Resolving Enantiomers of 2‑Hydroxy Acids by Nuclear Magnetic Resonance

Biologically important 2-hydroxy carboxylates such as lactate, malate, and 2-hydroxyglutarate exist in two enantiomeric forms that cannot be distinguished under achiral conditions. The D and L (or R, S) enantiomers have different biological origins and functions, and therefore, there is a need for a simple method for resolving, identifying, and quantifying these enantiomers. We have adapted and improved a chiral derivatization technique for nuclear magnetic resonance (NMR), which needs no chromatography for enantiomer resolution, with greater than 90% overall recovery. This method was developed for 2-hydroxyglutarate (2HG) to produce diastereomers resolvable by column chromatography. We have applied the method to lactate, malate, and 2HG. The limit of quantification was determined to be about 1 nmol for 2HG with coefficients of variation of less than 5%. We also demonstrated the method on an extract of a renal carcinoma bearing an isocitrate dehydrogenase-2 (IDH2) variant that produces copious quantities of 2HG and showed that it is the D enantiomer that was exclusively produced. We also demonstrated in the same experiment that the lactate produced in the same sample was the L enantiomer

Summary of mass spectral analysis in PMF-G disulfide bonding pattern determination.

<p>Mass spectral analyses was performed on the three-disulfide species of PMF-G purified by RP-HPLC. Differential treatment included proteolytic enzyme (Enz; chymotrypsin [C] or AspN [A]), reduction with dithiothreitol (DTT), and alkylation with iodoacetamide (addition of a carboxyamidomethyl (CAM) group). Observed monoisotopic masses were compared to theoretical masses with no free sulfhydryls, and mass shifts used to determine peptide modification. All assignments were confirmed by analysis of the fragmented ion series.</p

Structural Insights into the Evolution of a Sexy Protein: Novel Topology and Restricted Backbone Flexibility in a Hypervariable Pheromone from the Red-Legged Salamander, <i>Plethodon shermani</i>

<div><p>In response to pervasive sexual selection, protein sex pheromones often display rapid mutation and accelerated evolution of corresponding gene sequences. For proteins, the general dogma is that structure is maintained even as sequence or function may rapidly change. This phenomenon is well exemplified by the three-finger protein (TFP) superfamily: a diverse class of vertebrate proteins co-opted for many biological functions – such as components of snake venoms, regulators of the complement system, and coordinators of amphibian limb regeneration. All of the >200 structurally characterized TFPs adopt the namesake “three-finger” topology. In male red-legged salamanders, the TFP pheromone Plethodontid Modulating Factor (PMF) is a hypervariable protein such that, through extensive gene duplication and pervasive sexual selection, individual male salamanders express more than 30 unique isoforms. However, it remained unclear how this accelerated evolution affected the protein structure of PMF. Using LC/MS-MS and multidimensional NMR, we report the 3D structure of the most abundant PMF isoform, PMF-G. The high resolution structural ensemble revealed a highly modified TFP structure, including a unique disulfide bonding pattern and loss of secondary structure, that define a novel protein topology with greater backbone flexibility in the third peptide finger. Sequence comparison, models of molecular evolution, and homology modeling together support that this flexible third finger is the most rapidly evolving segment of PMF. Combined with PMF sequence hypervariability, this structural flexibility may enhance the plasticity of PMF as a chemical signal by permitting potentially thousands of structural conformers. We propose that the flexible third finger plays a critical role in PMF:receptor interactions. As female receptors co-evolve, this flexibility may allow PMF to still bind its receptor(s) without the immediate need for complementary mutations. Consequently, this unique adaptation may establish new paradigms for how receptor:ligand pairs co-evolve, in particular with respect to sexual conflict.</p></div

DEK overexpression in HNSCC cells increases glycolytic end products and TCA cycle intermediates.

<p>(A-B) PCA scores plot of C-SCC1 generated from normalized bucket intensities showing separation by metabolite presence between C-SCC1 R780 and R-DEK cells (A) and media (B). (C-D) Fold change in bucket intensities for each significantly changed metabolite between R780 and R-DEK cells (C) and media (D) arranged by magnitude of change. Error bars represent the SEM in fold change of the R780-DEK samples relative to the mean of the R780 controls. (E-F) The bucket intensity of metabolites in the unconditioned media (dashed red line) were compared to R780 (grey) and R-DEK (black) conditioned media samples to identify metabolites decreased (E) and those increased compared to control unconditioned media (F). (G) Metabolic pathway analysis highlighting metabolites identified by NMR that are differently regulated upon DEK overexpression. The metabolites identified are involved in various metabolic pathways including choline metabolism, protein and nucleotide synthesis, cellular redox state, aerobic glycolysis, and the TCA cycle. Abbreviations: p = phospho, Asn = asparagine, Phe = phenylalanine, GPC = glycerophosphocholine, NAD⁺ = nicotinamide adenine dinucleotide, Myo-ino = myo-inositol, Glu = glutamate, Gln = glutamine, α-keto = α-ketoglutarate, 2-OIC = 2-oxoisocaproate, Poly-Glu = polyglutamate, DMA = dimethylamine.</p

Surface models of PMF-G.

<p>(a) Alignment of PMF-G with a representative TFP (1IQ9), color coded by residue type (acidic, red; basic, blue; hydrophilic, purple; nonpolar, green; cysteine, yellow), with disulfide bonds denoted by the black lines; (b) secondary structure schematic comparing PMF-G (left) and a representative TFP (right; 1IQ9); (c) backbone model of PMF-G (20 lowest-energy conformers) with partially transparent surface rendering (both color coded N- to C-terminus, blue to red); (d) full surface rendering of PMF-G color coded by residue type (same color code as a); (e) surface rendering of 1IQ9 (same color code as a).</p

Measurements of structural and sequence variability in PMF.

<p>(a) Backbone amide (H_N) exchange H/D exchange rates measured by half life (in hours), with proline residues omitted; (b) Root mean squared fluctation (RMSF) per residue in the PMF structural ensemble (blue) and predicted from the random coil index (red); (c) spectral density functions at 0, ω_N, and ω_H; J(0) is sensitive to fast (ns) and slow (µs-ms) motions, J(ω_N) to motiions on time scales faster than (1/ω_N = 2 ns), and J(ω_H) to motions faster than ¹H (1/ω_H = 0.2 ns); (d) Sequence variability (Shannon entropy index) at each residue measured for all Class I PMFs, shaded according to likelihood of positive selection at each position (red p<0.01, orange p<0.05; yellow = neutral selection). Seven out of the nine non-conserved amino acids in finger 3 display signatures of positive selection, suggesting combined structural flexibility and rapid evolution in this region.</p

NMR-derived structural ensemble of PMF-G.

<p>(a) Backbone model of PMF-G with the twenty lowest-energy conformers, color coded from N- to C-terminus (blue to red), and peptide finger numbers denoted (1–3); (b) disulfide bonds in PMF-G from underside view (same color scheme as a) and a representative TFP (1IQ9, carbons in magenta, sulfurs in green).</p

Rates of molecular evolution on PMF-G.

<p>Putty model of PMF-G, with backbone width proportional to residue variability (Shannon-Weaver index in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096975#pone-0096975-g004" target="_blank">Figure 4D</a>), and color-coded according to the likely mode of molecular evolution (based on data from Wilburn et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096975#pone.0096975-Wilburn1" target="_blank">[20]</a>; backbone, black; purifying selection, blue; neutral selection, yellow; positive selection, 0.01≤p<0.05, orange; positive selection, p<0.01, red).</p

DEK overexpression increases glycolysis and the maximum rate of oxidative phosphorylation in NIKS and C-SCC1 cells.

<p>Seahorse XF24 Extracellular Flux Analyzer experiments using the mitochondrial stress test. (A-D) Quantification of oxygen consumption rate (OCR) measurements from 4 replicates of NIKS (A) and C-SCC1 (C) R780 and R-DEK samples taken three times at baseline and after treatment with the following pharmacological inhibitors of metabolism: oligomycin (ATP synthetase inhibitor), FCCP (and uncoupling agent), and rotenone and antimycin A (electron transport chain inhibitors). (B and D) Calculations from the mitochondrial stress test were as follows: non-mitochondrial respiration = oxygen consumed after treatment with electron transport chain inhibitors (rotenone and antimycin A). Basal OCR = baseline OCR minus non-mitochondrial respiration. ATP production = baseline OCR minus OCR after ATP synthetase inhibitor (oligomycin). Spare capacity = max OCR minus baseline OCR. Proton leak = OCR after oligomycin treatment minus OCR with electron transport chain inhibitors (rotenone and antimycin A). (E-H) The extracellular acidification rate (ECAR) was quantified for NIKS (E-F) and C-SCC1 (G-H) transduced with R780 or R-DEK. Quantification of glycolysis was calculated for baseline, maximum potential, and reserve potential in NIKS (F) and C-SCC1 (H). Reserve ECAR was calculated by subtracting baseline ECAR from maximum ECAR (oligomycin treated). Error bars represent the SEM of the 4 replicates. Statistical significance was determined using a t-test. Where indicated *<i>P</i> ≤ 0.05, **<i>P</i> ≤ 0.01, and ***<i>P</i> ≤ 0.001.</p

DEK overexpression drives glycolytic and glutathione pathways in NIKS and C-SCC1 cells, but uniquely stimulations TCA cycle intermediate accumulation in C-SCC1 cells.

<p>(A) Fold change in R-DEK compared to R780 bucket intensities for metabolites identified in NIKS (grey bars) and C-SCC1 cells (black bars). Metabolite names labelled in blue are decreased and those in red are increased in both cell lines. Metabolites labelled in green are either jointly but differentially regulated or only regulated in one cell line; in either case, the metabolite is higher in the C-SCC1 cells and/or lower in the NIKS with DEK overexpression. (B-E) Metabolites regulated in one or both normal/cancer cells are indicated within known associated metabolic pathways. (B) Metabolite products of aerobic glycolysis are increased in both cell lines (red) with an increase in glucose uptake in the R-DEK NIKS. (C) Glutathione and aspartate are decreased in both cell lines while metabolites surrounding this pathway are decreased in NIKS (green) and increased in C-SCC1 cells. (D) Choline and p-choline are decreased in both cell lines while GPC was differentially regulated. (E) Metabolites in and surrounding the TCA cycle are increased in the C-SCC1 cells (green) and either unchanged or decreased in the NIKS. Abbreviations: p = phospho, GPC = glycerophosphocholine, NAD+ = nicotinamide adenine dinucleotide, α-keto = α-ketoglutarate, OAA = oxaloacetate, ROS = reactive oxygen species.</p