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I was looking at the oxoglutarate dehydrogenase complex and saw the reaction mechanism for its E1-TPP mechanism, which results in the formation of a stabilized carbanion intermediate.
The mechanism begins with a protonation, but since the "goal" of the enzyme is to form the carbanion intermediate, it seems to me that the decarboxylation is the key part of the reaction. So why is this a dehydrogenase-catalyzed reaction and not a decarboxylase-catalyzed one?
I'm new to citric acid cycle and I'm judging this question based on my previous knowledge of biochemistry and organic chemistry. I've looked into several protein databases and shockingly it seems oxoglutarate dehydrogenase is the correct name for it.( See :Uniprot , PDB 1 , PDB 2 ) Although the nomenclature of biological enzymes sometimes does not sound right to me , I believe this naming is appropriate for this molecule. First take a look at these definitions:
(Definitions from Oxford Languages / Google)
Dehydrogenase : an enzyme that catalyses the removal of hydrogen atoms from a particular molecule, particularly in the electron transport chain reactions of cell respiration in conjunction with the coenzymes NAD and FAD.
Dehydrogenase (also called DH or DHase in the literature) is an enzyme belonging to the group of oxidoreductases that oxidizes a substrate by reducing an electron acceptor, usually NAD+/NADP+ or a flavin coenzyme such as FAD or FMN.
Decarboxylation is a chemical reaction that removes a carboxyl group and releases carbon dioxide (CO2). Usually, decarboxylation refers to a reaction of carboxylic acids, removing a carbon atom from a carbon chain
Here is why the usage of "decarboxylase" in the name of the enzyme is incorrect :
Recall amylase (Amylase-Wiki) . That enzyme consumes amylose as substrate and produces maltose out of it. It sounds weird if one calls it "Demaltase" just because it gives out maltose. Instead, in this case , consumption of amylose plays a more important role in digestion.
According to above definitions , the "goal" here is simply the movement of H+ and the electron to FAD (to generate FADH2) and then to NAD+ (To generate NADH), Not the removal of CO2. That is just a by-product resulting from the mechanism. see :Reaction
Here's why the usage of "dehydrogenase" in the name of the enzyme is correct : Take a look at this . The whole process involves the movement of one proton from thiamindiphosphate and one from CoA-SH and also 2 electrons from two S-H bonds (I've traced the Hydrogens for you. Trace the electrons yourself ! :) ) Recall the concepts of carbonyl functional group from organic chemistry. One way to perform any reactions on carbonyls is to use an acidic catalysis which is most of the times H+ resulted from H2SO4 or HCl.Take a look at this. Also see Morrison-boyd organic chemistry or Vollhardt's This H+ Activates the carbonyl functional group , allowing it to participate in nucleophilic substitution more easily. The addition of H+ to Carbonyl oxygen is reversible and thus it is important to optimize this equilibrium. The indicated enzyme operates on this equilibruim to gain the maximum yeild. Plus it may induce TPP to lose a H+ . These two processes (the removal of H+ from TPP and the addition of H+ to oxogultarate ) can not be done with the help of the enzyme , hence the name Dehydrogenase for the enzyme.
Why is this oxoglutarate dehydrogenase and not oxoglutarate decarboxylase? - Biology
Heterogeneity of the mitochondrial proteome in plants underlies fundamental differences in the roles of these organelles in different tissues. We quantitatively compared the mitochondrial proteome isolated from a non-photosynthetic cell culture model with more specialized mitochondria isolated from photosynthetic shoots. Differences in intact mitochondrial respiratory rates with various substrates and activities of specific enzymes provided a backdrop of the functional variation between these mitochondrial populations. Proteomics comparisons provided a deep insight into the different steady-state abundances of specific mitochondrial proteins. Combined these data showed the elevated level of the photorespiratory apparatus and its complex interplay with glycolate, cysteine, formate, and one-carbon metabolism as well as the decrease of selected parts of the tricarboxylic acid cycle, alterations in amino acid metabolism focused on 2-oxoglutarate generation, and degradation of branched chain amino acids. Comparisons with microarray analysis of these tissue types showed a positive, mild correlation between mRNA and mitochondrial protein abundance, a tighter correlation for specific biochemical pathways, but over 78% concordance in direction between changes in protein and transcript abundance in the two tissues. Overall these results indicated that the majority of the variation in the plant mitochondrial proteome occurred in the matrix, highlighted the constitutive nature of the respiratory apparatus, and showed the differences in substrate choice and/or availability during photosynthetic and non-photosynthetic metabolism.
Published, MCP Papers in Press, April 1, 2008, DOI 10.1074/mcp.M700535-MCP200
This work was supported by Grant CE0561495 from the Australian Research Council (ARC) through the Centres of Excellence Program and by Western Australia State Government support to the Centre for Computational Systems Biology. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
All microarray data are available from ArrayExpress under accession number E-ATMX-31.
EC Explorer1.2.1 With NAD + or NADP + as acceptor (740 organisms) 1.2.2 With a cytochrome as acceptor (12 organisms) 220.127.116.11 formate dehydrogenase (cytochrome) (3 organisms) 18.104.22.168 pyruvate dehydrogenase (cytochrome) 22.214.171.124 formate dehydrogenase (cytochrome-c-553) 126.96.36.199 carbon-monoxide dehydrogenase (cytochrome b-561) (9 organisms) 1.2.3 With oxygen as acceptor (136 organisms) 1.2.4 With a disulfide as acceptor (87 organisms) 188.8.131.52 pyruvate dehydrogenase (acetyl-transferring) (55 organisms) 184.108.40.206 oxoglutarate dehydrogenase (succinyl-transferring) (39 organisms) 220.127.116.11 created 1972, deleted 1978 18.104.22.168 3-methyl-2-oxobutanoate dehydrogenase (2-methylpropanoyl-transferring) (24 organisms) 1.2.5 With a quinone or similar compound as acceptor (43 organisms) 1.2.7 With an iron-sulfur protein as acceptor (148 organisms) 1.2.98. undefined (12 organisms) 1.2.99 With unknown physiological acceptors (24 organisms) 22.214.171.124 created 1961, deleted 1984 126.96.36.199 carbon-monoxide dehydrogenase (acceptor) 188.8.131.52 aldehyde dehydrogenase (pyrroloquinoline-quinone) 184.108.40.206 formaldehyde dismutase 220.127.116.11 formylmethanofuran dehydrogenase 18.104.22.168 carboxylate reductase (11 organisms) 22.214.171.124 aldehyde dehydrogenase (FAD-independent) (6 organisms) 126.96.36.199 glyceraldehyde dehydrogenase (FAD-containing) (4 organisms) 188.8.131.52 formate dehydrogenase (coenzyme F420) 184.108.40.206 4,4&prime-diapolycopenoate synthase (4 organisms) 1.16.1 With NAD + or NADP + as acceptor (172 organisms) 1.16.3 With oxygen as acceptor (73 organisms) 1.16.9 With a copper protein as acceptor (3 organisms)
Synthetic phosphonate analogs of pyruvate and 2-oxoglutarate (OG) are specific inhibitors of the reactions, catalyzed by key regulatory enzymes, the thiamine diphosphate (ThDP)-dependent pyruvate dehydrogenase (PDH) and 2-oxoglutarate dehydrogenase (OGDH), respectively (Bunik et al., 2015, 2016 Artiukhov et al., 2016 Bunik, 2017). In these analogs, the phosphonate group replaces the carboxyl group which undergoes the decarboxylation. The interaction of 2-oxo phosphonates with cognate dehydrogenases mostly results in the non-cleavable intermediates, mimicking the enzyme transition state (Kluger and Pike, 1979 Wagner et al., 2019). The tight, yet reversible, binding of the phosphonates to their cognate dehydrogenases allows for specific inhibition of 2-oxo acid dehydrogenase in vivo (Bunik et al., 2013 Artiukhov et al., 2016).
The well-known OGDH-encoded OGDH is a key enzyme in the mitochondrial tricarboxylic acid (TCA) cycle, whose mutations which impair the function are incompatible with life (Bunik, 2017). In contrast, the physiological significance of the DHTKD1-encoded isoenzyme 2-oxoadipate dehydrogenase (OADH), catalyzing oxidative decarboxylation of 2-oxoadipate (OA, Figure 1A), the common intermediate of the lysine and tryptophan catabolism, is much less obvious, as the DHTKD1 mutations often remain unnoticed. Besides, although OADH is assumed to function as a component of the 2-oxoadipate dehydrogenase multienzyme complex, analogous to the one formed by OGDH, our recent identification of the DHTKD1-encoded isoforms of OADH in mammalian tissues has supported our previous prediction from the sequence analysis (Bunik and Degtyarev, 2008), that OADH may also be active in the isolated state, catalyzing the non-oxidative decarboxylation (Boyko et al., 2020). The role of such function in detoxication of aldehydes (Bunik and Fernie, 2009) may underlie association of the dysregulated DHTKD1 expression with diabetes, obesity and cancer (Lim et al., 2014 Wu et al., 2014 Kieᐫus et al., 2015 Plubell et al., 2018 Timmons et al., 2018).
Figure 1. Structures of the dicarboxylic 2-oxo acids (A) as well as the corresponding phosphonate analogs (B) and their membrane-permeable precursors (C), studied in this work.
In order to discriminate physiological significance of the reactions catalyzed by the isoenzymes of 2-oxoglutarate dehydrogenase, encoded by the OGDH and DHTKD1 genes, in vivo, a homologous series of the phosphonate analogs of dicarboxylic 2-oxo acids (Figure 1B) has been synthesized (Artiukhov et al., 2020), namely the succinyl (SP), glutaryl (GP), and adipoyl (AP) phosphonates. Cellular experiments have shown specific action of the compounds on metabolomes, coinciding with their inhibition of the OGDH and OADH reactions, catalyzed by the partially purified OGDH and OADH. A number of other enzymatic reactions, employing 2-oxo acids, or their structural analogs, are not affected by the phosphonates (Artiukhov et al., 2020).
These promising findings justify a detailed study of the molecular mechanisms underlying the specific action of the phosphonate inhibitors on OADH and OGDH in vivo. To develop the most selective inhibitors in vivo, our current work aims at comparing the binding of the homologous phosphonate analogs of dicarboxylic 2-oxo acids to OADH and OGDH. We use inhibition kinetics to quantify the binding, and analysis of available structures to identify the molecular origin of the interaction specificity. The quantifications obtained by the kinetic study show a good agreement with the structural data available for the two isoenzymes and the known OGDH complexes with the phosphonates (Wagner et al., 2019). As a result, molecular basis of the selective regulation of the OGDH or OADH isoenzymes by the synthetic phosphonate inhibitors is revealed, providing new knowledge on the directed regulation of the target enzymes in cells and organisms. While SP, whose inhibition of OGDH was first published about three decades ago (Bunik et al., 1992), is by now well-recognized as a specific and efficient inhibitor of OGDH in vivo, our development of a similar inhibitor of OADH opens new ways to study the poorly understood biological role of this isoenzyme. Moreover, pharmacological tools to specifically affect the OADH function may be of therapeutic significance, as regulation of the DHTKD1 expression is observed in a number of pathological conditions, including diabetes, obesity and malignant transformation.
Enzymes of Mtb’s CCM, including some that subserve glycolysis, gluconeogenesis, the TCA cycle, and the glyoxylate shunt, make an important contribution to the pathogen’s virulence (5 ⇓ –7, 23, 35, 36). Evidence is emerging that some of these enzymes are not simply playing the metabolic roles for which they are annotated, but can protect the pathogen by participating in antioxidant or antinitroxidative defense. Such a role was first seen with DlaT (23, 37) and Lpd (5), and later with the isocitrate lyases (38). Here, we have added HOAS to that list and identified two four-component peroxidase systems, HOAS/DlaT/AhpD/AhpC and AceE/DlaT/AhpD/AhpC, that can sustain peroxidatic action with alternative sources of electrons besides NADPH and NADH, namely, α-KG and pyruvate. Finally, we have helped clarify the role of the KDHC in Mtb.
HOAS can function in KDHC in vitro. However, KDHC itself seems to play a limited role in Mtb’s TCA cycle during growth on glycolytic or fatty acid carbon sources. In the presence of KDHC, there is little flux of carbon through the node of the TCA cycle that KDHC controls (4) and deletion of its E1 has little or no impact on Mtb’s growth in those conditions, consistent with the low KDHC enzymatic activity observed here and elsewhere (15) and the minimal effect of HOAS deletion on metabolism when bypass pathways like the glyoxylate and GABA shunts are available. Perhaps SucCoA synthetase accounts for the sufficiency of SucCoA for biosynthetic purposes (39). In contrast, we observed drastic metabolic and growth phenotypes when HOAS-deficient Mtb was grown in glutamate. Incomplete metabolism of glutamate/α-KG in HOAS-deficient Mtb led to intracellular and extracellular accumulation of potentially toxic aldehydes, one of which, SSA (40), abrogated growth when added extracellularly.
Why did aldehydes accumulate intracellularly and extracellularly when HOAS-deficient Mtb was presented with extracellular glutamate? WT Mtb can proliferate exponentially with glutamate as its only carbon source. However, extracellular glutamate stopped Mtb from replicating if HOAS was absent or inactive. Glutamate enters the TCA cycle through oxidative deamination to α-KG or as succinate via the GABA shunt. Metabolomic analysis revealed a glutamate-dependent build-up of α-KG and SSA in both Δhoas and Δhoas::E956Ahoas. SSA could arise from HOAS-catalyzed nonoxidative decarboxylation of α-KG or as a product of the GABA shunt in HOAS-deficient strains. SSA can be oxidized to succinate by SSADHs GabD1 and GabD2 (rv0234c and rv1731) (13). GabD1 is inhibited by high concentrations of substrate SSA (41) as well as by glyoxylate (42). Not only could glutamate metabolism drive the GABA shunt but HOAS deficiency could lead to an increase in glyoxylate through a decrease in its disposition through the HOAS reaction. Thus, elevation of SSA could reflect the joint impact of two effects of HOAS deficiency: increased activity of the GABA shunt and inhibition of SSADH activity by increased glyoxylate. The toxic potential of SSA illustrates the principle that intermediary metabolism produces not just molecules that sustain life but life-threatening molecules as well, as shown for Mtb, which also accumulates branched chain α-ketoacids, propionate, maltose 1-phosphate, or glycerol phosphate when the relevant metabolic pathways are disrupted by chemical or genetic means (5, 8, 43 ⇓ ⇓ ⇓ –47).
Besides accumulation of growth-inhibitory SSA, other mechanisms may account for or contribute to the suppressive effect of glutamate on growth of HOAS-deficient Mtb, which stands in contrast to the ability of WT Mtb to cocatabolize different substrates, including glutamate, for optimal growth (4). In some bacteria, intracellular metabolites play a signaling role in carbon utilization. For example, in Escherichia coli, elevated α-KG inhibits enzyme I of the phosphotransferase system, blocking glucose uptake (19), and impairs cAMP synthesis, eliciting catabolite repression (48). Moreover, aldehydes acting as electrophiles can form transient adducts with select Lys residues to control enzyme activities and redirect CCM metabolism (49).
Except for dismutation reactions in which H2O2 or O2 − serves as both an oxidant and a reductant, previously described systems of enzymatic detoxification of reactive oxygen intermediates and RNIs ultimately depend on reducing equivalents from NADH or NADPH, for which CCM is the major source. However, Lpd, NADH, and NADPH are all known targets of RNIs (50 ⇓ –52). By drawing electrons directly from CCM metabolites, and bypassing Lpd, defense systems that use HOAS or AceE may act as important complements to the Lpd-dependent system.
The turnover number for the PNR/P complexes described here was lower than one might expect for a life-sparing defense. We speculate that the conditions used in vitro did not adequately recapitulate the conditions in the intact cell. We could demonstrate a peroxidase reaction, but reagent peroxynitrite destroyed CoA so rapidly that we could not demonstrate a PNR reaction. Nonetheless, the four-enzyme system containing either HOAS or AceE was capable of cyclic reduction of AhpC, and AhpC was shown to reduce peroxynitrite in an assay system that does not depend on CoA (31).
The peroxidase reaction in which HOAS participates appears to be physiologically relevant during Mtb’s infection of the mouse. The role of HOAS in avoiding toxicity from glutamate anaplerosis may be required for virulence as well, but such a role could not be evaluated by the present studies. By complementing HOAS-deficient Mtb with an active site point mutant, we established that the role of HOAS in both cases is catalytic, not merely structural. HOAS deficiency did not increase susceptibility to the other physiological stresses tested or lead to amino acid auxotrophy (21, 39).
In sum, under the conditions studied here and earlier (23) in vitro and in mice, Mtb uses the E1 and E2 components of its KDHC not for growth but for defense against nutritional imbalance and host immune chemistry.
Pediatric Neurology Part III
Pyruvate dehydrogenase and pyruvate carboxylase deficiency are the most common disorders in pyruvate metabolism. Diagnosis is made by enzymatic and DNA analysis after basic biochemical tests in plasma, urine, and CSF.
Pyruvate dehydrogenase has three main subunits, an additional E3-binding protein and two complex regulatory enzymes. Most frequent are deficiencies in PDH-E1α. There is a spectrum of clinical presentations in E1α deficiency, ranging in boys from severe neonatal lactic acidosis, Leigh encephalopathy, to later onset of neurological disease such as intermittent ataxia or dystonia. Females tend to have a more uniform presentation resembling nonprogressive cerebral palsy. Neuroradiological abnormalities such as corpus callosum agenesis are seen more frequently in girls, basal ganglia and midbrain disturbances in boys.
Deficiencies in the other subunits have also been described, but in a smaller number of patients.
Pyruvate carboxylase deficiency has three clinical phenotypes. The infantile type is characterized mainly by severe developmental delay, failure to thrive, and seizures.
The second type is characterized by neonatal onset of severe lactic acidosis with rigidity and hypokinesia. A third form is rarer with intermittent episodes of lactic acidosis and ketoacidosis. Neuroradiological findings such as cystic periventricular leukomalacia have been described.
Materials and Methods
Reagents and Tools table
|Reagent/Resource||Reference or source||Identifier or catalog number|
|B. diazoefficiens USDA 110 spc4||Regensburger and Hennecke (1983)||NA|
|E. coli BL21 pET42b::gabT2||This study||BC3422|
|E. coli BL21 pET42b::gabD9||This study||BC3421|
|E. coli BL21 pET42b:: gabD6||This study||BC4312|
|E. coli BL21 pET42b::odcB||This study||BC4308|
|E. coli BL21 pET42b:: gabD8||This study||BC4294|
|E. coli BL21 pET42b::argI2||This study||BC3424|
|E. coli BL21 pET42b::speB||This study||BC4265|
|E. coli BL21 pET42b::speB2||This study||BC4266|
|E. coli BL21 pET42b::argD||This study||BC4270|
|E. coli BL21 pET42b::datA||This study||BC4292|
|E. coli BL21 pET42b::aspC||This study||BC3417|
|E. coli BL21 pET42b::ilvB1||This study||BC3419|
|E. coli BL21 pET42b::gabD7||This study||BC4296|
|E. coli BL21 pET42b::gabD1||This study||BC3420|
|E. coli BL21 pET42b::odcA||This study||BC4269|
|E. coli BL21 pET42b::argI1||This study||BC3423|
|E. coli BL21 pET42b::aatB||This study||BC4293|
|E. coli BL21 pET42b::gabT3||This study||BC4271|
|S. meliloti CL150 (Rm1021 pstC + ecfR1 + )||Schlüter et al ( 2013 )||BC2175|
|S. meliloti CL150 nifD::Tn5-233||Lang et al (2018)||CL309|
|S. meliloti CL150 dctAB::aacC1||This study||BC4081|
|S. meliloti CL150 argI2:: aacC1||This study||BC3455|
|S. meliloti CL150 satABC::aacC1||This study||BC3451|
|S. meliloti CL150 ureGFE::aacC1||This study||BC4083|
|S. meliloti CL150 aspC::aacC1||This study||BC3457|
|S. meliloti CL150 argI1::aacC1||This study||BC3453|
|S. meliloti CL150 argI1::smR argI2::aacC1||This study||BC3766|
|S. meliloti CL150 artABCDE::aacC1||This study||BC3459|
|S. meliloti CL150 amtB::aacC1||This study||BC4085|
|M. truncatula Jemalong wt||Pecrix et al (2018)||NA|
|M. truncatula Jemalong lss||Schnabel et al ( 2010 )||NA|
|G. max cultivar Williams||NA|
|Oligonucleotides and sequence-based reagents|
|PCR primers||This study||Dataset EV5|
|Chemicals, enzymes and other reagents|
|Succinic semialdehyde||This work||NA|
|Sodium hypochlorite solution||VWR||BDH7038|
|Restriction enzyme SpeI||New England Biolabs||R0133|
|Restriction enzyme MfeI||New England Biolabs||R0589|
|GC6850 gas chromatograph instrument||Agilent Technologies|
|6550 accurate-mass quadrupole time-of-flight||Agilent Technologies|
|Agilent HILIC Plus RRHD column||Agilent Technologies|
|5500 QTRAP triple-quadrupole mass spectrometer||AB Sciex|
|HisTrap FF crude column||GE Healthcare||GE11-0004-58|
Materials and Methods
Bacterial strains, cultivation, and growth conditions
Sinorhizobium meliloti strain CL150 (Rm1021, pstC + ecfR1 + ) (Schlüter et al, 2013 ) was grown at 30°C in LB broth medium with 5 g NaCl per liter. Escherichia coli was grown in LB broth at 37°C. Where necessary, growth media were supplemented with antibiotics at the following concentrations: gentamicin, 10 μg/ml for E. coli and 30 μg/ml for S. meliloti when cultured in LB streptomycin, 200 μg/ml and ampicillin, 50 μg/ml.
Plant cultivation and inoculation assays
Medicago truncatula WT Jemalong seeds were surface-sterilized with 70% ethanol for five minutes and thoroughly rinsed with water. Seeds were imbibed with gentle agitation for at least four hours with two water changes and further imbibed overnight at room temperature without light. After imbibition, seeds were washed with water and germinated at 30°C for 24 h. Seedlings were planted into a sterile perlite substrate (Isoself, Knauf) within 300 cm 3 black plastic pots (Greenhouse, Elho). Plants were grown in plant growth shelves at room temperature with a controlled 16-h day and 8-h night cycle. During the light cycle, every pot received 2500 lumens using 36 W Fluora 77 OSRAM light bulbs. Plants were automatically watered with a droplet watering system (Micro-drip-system, Gardena Art. 8311-20 Art. 1407-20) at 3-day intervals with 80 ml of 10% BNM solution (Ehrhardt et al, 1992 ). Pots were covered with a transparent PET cylinder during growth. Three days after germination, plants were inoculated with 20 ml of a S. meliloti cell culture with a cell density of an OD600nm of 0.05 resuspended in 1 mM MgSO4. The inoculation time point was considered as day zero post-inoculation (dpi).
Glycine max cultivar Williams seeds were surface-sterilized with a wash in 100% ethanol for 5 min followed by a wash in 35% H2O2 for 15 min. Afterward, seeds were thoroughly rinsed with water. Seeds were germinated in 0.8% agar plates for 24–48 h at 25°C in the dark. Seedlings were planted on 200 cm 3 brown glass jars filled with autoclaved vermiculite (Vermisol) and 100 ml of Jensen media (Vincent, 1970 ). After transplantation, plants were inoculated with B. diazoefficiens (1 ml OD600 nm of 0.01). The inoculation time point was considered as day zero post-inoculation (dpi). Plants were grown in plant growth chambers at 28°C with a controlled 16-h day and 8-h night cycle as previously described (Göttfert et al, 1990 ). After 5 dpi, plants were watered every 3 days with sterile water.
Phenotypic characterization of plants to assess symbiotic nitrogen fixation
Symbiosis phenotypes such as nodulation frequency, plant dry weight, and nitrogenase activity were determined in M. truncatula WT plants 8 weeks post-inoculation with S. meliloti gene deletion mutants and wild-type control strains. Nitrogenase activity of M. truncatula was determined by measuring acetylene reduction to ethylene as previously reported. Plants were placed into 50 cm 3 sealed vials to which acetylene was injected to a final concentration of 2%. Ethylene production was measured after 3 h and 5 h of incubation on an analytic gas chromatograph instrument (GC6850, Agilent Technologies). The ethylene background was monitored and systematically removed from every measurement. To determine the average ethylene production per nodule, ethylene production was normalized by nodule number and duration of acetylene incubation. Nodulation frequency was determined by manually counting all the visible nodules per plant. For dry weight determination, plant shoots were dried at 85°C for at least 20 h prior individual plant weight measurement (ABS 80-40 N, KERN). Measurements from replicas were averaged and normalized to the S. meliloti CL150 WT reference strain. Nitrogenase activity, plant dry weight, and nodulation frequency were determined for at least 22 independent plants for each assayed S. meliloti strain.
Sinorhizobium meliloti bacteroid ultrastructure determination through scanning electron microscopy
Medicago truncatula nodules were harvested 10 weeks post-inoculation (wpi), cut open longitudinally, and collected in a vessel with fixative buffer (2.5% glutaraldehyde, 2% formaldehyde in 0.15 M Na-cacodylate with 2 mM CaCl2). The nodule containing vessel was subjected to vacuum for at least 30 min to allow the nodules to sink followed by microwave fixation in fresh fixative (PELCO BioWave Pro + ) and washed with fixative buffer. Nodules were stained through sequential incubation with (i) 2% OsO4, 1.5% K4Fe(CN)6 in 0.15 M Na-Cacodylate with 2 mM CaCl2 (ii) 1% thiocarbohydrazide (iii) 2% OsO4 (iv) 1% uranylacetate and (v) Waltons lead aspartate samples were washed with fixative buffer between every step. Samples were dehydrated through a graded ethanol series (25%, 50%, 75%, 100%, dry ethanol) and washed with dry acetone. Samples were placed in a graded series of Epon/Araldite in dry acetone (25%, 50%, 75%, 100%, 100%) and allowed to polymerize at 60°C for 3 days. Ultrathin sections (100 nm) were transferred to Si-wafer-chips and imaged on a FE-SEM FEI Magellan 400i operating at 1.8 kV and 0.8 nA with a 20 nm pixel size by backscatter electron detection.
Substrate specific stimulation of nitrogenase activity in isolated bacteroids
Sinorhizobium meliloti and B. diazoefficiens bacteroids were isolated from M. truncatula and G. max root nodules, respectively, according to following procedure. Plant roots were harvested 3 weeks (G. max) or 10 weeks (M. truncatula) post-inoculation with wild-type B. diazoefficiens 110spc4 strain (Regensburger & Hennecke, 1983 ) and S. meliloti CL150 strain, respectively, and rinsed thoroughly with water. Under anaerobic conditions (92% N2, 8% H2), nodules (250 mg to 1 g wet weight) were crushed in PBS (10 mM Na2HPO4, 1.8 mM KH2PO4, 137 mM NaCl, 2.7 mM KCl, pH 7.4) and filtered through three layers of gauze to remove debris. Bacteroid suspensions (2 ml corresponding to 4 × 10 6 bacteroid cells) were added to 15-ml sealed flasks to obtain reference measurements of nitrogenase activity in isolated bacteroids in the presence of plant crude extract. To assay stimulation of nitrogenase activities upon addition of substrates, bacteroid suspensions were washed two times with PBS under anerobic conditions and pelleted by centrifugation at 1,000 g. Recovered bacteroids (4 × 10 6 cells) were resuspended in 2 ml of induction media (2 μM biotin, 1 mM MgSO4, 42.2 mM Na2HPO4>, 22 mM KH2PO4, 8.5 mM NaCl, 21 nM CoCl2, 1 μM NaMoO4 pH 7.0) and added to 15-ml sealed flasks. Induction media were supplemented with either 7.4 mM succinate or 5 mM arginine or both substrates. Acetylene and oxygen were added to a final concentration of 5% and 0.01%, respectively, in the head space of each flask. Nitrogenase activity was determined for each sample through measuring the reduction of acetylene into ethylene after 1 and 3 h time points. Ethylene production was detected with gas chromatograph (GC6850, Agilent Technologies). Activities were normalized by nodule wet weight. Furthermore, relative activities were calculated by subtracting the baseline activity of washed bacteroid samples without substrate supplementation. Reported B. diazoefficiens and S. meliloti activity is the result of 17 and 4 independent preparations, respectively.
Substrate specific stimulation of ATP production in isolated bacteroids
Bradyrhizobium diazoefficiens bacteroids were isolated from 3 weeks post-inoculated G. max root nodules. Nodules (1 g wet weight) were crushed in PBS (10 mM Na2HPO4, 1.8 mM KH2PO4, 137 mM NaCl, 2.7 mM KCl, pH 7.4) and filtered through three layers of gauze to remove debris. Bacteroid suspension was pelleted by centrifugation at 2,500 g, and the supernatant (nodule extract) was saved for later usage. Bacteroid suspension (5 × 10 8 > cells) was washed twice with PBS and resuspended in 1 ml of induction media (2 μM biotin, 1 mM MgSO4, 42.2 mM Na2HPO4, 22 mM KH2PO4, 8.5 mM NaCl, 21 nM CoCl2, 1 μM NaMoO4 pH 7.0). To avoid ATP generation from aerobic respiration, the bacteroid suspension was placed under anaerobic conditions (92% N2, 8% H2) to perform the rest of the procedure. Bacteroids (200 μL) were incubated in 2 mL of nodule extract (obtained at the beginning of the protocol) or induction media without supplements or supplemented with either 7.4 mM succinate or 5 mM arginine or both substrates. ATP content was determined for each sample (using a 1:10 dilution) through ATP-dependent luciferase reaction (BacTiter-Glo Microbial Cell Viability Assay, Promega). Luminescence from luciferase activity was quantified with Victor3 multilabel plate counter (PerkinElmer).
To generate hyper-saturated transposon mutant libraries in S. meliloti, a previously described Tn5 mutagenesis procedure for Caulobacter crescentus was adapted (Christen et al, 2011 ). In brief, the Tn5 delivery ColE1 plasmid pTn5_gent_14N (Christen et al, 2016 ) was conjugated from an E. coli SM10 donor strain into a S. meliloti CL150 recipient strain. Separate transposon mutant libraries were generated and growth selected on rich medium (LB). For each condition, a total of sixteen independent conjugations were performed and replicate libraries were tagged using eight barcoded Tn5 derivatives. Transposon insertion mutants were selected on LB supplemented with gentamicin and streptomycin. Plates were incubated at 30°C for 2 days, and transposon mutant libraries from each plate were separately pooled, supplemented with 10% v/v DMSO (Sigma-Aldrich), and stored in 96-well deep-well plates at −80°C for further processing.
In planta selection of transposon mutant libraries
Transposon mutant pools were selected for infection and nodule formation in legume plants. For nodulation experiments, a Medicago truncatula lss super-nodulator mutant was used (Schnabel et al, 2010 ). Seeds were treated with concentrated H2SO4 (Sigma-Aldrich) for 5 min, thoroughly rinsed with sterile water, then sterilized with 7% NaClO (VWR chemicals) for 3 min, and again rinsed with sterile water. The seeds were then imbibed with gentle agitation for four hours with regular water changes and then incubated overnight in the dark at room temperature. After imbibition, seeds were rinsed with sterile water, placed in deep petri plates, and inverted for 24 h at 30°C to allow for the downward growth of the seedling roots. After removing seed coats, groups of 25 seedlings were planted on large square plates (Genetix) containing 1.2% buffered nodulation medium (Ehrhardt et al, 1992 ) supplemented with 0.1 nM aminoethoxy vinyl glycine. Altogether, 4,500 M. truncatula lss plant seedlings were grown at 22°C with a controlled 16-h day and 8-h night cycle (2500 Lumens using Osram Fluora L36 W/77 bulbs). Five days post-germination, M. truncatula lss seedlings were flood-inoculated with S. meliloti Tn5 mutant reference libraries initially selected on rich media conditions (LB). During inoculation experiments, input libraries were kept independent from each other. S. meliloti transposon input mutant pools were inoculated from 96-well storage plates, grown overnight, washed, and resuspended to an OD600 nm = 0.05 in 10 mM MgSO4. Plant roots were then aseptically inoculated with 5 ml of a dilute bacterial suspension, followed by removal of the excess bacterial suspension.
Recovery of transposon mutant libraries from nodules
After 6 weeks post-inoculation, nodules were harvested. To recover transposon mutants capable of infecting root nodules, a two-step surface sterilization protocol was employed. Nodule material was washed with 1% SDS and then treated with 70% ethanol for 5 min, followed by rinsing with sterile water. Next, nodules were treated for 3 min with 0.2% NaClO (VWR chemicals) followed by three rinses with sterile water. The surface-sterilized nodules were crushed in cold PBS (10 mM Na2HPO4, 1.8 mM KH2PO4, 137 mM NaCl, 2.7 mM KCl, pH 7.4) and filtered through three layers of gauze to remove debris. The filtered suspension containing bacteroids was plated on LB supplemented with gentamicin and streptomycin and grown for 2 days at 30°C. The recovered S. meliloti colonies were pooled and arrayed in 96-well plates and stored at −80°C for subsequent use.
Data analysis and mapping of transposon insertion sites
Raw sequencing data processing and read alignment were performed using a custom sequence analysis pipeline based on Python, Biopython (Cock et al, 2009 ), bwa (Li & Durbin, 2010 ), and MATLAB routines as previously described (Christen et al, 2016 ). Adapter sequences were detected using Python string comparison with a 15 bp search window. Demultiplexing into the different TnSeq selection experiments was performed according to a defined barcode sequence tag internal to the arbitrary primer sequence. Reads were aligned onto the S. meliloti 1021 NCBI reference genome (NC_003047, NC_003037, NC_003078 Barnett et al, 2001 Finan et al, 2001 Galibert et al, 2001 ) using bwa-07.12 (Li & Durbin, 2010 ). Insertion datasets were correlated with the genome annotation to analyze global insertion statistics and calculate transposon insertion occurrence and distributions within each annotated GenBank feature of the S. meliloti 1,021 genome (Galibert et al, 2001 )> as previously described (Christen et al, 2016 ).
Gene essentiality analysis across selection conditions
For k smaller than (n−i)/2 and n not to exceed 100, p2 was numerically calculated by calculating the number (h) of compositions of length i + 1> for n where each part does not exceed k by brute force. For k smaller than (n−i)/2 and n equals or exceeding 100, we approximated p2 by sampling the composition space by random simulation and counting the occurrences of compositions of length i + 1 for n where each part does not exceed k.
Construction of targeted gene deletions in Sinorhizobium meliloti
Sinorhizobium meliloti deletion mutants were generated by replacing the native gene with the aacC1 gene conferring gentamycin resistance using a one-step double homologous recombination procedure as detailed in Ledermann et al ( 2016 ). Flanking DNA regions covering 750 bp upstream and downstream of a target gene were PCR amplified (Dataset EV5) and subsequently fused to a central gentamicin resistance gene using splicing by overlapping extension PCR or Gibson assembly (Gibson et al, 2009 ) to produce gene replacement cassettes. The gene replacement cassettes were cloned via SpeI and MfeI into the pNPTS138 plasmid, which is non-replicative in S. meliloti and confers kanamycin resistance. Cloned plasmids were sequence confirmed and conjugated into S. meliloti strain CL150. Recombinants were selected on LB media supplemented with gentamicin and subsequently screened for kanamycin sensitivity. Single gene deletion strains were confirmed by PCR and sequencing. A similar deletion strategy was employed for construction of the double arginase mutant (∆argI1 ∆argI2) with a gene replacement cassette for argI1 carrying a spectinomycin marker gene. The resulting plasmid was conjugated into S. meliloti CL150 ∆argI2, and the double arginase double mutant strain was screened for kanamycin sensitivity before verification of constructed deletion by PCR and sequencing.
13 C arginine isotope tracing in Bradyrhizobium diazoefficiens bacteroids
Bradyrhizobium diazoefficiens bacteroids were isolated under anaerobic conditions from three weeks post-inoculated G. max root nodules according to Sarma and Emerich ( 2005 ) and Delmotte et al ( 2010 ). Nodules (10 g wet weight) were crushed in PBS (10 mM Na2HPO4, 1.8 mM KH2PO4, 137 mM NaCl, 2.7 mM KCl, pH 7.4). The homogenate was passed through four layers of cheesecloth (pre-moistened with PBS) into a sterile centrifuge tube, re-extracted several times with buffer, and centrifuged at 400 g for 10 min. The resulting pellet was resuspended twice in PBS and centrifuged at 8,000 g for 20 min. The pellet was dispersed into the extraction buffer (2 ml/g original weight of the nodule) and was layered onto a pre-equilibrated Ludox gradient consisting of 25% (10 ml) Ludox and 75% PBS (30 ml). The gradient tubes were centrifuged in an SW-28 rotor at 10,000 g for 35 min at 4°C in a Beckman L8–55 ultracentrifuge. The bacteroid layer with a density of 1.09 g/ml was collected. The bacteroid pellet was suspended in distilled water and collected by centrifugation at 10,000 g. For labeling assays, bacteroids were resuspended in 10 ml volume in PBS to a final OD of 4.0. After the addition of succinate (5 mM) and 13 C arginine (5 mM), bacteroids were incubated at RT under microaerobic condition (0.1% v/v) and samples of 250 μl were taken at regular time intervals, filtered on a PVDF 0.45 μl membrane and immediately washed with 1.0 ml H2O Chromasolv. The filter with the cell pellet was extracted in 3 ml (40% MeOH, 40% acetonitrile and 20% H2O) at −20°C for 1 h, stored at −80°C, and finally dried in a SpeedVac. The metabolite extracts were resuspended in 100 μl MilliQ water, and metabolites were analyzed using a HILIC method. 5 μl of metabolite extract was injected on an Agilent HILIC Plus RRHD column (100 mm × 2.1 mm × 1.8 μm Agilent). The gradient of mobile phase A (10 mM ammonium formate and 0.1% formic acid) and mobile phase B (acetonitrile with 0.1% formic acid) was as follows: 0 min, 90% B 2 min, 40% B 3 min, 40% B 5 min, 90% B and 6 min, 90% B. The flow rate was held constant at 400 μl min −1 . Metabolites were detected on a 5500 QTRAP triple-quadrupole mass spectrometer in positive mode with MRM scan type (AB Sciex, Foster City, CA). The raw data were processed and analyzed by custom software using MATLAB (MathWorks).
15 N arginine isotope tracing in Bradyrhizobium diazoefficiens bacteroids
Bradyrhizobium diazoefficiens bacteroids were isolated from 3 weeks post-inoculated G. max root nodules under anaerobic conditions as described above. Bacteroids were resuspended (1 ml/g nodule wet weight) in 2 ml of tracing media (2 μM biotin, 1 mM MgSO4, 42.2 mM Na2HPO4, 22 mM KH2PO4, 8.5 mM NaCl, 21 nM CoCl2, 1 μM NaMoO4 pH 7.0, 10 mM NH4Cl, 7.4 mM succinate, and 5 mM 15 N arginine). Bacteroid suspensions were incubated at room temperature under microaerobic conditions (0.1% v/v). Aliquots (20 μl) of the enzymatic reaction were sampled over the time series, and reaction was blocked by adding 180 μl of ice-cold methanol. Relative metabolite abundances were determined by non-targeted flow injection analysis as described previously (Fuhrer et al, 2011 ). Mass spectra were recorded in negative-ionization profile mode from m/z 50 to m/z 1,000 on an Agilent 6550 accurate-mass quadrupole time-of-flight instrument with a frequency of 1.4 spectra/s using the highest resolving power (4 GHz HiRes). The source gas temperature was set to 225°C, with 11 l min −1 drying gas and a nebulizer pressure of 20 psig. The sheath gas temperature was set to 350°C, and the flow rate was 10 l min −1 . Electrospray nozzle and capillary voltages were set at 2,000 and 3,500 V, respectively.
Purification of recombinant proteins
Coding sequences of interest were amplified by PCR (Dataset EV5) and cloned into pET42 expression plasmids inframe to a C-terminal (His)6-tag (6xHis). The resulting vectors were sequence-verified and electroporated into BL21 rosetta pLys strains. E. coli BL21 harboring the expression vectors were grown at 30°C in LB medium containing chloramphenicol (20 mg/l) and kanamycin (30 mg/l). When the cultures reached an OD600nm of 0.4, isopropyl-β-D-thiogalactopyranoside (IPTG) was added to a final concentration of 0.5 M. After the addition of IPTG, the culture was grown for 2–4 h more at 30°C to induce the expression of the recombinant proteins. Grown cells were harvested by centrifugation at 5,095 g for 10 min at 4°C. The resulting pellet was either shock frozen with liquid N2 and stored at −80°C or immediately used for protein extraction and purification. To purify the recombinant proteins, cells were resuspended in NPI-buffer (20 mM sodium phosphate, 500 mM NaCl, pH7.4) supplemented with 10 mM imidazole and disrupted using a French press (SLM Instruments Inc.). Lysates were cleared by centrifugation at 17,000 g for 15 min at 4°C to remove cell debris. The supernatant was loaded on HisTrap FF crude column (GE Healthcare) previously equilibrated with NPI-buffer supplemented with 10 mM imidazole. The column was washed twice with NPI-buffer supplemented with 10 mM and 20 mM imidazole, respectively. The purified enzymes were eluted with NPI-buffer supplemented with 250 mM imidazole, concentrated by ultrafiltration with Amicon Ultra-4 centrifugal filters (Merck Millipore), and dialyzed against NPI-buffer containing 10% (v/v) glycerol. Protein concentration was determined using the Pierce BCA Protein Assay kit (Thermo Scientific). Protein samples were shocked frozen with liquid N2 and stored at −80°C until use.
Chemical synthesis of succinate semialdehyde, 4-aminobutanal, and 4-guanidinobutanal
The arginine transamination metabolic network contains a set of 13 intermediates. Thereof, three intermediates succinate semialdehyde, 4-aminobutanal, and 4-guanidinobutanal are not commercially available and were chemically synthesized for use in subsequent enzyme characterization studies as aldehyde dehydrogenase substrates.
Succinate semialdehyde was synthesized according to the procedure as previously described (Bruce et al, 1971 ). In a 15 ml Falcon tube, a solution of monosodium glutamate (169 mg, 1 mmol) in 5.0 ml distilled water was exposed to a gentle flow of N2 for 5 min. An equimolar amount of chloramine T (227 mg, 1 mmol) was added to the solution and dissolved by heating the solution to 60°C. After further incubation at 60°C for an additional 15 min, the mixture was cooled to 25°C and adjusted to pH 2.0 with concentrated HCl and degassed. Reaction by-products were crystallized by placing the mixture on ice and removed by filtration. The aqueous phase was extracted with diethyl ether 3 times, organic phases were combined, and the water fraction was discarded. Diethyl ether was evaporated yielding an aqueous solution of 1% of the starting material and a concentration of approximately 2 M succinic semialdehyde.
4-guanidinobutyraldehyde was prepared from L-arginine according to Tanaka et al ( 2001 ). The procedure follows a similar reaction scheme as detailed for the synthesis of succinic semialdehyde with the following modifications. As starting material, arginine hydrochloride (210 mg, 1 mmol) was dissolved. Upon addition of chloramine T (227 mg, 1 mmol), the mixture was adjusted to a pH 6.5 with 1 N HCl (50 μl) and heated to 60°C. Prior extraction with diethyl ether, the solution was adjusted to pH 13.5 with 10 N NaOH.
4-aminobutyraldehyde was prepared by the hydrolysis of 1.0 ml of 0.5 M 4-aminobutyraldehyde diethyl acetal (Sigma-Aldrich). 172 μl 4-aminobutyraldehyde diethyl acetal was dissolved in 2 ml H2O, and 1 N HCl was added to acidify to pH 3.0. After incubation for 30 min at 30°C, the reaction mixture was titrated to pH 10.0 with 1 N NaOH. The crude reaction product 4-aminobutanal was extracted from the water phase with five consecutive extraction steps using 1 ml diethyl ether. Ether fractions were combined, and the solvent was removed by evaporation. The reaction product 4-aminobutyraldehyde was obtained as a colorless liquid of 40.0 mg mass.
Enzyme characterization and activity assays
Transaminases were assayed for enzymatic activity according to the following procedure. A total of 30 μg purified enzyme were added to 200 μl reaction mixture containing 1 mM arginine, 1 mM ornithine, 1 mM citrulline, 1 mM agmatine, 1 mM putrescine, 1 mM 4-guanidinobutanoate, 1 mM 4-aminobutanoate, 10 mM of pyruvate, 1 mM MgCl2, 1 mM MnCl2, and 100 μM pyridoxal phosphate in 50 mM PBS pH 7.4 followed by incubation at 25°C. Aliquots (20 μl) of the enzymatic reaction were sampled over the time series, and reactions were blocked by adding 180 μl of ice-cold methanol. Ureohydrolases were assayed for enzymatic activity according to the following procedure. A total of 30 μg purified enzyme were added to 200 μl reaction mixture containing 1 mM arginine, 1 mM agmatine, 1 mM 4-guanidinobutanoate, 10 mM of pyruvate, 1 mM MgCl2, 1 mM MnCl2, and 100 μM pyridoxal phosphate in 50 mM PBS pH 8.0, previously incubated at 25°C for 30 min with 0.15 ng/μl purified AspC followed by heat inactivation at 65°C for 5 min to generate 5-guanidino-2-oxopentanoate (GOP) and guanidinobutanal, from arginine and agmatine transamination, respectively. Ureohydrolase reactions were done at 25°C. Aliquots (20 μl) of the enzymatic reaction were sampled over the time series, and reaction was blocked by adding 180 μl of ice-cold methanol. Decarboxylases were assayed for enzymatic activity according to the following procedure. A total of 30 μg purified enzyme were added to 200 μl reaction mixture containing 1 mM arginine, 1 mM ornithine, 1 mM citrulline, 10 mM of pyruvate, 1 mM MgCl2, 1 mM MnCl2, 100 μM pyridoxal phosphate, and 500 μM thiamine pyrophosphate in 50 mM PBS pH 8.0, previously incubated at 25°C for 30 min with 0.15 ng/μl purified AspC followed by heat inactivation at 65°C for 5 min to generate 5-guanidino-2-oxopentanoate (GOP) from arginine transamination. Decarboxylase reactions were done at 25°C. Aliquots (20 μl) of the enzymatic reaction were sampled over the time series, and reactions were blocked by adding 180 μl of ice-cold methanol. Substrate consumption and product formation of transaminases, ureohydrolases, and decarboxylases reactions were determined by non-targeted flow injection MS analysis as described previously (Fuhrer et al, 2011 ). Dehydrogenase activities were assayed from cell lysates of BL21 rosetta pLys strain expressing S. meliloti dehydrogenases according to the following procedure. Cell lysates, corresponding to 20 μg of dehydrogenase enzymes, were added to a substrate mixture containing 1 mM succinate semialdehyde or 1 mM 4-guanidinobutanal or 1 mM 4-aminobutanal in 10 mM PBS pH 10.0 supplemented with 1 mM NAD + . Dehydrogenase reaction was determined by the conversion of NAD + into NADH + H + , which was measured by the increase in absorbance at 340 nm.
Biochemical reconstitution of the catabolic arginine transamination network
To reconstruct a functional catabolic arginine transamination network in vitro, a multienzyme assay comprising 14 purified enzymes was established. A reaction buffer containing of 500 μM thiamine pyrophosphate, 100 μM pyridoxal phosphate, 2 mM NAD, 1 mM MgCl2, and 1 mM MnCl2 in 50 mM PBS pH 8.0 was prepared. Purified enzymes (25 μg each) from the catabolic arginine transamination network (AspC, AatB, ArgD, GabT2, DatA, ArgI1, SpeB, SpeB2, IlvB1, OdcA, OdcB, GabD1, GabD6, and GabD7) were added one by one and gently mixed into the reaction mixture. After the addition of all enzymes, 20 mM pyruvate and 2 mM arginine were added and gently mixed. As a control, the enzyme mix was incubated in a reaction buffer lacking arginine and pyruvate. Aliquots (15 μl) of the enzymatic reaction were sampled over the time series, and reaction was blocked by adding 135 μl of ice-cold methanol. Substrate consumption and product formation of enzymatic reactions were determined by non-targeted flow injection MS analysis as described previously (Fuhrer et al, 2011 ).
The datasets produced and presented in this study are available as Datasets EV1–EV5.
Yet α-lipoic acid functions to increase acetylcholine twice, first through pyruvate decarboxylase — along with thiamine — and then again via choline acetyltransferase.
Acetylcholine also increases strength by creating the action potential of nerves controlling the muscles.
This had been a good study yet simple and brief. The two authors had, however, done a follow-up which had confirmed the initial observation and had helped elucidate some finer details:
This study had used the exact same materials and methods as the former, yet had included dialysis equipment and a few other reagents.
They had found that after the purified enzyme had been dialyzed it’d lost it’s activity, implying that some necessary factor had been removed.
‘Dialysis of the preparation for 24 h caused almost complete loss of activity.’ ―Haugaard
The enzyme choline acetyltransferase had no known cofactor at that time, so this had come somewhat as a surprise.
Yet despite its initial inactivation, the enzyme had regained function upon the addition of dihydrolipoic acid:
They had also tested some other biological “reducing agents” for activity, yet glutathione, vitamin C, and NADH all failed to increase acetylcholine.
This rules out the possibility that dihydrolipoic acid was simply acting as a nonspecific electron donor, although no electrons are consumed in the process of acetylcholine synthesis.
‘It can be seen that the activity of the enzyme is quite low compared to the activity that can be reached after addition of dihydrolipoic acid.’ ―Haugaard
The authors had eventually come to the conclusion, and perhaps rightly so, that lipoic acid had been acting a coenzyme — its well-established function in five other enzymes.
In two of the five known lipoic acid enzymes — i.e. pyruvate decarboxylase and acetoin dehydrogenase — an series of enzyme-bound dihydrolipoic acid molecules carry acetyl groups from thiamine to coenzyme A.
ENVIRONMENTAL EFFECTS ON RESPIRATORY PROCESS
A large number of measurements have been made concerning gas exchange (i.e. rates of photosynthesis, respiration and transpirations) of different plants growing under contrasting conditions ( Lambers, Chapin & Pons 2008 ). These measurements have yielded a mass of experimental results, some of which have been previously discussed. The enormous variety of alternative respiratory substrates and metabolic pathways makes plant respiration remarkably flexible especially in response to changing environmental circumstance. For instance, it has been shown that oxygen isotope discrimination during plant respiration seems to be independent of temperature over the range of temperature normally encountered during growth ( Macfarlane et al. 2009 ). These authors also observed that there is a relatively large temperature dependence of the respiration rate, suggesting that there was little substrate limitation to respiratory rate in the leaves of healthy plants ( Macfarlane et al. 2009 ). Thus, it seems reasonable to assume that enzyme capacity is the main limitation of respiratory rate, and the reduction state of the ubiquinone pool varies little or none with measurement of temperature.
It has been suggested that higher temperatures reduce net carbon gain by increasing plant respiration more than photosynthesis. In fact, the light-saturated photosynthesis rate of C3 crops such as wheat and rice is at a maximum for temperatures from about 20–32 °C, whereas total crop respiration shows a steep non-linear increase for temperatures from 15 to 40 °C, followed by a rapid and nearly linear decline ( Porter & Semenov 2005 ). Although the stimulation of C3 photosynthesis by growth at elevated atmospheric [CO2] can be somewhat predicted with confidence, the nature of changes in respiration remains uncertain ( Leakey et al. 2009b ). The primary reason for uncertainty is that the mechanisms of plant respiratory responses to elevated [CO2] are not fully understood ( Gifford 2003 Leakey et al. 2009b ). In fact, the results observed in the literature are somehow contradictories and have shown that plant respiration may increase as much as 37%, decrease as much as 18%, or even not change at all with increased [CO2] (e.g. ( Drake et al. 1999 Gifford 2003 Leakey et al. 2009a ). In a recent free air carbon dioxide enrichment (FACE) study where soybean was grown at elevated [CO2] (550 ppm), the stimulated (37%) rate of night-time respiration was associated with the additional carbohydrate available from enhanced photosynthesis at elevated CO2 ( Leakey et al. 2009a ). Although at the leaf and plant scales, stimulated respiration at elevated [CO2] may reduce net carbon balance, it is possible, nevertheless, that such stimulation could facilitate increased yield by providing greater energy for export of photoassimilate from source organs to sink tissues. However, the precise role of plant respiration in augmenting the sink capacity remains fragmented ( Gonzalez-Meler, Taneva & Trueman 2004 ).
Considerable research effort has additionally been directed towards the adaptive responses of respiratory metabolism to low oxygen concentrations. An important environmental stress condition that rapidly leads to the depletion of molecular oxygen within plant organs is flooding or water logging of the soil ( Bailey-Serres & Voesenek 2008 ). The most immediate effect on soil flooding is a decline in the oxygen concentration and a consequent decrease in aerobic root respiration leading to a restriction in ATP production. Furthermore, low availability of oxygen to plant cells can also occur under optimal growth conditions, because of the relatively high resistance to diffusion of oxygen through plant tissues ( van Dongen et al. 2011 ). Steep oxygen gradients have been observed in various plant tissues such as roots, stems, seeds or tubers ( Armstrong et al. 1994 Geigenberger et al. 2000 van Dongen et al. 2003 Borisjuk & Rolletschek 2009 Zabalza et al. 2009 ). Moreover, during development, local oxygen concentrations can vary, depending on the metabolic activity of the tissue ( van Dongen et al. 2003 Benamar et al. 2008 ). Therefore, the metabolic responses to low oxygen are directly involved in optimizing the plant's energy status while consuming as little oxygen as possible ( van Dongen et al. 2011 ).
It is well known that both metabolic and anatomical adjustments are important strategies in order to allow plants to cope with spatial and temporal variations of the oxygen availability. The major structural change is an increased formation of aerenchyma to lower the resistance to oxygen diffusion into the respiring tissue ( Drew, He & Morgan 2000 Jiang et al. 2010 ). From a metabolic perspective, the hypoxic responses includes the down-regulation of a suite of energy-, and therefore, oxygen-consuming, metabolic pathways ( Geigenberger 2003 ). Examples of such metabolic adaptations to hypoxia include the down-regulation of storage metabolism ( Geigenberger et al. 2000 ), the switch from invertase to sucrose synthase routes during sucrose hydrolysis ( Bologa et al. 2003 Huang, Colmer & Millar 2008 ) and the inhibition of mitochondrial respiration ( Gupta, Zabalza & van Dongen 2009 Zabalza et al. 2009 ). It seems reasonable to assume that these responses are already initiated before oxygen becomes limiting as a substrate for respiration. Therefore, it has been suggested that these metabolic changes are important components of the survival strategy as they considerably extend the period of hypoxia that a plant can withstand ( van Dongen et al. 2011 ).
Limited water availability, on the other hand, impairs plant growth and is one of the main issues of future climate changes ( Ciais et al. 2005 Loreto & Centritto 2008 ). Several studies on the effect of severe drought stress on respiratory pathways have revealed contrasting results, as respiration remained unaltered in soybean ( Ribas-Carbo et al. 2005 ), increased in wheat ( Bartoli et al. 2005 ), and decreased in bean and pepper ( Gonzalez-Meler, Matamala & Penuelas 1997 ). Nevertheless, the effects of mild to moderate water stress were relatively small on the mitochondrial activity of several key TCA cycle enzymes in two CAM species ( Herppich & Peckmann 2000 ). However, changes in the in vivo activities of the cytochrome oxidase (COX) and alternative oxidase (AOX) pathways, measured with the oxygen isotope fractionation technique that has been demonstrated to be the most reliable technique for the studies of electron partitioning between the two main respiratory pathways ( Ribas-Carbo et al. 1995 Day et al. 1996 ), have been reported by Ribas-Carbo and colleagues ( Ribas-Carbo et al. 2005 Flexas et al. 2006 ). In their study on soybean ( Ribas-Carbo et al. 2005 ), a decrease in COX activity was detected in leaves during severe drought stress, while AOX activity increased. Accordingly, despite complex I dysfunction and hence altered redox balance, the CMSII mutant seems to be able to adjust its photosynthetic machinery during and after drought stress to reduce photo-oxidation and to maintain the cell redox state and the ATP level ( Galle et al. 2010 ). Notwithstanding, identifying whether, and to what extent, plant species-specific factors and/or experimental conditions affect in vivo respiratory pathways, particularly the TCA cycle, under drought stress, awaits further studies.
It is clear, however, that although there have been a range of studies analysing changes in respiratory rates in response to light, temperature and CO2 ( Day et al. 1985 Atkin et al. 1997 Scheurwater et al. 2000 Kruse, Rennenberg & Adams 2011 ), our knowledge of the environmental impact on plant respiration and the TCA cycle remains fragmented. Although the global response of respiration is well characterized, the specific response of the TCA cycle enzymes and intermediates has only been described in a limited number of conditions. Nevertheless, the molecular, enzymatic and metabolic responses were observed in the case of biotic stress such as to moderately low nitrogen ( Tschoep et al. 2009 ), low carbon ( Gibon et al. 2006, 2009 Osuna et al. 2007 Usadel et al. 2008b ), low potassium ( Armengaud et al. 2009 ), small decreases in temperature ( Usadel et al. 2008a ) and water deficit ( Hummel et al. 2010 ), and this was interpreted as an adaptive response to maintain carbon flux through the TCA cycle. Moreover, a robust link between circadian-clock function and metabolic homeostasis in the TCA cycle was recently suggested ( Fukushima et al. 2009 ). Further studies are clearly needed to explore the interactions of mitochondrial non-phosphorylating pathways with photosynthetic processes and cell homeostasis under stressful conditions.
In summary, it is evident that there are several modes of regulation of the TCA cycle activity. For instance, proteomic studies have indicated that the TCA cycle enzymes (aconitase, succinyl-CoA ligase isocitrate, malate, pyruvate and succinate dehydrogenases) are potential targets for redox regulation ( Balmer et al. 2004 ). These results, associated with the allosteric properties of succinyl CoA ligase ( Studart-Guimarães et al. 2005 ) and with the ability to assess free, as opposed to bound, NADH levels ( Kasimova et al. 2006 ) when coupled with observations that glycolitic enzymes are functionally associated to the outer mitochondrial membrane ( Giege et al. 2003 ), suggest that many aspects of the regulation of TCA cycle remain to be elucidated. Additionally, there is a wealth of evidence suggesting that the TCA cycle is inhibited in the light as well as being transcriptionally down-regulated however, it is also equally clear that respiration remains active at considerable levels in illuminated leaves ( Atkin et al. 2000a Nunes-Nesi et al. 2008 ). Therefore, it seems likely that the physiological purpose for regulation is not the control of respiration per se but of other metabolic processes mediated by respiratory metabolism.
In assessing the merits of FBA as a tool to predict net CO2 evolution rates of plant tissues, we have been restricted by the number of tissue types and environmental conditions for which experimentally constrained metabolic flux maps are available as a point of comparison/validation. This is mainly because of the limited number of experimental systems that are suitable for the more tractable steady-state stable isotope MFA approach. In particular, because flux quantification in photosynthetic tissues requires the more challenging analysis of labelling time-courses, there is a shortage of quantitative flux maps for such tissues. So, although there have been several detailed FBA studies of photosynthetic tissues of higher plants (Montagud et al. 2010 de Oliveira Dal'Molin et al. 2010b Chang et al. 2011 Saha, Suthers & Maranas 2011 Nogales et al. 2012 ), at the time of writing, the only comparable experimental flux map of a sufficiently large-scale metabolic network is for the cyanobacterium Synechocystis (Young et al. 2011 ). Although the requirement for calculation of fluxes from dynamic labelling patterns is both experimentally and computationally more demanding, there are several groups that have been developing the necessary experimental and analytical tools for flux analysis in leaves in the light (Huege et al. 2007 Hasunuma et al. 2010 Keerberg et al. 2011 Lattanzi et al. 2012 ) and it is likely that flux maps will emerge in due course. Recently, a major step forward towards this goal was made with the publication of an analysis of the dynamic label redistribution of label from 13 CO2 supplied to Arabidopsis rosette leaves, from which a small set of fluxes were calculated (Szecowka et al. 2013 ). This paper establishes the experimental, analytical and mathematical frameworks that will allow a more systematic analysis of metabolic network fluxes in leaves and will facilitate the assessment of FBA for predicting CO2 evolution profiles in the dominant tissues of higher plants.
In summary, it is clear that FBA has the potential to predict fluxes through the CO2-consuming and CO2-generating processes in plant tissues, and based on existing work, it should be capable of predicting how the CO2 evolution profile will change in response to environment. Given the increasing interest in FBA as a tool to examine plant metabolic networks and the acceleration of sequencing of diverse plant genomes, there is every reason to expect that a more sophisticated, species-specific prediction of plant net CO2 evolution could ultimately be incorporated into higher-level ecosystem models.