1. Introduction
Sulfate-reducing prokaryotes
(SRPs) are anaerobic bacteria and archaea ubiquitously distributed in
nature. They can couple dissimilatory sulfate reduction (DSR) to the
oxidation of organic substrates or hydrogen/carbon dioxide in their
energy metabolism, and therefore function as crucial mediators of the
global sulfur and carbon cycle
(Muyzer & Stams, 2008; Rudolf K.
Thauer et al., 2007). The corrosive and toxic hydrogen sulfide resulting
from DSR is responsible for significant damage to the environment and
industrial processes, and is also
implicated in human health problems (Beech & Sunner, 2007; Goldstein et
al., 2003; Loubinoux et al., 2002; Pikaar et al., 2014; Rückert, 2016;
Wang, 2012). On the other hand, the biogenically produced sulfide can be
utilized beneficially to remove and recover heavy metals from
groundwater and wastewater by capturing the dissolved metals into
insoluble metal sulfides (Hao et al., 2014; K. Tang et al., 2009). In
addition, the low redox potentials achieved by SRPs enable them to
reduce several toxic metals and radionuclides, including divalent
mercury [Hg(II)] and the oxyanions of chromium [Cr(VI)], uranium
[U(VI)], and arsenic [As(V)] (Bruschi et al., 2007; Lloyd,
2003). SRPs exhibit versatile energy metabolic capacities by using
various electron donors and carbon sources under a wide variety of
environmental conditions, which also enables them to degrade municipal
organic wastes, hydrocarbons, and
crude oil in industrial and
environmental applications (Hao et al., 2014; Ollivier et al., 2007;
Rabus et al., 2013; Rabus et al., 2015). A fundamental understanding of
the energy metabolism of SRPs growing with different energy sources is
thus crucial for improving their performance in SRP-based
biotechnologies, as well as for controlling their activity when they are
undesired.
Extensive genomic and molecular studies of SRPs have been conducted in
recent years, providing an enormous amount of information on their
genetics, physiology, and biochemistry (Muyzer & Stams, 2008; P. M.
Pereira et al., 2008; Rabus et al., 2015; Walker et al., 2009). However,
a systematic understanding of their energy metabolism remains elusive
(Muyzer & Stams, 2008; Rückert, 2016). For instance, DSR has long been
recognized to be associated with energy conservation via oxidative
phosphorylation, which implies an electron transport chain translocating
charges across the cell membrane and generating a proton motive force
(pmf) (Fitz & Cypionka, 1989; Kobayashi et al., 1982; Peck, 1960; Wood,
1978). However, the terminal reductase of the DSR pathway, dissimilatory
sulfite reductase (DsrAB), is located in the cytoplasm, thus failing to
be directly involved in the membrane-associated charge translocation (I.
A. C. Pereira et al., 2011; Rabus et al., 2015). The interaction of
DsrAB with the membrane-bound electron transport chain to generate a pmf
remains a mystery. More importantly, previous analyses indicated that
the composition of energy metabolism proteins could vary significantly
from one SRP to another (Rabus et al., 2015; Zhou et al., 2011), whereas
current biochemical and molecular studies on SRPs are primarily focused
on a few model organisms only, such as Desulfovibrio
desulfuricans and Desulfovibrio vulgaris (Barton & Fauque,
2009; Rabus et al., 2013). Besides, SRPs can switch from one mode of
energy generation to another in response to shifting nutrient
availability (P. M. Pereira et al., 2008; Walker et al., 2009). For
these reasons, a systematic database describing the energy metabolic
properties of SRPs is highly sought after.
Genome-scale metabolic models (GEMs) can systematically compile genomic,
biochemical, genetic and high-throughput omics data, thereby forming a
mathematically-structured knowledge-base for investigation of metabolic
capabilities, generation and testing of hypotheses, and analysis of
growth characteristics (King et al., 2016; Thiele & Palsson, 2010). By
using GEMs, not only engineers can obtain broader insights into the
energy metabolism of SRPs, but also can quantitatively predict the
metabolic behaviors of SRPs in engineered ecosystems. Despite their
utility, high-quality GEM
construction is a time-consuming and labor-intensive process, involving
multiple computational and experimental steps, which limits their
application to only a few well-understood model organisms (J. Monk et
al., 2014; Thiele & Palsson, 2010).
To address this challenge,
automated GEM reconstruction tools have been developed that can generate
an increasing number of GEMs (Gu et al., 2019; Machado et al., 2018).
Nevertheless, without manual curation, the quality of an automatically
generated draft GEM is always low, due to lower-confidence annotations,
extensive gap-filling, and incompleteness and/or inaccuracy of the
existing databases involving genes, enzymes, reactions, and metabolites
(Edirisinghe et al., 2016; Gu et al., 2019). Many of these problems can
be avoided by using a simplified core metabolic model comprised of only
the well-studied and biologically critical pathways (Edirisinghe et al.,
2016; J. D. Orth et al., 2010a), thus significantly simplifying the
laborious construction process
while maintaining the utility of GEMs.
The core metabolic model aims at a
focused understanding of central and energy metabolism. The chosen
pathways in the model are often the subjects of textbooks and should be
familiar to most readers with basic biochemistry knowledge. To further
improve the accuracy and efficiency of metabolic model construction,
comparative genomics has been integrated into GEMs by using curated
reference GEMs from closely related organisms to derive multiple
metabolic models for various strains of a single species (J. M. Monk et
al., 2013) or multiple species of a single genus (Seif et al., 2018).
In view of the above, this study aims to develop
an efficient approach for
constructing metabolic models of multiple organisms, and to apply it to
24 SRP species to study their energy metabolism quantitatively and
systematically. To this end, a
combination of comparative genomics via pan-genome analysis and the
concept of core metabolic model has been applied. The global workflow
illustrated in Figure 1 can be divided into four major steps: 1)
pan-genome analysis of SRPs to obtain orthologous genes encoded proteins
related to central and energy metabolism; 2) pairwise comparison of the
gene-protein relations from pan-genome analysis and the reaction-protein
relations from the model template to obtain gene-protein-reaction (GPR)
associations for construction of 24 SRP metabolic models; 3) validation
of the metabolic model of the model SRP Desulfovibrio vulgarisHildenborough (DvH); and 4) Flux balance analysis (FBA) of the developed
models to evaluate the energy metabolism of SRPs under different growth
conditions.