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\begin{document}

\title{Lactate vs butyrate production during mixed culture glucose fermentation
driven by substrate availability as determined by metaproteomics}



\author[1]{Robert Hoelzle}%
\author[1]{Daniel Puyol}%
\author[1]{Bernardino Virdis}%
\author[1]{Damien Batstone}%
\affil[1]{The University of Queensland}%


\vspace{-1em}



  
  \date{June 13, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
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\selectlanguage{english}
\begin{abstract}
Mixed-culture fermentation provides a means to recycle carbon from
complex organic waste streams into valuable feedstock chemicals. Using
complex microbial consortia, individual systems can be tuned to produce
a range of biochemicals to meet market demand. However, the metabolic
mechanisms and community interactions which drive product expression
changes under differing conditions are currently poorly understood.
Furthermore, predictable product transitions are currently limited to
pH-driven changes between butyrate and ethanol, and chain-elongation
(fed by CO2, acetate, and ethanol) to butyrate, valerate, and hexanoate.
Lactate, a high-value biopolymer feedstock chemical, has been observed
in transition states, but sustained production has not been described.
In this work, a continuous stirred bioreactor was operated at low pH
(5.5) with substrate concentration varied between limiting and
non-limiting conditions. Using glucose as a model substrate, two
sustained operational states were defined: butyrate production during
substrate limitation, and lactate production in the non-limited state.
Through SWATH-MS metaproteomics and 16S rDNA community profiling, the
mechanism of change between butyrate and lactate was described primarily
by redirected carbon flow through the methylglyoxal bypass by
Megasphaera under substrate non-limiting concentrations. Crucially,
butyrate production resumed upon return to substrate-limited conditions,
demonstrating the reversibility of this transition.%
\end{abstract}%



\sloppy


Lactate vs butyrate production during mixed culture glucose fermentation
driven by substrate availability as determined by metaproteomics

Advanced Water Management Centre, The University of Queensland,
Brisbane, Qld 4072, Australia

Australian Centre for Ecogenomics, The University of Queensland,
Brisbane, Qld 4072, Australia

School of Earth and Environmental Sciences, The University of
Queensland, Brisbane, Qld 4072, Australia

Group of Chemical and Environmental Engineering, King Juan Carlos
University, 28933 M\selectlanguage{ngerman}óstoles, Madrid, Spain

*Correspondence should be addressed to: Robert D. Hoelzle, School of
Earth and Environmental Sciences, The University of Queensland, St.
Lucia, QLD 4072, Australia Phone: +61 7 3365 2108; Fax: +61 7 3365 6899;
E-mail: r.hoelzle@.uq.edu.au

\emph{}

\emph{Abstract}

Mixed-culture fermentation provides a means to recycle carbon from
complex organic waste streams into valuable feedstock chemicals. Using
complex microbial consortia, individual systems can be tuned to produce
a range of biochemicals to meet market demand. However, the metabolic
mechanisms and community interactions which drive product expression
changes under differing conditions are currently poorly understood.
Furthermore, predictable product transitions are currently limited to
pH-driven changes between butyrate and ethanol, and chain-elongation
(fed by CO\textsubscript{2}, acetate, and ethanol) to butyrate,
valerate, and hexanoate. Lactate, a high-value biopolymer feedstock
chemical, has been observed in transition states, but sustained
production has not been described. In this work, a continuous stirred
bioreactor was operated at low pH (5.5) with substrate concentration
varied between limiting and non-limiting conditions. Using glucose as a
model substrate, two sustained operational states were defined: butyrate
production during substrate limitation, and lactate production in the
non-limited state. Through SWATH-MS metaproteomics and 16S rDNA
community profiling, the mechanism of change between butyrate and
lactate was described primarily by redirected carbon flow through the
methylglyoxal bypass by \emph{Megasphaera} under substrate non-limiting
concentrations. Crucially, butyrate production resumed upon return to
substrate-limited conditions, demonstrating the reversibility of this
transition.

\emph{Keywords}

Lactate production; butyrate; mixed-culture fermentation; metabolic
regulation; carbon recycling

\emph{Introduction}

Fermentation is a key central step in biologically treating organic
waste in which waste carbon can be recycled into valuable short-chain
fatty acids, alcohols, and other organic (bio)chemicals (Agler, Wrenn,
Zinder, \& Angenent, 2011; Angenent et al., 2016; De Groof, Coma, Arnot,
Leak, \& Lanham, 2019). While the definition is not universally agreed
upon, fermentation can generally be described as biological catabolism
without the use of an external electron acceptor (such as
O\textsubscript{2}, NO\textsubscript{3}\textsuperscript{-}, or
SO\textsubscript{4}\textsuperscript{2-}), and commonly relies on
substrate-coupled electron transfer reactions for energy generation
(El-Mansi, Bryce, Demain, \& Allman, 2006). As such, microbes have
evolved a variety of metabolic pathways to balance their carbon and
electron loads (Hoelzle, Virdis, \& Batstone, 2014). These pathways
generate the variety of chemical products inherent to fermentation.

Angenent et al. (2004), Spirito et al. (2014), and Hoelzle et al. (2014)
identified several advantages of fermentation by mixed cultures, or
natural consortia of microbes, over defined cultures. Key among these
are 1. diverse mixed cultures provide a range of metabolic functional
clades, which allows flexibility to consume a wide variety of
substrates; 2. functional redundancy between disparate microbial groups
ensures production robustness toward substrate contaminations and
process disturbances; and 3. reduced equipment and operational costs due
to lack of need to sterilize.

To date, research on mixed-culture fermentation (MCF) has primarily
focused on carbohydrate-based pH-driven processes, which produce
primarily acetate and butyrate at low pH and acetate and ethanol at high
pH (Atasoy, Eyice, Schnürer, \& Cetecioglu, 2019; Lu, Slater, Mohd-Zaki,
Pratt, \& Batstone, 2011; Mohd-Zaki et al., 2016; M F Temudo, Muyzer,
Kleerebezem, \& van Loosdrecht, 2008; Margarida F. Temudo, Mato,
Kleerebezem, \& Van Loosdrecht, 2009; Margarida F Temudo, Kleerebezem,
\& van Loosdrecht, 2007), and chain elongation of syngas fermentation
products, which produces hexanoate and caprylate (Dams et al., 2018;
Kucek, Spirito, \& Angenent, 2016; Spirito, Marzilli, \& Angenent, 2018;
Spirito et al., 2014). While these production modes have proven
reproducible, these products represent only a limited range of possible
MCF\selectlanguage{english}-generated biochemicals (Hoelzle et al., 2014; Jang et al., 2012).
For instance, reproducible MCF production modes have not been described
for lactate and propionate, which are higher-value products used to
produce biopolymers such as polylactic acid (PLA),
poly(glycolate-co-lactate-co-3-hydroxybutyrate), and as a precursor to
polyhydroxyalkanoates (PHA), as well as vinyl propionate respectively.
Instead, these products are currently produced in pure-culture
fermentation systems (Alves de Oliveira, Komesu, Vaz Rossell, \& Maciel
Filho, 2018; Gonzalez-Garcia et al., 2017; Hofvendahl \& Hahn-Hagerdal,
2000). However, lactate in particular has been observed as a major
product during transient states and periods of increased substrate
loading in anaerobic digestion (to methane), a process in which MCF is
the generating step for methanogenic substrates (Eng, Fernandes, \&
Paskins, 1986; Romli, Keller, Lee, \& Greenfield, 1995). This suggests
that high substrate concentration could act as a control for lactate
formation in MCF, potentially opening up more economical methods of
production.

In order to control the production of a given biochemical, it is
necessary to understand the underlying community assembly dynamics and
metabolic mechanisms which result in its production. Gonzalez-Cabaleiro
et al. (2015) have developed a metabolic model for the pH-driven
substrate-limited system which successfully describes the
acetate-butyrate to acetate-ethanol switch based on electron balancing
and modes of active product transport across the cell membrane.
Advancements in meta-omics approaches now make it possible to describe
these determinative factors through observed changes in gene expression
and protein formation (Grobbler et al., 2014; Matallana-Surget, Jagtap,
Griffin, Beraud, \& Wattiez, 2017; Singleton et al., 2018; Wang \&
Kuruc, 2019; Woodcroft et al., 2018).

In this research, high substrate concentration is explored as a control
strategy for alternative MCF production modes by transitioning between
substrate-limited and substrate non-limited conditions. Furthermore,
community assembly dynamics and functional metabolic expression are
described through a combination of 16S rDNA amplicon community profiling
and SWATH-MS metaproteomics.

\par\null

\emph{Materials and Methods}

2.1. Experimental approach

A continuous flow stirred-tank bioreactor (Supplementary Fig. 1) was
operated for 22 weeks under a range of substrate concentrations using
glucose as a model substrate. Initial operation matched previously
reported conditions for acetate-butyrate production with substrate
concentration limited at 5 g\textsubscript{glu}*L\textsuperscript{-1},
hydraulic retention time fixed at 1 day, pH at 5.5 and, temperature at
30degC (Lu, Slater, Mohd-Zaki, Pratt, \& Batstone, 2011; Mohd-Zaki et al.,
2016; M F Temudo, Muyzer, Kleerebezem, \& van Loosdrecht, 2008;
Margarida F Temudo, Kleerebezem, \& van Loosdrecht, 2007). Though
conditions for both acetate-butyrate and acetate-ethanol production
modes have previously been described, acetate-butyrate was chosen as the
base production mode on the hypothesis that the lower electron sinking
capacity of butyrate compared to ethanol production offers greater
potential for expression of alternative pathways for electron mediation
(Hoelzle et al., 2014). Full description of bioreactor setup and control
is provided in the Supplementary Methods.

The bioreactor was sampled for product composition every 2-4 days. After
establishment of steady state acetate-butyrate production (as evidenced
by a \textless{}10\% variation in the 2 primary products over 3
consecutive samples), sampling was expanded to include microbial
community structure and protein expression over the next 6 samples
(\textasciitilde{}1.5 weeks). After initial steady state operation, the
substrate feed concentration was increased from 5 to 10
g\textsubscript{glu}*L\textsuperscript{-1}, then to 15, 20, and finally
back to 5 g\textsubscript{glu}*L\textsuperscript{-1}, establishing
steady state operation at each concentration. Hence loading rates ranged
from 5 to 20
g\textsubscript{glu}*L\textsuperscript{-1}*d\textsuperscript{-1}.

Due to a failed extraction from one of the protein samples (third sample
of fifth steady state period), protein analysis was limited to 5
randomly selected samples per steady state period. 16S rDNA amplicons
were then sequenced for samples matching the first, third, and final
protein samples (Supplementary Table 1). Product composition was then
correlated to the community and protein expression analyses to describe
the response at a metabolic level at each steady state loading rate.

2.2. Media

Biomass was activated overnight in tryptone-glucose-yeast extract medium
before inoculating into freshwater basal anaerobic (BA) media
(Bastidas-Oyanedel, Mohd-Zaki, Pratt, Steyer, \& Batstone, 2010; Widdel
\& Bak, 1992). The reactor was then fed with BA media as described
above.

A separate tryptone-glucose-yeast extract (TGY) medium was used for
culture activation before inoculation into the bioreactor. Details on
media preparation are available in the Supplementary Methods.

2.3. Inoculum

A diverse mixed culture was required in order to ensure exploration of
the interacting effects between a variety of microbial functional
groups. Granules from the upflow anaerobic sludge blanket digester
reactor at Golden Circle Cannery in Brisbane, Australia were chosen as
the inoculation community. This digester reactor is fed on
carbohydrate-rich fruit and vegetable canning wastewater.

The granules were collected less than 48 hours before beginning the
experiment. To activate the community, granules were crushed in a
plastic bag by hand using a roller in order to break up the granule
structure while not affecting community composition (Juste-Poinapen,
Turner, Rabaey, Virdis, \& Batstone, 2015). The crushed granules were
then injected at 2\% v/v into TGY media, then activated overnight at
30degC. Activated culture was then inoculated into the bioreactor at 1\%
v/v. The bioreactor pH was adjusted to 7.0, and then continuous feed
began once the pH decreased to 5.5.

To ensure continuation of the same culture throughout the experiments,
broth samples were taken at weekly intervals from the fermenter and
stored in 20\% glycerol at -80degC in 2.0 mL aliquots. In the event of
reactor failure, the reactor was re-started from the most recent broth
sample, activated overnight in TGY media.

2.4. Analytical Methods

2.4.1. Metabolite Analysis

Substrate and product metabolites were determined by HPLC (1050 Series,
Hewlett-Packard, USA). 2.0 mL broth samples were first centrifuged at
10,000 x g for 10 min. The supernatant was then passed through a sterile
0.22 \selectlanguage{greek}µ\selectlanguage{english}m filter to remove remaining cell mass. Filtered supernatant was
then mixed 3:1 with 0.02 N H\textsubscript{2}SO\textsubscript{4} (making
0.005 N), and analyzed via HPLC using a 7.7x300 mm Hi-Plex H column with
associated guard column and inline filter (Agilent, USA) heated to
40.0\selectlanguage{ngerman}°C (TC-50/CH-30, Eppendorf, Germany). The mobile phase was 0.005 N
sulfuric acid at 0.800 mL·min\textsuperscript{-1} and the sample
injection volume was 30 \selectlanguage{greek}μ\selectlanguage{english}L. Products were detected via RID (1047,
Hewlett-Packard, USA) at 30\selectlanguage{ngerman}°C and a detection range of
32×10\textsuperscript{-5} RIU.

Complete carbon product composition was confirmed by balancing measured
and theoretical chemical oxygen demand (COD), a measure of oxidation
state. Measured COD of soluble components (SCOD), was determined by COD
Cell Test (14555, Merck Millipore, MA, USA) and measured on a
Spectroquant® Move 100 colorimeter (Merck Millipore, MA, USA). SCOD
measurements used the same samples as those for HPLC measurement.
Theoretical COD was calculated from HPLC-measured concentrations based
on complete oxidation stoichiometry.

2.4.2. 16S rDNA Amplicon Sequencing

The community profile was assessed 3 times per steady state period,
aligned with the first, middle, and final protein samples. DNA was
extracted from the cell pellet generated during chemical analysis
centrifugation. The pellet was first washed in 2.0 mL of 4°C PBS
solution and centrifuged again at 10,000 x g for 10 min. The supernatant
was discarded and the pellet was resuspended again in 1.0 mL of PBS, and
samples were stored at -20°C until DNA extraction. DNA was extracted
using the FastDNA\selectlanguage{english} SPIN Kit for Soil (MP Biomedicals, Santa Ana,
California) via the manufacturer's protocol. Illumina sequencing of the
16S rDNA gene, PCR amplified using iTAG 16S 926F and 1392wR primers
(Engelbrektson et al., 2010), was then performed by the Australian
Centre for Ecogenomics (The University of Queensland, Brisbane,
Australia).

2.4.3. Protein Extraction and SWATH-MS

Proteins were assessed 5 times per steady state period. Biomass was
prepared similarly to the amplicon samples. Final resuspension was in
500 \selectlanguage{greek}µ\selectlanguage{english}L of protein extraction solution, which consisted of 77 mg
dithiotheretol and 1 tablet of cOmplete\textsuperscript{TM} Protease
Inhibitor Cocktail (Roche, Switzerland) dissolved into 10.0 mL of B-PER
II (Thermo Fisher Scientific Inc, USA). Extraction solution was stored
in the dark at 4\selectlanguage{ngerman}°C and used within 48 hours. Proteins were extracted and
analyzed via SWATH-MS using the method described by Grobbler et al.
(2014). All samples were prepared for MS analysis at equivalent total
protein concentrations. A portion of each prepared sample was mixed into
a pooled sample for protein identification.

2.5. Bioinformatics

2.5.1. Community Profiling

Community structure was determined from amplicon reads using QIIME 2
v2017.10 (Caporaso et al., 2010). Lineages (features) were generated
using `deblur', with reads truncated to 225 base pairs, then evenly
rarefied based on the lowest sample lineage count using `feature-table
rarefy'. Read alignments were manually verified (Supplementary Note 1,
Supplementary Figs. 2 and 3), then blasted against the Silva 132
database at 99\% similarity using `feature-classifier' to generate
taxonomic labels, which were truncated at the genus level.

The heatmap and dendrogram were generated in R using the pheatmap
package from square-root transformation of the genus-aggregated relative
lineage abundance data, averaged for each operational level. Shannon
diversity index was calculated in R from the whole lineage profile for
each sample via the `diversity' function of the vegan package.

2.5.2. Metaproteomics

Proteins were identified from the pooled sample in ProteinPilot (v5.0,
SCIEX, Framingham, MA, USA) using a reference proteome consisting of all
bacteria, archaea, and fungi on the UniProt database (Bateman et al.,
2015). Ion fragments from individual samples were then mapped to
identified proteins in PeakView (v2.1, SCIEX, Framingham, MA, USA) at 5
peptides per protein, 3 transitions per peptide, 99\% peptide confidence
threshold, 1.0\% global false discovery rate threshold (calculated in
ProteinPilot using the Paragon algorithm), 5.0 min XIC extraction
window, and 50 ppm XID width.

Identified proteins were assigned functionality (i.e. amino acid
synthesis, butyrate synthesis, etc) based on UniProt listing, and
taxonomy was aggregated to genus for consistency with the community
profile. Ion intensity tables from PeakView were converted to the
``label free LCMS'' 10-column table format for processing in MSstats
v3.3.12 (Choi et al., 2014).

Raw ion data was processed in MSstats using `dataProcess' with default
settings. The default normalization setting in MSstats equalizes the
median protein signals across samples. This effectively weights the
expression analysis toward taxa with greater representation in the
sample. To account for this, functional expression of the proteome was
assessed individually by genus. Raw mass spec intensity data was grouped
by genus before processing through `dataProcess'. The `groupComparison'
function was then used to compare expression, measured as
log\textsubscript{2}-fold change (L2FC), between different levels or
groups of levels as described in the text. After expression analysis
within individual genera, results were regrouped and assessed by
metabolic function. Adjusted p \textless{} 0.05 was used to define
significant differences in expression between comparison groups.

2.6. Statistical Methods

5.6.1. 95\% Confidence Intervals

95\% confidence intervals were calculated via the following equation:

\(\text{CI}_{95}=\ \pm\text{\ t}_{0.025,n-1}\times\frac{\sigma}{\sqrt{n}}\)Eq. 1

Where \selectlanguage{greek}\emph{σ} \selectlanguage{english}and \emph{n} are the standard deviation and number of
samples, respectively. For proteomics data, standard error (SE) from the
`groupComparison'' function of MSstats was used in place of \selectlanguage{greek}\emph{σ}
\selectlanguage{ngerman}·\(\left(\sqrt{n}\right)\)\textsuperscript{-1}.

2.6.2. Analysis of Covariance

Product composition was normalized by \%SCOD, and then compared between
operational levels by analysis of covariance (ANCOVA) via the `aov'
function in R, with time from the start of steady state as the
covariant. Adjusted p-values and 95\% confidence intervals were then
calculated using `TukeyHSD' in R.

2.6.3. Canonical Correlation Analysis

Canonical correlation analysis (CCA) was used to assess correlations
between metabolite, community profile, and metaproteomic datasets. CCA
was carried out in R using the `cca' function of the vegan package.
Metabolite analyses used the \%COD-normalized profile, community profile
analyses used the genus-aggregated relative abundance profile, and
metaproteome analyses used ``Abundance'' data from the `dataProcess'
function of MSstats, which was averaged together for each individual
metabolic pathway or function.

\emph{Results}

3.1. Steady State Product Analysis

Steady state was achieved at all operation levels after 1-2 weeks of
transitional operation (Supplementary Fig. 4). Initial operating
conditions (5 and 10 g\textsubscript{glu}·L\textsuperscript{-1})
produced acetate and butyrate as the major products (Fig. 1), consistent
with previous studies (Atasoy, Eyice, Schnürer, \& Cetecioglu, 2019; Lu
et al., 2011; Mohd-Zaki et al., 2016; M F Temudo et al., 2008; Margarida
F Temudo et al., 2007). This product profile will be referred to as
``HBu-type''. Production then transitioned to acetate and lactate as the
major products, or ``HLa-type'', at 15 and 20
g\textsubscript{glu}·L\textsuperscript{-1}. HBu-type fermentation
corresponded with complete glucose consumption (Table 1), making this a
substrate-limited condition. By contrast, HLa-type fermentation
corresponded with incomplete glucose consumption, making this a
substrate non-limited condition. Importantly, HBu-type fermentation was
regained when the substrate concentration was reduced from 20
g\textsubscript{glu}·L\textsuperscript{-1} back to 5
g\textsubscript{glu}·L\textsuperscript{-1}, showing reversibility of the
production types. Theoretical COD of the product composition balanced to
between 90-110\% of measured SCOD except for level 15, which balanced to
111±1\%, confirming measurements of all major products.

ANCOVA comparison of normalized product composition between operational
levels revealed that butyrate and acetate production in the first 5
g\textsubscript{glu}·L\textsuperscript{-1} condition (L5a) were
significantly different (p \textless{} 0.05) than the other two HBu-type
levels (Supplementary Table 2). In addition to high acetate and butyrate
composition, L5a also had high hexanoate composition (10±4\%). For the
remainder of the levels, including the second 5
g\textsubscript{glu}·L\textsuperscript{-1} level (L5b), hexanoate was
produced only as a minor product. This initial 5
g\textsubscript{glu}·L\textsuperscript{-1} condition will therefore be
assessed as a separate operational mode from the other HBu-type levels.

No products were produced significantly differently between the 15
g\textsubscript{glu}·L\textsuperscript{-1} and 20
g\textsubscript{glu}·L\textsuperscript{-1} conditions. These will
therefore be discussed jointly as the ``High'' levels based on substrate
concentration. For the second 5
g\textsubscript{glu}·L\textsuperscript{-1} (L5b) and 10
g\textsubscript{glu}·L\textsuperscript{-1} conditions, minor products
formate, propionate, and valerate were produced significantly
differently. However, major products butyrate and acetate, as well as
the remaining minor products, were not produced significantly
differently. These operational levels will therefore also be discussed
jointly as the ``Low'' levels. Interestingly, production of formate and
propionate were not significantly different between levels 5a and 5b.
This may indicate that formate is most readily produced at very low
organic loading rates, while propionate is most readily produced at
higher organic loading rates.

\par\null

3.2. Taxonomic Analysis

Dominant lineages across all operational levels were from the
genera\emph{Bifidobacterium} , \emph{Ethanoligenens} ,
\emph{Megasphaera} ,\emph{Pectinatus} , and \emph{Dokdonella} , and
microbial communities from the Low and High levels were each more
similar internally than to each other or to L5a (Fig. 2).
\emph{Clostridium} lineages were generally more abundant in the Low and
High levels than in L5a, while L5a had higher abundances of lineages
from \emph{Olsenella} and the order\emph{Bacteroidales} .

Three separate aggregated genera individually make up at least 20\% of
the community in at least one operational condition: \emph{Megasphaera}
,\emph{Ethanoligenens} , and \emph{Bifidobacterium} .
Individual\emph{Megasphaera} lineages (Supplementary Fig. 5) were more
abundant in the Low levels (9.6±0.9\% average lineage abundance) than in
the High levels (3.0±0.7\%) and L5a (6±1\%), and combined made up the
dominant group in both L5a and the Low levels (25\% and 38\% summed
abundance of lineages, respectively). \emph{Ethanoligenens} lineages
were more abundant in the High levels (7±1\%) than in the Low levels
(2.3±0.5\%). They were overall similarly abundant in L5a and the High
levels, though a low abundance third lineage resulted in high variance
(6±13\%). Combined, \emph{Ethanoligenens} lineages made up the defining
major group in the High levels (20\%). \emph{Bifidobacterium} lineages
made up the most abundant group in the High levels at a combined 27\%,
though they were similarly abundant in L5a and the Low and High levels
at 3±3\%, 4±2\%, and 7±4\%, respectively. Though different lineages were
dominant under different conditions, all of the conditions maintained
similar community diversity, ranging from 3.15 in L20 to 3.45 in L5b.

\par\null

3.3. Metaproteomic Analysis

SWATH-MS analysis identified 2565 peptide ions, which mapped to 383
positively identified proteins from 44 genera (Supplementary Data). In
order to verify comparisons between 16S rDNA-based and protein-based
taxonomic assignments, the genus\selectlanguage{english}-aggregated community and metaproteomic
abundance profiles were assessed together via CCA (Fig. 3). The greatest
proportion of co-variance between these data sets (38.8\%, CCA 1) is
explained by changes in lineage abundance between the Low and High
levels, confirming the taxonomic assignments. This could indicate that
the primary variation in measured protein expression is due to changes
in lineage abundance. However, this trend in covariance does not extend
to CCA 2, likely due to high expression of metabolic versus structural
proteins in the varying conditions. In order to eliminate lineage
abundance effects on protein expression analysis, further metaproteomic
assessments were analyzed separately within each genus. (Supplementary
Fig. 6).

After grouping proteins by metabolic function (Supplementary Table 3),
those involved in carbon energy metabolism were assessed for L2FC
expressional differences between the Low and High levels (Fig. 4).
Consistent with the high butyrate production in the Low levels, all
significant up-expression of proteins from the acetyl-CoA-to-butyrate
pathway was in the Low levels, and these were exclusively
from\emph{Megasphaera} . Proteins from both propionate pathways were
also generally more up-expressed in the Low levels (from
\emph{Megasphaera} ,\emph{Anaerovibrio} , and \emph{Megamonas} ) than in
the High levels (\emph{Dorea} ), as were proteins from glycerol
production (\emph{Megasphaera} ) and consumption (\emph{Clostridium} ).
The majority of electron mediating cofactors (EMCs) were up-expressed in
the Low levels by \emph{Megasphaera} , though this function was
up-expressed in the High levels by \emph{Bifidobacterium} .

In the High levels, where acetate and lactate were more highly produced,
proteins from these pathways were generally more up-expressed than in
the low levels. This is especially true for the methylglyoxal bypass
pathway of lactate production in \emph{Megasphaera} , which despite this
genus being more than 3x lower abundance in the High levels,
up-expressed glyoxalase by 5.2+-0.2 L2FC.

Consistent with the higher substrate load in the High levels, proteins
for central carbon metabolism (glycolysis and the pentos-phosphate
pathway) were generally more up-expressed in the High levels from a
variety of lineages, especially \emph{Clostridium}
,\emph{Bifidobacterium} , and \emph{Ethanoligenens} . Yet, overall
proteins for substrate uptake, such as ABC-type and PTS system sugar
transporters, were roughly equally as up-expressed in both High and Low
levels. Expression of substrate uptake proteins does vary by lineage,
however, with \emph{Megasphaera} more active in the High levels (again,
despite lower abundance) and \emph{Clostridium} more active in the Low
levels.

\emph{Discussion}

4.1. Product Spectrum and Community Profile

The HBu-type MCF production mode has been well-established at low pH and
substrate-limited conditions in previous research (Lu et al., 2011;
Margarida F Temudo et al., 2007), and a metabolic control mechanism for
the transition between high and low pH has been proposed and supported
in models (Gonzalez-Cabaleiro et al., 2015; Rodriguez, Kleerebezem,
Lema, \& van Loosdrecht, 2006). While high organic loading rate has been
proposed as a mechanism to promote chain elongation (De Groof et al.,
2019), elevated production of reduced acid products such as lactate and
propionate has only been reported during transition states (Eng et al.,
1986; Voolapalli, 2001). Sustained HLa-type MCF production has not
previously been reported, nor has the metabolic mechanism been described
for transition to this production state.

A typical community profile assessment of the production data, such as
those reported by Mohd-Zaki et al. (2016) and Atasoy et al. (2019),
shows that the defining product spectrum can largely be attributed to
the major taxonomic lineages within each operational condition. Butyrate
and acetate are primary products of \emph{Megasphaera} (Marounek,
Fliegrova, \& Bartos, 1989; Weimer \& Moen, 2013),
\emph{Pectinatus}(Watier, Dubourguier, Leguerinel, \& Hornez, 1996),
and\emph{Bifidobacterium} (de Vries \& Stouthamer, 1968; Wolin, Zhang,
Bank, Yerry, \& Miller, 1998), while lactate and acetate are primary
products of \emph{Bifidobacterium} and \emph{Ethanoligenens} (Castro,
Razmilic, \& Gerdtzen, 2013; Xing et al., 2006).. However, what is not
clear from the metabolite and community profile datasets alone is why
the community transitions from predominantly \emph{Megasphaera}
and\emph{Bifidobacterium} to \emph{Ethanoligenens}
and\emph{Bifidobacterium} . Our metaproteome analysis offers insights
into the metabolic mechanisms which generate this transition, and
therefore the transition to HLa-type production. We begin with an
assessment of the HBu-type to HLa-type transition by comparing the Low
and High levels, then follow with a brief analysis of the apparent
outlier L5a condition.

\par\null

4.2. Change in Pathway Expressions between Low and High Levels

Upon plotting pathway expression on a model MCF metabolic network
(Hoelzle et al., 2014), it is evident that, consistent with the total
protein expression profile, \emph{Megasphaera} accounts for most of the
up-expressed pathways in the Low levels, while \emph{Bifidobacterium}and
\emph{Ethanoligenens} account for most up-expressed pathways in the High
levels (Fig. 5). \emph{Clostridium} is the exception, with the total
protein expression profile suggesting it should be most active in the
High levels. However, the majority of the High level
up-expressed\emph{Clostridium} enzymes are related to biomass synthesis
(Supplementary Table 3), while the carbon metabolism enzymes are
generally more highly expressed in the Low levels.

The trends in pathway expression align with the trends of product
expression, though no enzymes for ethanol production were detected,
while enzymes for formate production, detected in\emph{Bifidobacterium}
, did not significantly change expression.\emph{Megasphaera} was
responsible for high butyrate production in the Low levels, having
up-expressed enzymes for the acetyl-CoA-to-butyrate pathway. The
increased acetate production in the High levels was due to increased
expression of the acetyl\selectlanguage{english}-CoA-to-acetate pathway in\emph{Ethanoligenens}
. High lactate production in the High levels was due to up-expression of
two pathways: pyruvate reduction by\emph{Bifidobacterium} and the
methylglyoxal bypass by\emph{Megasphaera} . Propionate production may
also occur via two pathways, each of which were up-expressed in the Low
levels. However, propionate was not produced as a significantly greater
proportion of product COD in either the High or Low levels.

In addition to the metabolic changes associated with product formation,
several other key metabolic functionalities changed expression in the
system. \emph{Bifidobacterium} up\selectlanguage{english}-expressed glycogen synthesis in the
Low levels, suggesting high substrate competition during the
substrate-limited condition. In the High levels,
\emph{Bifidobacterium}up-expressed glucose uptake, glycolysis, and
electron mediation.\emph{Megasphaera} up\selectlanguage{english}-expressed glucose uptake in the
High levels and electron mediation in the Low levels. Additionally,
glycerol appears to have been produced by \emph{Megasphaera} and
consumed by\emph{Clostridium} in the Low levels, though it was not
detected in the product spectrum.

\par\null

4.3. Lactate Production is Triggered by Expression of the Methylglyoxal
Bypass

The change from HBu-type fermentation in the Low levels to HLa-type
fermentation in the High levels appears largely attributable to the
change in metabolic activity of \emph{Megasphaera} (Fig. 6). During
HLa-type fermentation, \emph{Megasphaera} down-expressed enzymes
involved in butyrate formation, pyruvate oxidation, and a number of
flavoproteins and oxidases involved in electron mediation.
Additionally,\emph{Megasphaera} changed activity in the glycolysis
side-branch pathways from lactate reduction and glycerol production to
lactate generation via up-expression of glyoxylase, which removes
methylglyoxal by hydration to lactate (Hoelzle et al., 2014).
Methylglyoxal is a toxic byproduct of glycolysis which is produced from
dihydroxyacetone-P when glucose consumption outpaces phosphate uptake
(Booth et al., 2003; Ferguson, Totemeyer, MacLean, \& Booth, 1998). It
is hypothesized that methylglyoxal forms under these conditions as a
mechanism to slow down substrate consumption by requiring the cell to
spend energy detoxifying methylglyoxal to lactate. This mechanism is
consistent with the observed increase in substrate uptake activity of
\emph{Megasphaera} and corresponding decrease in relative abundance. The
required redirection of cellular energy also agrees with the observed
decrease in activity for electron mediation and production of butyrate
and propionate by\emph{Megasphaera} during up-expression of the
methylglyoxal bypass. Given that phosphate is supplied in excess and the
rest of the community did not show the same response, \emph{Megasphaera}
may have a less efficient phosphate uptake mechanism than other species.
However, no direct evidence of limited \emph{Megasphaera} phosphate
uptake could be discerned from the metaproteome or from the literature.

In addition to the increased methylglyoxal bypass activity and
subsequent reduction of the \emph{Megasphaera}
abundance,\emph{Bifidobacterium} also increased lactate production
activity in the High levels. Increased expression of this pathway was
matched by increased glucose uptake, glycolysis, and electron mediation
activity. This suggests that lactate production in
\emph{Bifidobacterium} was increased as a means to sink the excess
electrons resulting from increased glucose oxidation when glucose was no
longer limiting (Table 1).

A CCA of the entire proteome (including L5a) confirms that variation in
lactate production though the operational levels is positively
associated to increased expression of both lactate synthesis and
especially the methylglyoxal bypass (Fig. 7). Additionally, the inverse
relationship along CCA1 between lactate formation and expression of the
lactate-consuming acrylate pathway of propionate formation highlights
the other key metabolic shift away from lactate
consumption.\emph{Megasphaera} is known to reduce lactate to propionate
as an electron sink, and will preferentially consume lactate through
this pathway when available (Hino \& Kuroda, 1993; Hino, Shimada, \&
Maruyama, 1994). The combination of down-expression of this pathway with
the decreased abundance of \emph{Megasphaera} in the High levels greatly
reduced the effect of this lactate\selectlanguage{english}-consuming mechanism. Together, these
correlations confirm that HLa-type fermentation was initiated by a
decline in the butyrate-producing and
lactate\selectlanguage{english}-consuming\emph{Megaspheara} population resulting from expression
of the methylglyoxal bypass.

4.4. Hexanoate Production in Level 5a

While the major products formed in L5a were similar to L10 and L5b, they
were each produced at about 10 COD\% lower yield in L5a (Fig. 1).
Meanwhile, hexanoate was produced at about 10x higher yield in L5a. The
co-dominance of \emph{Ethanoligenens} and \emph{Megasphaera} in this
level (Fig. 2) suggests that this product spectrum divergence is
community-linked rather than an expression difference within the same
community.

\emph{Megasphaera} is known to produce hexanoate via chain elongation
through the reverse \selectlanguage{greek}β-\selectlanguage{english}oxidation pathway, which utilizes butyrate pathway
enzymes and their analogues to elongate ethanol and acetate to butyrate,
hexanoate, and other high-carbon organic acids (Agler et al., 2011;
Spirito et al., 2014). Comparing the enzyme expression of L5a to L5b
confirms that there was no significant difference in expression of
butyrate pathway enzymes between levels 5a and 5b (Supplementary Fig.
7). The expressed functionalities that do change between L5a and L5b are
the up-expression of electron mediation and acetate synthesis in L5a,
and oxidative stress in L5b. The up-expressed acetate functionality is
from \emph{Ethanoligenens} . Although ethanol synthesis enzymes were not
identified in this study, it is likely that this functionality was also
up-expressed by \emph{Ethanoligenens} in L5a due to this lineage's use
of ethanol as an electron sink during acetate production (Castro et al.,
2013; Xing et al., 2006). \emph{Ethanoligenens} therefore seems the
likely source of the chain elongation substrates acetate and ethanol.
Use of reverse \selectlanguage{greek}β-\selectlanguage{english}oxidation to elongate butyrate to hexanoate could
therefore have been induced by the availability of chain elongation
substrates rather than any change in expression of the butyrate/reverse
\selectlanguage{greek}β-\selectlanguage{english}oxidation pathway enzymes.

\emph{Conclusions}

The reliable transition between sustained butyrate and lactate
production at pH 5.5 was demonstrated by shifting from substrate limited
to non-limited conditions. This represents a new MCF production mode for
lactate, a high-value biopolymer feedstock chemical. The product
transition corresponded with a change in the community profile resulting
from expression of the methylglyoxal bypass in \emph{Megasphaera} during
high substrate loads. This metabolic mechanism can be included in future
MCF models to describe the changes in product generation in substrate
non-limiting conditions, and points to the important interplay between
MCF conditions, community structure, and product profile. Furthermore,
the protein expression profiles suggested a likely syntrophic
relationship between \emph{Ethanoligenens} and \emph{Megasphaera} in
chain elongation via reverse \selectlanguage{greek}β-\selectlanguage{english}oxidation to generate hexanoate at the
lowest substrate concentration, though hexanoate production was not
recoverable.

Combined metabolite and community profile analyses alone were not
sufficient to uncover these mechanisms, and incorporation of
expressional analysis through metaproteomics proved critical. While the
metaproteome and community profiles could be linked for major lineages,
future incorporation of metagenomes would likely uncover additional
mechanisms by enabling creation of a custom protein database.

\emph{Acknowledgements}

The authors would like to thank Christy Grobbler for assistance with
protein extractions, Amanda Nouwens for assistance with protein
identification, and Yang Lu for assistance with community profiling. The
authors declare no competing financial interests.

This study was supported by the Australian Research Council's Discovery
Projects funding scheme (project number DP0985000). RH acknowledges
support from the American Australian Association Sustainability
Fellowship.

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\emph{Figure Legends}

\textbf{Figure 1:} Time-course product data of the steady state periods
by COD composition. Confidence intervals for major products at each
level were consistently \selectlanguage{english}[?]10\% of the mean. Vertical bars separate the
individual operational levels.

\textbf{Figure 2:} Heatmap of square root-transformed relative abundance
of genus-aggregated lineages, as well as the level Shannon diversity
index (H), each averaged across all samples from each operational level.
Genera with relative abundance of \textless{}0.50\% are aggregated into
``Other''. All labelled taxa are from the Bacteria domain. The
dendrogram and Shannon diversity are based on the non-aggregated
community profile.

\textbf{Figure 3:} CCA of the genus-aggregated 16S rDNA community and
protein abundance profiles across operational levels. Only the 10 most
abundant genera (averaged between both the community and protein data
sets) are highlighted. Individual 16S rDNA genus aggregates are plotted
as grey points with highlighted genera as orange Xs and labelled in
italic text. Highlighted protein genus aggregates are plotted as vectors
and labelled in greyed text. Non-highlighted proteins are not shown.
Individual samples are plotted by color and shape according to their
operational level. Protein samples without corresponding community
samples were not used.

\textbf{Figure 4:} Volcano plot depicting significant expressional
changes of all identified carbon energy metabolism proteins. Expression
comparison is represented on the horizontal axis as
log\textsubscript{2}-fold change (L2FC) between Low (blue, negative L2FC
values) and High (red, positive L2FC values) levels. Significance is
represented on the vertical axis as the --log\textsubscript{10} of the
adjusted p-value from MSstats `groupComparison' output, where p = 0.05
is plotted as a dashed line at 1.30. Points above the line (p
\textless{} 0.05) are considered statistically significant. Proteins are
categorized by pathway or metabolic function as represented by shape and
described in the key. Proteins from key pathways (acetate, butyrate,
lactate, and substrate uptake) are labelled according to the lineage
they were found in.

\textbf{Figure 5:} Expression map of mixed culture carbon and energy
metabolism. Text color indicates the condition under which the product
is more highly produced. L2FC for significantly up-expressed proteins
from each pathway were averaged separately within the High and Low
conditions. The higher of the two averages is represented by arrow color
and width, and the lineages responsible for the up-expression are
labelled next to the pathway. Arrows representing glucose uptake and
electron mediation functions, having clear lineage differentiation
between High and Low levels, are shown for both conditions.

\textbf{Figure 6:} CCA of pathway expression and product concentrations.
Individual metabolic pathways are plotted as grey points, and select
carbon metabolism pathways highlighted with black symbols.
Genus-normalized protein abundances were averaged for each pathway
category (Supplementary Table 3) within each sample to generate the
pathway expression data. Products are plotted as vectors and labelled
accordingly. Individual samples are plotted by color and shape according
to their operational level.

\textbf{Hosted file}

\verb`Table 1.docx`  available at \url{https://authorea.com/users/350167/articles/475076-lactate-vs-butyrate-production-during-mixed-culture-glucose-fermentation-driven-by-substrate-availability-as-determined-by-metaproteomics}\selectlanguage{english}
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