Abstract
Background
Syntrophic propionate- and acetate-oxidising bacteria (SPOB and SAOB)
play a crucial role in biogas production, particularly under high
ammonia conditions that are common in anaerobic degradation of
protein-rich waste streams. These bacteria rely on close interactions
with hydrogenotrophic methanogens to facilitate interspecies electron
transfer and maintain thermodynamic feasibility. However, the impact of
mixing-induced disruption of these essential syntrophic interactions in
biogas systems remains largely unexplored. This study investigates how
magnetic stirring and orbital shaking influence degradation dynamics,
microbial community composition, and gene expression in syntrophic
enrichment communities under high-ammonia conditions.
Results
Stirring significantly delayed the initiation of propionate degradation
in one culture and completely inhibited it in the other two parallel
cultures, whereas acetate degradation was less affected. Computational
fluid dynamics modelling revealed that stirring generated higher shear
rates (~ 20 s^−1) and uniform cell distribution, while shaking led to
lower shear rates and cell accumulation at the bottom of the culture
bottle. Visual observations confirmed that stirring inhibited floc
formation, while shaking promoted larger flocs compared to the static
control condition, which formed smaller flocs and a sheet-like biofilm.
Microbial community analysis identified substrate type and degradation
progress as primary drivers of community structure, with motion
displaying minimal influence. However, metatranscriptomic analysis
revealed that motion-induced gene downregulation was associated with
motility, surface sensing, and biofilm formation in SAOB and another
bacterial species expressing genes for the glycine synthase reductase
pathway. Stirring also suppressed oxalate–formate antiporter expression
in SPOB, suggesting its dependence on spatial proximity for this
energy-conserving mechanism. The strongest gene expression changes of
stirring were observed in methanogens, indicating a coupling of the
first and last steps of hydrogenotrophic methanogenesis, likely an
adaptive strategy for efficient energy conservation. Other
downregulated genes included ferrous iron transporters and electron
transfer-associated enzymes.
Conclusions
This study highlights that stirring critically disrupts the initial
syntrophic connection between SPOB and methanogens, whereas SAOB
communities exhibit greater tolerance to shear stress and disruptive
conditions that inhibits aggregate formation. These findings emphasize
the importance of carefully managing mixing regimes, especially when
attempting to reactivate ammonia-tolerant syntrophic propionate
degraders in biogas systems experiencing rapid propionate accumulation
under high-ammonia conditions.
Graphical abstract
[28]graphic file with name 13068_2025_2644_Figa_HTML.jpg
Supplementary Information
The online version contains supplementary material available at
10.1186/s13068-025-02644-3.
Keywords: Syntrophic propionate-oxidizing bacteria, Syntrophic
acetate-oxidizing bacteria, Methanogens, Anaerobic digestion, Mixing,
Flocculation, Computational fluid dynamics, Interspecies electron
transfer
Introduction
To function effectively, biotechnological systems rely on a synergistic
interplay between microbiology, chemistry, and technology. An example
of this is the anaerobic digestion (AD) process, wherein productivity
is directly linked to microbial activity which is, in turn, governed by
both chemical conditions and process technology [[29]1]. AD is a
well-established biotechnology that converts organic waste into
renewable energy (biogas), green chemicals, and sustainable fertiliser
[[30]2]. These products are vital components in the transition to a
more carbon–neutral society, as they offer sustainable alternatives to
fossil-derived fuels and chemicals. In addition, the use of sustainable
fertilisers promotes nutrient recycling and reduces the reliance on
mineral fertiliser, which is responsible for a significant amount of
greenhouse gas emissions during both production and application
[[31]3–[32]5].
From a microbial perspective, the AD process involves a continuous
interaction between distinct microbial species which are responsible
for transforming complex compounds into methane. This process is
generally divided into four stages. In the first hydrolytic stage,
proteins, fats, and carbohydrates are broken down into amino acids,
fatty acids, and sugars. This is followed by the acidogenesis and
acetogenesis stages, where these intermediates are further converted
into volatile fatty acids (VFA), alcohols, ammonia, carbon dioxide
(CO[2]), and hydrogen (H[2]). During the last step, methane is formed
from either acetate (acetoclastic methanogenesis) or by the reduction
of CO[2], typically using H[2] or formate as a reducing agent
(hydrogenotrophic methanogenesis). By taking chemistry and process
technology into consideration, the microbial interplay becomes more
complex, as the various species can be distinctly affected by certain
process conditions. This is particularly evident during ammonia
inhibition, which frequently occurs in AD processes that degrade
protein-rich materials, such as chicken manure or slaughterhouse waste.
Although the use of protein-rich substrates offers a high methane yield
potential and produces ammonia-rich digestate with a significant value
as a fertiliser, elevated ammonia levels can inhibit key members of the
microbial community. Acetoclastic methanogens, which convert acetate to
methane, are notably sensitive to elevated ammonia concentrations
[[33]6–[34]8], and their reduced activity under these conditions leads
to an acetate build-up. Moreover, the inhibition of microorganisms that
are responsible for degrading other acids, such as propionate, often
results in additional VFA accumulation [[35]9]. This further diminishes
process performance and methane yield and can, in severe cases, lead to
a complete process failure.
At high ammonia levels, an alternative route for acetate conversion to
methane frequently emerges, where syntrophic acetate oxidising bacteria
(SAOB) oxidise acetate into CO[2] and H[2]. For propionate degradation,
syntrophic propionate oxidising bacteria (SPOB) convert propionate into
acetate, CO[2,] and H[2]. The formed acetate can then be used by either
acetoclastic methanogens in low ammonia conditions [[36]10, [37]11] or
by SAOB under high ammonia conditions [[38]12, [39]13]. While SAOB are
crucial for the AD process under high ammonia conditions, SPOB convert
propionate in both low and high ammonia conditions, although the genera
of SPOB observed at high ammonia levels typically differ from those
that are active under lower ammonia conditions [[40]12, [41]14].
Both the acetate and propionate oxidation reactions are exothermic
under standard conditions and rely on a hydrogenotrophic methanogenic
partner to consume the products, thereby making the reaction
thermodynamically feasible. This mutualistic cooperation involves the
acid oxidiser producing an excess of reducing equivalents during
oxidation, which the methanogen uses to reduce CO[2]. These reducing
equivalents are subsequently transferred through a mediated
interspecies electron transfer using either H[2] or formate [[42]15,
[43]16]. Direct electron transfer between cells has also been suggested
as a possible mechanism; however, the extent to which this occurs
during syntrophic acid oxidation is currently unclear. For mediated
transfer, formate is considered to be more efficient than H[2] for
electron transfer over longer intracellular distances, due to its
higher solubility but lower diffusivity [[44]15, [45]17]. However,
regardless of whether H[2] or formate is used for electron transfer,
close proximity between the syntrophic bacteria and the methanogen
enhances the efficiency of transferring reducing equivalents between
the cells.
The formation of flocs, also referred to as flocculation, is a common
microbial strategy to reduce cell-to-cell distance. This has been
observed in mesophilic syntrophic acetate and propionate oxidising
enrichment cultures under high ammonia conditions [[46]13], as well as
in other syntrophic cultures cultivated under mesophilic and low
ammonia conditions [[47]18]. The importance of cell proximity was also
highlighted in a study of high ammonia mesophilic enrichment cultures,
which found a higher abundance of syntrophs within flocs compared to
platonic cells [[48]19]. Furthermore, in low ammonia and thermophilic
conditions, microscopic observations revealed that a SPOB and a
hydrogenotrophic methanogen grew as free-living cells in monoculture
but co-aggregated when cultivated together in a syntrophic propionate
degrading coculture [[49]20]. These results raise the question of
whether the observed flocculation is primarily driven by its role in
syntrophic acid degradation or if it arises from other reasons commonly
associated with microbial aggregation, such as protection from
environmental stress, nutrient cross-feeding, exchange of genetic
material, or attachment to solid surfaces [[50]21–[51]24]. The
protective function is particularly relevant in high-ammonia
environments, where microbial communities must withstand significant
stress. This underscores the importance of integrating microbiological
insights with process technology, as reactor mixing can significantly
influence microbial flocculation.
In AD, rapid mixing has been suggested to disrupt spatial proximity
between hydrogen producers and consumers [[52]25], and it has also been
proposed to negatively impact propionate degradation [[53]26]. Although
the effects of mixing on the biogas community and process performance
[[54]27, [55]28] has been previously investigated, no studies have
specifically examined the impact on syntrophic acid oxidising
communities, which are highly reliant on close cell proximity for
optimal function. Studying syntrophic interactions within the complex
web of microbial interactions of the AD process makes it challenging to
isolate the effects of mixing on syntrophic acid oxidation, from those
on other stages in the process, such as acid production rates. In
addition, the low abundances of the syntrophic community commonly
occurring in AD [[56]29] obstructs studying the effects on a molecular
level.
The objective of this study is to investigate how disruptive motion
affects the activity of syntrophic acid-oxidizing communities under
high-ammonia conditions. Specifically, we aimed to determine whether
harsh stirring disrupts flocculation, impact on the acid degrading
activities, and alters electron transfer mechanisms. We hypothesized
that obstructed flocculation would lead to a lower expression of
biofilm-associated genes and a shift in electron transfer mechanisms
toward formate-mediated transfer. To test this, we used mesophilic
enrichment cultures degrading either acetate or propionate under
high-ammonia conditions and subjected them to two different mixing
modes: magnetic stirring and orbital shaking. The impact of these
conditions on degradation rates, microbial community composition, and
microbial metabolic activities were analysed using molecular analyses.
Furthermore, computational fluid dynamics (CFD) modelling was employed
to assess the hydrodynamic forces and fluid motion generated by
different mixing modes. CFD modelling provided insights into variations
in shear rates and particle distribution, serving as a proxy for
microbial dispersion. Given the essential role of syntrophic
communities in preventing VFA accumulation in high-ammonia biogas
reactors, understanding their response to mixing can help optimize VFA
degradation and improve biogas process stability.
Materials and methods
Source of microbial community and batch cultivation setup
The syntrophic propionate-oxidising and acetate-oxidising enrichment
cultures were derived from mesophilic laboratory-scale continuous
stirred-tank reactors (CSTRs), previously described by Singh et al.
[[57]12]. In short, these CSTRs were inoculated with sludge from a
high-ammonia biogas reactor degrading food waste and supplemented with
albumin. The CSTRs were continuously fed with a bicarbonate-buffered
medium containing ammonium chloride (0.3 M) and sodium propionate (0.1
M) as substrate [[58]9]. The CSTRs were operated for over 144 days
before the enrichment cultures were transferred to anaerobic serum
bottles.
The anoxic bicarbonate-buffered medium was prepared as described by
Westerholm et al. [[59]30] and supplemented with ammonium chloride (0.3
M NH[4]Cl). However, unlike previous publications [[60]12, [61]13,
[62]19] the enrichment cultures in this study were cultivated through
multiple sequential transfers into medium without the addition of yeast
extract. This repeated transfer process ensured the gradual elimination
of all carbon sources except for the acids (excluding the reducing
agent cysteine).
For the preparation of cultivation batches for this study, 0.225 L of
the medium without yeast extract containing either 50 mM sodium acetate
(A) (CA, StA, ShA) or 50mM sodium propionate (P) (CP, StP, ShP) was
transferred to serum bottles (0.5 L) while flushing with N[2]. At this
stage, stirrer magnets were added to the bottles designated for
stirring motion (St) (StP, StA). The bottles were each sealed with a
butyl stopper and the gas phase was exchanged to N[2]/CO[2]. The
bottles were autoclaved at 121 °C for 20 min, then allowed to cool to
room temperature, before adding sterile-filtered solutions containing
vitamins, trace elements, and the reducing agents cysteine–HCL (0.5
g/L final concentration) and Na[2]S (0.24 g/L final concentration),
reaching a working volume of 0.25 L. The batches were inoculated with
12.5 mL (5% v/v) of the aforementioned enrichment culture grown in a
medium without yeast extract. Triplicate batches were incubated for
each setting in the dark at 37 °C. The control batches (C) were
incubated under static conditions. Batches designated for shaking (Sh)
(ShA, ShP) were placed on an orbital shaker (Orbitron Orbital Schüttler
Shaker, Infors HT, Switzerland) with a circular motion radius of 14.5
mm and a frequency of 2Hz (120 rpm). Batches designated for stirring
(StA, StP) were placed on magnetic stirrers set to an approximate speed
of 700 rpm. The stirring speed was estimated by analysing slow-motion
video footage of the magnetic stirrer, counting revolutions, and
dividing by the video recording time. The final rotational speed was
calculated as the average of three separate recordings. Consequently,
in this setup triplicate batches were exposed to either: static (C),
gentle shaking under relative mild conditions (Sh) and intense,
high-speed stirring conditions (St). Once the initially supplemented
substrate was depleted, it was replenished to a concentration of 50 mM
on day 111 for acetate-fed cultures, and day 300 for propionate-fed
cultures and again incubated under respective conditions.
Chemical analytical methods
For chemical analysis, liquid (2 mL) and gas (1 mL) samples were
extracted using syringes. Propionate and acetate levels were measured
using high-performance liquid chromatography (HPLC), as previously
described by Westerholm et al. [[63]30]. Methane and CO[2] composition
of the headspace were measured using gas chromatography as described by
Westerholm et al. [[64]31]. Pressure measurements of the headspace were
taken each time liquid and gas samples were extracted, using a handheld
pressure meter (GMH 3111, Gresinger). Pressure measurements were also
regularly taken to monitor whether acid degradation had started during
the long lag phases of the batch cultivations.
Optical inspection of floc-formation
Images were taken of the batch assays 56 and 104 days after incubation.
Bottles were placed on a sheet of acrylic glass, and images were taken
from beneath and from the side of the bottle.
To microscopically visualise aggregates and methanogenic activity
(based on F[420] autofluorescence) a droplet of sample culture was
placed on a microscope slide. Micrographs were captured using a
fluorescent microscope (Lumascope LS720, Etaluma) at
60 × magnification. F[420] autofluorescence of methanogens was
visualised using a 370–410 nm excitation filter and a 429–462 nm
emission filter. Microscopic inspection was performed on all stirred
propionate samples, and on a representative replicate from the control
and shaking batch assays.
Computational fluid dynamics modelling
A CFD model was constructed to determine the impact of stirring and
orbital shaking on the hydrodynamic conditions and cell distribution.
Two complementary simulations were constructed for each mode of motion.
Light particles (1 µm) were used as a proxy to represent the cells and
small-scale aggregates, which were initially homogeneously dispersed
throughout the fluid. The cultivation batches in serum bottles were
modelled up to the top of the working liquid height using finite
element-based solver COMSOL Multiphysics (v 6.2) [[65]32]. The stirring
was modelled through a moving mesh with a domain rotation corresponding
to 700 rpm. For the shaking motion, the same control volume used for
stirring was applied, but without the magnet. Modelling was conducted
in COMSOL in Multiphysics using mesh deformation with the following
relations:
[MATH: Mesh displacement
inXRcos(2πω)Mesh displacement
inY0Mesh displacement
inZRsin(2πω) :MATH]
where R is the radius of rotation and ω the frequency with values of
0.0145 [m] and 2 [Hz], respectively.
The governing equations, conservation of momentum and mass, take the
form of Reynolds Averaged Navier–Stokes (RANS) equations for turbulent
flow. The Realizable
[MATH: k-ε :MATH]
model has been used as the turbulence model, which considers two
additional transport equations, namely the turbulent kinetic energy and
dissipation to be solved along with RANS. This is the most commonly
used turbulence model within the community to simulate the hydrodynamic
environment in bioreactors [[66]33, [67]34] and its performance
was validated using Particle Image Velocimetry by Sucosky et al.
[[68]35]. The calculated Re are 6992 and 1597 for the stirring and
orbital shaking table, respectively, which suggests turbulent flow as
they are above the critical Re of 1000 [[69]36]. The characteristic
lengths used in the model were the length of the magnet stirrer and the
translational motion diameter, defined as 2R for the orbital
shaking-table. The cultivation medium, being an aqueous solution, was
treated as water for the CFD analysis, and it was assumed that its
density and viscosity were equal to water at 20 °C. A no-slip boundary
condition was used for the walls, and in the case of the orbital
shaker, the walls followed the frame motion. A symmetry plane was
applied at the free surface of the liquid, and gravitational forces
were accounted for as volume forces.
To investigate the effect of hydrodynamic forces on floc distribution,
a phase transport module was employed to solve for the mass transport
of two species; water as liquid and microbes or microbial aggregates as
solid particles, with a diameter of 1 µm and 1% heavier than water,
purposefully selected to approximate the size and density of microbial
cells [[70]13, [71]37]. The initial volume fraction of heavier
particles was set at 0.1, with a homogenous distribution. The
Multiphysics coupling between turbulent flow and phase transport was
carried out through the Mixture Model from COMSOL Multiphysics. The
selected slip and mixture viscosity models are Hadamard-Rybczynski and
Krieger, respectively [[72]32].
The computational mesh uses tetrahedral elements for the bulk flow and
4 layers of prism elements to capture the larger gradients near the
wall. The grid convergence index [[73]38], based on the volume average
of shear rate and velocity, showed a GCI value of less than 1% for a
mesh with the control volume consisting of 166,729 elements compared to
a finer mesh. The selected mesh had element size distribution ranges
from 4.63 × 10^–4 to 9.83 × 10^–3. The implicit time-dependent solver
was run until the average volume values of velocity and shear rate
changed by less than 1% for the last two consecutive times, indicating
that the solution had reached a steady state. This was established
after 20 s for the magnetic stirring and 50 s for the orbital shaker.
For example, in the earlier case, the comparison of volume average
shear rate and velocity between times 20 s and 21 s had indicated
errors of 0.03% and 0.4%, respectively. The post-processing considered
the hydrodynamic force, i.e., contours of shear rate and volume
fraction of solid particles, denoting the distribution of microbes or
microbial aggregates, which were initially uniform. Two circumferential
planes located at the bottom and top of the computational domains were
selected to study the effect of stirring or shaking.
16S rRNA gene amplicon sequencing, data analysis and qPCR
16S rRNA gene amplicon sequencing was conducted to monitor the
microbial community structure over time. Due to differences in
degradation dynamics between experimental setups, liquid samples (5 mL)
were taken at various timepoints to obtain samples representing
different stages of degradation. The samples were taken as follows:
acetate degrading batch cultures (CA, ShA, StA) on days 49, 69, 97,
111, 125, and 139; propionate degrading control and shaking motion
batch cultures (CP, ShP) on days 111, 139, 160, 216, 230, 251, 300,
328, and 338; stirred propionate culture (StP1) on days 196, 216, 230,
251, 280, 300, 328, and 338. Due to the absence of propionate degrading
activity, the stirred propionate cultures StP2 and StP3 were sampled
only on days 196, 251, and 300. The samples were stored at – 20 °C
until DNA extraction. After thawing, total DNA was extracted using the
DNAeasy Blood and Tissue Kit (Qiagen) according to the manufacturer’s
instructions. Construction of 16S rRNA gene amplicon libraries using
primers 515F [[74]39] and 805R [[75]40] and Illumina sequencing was
performed as described by Muller et al. [[76]41]. Paired-end sequencing
was conducted using an Illumina MiSeq instrument (Eurofins, Germany) at
SciLifeLab Stockholm, Sweden.
The raw sequencing data underwent a multistep pre-processing pipeline.
Due to the initial poor quality of the reverse reads, the first 42
bases were removed from the reverse reads using Trimmomatic (v 0.39)
[[77]42]. Subsequently, primer and adapter sequences were removed using
Cutadapt (v 4.7) [[78]43]. Amplicon sequence variants and abundance
tables were generated in R (v 4.2.1) using the dada2 package (v 1.24.0)
[[79]44]. In the dada2 pipeline, forward and reverse sequences were cut
to 272 and 182 bp, respectively, with a quality threshold of
maxEE = (2, 7) and a default value of truncQ = 2. For taxonomic
assignment of the amplicon sequence variants, a reference database
formatted specifically for dada2 was utilised [[80]45]. This database
was derived from the Genome Taxonomy Database (GTDB) release 207
[[81]46]. The phyloseq package (v 1.40) [[82]47] was used for
subsequent data handling and visualisation of the microbial community
structure. Non-metric Multidimensional Scaling (NMDS) was applied to
assess the similarity of the community structure between experimental
setups. The distance matrix used for the NMDS was created using
Bray–Curtis distance.
To compensate for the restricted specificity of archaeal 16S rRNA genes
by the primers used in the present study [[83]48], quantitative PCR
(qPCR) analyses were conducted on samples taken from the propionate and
acetate enrichment batches at the following timepoints: CA, ShA, and
StA on days 49, 69, 126, and 139; CP and ShP on days 139, 196, 251, and
328; and StP1 on days 216, 251, 300, and 328. The qPCR was conducted
using primers MMBf (5ʹ-ATCGRTACGGGTTGTGGG-3ʹ) and MMBr
(5ʹ-CACCTAACGCRCATHGTTTAC-3ʹ) to determine the 16S rRNA gene level of
methanogens of the order Methanomicrobiales [[84]49]. Total bacteria
were assayed using the forward primer (5ʹ-GTGITGCAIGGIIGTCGTCA-3ʹ) and
reverse primer (5ʹ-ACGTCITCCICICCTTCCTC-3ʹ) based on primers described
in Maeda et al. [[85]50]. The qPCRs were performed in a 20 μL reaction
mixture that consisted of 3 μL DNA sample, 10 μL ORA™ SEE qPCR Green
ROX Master Mix (HighQu), 1 μL of each primer (10 μM). The qPCR protocol
for quantification was as follows: 7 min at 95 °C, 40 or 55 cycles of
95 °C for 40 s, annealing at 66 or 61 °C (for the order
Methanomicrobiales and total bacteria, respectively) for 1 min and
72 °C for 40 s, and melting curve analysis at 95 °C for 15 s, followed
by 1 min at 55 °C and finally at 95 °C for 1 s. All reactions were
carried out in a CFX Duet Real-Time PCR System (BioRad).
RNA extraction, sequencing
For RNA extraction, the microbial cultures were sampled during the
exponential phase of acid degradation, after the second substrate
addition, to ensure sufficient biomass and active acid degradation.
Sampling occurred on day 125 for acetate-fed cultures (CA, StA, ShA)
and on day 338 for propionate-fed cultures (CP, StP, ShP). Total RNA
was extracted using a chloroform/phenol-based method, followed by rRNA
depletion using riboPOOL™ (as outlined in the RNA extraction and
depletion protocol by Perman/Weng [[86]51]). To sample any flocs formed
during cultivation, bottles were gently inverted to allow floc
sedimentation. For acetate-fed cultures, a single 50 mL sample was
taken from each bottle and transferred to N[2]-flushed Falcon tubes
that were kept on ice. For propionate-fed cultures, triplicate 50 mL
samples were collected from each bottle and similarly transferred to
N[2]-flushed Falcon tubes on ice. All tubes were centrifuged at 5000g
and 4 °C for 10 min, the supernatant was discarded, and the cell
pellets were pooled in 1 mL chilled TRIzol (Thermo Fisher Scientific,
MA, USA) and 0.2 mL chloroform. For acetate-fed cultures, pooling was
carried out on a biological replicate level, yielding one sample per
motion type (CA, StA, ShA). For propionate-fed cultures, technical
replicates were pooled, producing one sample per biological replicate
(CP1–3, StP, ShP1–3). Two of the stirred propionate cultures did not
degrade propionate (StP2, StP3) and failed to yield sufficient RNA for
analysis. Resultingly, two technical replicates were extracted from the
stirred propionate culture that did degrade the acid, herby referred to
as StP1 and StP12. RNA extraction was performed using the Quick-RNA
Fecal/Soil Microbe Microprep Kit (Zymo Research, CA, USA) with an
additional Dnase I treatment step. Extracted RNA samples were stored at
− 80 °C. Ribosomal rRNA was depleted using pan-prokaryote riboPOOL
probes with streptavidin-coated Dynabeads (MyOne Streptavidin C1,
Invitrogen #65001). Depleted RNA was purified using ethanol
precipitation and stored at − 80 °C until submission for sequencing.
RNA concentration and quality was assessed using a 2100 Bioanalyzer
System with RNA 6000 Nano Kit (Agilent Technologies Inc., CA, USA). The
rRNA-depleted RNA was sequenced using paired-end sequencing (2 × 150
bp) on an Illumina NovaSeq X Plus (Eurofins, Germany), using one lane
of a 10B flow cell. Sequencing was conducted at the SNP&SEQ platform
(SciLifeLab, Uppsala, Sweden).
RNA analysis
Raw sequences were trimmed off adapters using Cutadapt (v 4.0) [[87]43]
and quality filtered using Trimmomatic (v 0.39-2) [[88]42]. In
silico-removal of rRNA was carried out using SortMeRNA (v 2.1b)
[[89]52]. Quality controlled and trimmed reads were then quantified
through a reference-based approach using Salmon (v 1.9.0) [[90]53].
Metagenome-assembled genomes (MAGs) from a previous study investigating
the same enrichment culture were used for read mapping [[91]13], and
complemented with a single MAG affiliated to the genera Alkaliphilus
extracted from the high ammonia CSTRs from which the present enrichment
culture originates [[92]12]. Quantification results were outputted as
raw counts. Differential gene expression analysis was conducted in R
using DESeq2 (v 1.36.0) [[93]54] and results were visualised using the
packages pheatmap (v 1.0.12) [[94]55] and ggplot2 (v 3.5.0) [[95]56].
For the main differential gene expression analysis investigating the
effects of motion, only propionate fed samples were used. Genes with
low expression (less than 2 samples with a gene count of at least 10)
were sorted out prior to analysis. P values were adjusted (p-adj) using
the Benjamin–Hochberg method [[96]57]. Genes were assigned as
differentially expressed if they had an absolute value of log twofold
change (log2FC) > 1.5 and an adjusted p value of < 0.05. For the
principal component analysis (PCA), genes with low expression were
filtered out as described above and transformed using
regularised-logarithm transformation (rlog) of the Deseq2 package.
GapMind [[97]58] was used to identify likely biosynthetic pathways for
all amino acids, the associated coding sequences (candidate genes) to
each pathway, and the completeness of each pathway for each respective
MAG. All reported candidate genes were included in the gene sets used
for subsequent pathway enrichment analysis and heatmap visualisation.
When multiple candidates were associated with a single gene, the
aggregated count was displayed in the heatmaps. Pathway enrichment was
tested assuming a hypergeometric distribution with the p value
calculated as the probability of observing at least n pathway-related
genes among the differentially expressed genes and was tested for up-
and down-regulated genes separately. This was done using the R-basic
stat function phyper (q−1, m, n, k, lower.tail = FALSE):
q = number of differentially expressed genes in pathway list.
m = number of genes in pathway list.
n = number of background genes not in pathway list.
k = number of differentially expressed genes.
where the background gene set was defined as all genes that passed the
initial filtering for low expression (at least two samples with
counts > 10). The p value was adjusted for multiple testing according
to the Benjamin–Hochberg procedure [[98]57].
Results and discussion
Degradation of acetate and propionate
The acetate-fed cultures had similar degradation rates regardless of
the type of motion but stirring extended the lag phase in StA cultures
with 7–30 days as compared to cultures grown in static and shaken
conditions (CP, ShP, Fig. [99]1, Table [100]1, Supplementary Data SE1
and SE2). In accordance with previous studies of the enrichment culture
[[101]19], the lag phase was considerably shorter for the second
degradation (following the re-addition of 50 mM acetate, Table [102]1).
This difference is likely due to cells and essential enzymes already
being present at the point of re-addition. The slightly faster
degradation observed in stirred cultures during the second degradation
is likely explained by the shorter starvation period between the
consumption of the initially supplemented acetate and its re-addition
(Fig. [103]1).
Fig. 1.
[104]Fig. 1
[105]Open in a new tab
Degradation dynamics of acetate-fed (top) and propionate-fed (bottom)
cultures subjected to different types of agitative motion: no motion
(C, left), orbital shaking (Sh, middle), and magnetic stirring (St,
right). Solid lines show the acetate concentration, while dashed lines
show the propionate concentration (mM) and replicates are shown in
different colours. For acetate-fed cultures, additional acetate (up to
50 mM) was added on day 111 and RNA extraction was performed on day
125. In the propionate-fed cultures, propionate was added on day 300
and RNA was extracted on day 338. Note the differing scales on the
x-axes for acetate-fed and propionate-fed cultures
Table 1.
Cultivation metrices of acetate-fed (CA, ShA, StA) and propionate-fed
(CP, ShP, StP) cultures subjected to different types of agitative
motion: no motion (C), orbital shaking (Sh), and magnetic stirring (St)
Sample First degradation After substrate spiking
Substrate (50 mM) Motion Lag phase (days) Acid deg. rate (mM/day)
Acetate accum. (mM)^a Lag phase (days) Substrate conc. at RNA
extraction (mM)
CA Acetate Static 49 1.70–1.77 – 0 21–24 (acetate)
ShA Shaking 49 1.72–1.94 – 0 19–25 (acetate)
StA Stirring 56–79 1.73–1.86 – 0 3–12 (acetate)
CP Propionate Static 181 0.40–0.70 19 0–13 16–24 (propionate)
ShP Shaking 139–188 0.48–0.76 33 27–34 2–39 (propionate)
StP1^b Stirring 237 0.62 26 34 34 (propionate)
[106]Open in a new tab
^aMaximum acetate level during propionate degradation
^bOnly one of the triplicate propionate-fed cultures started to degrade
propionate during stirring
In the propionate batch assays (CP, ShP, StP) the impact by motion on
the lag phase was more pronounced. Strikingly, stirring had a negative
impact on propionate degradation, as it only began in one (StP1) of the
three replicates. The disruptive effect of stirring was further evident
from the prolonged lag phase of over 50 days in StP1, compared cultures
grown under static conditions (CP). Conversely, shaking motion (ShP)
reduced the lag phase by approximately 40 days compared to the static
condition in two of the replicates. Interestingly, once propionate
degradation commenced, the degradation rates remained relatively
similar across all conditions (Table [107]1). However, propionate
degradation remained significantly slower than acetate degradation,
consistent with previous studies on the enrichment culture when
cultivated in a medium containing yeast extract (Supplementary Data
SE2) [[108]13, [109]19]. Furthermore, similar to SAO, the propionate
degrading cultures all had shorter lag phases after substrate spiking
compared to the first degradation (Table [110]1).
Contrary to the biphasic utilisation of propionate previously observed
in enrichment cultures grown in yeast extract-supplemented medium
[[111]13], the present study revealed simultaneous degradation of
acetate and propionate during the initial phase. This simultaneous
degradation may be linked to the longer lag phase observed in the
present study, likely due to the lack of crucial nutrients typically
provided by the yeast extract. During this extended lag phase, the
minimal degradation of propionate could allow the SAOB community to
acclimate cultivation conditions, priming them for acetate degradation.
As a result, once propionate degradation enters the logarithmic phase,
the SAOB are already prepared to efficiently convert the acetate
produced. Interestingly, despite the extended lag phase, the
degradation rates were ultimately comparable between cultures with and
without yeast extract supplementation once degradation had commenced
(Supplementary Data SE2).
CFD modelling and floc formation observation
The CFD modelling showed that the stirring motion generated high shear
rates, which intensified with vertical depth as proximity to the
magnetic stirrer increased, reaching a maximum of ~ 20 s^−1
(Fig. [112]2). Contrastingly, the shaking motion resulted in relatively
lower shear rates (maximum ~ 5 s^−1) and showed minimal variation with
vertical depth. Consequently, the hydrodynamic forces acting on solid
particles were comparatively greater for magnetic stirring than for
orbital shaking.
Fig. 2.
Fig. 2
[113]Open in a new tab
Contour plots depicting shear rates (s^−1) for magnetic stirring (top
plane: a bottom plane: b and orbital shaking (top plane: c bottom
plane: d). Logarithmic scale has been used for both colour legends to
highlight the variation in shear rates
Regarding the concentration of particles, mapping the volume fraction
against the shear rate revealed that the highest solid particle
concentrations occurred in areas with steep shear rate gradients,
particularly near the bottom and along the walls (Fig. [114]3). During
the shaking motion, a considerably higher volume fraction was observed
at the bottom plane, likely due to the relatively weak but higher
gradient shear rates that were generated by shaking, which were
insufficient to counteract the negative buoyancy of the particles. The
stirring motion gave rise to minor differences in particle
distribution, mostly confined to the bottom of the liquid located close
to the wall.
Fig. 3.
Fig. 3
[115]Open in a new tab
Contour plots corresponding to volume fraction of solid particles,
unitless, with an initial value of solid particles 0.1 (1% of total
volume) with homogenous distribution for both motions with magnetic
stirring to the top and orbital shaking at the bottom. The deviation
greater than 0.1 indicates regions with higher concentration of solid
particles
The results from the CFD modelling indicate that cells in the different
setups were subjected to varying shear stress and fluid motion, which
likely significantly impacted their ability to form and sustain
cellular aggregates. For cell aggregation to occur, cells must be in
close proximity to each other or to a surface, as this enables adhesive
forces and the development of a shared extracellular matrix [[116]21,
[117]59]. In addition to motions influencing the initial cell
aggregation, shear rates can also affect the size of the flocs. This is
because higher shear rates impose an upper limit on floc size, leading
to the formation of smaller flocs [[118]60]. The present study
demonstrated that stirring clearly prevented the formation of visible
flocs due to its disruptive effect on aggregation.
Under shaking conditions, the enhanced volume fraction of particles
(cells) likely increased the likelihood of initial cell–cell contact,
while lower shear rates than in the stirring favoured the establishment
of stable cell–cell adhesion. In line with these hypotheses, visual
observations of the cultures revealed that shaking conditions resulted
in the largest flocs (Supplementary note 1, Supplementary Fig. S1).
This could be attributed to increased particle collision than under
static conditions, facilitating the establishment of cell–cell
adhesion. The observation aligns with previous studies which have
demonstrated that moderate shear forces is necessary for the formation
of a specific type of compact cellular aggregates, known as granules
[[119]61], and that turbulence promotes cellular aggregation and
biofilm formation [[120]62]. In addition, a study of a co-culture of
syntrophic butyrate oxidising bacteria and a hydrogenotrophic partner
showed that physical disruption through shaking or ultrasound treatment
did not affect the tendency of aggregation [[121]63].
For microscopic inspection, all samples were collected from the top
phase of the culture assays to prevent any disturbance of the formed
flocs and biofilms, favouring the sampling of platonic over aggregated
cells. Nevertheless, aggregates were still observed in the shaking
motion samples and, surprisingly, in one of the stirred replicates that
was unable to degrade propionate (StP3, Supplementary Fig. S2). The
presence of aggregates in the shaking motion samples, but not in the
static control samples, aligns with the enhanced volume fraction of
particles and the fluidic dynamics predicted by the CDF model under
shaking motion. The presence of microscopic flocs in one of the stirred
cultures implies that even under the high shear conditions, initial
cell–cell adhesion and the formation of microscopic flocs consisting of
a few cells is possible. However, although syntrophic relationships had
been established by the second substrate addition in StA and StP1, the
impact of shear stress under stirring conditions remained significant
throughout the experimental period. Even after interactions were
formed, microbial communities had to continuously withstand mechanical
forces, potentially affecting their structural integrity, metabolic
activity, and ability to establish new interspecies connections.
Methanogenic autofluorescence was observed in all samples except StP2,
which is one of the stirred replicates that was unable to degrade
propionate.
Based on these results, it can be hypothesised that stirring and high
shear stress impair propionate degradation more than acetate
degradation due to the SPOB’s greater reliance on flocculation and
proximity to the methanogenic partner compared to the SAOB. One
potential explanation is that the SPOB may require closer proximity to
the methanogenic partner for transferring reducing equivalents, such as
through H[2]-mediated or direct electron transfer. If hydrogen serves
as the primary carrier of reducing equivalents for the SPOB, the close
spatial arrangement of the hydrogen producer and consumer within flocs
or aggregates creates localised micro-environments, wherein hydrogen
partial pressures are lower than the surrounding medium [[122]64].
Stirring disrupts this arrangement, increasing the distance between
consumer and producer, and thus elevates the hydrogen partial pressure
that the producer is exposed to. It is possible that the SPOB is more
sensitive to this elevated partial pressure than the SAOB. Another
possibility is that SPOB are dependent on flocculation for protection
to the high ammonia levels. A deeper understanding of how syntrophic
organisms are affected by and protect themselves from high ammonia
levels is needed to determine whether this is indeed the case.
For all stirred samples, it could be hypothesized that the higher shear
stress enhanced fluid motion and reduced the boundary layer around
microbial cells, improving mass transfer efficiency and allowing
substrates to reach cells more rapidly. However, the negative impact of
stirring on the acid degradation capacity of the communities,
particularly the SPO community, suggests that any potential benefits
from enhanced mass transfer were outweighed by the disruption of
essential cell-to-cell interactions.
Microbial compositions and dynamics
Approximately 87% of the total reads mapped to amplicon sequence
variants taxonomically resolved to the species level. NMDS ordination
plots on relative abundance showed a distinct separation between
samples fed with acetate and those fed with propionate. However, no
clear clustering was observed based on mode of motion (Supplementary
Fig. S3 A). Instead, the degree of substrate degradation emerged as the
primary determining factor (Supplementary Fig. S3B, C).
The microbial compositions and dynamics were similar across all
acetate-fed cultures (Fig. [123]4, Supplementary Fig. S4). During the
lag phase, known SAOB of the genera Syntrophaceticus (46–95%) and
Tepidanaerobacter (13–29%) dominated. Once acetate oxidation had
started, the relative abundance of Syntrophaceticus increased to > 90%
for most of the replicates and remained relatively stable until the
second addition of acetate. Members of this genus are frequently
highlighted as the main SAOB in AD operating at high ammonia conditions
[[124]65, [125]66]. In the present study, an unclassified species
belonging to the genera Alkaliphilus increased in relative abundance
(from 0–3% to 11–49%) during this second acetate degradation, while
acetate was still present in high levels. The activity of
Acetomicrobium and Alkaliphilus in this study is particularly
noteworthy, as these genera are frequently detected in biogas processes
with high acetate levels, in association with syntrophic bacteria and
hydrogenotrophic methanogens [[126]67–[127]69]. They have also been
observed during hydrogen injection in power-to-gas applications
[[128]70], although their precise role in these systems remains
unclear.
Fig. 4.
[129]Fig. 4
[130]Open in a new tab
Bubble plot showing the relative abundance of microbial genera over
time in acetate-fed (CA, ShA, StA) and propionate-fed (CP, ShP, StP1–3)
cultures. All cultures, except stirred propionate-fed, show the
relative abundance of merged counts across replicates. Stirred
propionate-fed cultures (StP1–3) display individual replicate data
instead. This distinction was made, because stirred propionate cultures
exhibited substantial differences in ability to degrade propionate. For
a detailed view of replicate-specific data for each condition, see
Supplementary Figs. S4 and S5. Bubble size represents relative genus
abundance, with genera with less than 2% relative abundance grouped as
"Minor genera (< 2%)”
During propionate degradation in CP, ShP, and StP1, the microbial
community was initially predominated by the genus Tepidanaerobacter,
comprising over 50% relative abundance in most samples (Fig. [131]4,
Supplementary Fig. S5). In addition, low to intermediate relative
abundances of SAOB of the genera Syntrophaceticus, the SPOB candidate
'Ca. Syntrophopropionicum' [[132]12, [133]13, [134]19], Acetomicrobium,
and an uncultured bacterial group belonging to the Family
Tepidimicrobiaceae (referred to as Mt11) were observed. Once propionate
oxidation was initiated, the relative abundance of 'Ca.
Syntrophopropionicum' increased to 46–77%. As acetate levels raised, so
did the relative abundance of Syntrophaceticus. Acetomicrobium remained
at low but persistent relative abundance throughout the degradation in
the propionate-fed cultures, with an increasing trend toward the end of
the experiment in some samples (CP1–3, ShP1, StP1). Following the
second addition of propionate on day 300, the microbial community
structures remained relatively stable, except for a few samples which
showed a further increase in the relative abundance of 'Ca.
Syntrophopropionicum', reaching higher abundances than those observed
during the first degradation. The stirred samples that failed to
degrade propionate (ShP2 and ShP3) exhibited microbial compositions
similar to the lag-phase of the other cultures, with no observed
increase in the relative abundance of the SPOB nor the SAOB as the
experiment progressed.
qPCR analyses targeting the order Methanomicrobiales (including the
genus Methanoculleus) revealed an abundance ranging from 5.52–8.50
log[10] gene copies/ng DNA, while the total bacteria gene abundance was
8.68–12.27 log[10] gene copies/ng DNA in the acetate- and
propionate-fed cultures (Supplementary Fig. S6, Supplementary Data
SE3). This indicates that the total bacterial community was
consistently more abundant than the hydrogenotrophic methanogens (i.e.,
Methanomicrobiales) across all conditions. However, qPCR data showed
relatively consistent presence of methanogens in all samples over the
course of the study. Furthermore, while qPCR data showed similar
abundance of Methanomicrobiales in both acetate- and propionate-fed
cultures, this was not reflected in the 16S rRNA sequencing data
(Fig. [135]4), where Methanoculleus was not detected in acetate-fed
cultures.
RNA extraction, sequencing, and differential expression analysis
Approximately 53% of raw metatranscriptomic reads were classified as
rRNA and subsequently removed in-silico. Of the total mRNA reads, 71%
mapped to the reference MAGs, with the majority mapping to the SAOB S.
schinkii (34%), followed by the hydrogenotrophic methanogen 'Ca.
Methanoculleus ammoniitolerans' (32%), the SPOB 'Ca. S.
ammoniitolerans' (24%), and members of the genera Alkaliphilus (3%) or
Acetomicrobium (2%) (Supplementary Fig. S7). PCA analysis of the
transformed and normalised count data showed a distinct clustering
based on substrate type for all samples. However, the PCA using only
propionate samples showed a distinct clustering according to the mode
of motion (Supplementary Fig. S8).
In the acetate fed samples, the SAOB S. schinkii exhibited the highest
transcriptional activity, followed by the methanogen and Alkaliphilus.
In propionate-fed samples, the methanogen and the SPOB displayed the
highest activity, while Alkaliphilus was low in activity. The activity
of SAOB was considerably lower in the propionate-fed samples compared
to the acetate-fed, with moderate expression observed only in the
stirred samples (StP) and one of the static control samples
(Supplementary Fig. S7).
Based on the transcriptional activity and 16S rRNA gene-based microbial
abundance, the MAGs for the syntrophic partners (SAOB, SPOB,
methanogen) and Acetomicrobium were included in the differential
expression analysis. As Alkaliphilus had low activity in the
propionate-fed samples, this MAG was excluded in this analysis. All
differential expression comparisons were made relative to the static
control samples unless otherwise stated. For instance, the phrase
“downregulated in stirred samples” indicates that the expression levels
were lower in stirred samples compared to the static control. The term
“motion” is used to collectively refer to the samples subjected to
either stirring or orbital shaking. It is important to note that for
stirred propionate-fed samples, RNA was extracted from two technical
replicates of the same bottle (StP1), as the other two bottles lacked
sufficient biomass for RNA extraction.
In the differential gene expression analysis, a total of 1396
differentially expressed genes (|log2fc|> 1.5 and p-adj < 0.05) were
identified, mostly originating from the methanogenic MAG (Supplementary
Fig. S9). For the other species, most differentially expressed genes
were downregulated under both modes of motion. A Venn-diagram revealed
a substantial overlap in differentially expressed genes between the two
modes of motion for the methanogen and the SPOB, demonstrating that
these changes occurred under both conditions (Supplementary Fig. S10).
In contrast, the SAOB and the Acetomicrobium sp. showed less overlap,
suggesting that the two motions induced regulation of a more distinct
sets of genes in these species.
The following section discusses the differentially expressed genes
related to core metabolic pathways, energy conservation, and microbial
interactions and their surrounding genomic region. Emphasis has been
placed on annotated genes, where surrounding genes in the genome
exhibit a similar log-fold change. The data sheet containing the
expression data and differential expression results, primarily used for
analysis, can be found in Supplementary Data SE4.
Main metabolic pathways and energy conservation
Consistent with previous studies of the enrichment culture [[136]13],
the transcriptomic data in the present study demonstrated that the SPOB
'Ca. S. ammoniitolerans' expressed genes for all steps of propionate
oxidation through the methylmalonyl-CoA pathway. The SAOB S. schinkii
expressed genes for the Wood–Ljungdahl pathway, used in the reverse
direction during syntrophic acetate oxidation [[137]71], whereas the
methanogen 'Ca. Methanoculleus ammoniitolerans' expressed genes for
hydrogenotrophic methanogenesis. These core metabolic genes were highly
expressed by the species across all investigated conditions
(Supplementary Data SE4).
For the SPOB, the shear forces induced by stirring caused a significant
downregulation of an oxalate–formate antiporter (OxlT) located next to
genes encoding a CoA-transferase (Fig. [138]5). In anaerobic bacteria,
these enzymes have been shown to import oxalate (a divalent anion) by
exchanging formate and, through a series of steps, convert the imported
oxalate to formate, which is then exchanged for a new oxalate molecule,
thereby closing the cycle. For each cycle, one intracellular proton is
consumed, creating a proton gradient [[139]72, [140]73] that could be
used for ATP production (Fig. [141]5). This antiporter has previously
been suggested to facilitate formate export in a syntrophic butyrate
oxidising bacteria [[142]74] or contribute to ATP production in
syntrophic alkane-degrading bacteria [[143]75]. In addition, prior
research has linked increased expression of the oxalate–formate
antiporter to faster propionate oxidation in thermophilic,
ammonia-tolerant SPOB [[144]14]. In combination with the higher
expression of the oxalate–formate antiporter in aggregated states
(static/shaking) in the present study, these findings suggest that this
oxalate–formate cycling activity is dependent on cellular proximity and
possibly requires interactions with other members of the syntrophic
community. This activity could serve as a potential indicator of a
well-performing SPOB community, and future culture studies of the SPOB
may shed some light on this. Another question to address is the source
of the oxalate in the culture medium, as no gene expression linked to
oxalate production or transport was found in any of the other species
in the culture.
Fig. 5.
[145]Fig. 5
[146]Open in a new tab
Graphical overview of the highlighted differentially expressed genes in
stirred or orbital shaken samples relative to the static control
samples. The red and green squares represent differentially
downregulated and upregulated genes, respectively. White squares
indicate that no differential change in expression was observed
For the SAOB, the gene expression of the reductive Wood–Ljungdahl
pathway and energy conserving systems were relatively unaffected by the
motions, which correlated with the observation of the relatively
similar acetate degradation profiles of the batch assays. Rather, the
most pronounced effects were observed for the methanogen, where both
motions evoked upregulation of most of the genes involved in the
hydrogenotrophic methanogenesis and a formate transporter. Furthermore,
based on the genomic localisation of differentially expressed genes it
appears that stirring induced the methanogen to increase the expression
of genes involved in the so-called Wolfe cycle, a key energy conserving
strategy in hydrogenotrophic methanogenesis (Fig. [147]5). The cycle
plays a crucial role in regenerating reduced cofactors needed for
hydrogenotrophic methanogenesis by coupling the exergonic reduction of
heterodisulfide (CoM–S–S–CoB) to the endergonic reduction of
ferredoxin, using H[2]. Alternatively, ferredoxin can be reduced
through the energy-converting hydrogenases (Eha, Ech). During shaking,
the energy-converting hydrogenase Eha was downregulated, with
significant decreases observed in subunits ehaB and ehaD. Genes
encoding the enzyme formylmethanofuran dehydrogenase (Fwd), which
catalyse the first step of hydrogenotrophic methanogenesis, wherein
reduced ferredoxin is consumed, were distributed at multiple different
genomic regions (Supplementary Fig. S11). These fwd genes were located
at distinct genomic loci, next to genes associated with either the
Wolfe cycle, the energy-converting hydrogenases, or, intriguingly, next
to a formate dehydrogenase. The formate dehydrogenase was downregulated
under both motions, and the Fwd-gene downregulated in response to
stirring. This co-localisation could suggest that the formate
dehydrogenase is involved in ferredoxin reduction. However, formate has
a redox potential similar to H[2], and like hydrogen cannot reduce
ferredoxin directly. To our knowledge, no such route of ferredoxin
reduction via formate has been previously described. The Fwd encoding
genes located next to the energy converting hydrogenase Eha were not
differentially expressed. Furthermore, the methanogen upregulated a
V/A-type ATPase during stirring, used to convert proton/sodium
gradients to ATP, which can be related to the strive to maintain
cellular activity during these harsh conditions.
In addition, both motions induced the methanogen to downregulate a
quinone-modifying oxidoreductase, subunit QmoC. This enzyme is found in
sulfate-reducing bacteria, where it is thought to play a role in the
electron transfer from menaquinone to the terminal electron acceptor
sulfate [[148]76]. However, while QmoC is membrane-bound, our analysis
indicated the differentially expressed QmoC to be cytoplasmic. The
operon also contained a downregulated heterodisulfide reductase subunit
(Hdr-A) and multiple hypothetical genes with similarities to
iron–sulfur proteins, indicating involvement in electron transfer. A
similar protein complex has been suggested to be involved in ferredoxin
reduction through a QmoC/HdrA/Mvh-hydrogenase complex [[149]77].
However, in the methanogen of this study, the operon lacked genes
encoding the Mvh-hydrogenase, although genes for this enzyme were
expressed and located elsewhere in the genome.
The transcriptomic activities of the Acetomicrobium sp. in propionate
and acetate cultures and the Alkaliphilus sp. in acetate cultures were
notable findings, considering the absence of any other sources for
growth apart from the acids in the present study. These species
expressed some genes of the Wood–Ljungdahl pathway but lacked key genes
for the complete pathway. However, both species expressed genes related
to the glycine synthase reductase pathway [[150]78], suggesting its use
for either acetate consumption or production. The Alkaliphilus MAG was
not included in the differentially expressed gene analysis, as the
expression by this MAG was negligible in propionate samples. A
description on general expression and discussion on genomic potential
for the Alkaliphilus MAG can be found in Supplementary note 2. Under
stirred conditions, the Acetomicrobium sp. downregulated a glycine
dehydrogenase and a dihydrolipoyl dehydrogenase, both components of the
glycine cleavage system. They located within an operon that also
contained a downregulated diguanylate cyclase, an enzyme regulating the
ubiquitous second messenger cyclic di-GMP, which is an important
regulator of bacterial behaviour, modulating processes such as biofilm
formation and motility [[151]79]. The reduced expression of diguanylate
cyclase during stirring, when cells were more dispersed, suggests a
possible link between glycine cleavage system activity and the
flocculation state.
Microbial interactions, motility, and biofilm formation
We expected that the motions, particularly stirring, would influence
the expression for genes associated with motility, biofilm formation,
and cell–cell interactions. However, in the SPOB no characterised genes
linked to these functions were found to be differentially expressed.
Nevertheless, the congregation of cells induced by orbital shaking
caused the SAOB to upregulate an operon encoding tight adherence
proteins (tadB, tadC) and pilus assembly proteins (CpaE, CpaF, CpaB).
Expression of this operon was almost absent in static and stirred
cultures. Some of these genes (tadB, tadC, CpaF) are known to be
associated with the capability of colonisation of the mucus layer
surrounding phytoplankton by oceanic bacteria [[152]80], thus
indicating a potential role in floc-formation of the SAOB during
shaking. Shaking motion also caused the SAOB to downregulate an operon
encoding ABC transporters involved in lipoprotein transport and the
efflux of antimicrobial peptides. One of the genes in this operon
showed BlastP similarity to bacteriocins, which are antimicrobial
peptides that inhibit similar or closely related species, thereby
offering a competitive advantage to the producer in the competition for
resources and ecological niches [[153]81]. Since cultures subjected to
shaking motion formed the largest flocs, the downregulation of this
bacteriocin system could represent an adaption to the close proximity
to partner species on which they depend for metabolic activity.
Moreover, both motions caused the SAOB and the Acetomicrobium sp. to
downregulate type IV pilus containing operons, including twitching
motility proteins. Twitching motility allows bacteria to move over
surfaces, and it is plausible that this mode of motility was only
beneficial in the absence of shear stress and fluidic movement, as was
the case for the static cultures. This is supported by the observation
of surface-attached biofilms and biomass aggregation at the bottom of
the culture bottles in static cultures, while both stirring and shaking
seemed to prevent such formations. Furthermore, contact with surfaces
has been shown to enhance the expression of type IV pilus in bacteria
of the genus Pseudomonas [[154]82, [155]83]. Therefore, it is possible
that contact with the bottom of the bottle during static conditions
contributed to the higher expression of type IV pilus in SAOB and
Acetomicrobium sp. In addition to type IV pilus genes, the
Acetomicrobium sp. downregulated a Flagellar hook-associated protein
(FlgK) in response to both motions. The hook-associated protein plays a
crucial role in connecting the flagellar hook to the flagellar
filaments [[156]84], suggesting increased motility under the static
control conditions.
For the methanogen, both types of motions invoked upregulation of an
operon containing numerous hypothetical proteins with BlastP similarity
to “von Willebrand A (VWA) domain-containing protein”. While VWA
domain-containing proteins are well-studied in eukaryotes, and are
associated with cell adhesion and extracellular matrix proteins, less
is known about their roles in prokaryotes [[157]85]. The operon also
included genes involved in methanogenesis (mer and mtd) of which mtd
was upregulated. In addition, the operon contained genes encoding a
Tubulin-like protein (CetZ), which in Haloferax volcanii regulates cell
shape, contributed to the rod-shaped morphology needed for normal
swimming motility [[158]86]. However, considering that Methanoculleus
species are irregular cocci this protein likely has a different role in
this species. Furthermore, both motions evoked upregulation of
unannotated genes, with BlastP similarities to proteins containing
domains that indicate an involvement in adhesion and cell–surface
interactions, namely: fasciclin-domain which is involved in ancient
cell adhesion in plants and animals [[159]87], PKD-domain found surface
layer proteins of archaea [[160]88], and SdrD B-like-domain which is
important for cell adhesion [[161]89].
Transport, ammonia tolerance, amino acid, and other noteworthy gene
expression changes
It is noteworthy that both the SPOB and the methanogen downregulated
ferrous iron transport proteins under both types of motions. Ferrous
iron is the predominant form of iron in anaerobic environments, and the
downregulation of the transport systems may suggest that iron is a
limited resource in the static control conditions, where mass transfer
is determined by diffusion rates. In a separate experiment, the
influence of iron level was tested under static conditions (data not
shown). The addition of extra iron (ferric or ferrous 250 µM) seemed to
have a negative effect on the syntrophic acid degradation, suggesting
that iron deficiency is unlikely to be a major constraint in the
current study. However, considering the interaction between iron and
sulphide [[162]90], it is possible that the iron addition inadvertently
caused deficiency in bioavailable sulphur. Other differentially
expressed transport proteins included downregulation of zinc transport
under stirring by SPOB, whereas the methanogen upregulated ABC
transport of nickel, iron, and molybdate/tungsten under stirred
conditions and downregulated a phosphate/Na^+ symporter under both
motions. The Acetomicrobium sp. exhibited differential expression in
genes for the transport of aromatic amino acids, with one operon
downregulated during shaking motion and another upregulated under
stirring motion. This suggests an adaption strategy to cope with the
more challenging conditions associated with stirring. An ABC importer
for peptide/nickel was also downregulated in stirred samples for the
Acetomicrobium sp. (Fig. [163]5). In the methanogen, changes in amino
acids metabolism included downregulation of genes involved in the
synthesis of branched-chain amino acids (valine, isoleucine, leucine)
under both motions. Conversely, several genes encoding
phenylacetate-CoA ligase family protein, which are involved in
phenylalanine and tyrosine metabolism, were upregulated under both
motions.
Cultures were grown in high ammonia conditions (0.3 M) and many enzymes
involved in ammonia-related reactions or osmotic stress showed
differential expression. Notably, many of these enzymes exhibited
similar expression patterns for both modes of motion. These systems
could assimilate ammonia, thereby reducing intracellular ammonia
levels, or to synthesise compatible solutes that help the cells manage
ammonia stress. Furthermore, cellular aggregation could be employed by
certain microbes to mitigate osmotic stress caused by the high ammonia
[[164]91]. If so, the disruption of aggregates through agitative
motion, especially stirring, could undermine this strategy. This could
explain the observed changes in expression between static and mixed
cultures. For SPOB, both motions induced downregulation of aspartate
ammonia lyase, converting ammonia and fumarate into L-aspartate. As
fumarate is an intermediary of the methylmalonyl CoA pathway, the
observed expression changes may reflect a shift in the SPOB’s
metabolism toward catabolic processes in response to motion. This
enzyme has also been shown to be upregulated in iron starved conditions
in various bacterial species [[165]92–[166]95]. Moreover, the shaking
motion evoked a downregulation of a NADPH dependent glutamate synthase
in the SPOB, an enzyme that in conjunction with glutamine synthase is
an important pathway of ammonia assimilation [[167]96]. However, genes
encoding glutamine synthase were located at other genomic locations and
not differentially expressed. Glutamate is also an important
counter-ion to potassium, a ion-pair which accumulates as a short-term
response to osmotic shock [[168]97]. However, given the long-term
exposure to elevated ammonia levels, this function of glutamate is less
probable for the species. Both motions caused the SAOB to upregulate a
carbamate kinase, an enzyme that utilises ammonia and CO[2] to produce
carbamoyl phosphate. The operon also included an ornithine
carbamoyltransferase, involved in arginine synthesis, which was
upregulated in stirred cultures. In the methanogen, two operons with a
potential role in ammonia assimilation were differentially expressed.
The first operon included a carbonic anhydrase which converts carbon
dioxide into carbonate, upregulated under both motions (Fig. [169]5).
Surrounding genes encoded a carbamoyl-phosphate synthase, which
combines bicarbonate and ammonia to form carbamoyl phosphate, an
intermediary in pyrimidine synthesis and the urea cycle. The cluster
also contained genes encoding a transport system for the compatible
solute glycine betaine, which could indicate that the operon is
involved in ammonia tolerance. The transport system was, however,
downregulated in response to stirring, directly contrasting the
carbonic anhydrase. The second operon was downregulated under both
motions and contained a hydroxylamine reductase, which catalysed the
reversible oxidation of ammonia to hydroxylamine (NH[2]OH),
transferring electrons to an electron acceptor.
Finally, although certain differentially expressed genes were
interesting, they did not fit into any of the previous categories. For
the SAOB, the shear forces by stirring induced upregulation of two
genes encoding a zinc dependent alcohol dehydrogenase, one of which was
among the most highly expressed for the species. Alcohol dehydrogenases
catalyse the reversible conversion of aldehydes/ketones to their
corresponding alcohol while simultaneously oxidising NADH. In S.
schinki, the enzyme has been proposed to partake in ethanol degradation
in conjunction with acetaldehyde dehydrogenase, converting ethanol to
acetyl-CoA, reducing NAD^− in the process [[170]71]. This gene has been
shown to be expressed by Zhaonella formicivorans in co-culture with a
methylotrophic methanogen, utilizing the fourth mode of syntrophy,
where methanol serves as an electron carrier [[171]98]. However, this
is unlikely to be the function of the alcohol dehydrogenase gene in S.
schinki, as this species lacks several key genes for the
methanol-producing pathway and was not associated with a methylotrophic
methanogen in the enrichment culture. The high upregulation of this
alcohol dehydrogenase containing operon is a notable finding, although
its function and high expression remains enigmatic.
Differences in gene expression between acetate- and propionate-fed cultures
and potential coordinated cross-feeding of amino acids
The microbial composition varied between acetate- and propionate-fed
cultures, with the primary distinction being the presence of the SPOB
in the propionate-fed cultures and the Alkaliphilus sp. in the
acetate-fed cultures. To investigate how both substrate type and
microbial composition influenced gene expression in the SAOB,
methanogen, and Acetomicrobium sp., a differential gene expression
analysis was conducted (Supplementary Data SE5). This analysis compared
the transcriptomes of all acetate-fed (three in total) with all
propionate-fed cultures (nine in total). Changes in gene expression are
reported relative to the propionate-fed cultures, with upregulation
indicating higher expression in acetate-fed than in propionate-fed
cultures.
Both the SAOB and methanogen downregulated several formate
dehydrogenases and formate transporters in the acetate-fed cultures.
Given that formate and hydrogen are considered as the primary carriers
of reducing equivalents in syntrophic cultures, an upregulation of
hydrogenases would typically be expected if the flux of reducing
equivalents is to be maintained. However, no such upregulation was
observed for the methanogen. Furthermore, the SAOB, contrary to
expectations, downregulated hydrogenases [NiFe] 4e (ech) and [NiFe] 3B.
These finding indicates that electron transfer in the acetate-fed
cultures is more extensively routed through alternative pathways, such
as direct electron transfer, or that the overall flux was reduced. For
core metabolic pathways, gene expression remained largely unchanged,
except for the upregulation of methenyltetrahydromethanopterin
cyclohydrolase by the methanogen (step M3, Fig. [172]5).
Regarding pilus and flagellar systems, the SAOB downregulated most
genes associated with the pilus system (pilABC), while genes involved
in tight adherence and twitching motility were upregulated in the
acetate culture. The methanogen downregulated the archaeal type IV
pilus assembly protein (pilA), which is shown to be important for
surface adherence [[173]99]. These adaptions may contribute to the more
pronounced surface attached biofilm growth observed in acetate-fed
cultures (Supplementary Fig. S1).
Interspecies amino acid exchange is a particularly relevant aspect of
the present study, as the metagenomic analyses indicated that all
syntrophic microorganisms are auxotrophic (Supplementary Data, SE6 and
SE7) and that they are cultivated under amino acid-limited conditions,
aside from cysteine. The necessity of cross-feeding in the enrichment
culture is further supported by the requirement of yeast extract by the
SAOB S. schinkii when grown in pure-culture [[174]30]. Experimental
validation of auxotrophy in 'Ca. M. ammoniitolerans' and 'Ca. S.
ammoniitolerans' awaits their isolation but SPOB such as P.
thermopropionicum and several Methanoculleus species require the
presence of yeast extract in mono-cultivation [[175]100–[176]102].
Still, our findings demonstrate that the propionate oxidiser is not
essential for providing amino acids in acetate-degrading community, as
these cultures managed to grow without yeast extract, even in the
absence of the propionate oxidiser. Even though, no enrichment of
amino-acid-related genes was observed for the methanogen, the SAOB had
enrichment of upregulated genes involved in the synthesis of the
branched-chain amino acids leucine and isoleucine in the acetate-fed
cultures. The SPOB expressed the necessary genes for the leucine and
isoleucine synthesis as observed in the propionate-fed cultures
(Supplementary Fig. S12), indicating that the exchange of
leucine/isoleucine from the SPOB to the SAOB is possible. However,
these expression changes may not solely link to microbial interactions,
as they could also result from the differences in acetate or propionate
concentrations. For example, Escherichia coli can synthesise isoleucine
by converting propionate to 2-ketobutyrate through an alternate pathway
under anaerobic conditions in the presence of propionate [[177]103].
In acetate-fed cultures, the SAOB exhibited enrichment of downregulated
genes involved in threonine synthesis. Both the SPOB, the SAOB, and the
Alkaliphilus sp. have the capability to produce threonine, complicating
the ability to draw conclusions as to why the SAOB is downregulating
its synthesis. Potential explanations include threonine provision by
the Alkaliphilus sp. in acetate-fed cultures or overproduction and
provision of threonine by the SAOB in propionate-fed conditions.
Furthermore, the Acetomicrobium sp. showed enrichment of upregulated
genes for tryptophan synthesis in acetate-fed conditions. This species
had a complete set of genes for the synthesis of the tryptophan, unlike
both the Alkaliphilus sp. and the SPOB, which lacked a complete set of
genes. Taken together, the upregulation could indicate that
Acetomicrobium sp. is providing the Alkaliphilus sp. with tryptophan in
acetate-fed cultures. Tryptophan is a relatively costly amino acid to
produce [[178]104] and these amino acids have been shown to promote
stronger auxotrophic interactions than the exchange of amino acids that
are cheaper to produce [[179]105]. Tryptophan has previously been
suggested to play a role in coordinated cross-feeding between the SPOB
Pelotomaculum schinkii and Methanospirillum hungatei [[180]106].
However, given the complex and interconnected nature of interaction
within these syntrophic communities further research is needed to
confirm these findings.
Conclusion
This study provides critical insights into how different mixing
strategies affect syntrophic acid-degrading cultures, which play a key
role in AD processes. Stirring substantially hindered initiation of
syntrophic propionate oxidation while having minimal effect on
syntrophic acetate oxidation. CFD modelling showed that stirring
generated high shear rates and resulted in an even particle (cell)
distribution, while shaking induced lower shear rates with more
pronounced spatial gradients, causing cells to concentrate near the
bottom. As a result, shaking reduced the lag phase and promoted the
formation of larger microbial aggregates in the syntrophic propionate
oxidizing community. This suggests that the movement, characterized by
low shear stress, facilitated the initial connection and enhanced
cell-to-cell interactions between SPOB and the methanogen.
Although motion had a limited impact on microbial community composition
and the acid degradation rate, it triggered several key transcriptional
changes in the syntrophic community (SAOB, SPOB, and methanogen), and
the two species expressing the glycine synthase reductase pathway.
Notably, stirring led to downregulation of an oxalate–formate
antiporter in the SPOB. The higher expression of this system in the
biofilm/aggregate forming cultures suggests a reliance on proximity for
this activity, which may be linked to ATP production. Another notable
effect was the upregulation of genes associated with hydrogenotrophic
methanogenesis, particularly under stirred conditions, where the
connection between the first and the last step in the hydrogenotrophic
methanogenesis to regenerate ferredoxin appeared to have a prominent
role. This has been suggested as an important strategy for methanogens
for energy conservation. In addition, shaking caused the upregulation
of tight adherence and pilus assembly genes in the SAOB, likely
promoting cell aggregation. Conversely, both motions caused
downregulation of motility-related genes in the SAOB and a
Acetomicrobium sp., suggesting that motility was primarily beneficial
under static conditions. Furthermore, pathway enrichment revealed
potential cross-feeding of the branched amino acids leucine and
isoleucine in propionate-fed cultures.
In particular, this study demonstrated that mixing and high shear
stress disrupt the initial cell-to-cell connections between syntrophic
propionate-oxidising bacteria and hydrogenotrophic methanogens, thereby
negatively affecting the propionate degradation. These findings
highlight the need to carefully control mixing strategies in anaerobic
digesters, especially during periods of rising propionate levels when
these communities need to establish themselves. Excessive shear stress
may hinder the initiation of key syntrophic processes, particularly
propionate oxidation, potentially leading to higher VFA accumulation
and process instability. Conversely, moderate agitation that promotes
microbial aggregation without excessive disruption could enhance
syntrophic efficiency and biogas yield. These results have deepened the
understanding of how hydrodynamic forces affect syntrophic
interactions, which is crucial for optimizing anaerobic reactor design
to maximize performance and stability, particularly under high-ammonia
and high-VFA conditions. Furthermore, this knowledge could be leveraged
in designing systems specifically aimed at producing propionate and
other VFAs as end-products by adjusting mixing conditions to minimize
syntrophic propionate degradation.
Supplementary Information
[181]Supplementary Material 1.^ (4.2MB, docx)
[182]Supplementary Material 2.^ (6MB, xlsx)
[183]Supplementary Material 3.^ (33.3KB, docx)
Author contributions
NW: Experimental design, Laboratory work, Analysis, Data visualisation,
writing (original draft, reviewing and editing) MW: Experimental
design, Supervision, Writing (original draft, reviewing, and editing),
Project administration, Funding acquisition HNN: All analysis and
visualisation of computational fluid dynamics modelling, Writing
(original draft sections on CFD modelling, reviewing and editing) All
authors reviewed and approved the manuscript.
Funding
Open access funding provided by Swedish University of Agricultural
Sciences. Research reported in this publication was supported by the
HR2020 ERC—Grant European Research Council (Grant Number 948138), the
Swedish Research Council (VR, Grant Number 2019-03846) and the Novo
Nordisk Foundation (grant number NNF23OC0081830).
Availability of data and materials
All data generated or analysed during this study is included in this
published article and its supplementary information files. The
sequencing data generated and analysed in this study is available in
the NCBI BioProject (PRJNA1187519). The 16S rRNA gene sequencing data
have the SRA accession numbers SRR31396638-SRR31396766, and the
metatranscriptomic sequencing data have the accession numbers
SRR31439851-SRR31439861.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
References