 80% methanol solution and 5 µL ribitol
(5 mg/mL). The tubes were vortexed for 60 seconds and ground for 180 seconds at
60 Hz, sonicated for 30 minutes at 4°C, and centrifuged at 12,000 rpm and
4°C for 10 minutes (Eppendorf). Two hundred microliter of supernatant was
transferred into a new tube, dried under nitrogen, and then added to 35 ul
O-Methylhydroxylame solution (Merck), vortexed for 30 seconds, and incubated at
37°C for 90 minutes. The extractives were derived with 35 µL
BSTFA, reacted at 70°C for 60 minutes, and kept at room temperature for
30 minutes until analysis. Samples were analyzed on a DB-5 column (60 m ×
0.25 mm × 0.25 µm, Agilent). The detector and injector
temperatures were 280°C. The oven temperature was held at 70°C for
5 minutes then raised to 200°C at a rate of 10°C per minute, then
raised to 280°C at 5°C per minute and held for 10 minutes. Helium
was used for column carrier gas at a constant flow rate of 1 mL/min, and split
(20:1) injection mode was used. The temperature of the ion source was
230°C, the electron impact mass spectra were recorded at 70 eV ionization
energy, and the GC-MS (gas chromatography–mass spectrometry) analysis was
carried out in 30–550 mass range scanning mode (Thermo Scientific ISQ
7000). To analyze the variation of VOCs across the samples sent for GC/MS analysis, any
duplicated compound reads were removed, only keeping the highest similarity
matches of each compound. Compounds related to plastic components/degradation,
human metabolites, or low-similarity compounds were also removed as background
or contamination. Then, distance matrices were calculated and ordinated of the
VOCs and samples . Four outlier
samples were removed prior to downstream analyses (YL_C, A2_C, A6_B, and A7_C).
To assess the impact of AAB treatment on overall community VOC profiles, we ran
PERMANOVA models with treatment (AAB strain) and AAB presence (vs yeast-LAB
only) as predictors. We identified four clusters of samples using hierarchical
clustering (method Ward D2) and plotted the resulting dendrogram . We used Kruskal-Wallis tests (with
FDR corrections for multiple comparisons) to assess if any VOCs were
significantly enriched by AAB presence, treatment, or cluster. 

