Biochemical, kinetic, and spectroscopic characterization of Ruegeria pomeroyi DddW - A mononuclear iron-dependent DMSP lyase

The osmolyte dimethylsulfoniopropionate (DMSP) is a key nutrient in marine environments and its catabolism by bacteria through enzymes known as DMSP lyases generates dimethylsulfide (DMS), a gas of importance in climate regulation, the sulfur cycle, and signaling to higher organisms. Despite the environmental significance of DMSP lyases, little is known about how they function at the mechanistic level. In this study we biochemically characterize DddW, a DMSP lyase from the model roseobacter Ruegeria pomeroyi DSS-3. DddW is a 16.9 kDa enzyme that contains a C-terminal cupin domain and liberates acrylate, a proton, and DMS from the DMSP substrate. Our studies show that as-purified DddW is a metalloenzyme, like the DddQ and DddP DMSP lyases, but contains an iron cofactor. The metal cofactor is essential for DddW DMSP lyase activity since addition of the metal chelator EDTA abolishes its enzymatic activity, as do substitution mutations of key metal-binding residues in the cupin motif (His81, His83, Glu87, and His121). Measurements of metal binding affinity and catalytic activity indicate that Fe(II) is most likely the preferred catalytic metal ion with a nanomolar binding affinity. Stoichiometry studies suggest DddW requires one Fe(II) per monomer. Electronic absorption and electron paramagnetic resonance (EPR) studies show an interaction between NO and Fe(II)-DddW, with NO binding to the EPR silent Fe(II) site giving rise to an EPR active species (g = 4.29, 3.95, 2.00). The change in the rhombicity of the EPR signal is observed in the presence of DMSP, indicating that substrate binds to the iron site without displacing bound NO. This work provides insight into the mechanism of DMSP cleavage catalyzed by DddW

Genome modeling system: A knowledge management platform for genomics

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms

Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

We expanded GWAS discovery for type 2 diabetes (T2D) by combining data from 898,130 European-descent individuals (9% cases), after imputation to high-density reference panels. With these data, we (i) extend the inventory of T2D-risk variants (243 loci,135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency 2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence).Peer reviewe

Proposed mechanisms for the mononuclear iron dependent DMSP lyase, DddW.

<p>DddW binds to Fe(II) cofactor to which the substrate can coordinate in either monodentate or bidentate modes. (A) His81 can act as a nucleophile to remove a hydrogen atom from the α-carbon of DMSP to form acrylate. (B) A hypothetical water molecule can be activated by His81, which then acts as a nucleophile in initiating catalysis. (C) Tyr89 located near the active site can initiate the elimination reaction cleaving DMSP.</p

The pH dependence of DddW lyase activity.

<p>The optimal pH was determined by comparing the initial velocities (V_i) of reactions containing 2 μM apo-DddW, 2 μM Fe(II), and 10 mM DMSP in varying buffer solutions. The buffers used are as follows: 50 mM MES 20 mM NaCl (pH 5.5, 6.0, 6.5), 50 mM HEPES 20 mM NaCl (pH 7.0, 7.5, 8.0), 50 mM Tris-HCl 20 mM NaCl (pH 8.5, 9.0).</p

Stoichiometry of Fe(II) binding to DddW.

<p>2 μM apo-DddW (under tight-binding conditions) was titrated with increasing concentrations of Fe(NH₄)₂(SO₄)₂ and the fluorescence intensity was monitored. The titration data were analyzed by nonlinear curve fitting using Eq (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127288#pone.0127288.e003" target="_blank">3</a>) to produce the solid line. Upon data fitting, the stoichiometric ratio of Fe(II) to DddW monomer was determined to be 1:1.</p

Spectral properties of Fe(II)-bound DddW.

<p>(A) UV-visible spectra of the reaction of as-isolated DddW in the presence of Fe(II) and Cu(II). All spectra with Fe(II) had an enzyme concentration of 370 μM. Trace in black, apo-DddW, green, apo-DddW in presence of 370 μM Fe(II) red, apo-DddW+Fe(II) after bubbling with NO gas. The absorption maximum is at 340 nm with a shoulder at 430 nm. Inset: Spectrum of 1 mM apo-DddW in the presence of Cu(II). The spectral feature at 550 nm is due to a charge transfer transition of DddW with Cu(II). (B) EPR spectra of: (top) 18 μM apo-DddW with Fe(II); (bottom) Fe(II)-DddW in the presence of 25 mM DMSP. The spectra were collected at microwave frequency, 9.43 GHz; receiver gain, 2 x 104; modulation frequency, 100 kHz; temperature, 4 K; microwave power, 200 microwatts; 83.89 s sweep time, and 16 scans.</p

Dependence of initial velocity (V_i) of DddW catalyzed lyase reaction on DMSP concentrations in the presence of Fe(II) and Mn(II).

<p>Apo-DddW (2 μM) was mixed with an equimolar amount of Fe(NH₄)₂(SO₄)₂ and 300μM MnCl₂. To this reaction mixture, varying concentrations (0.5–35 mM) of DMSP was added. The reactions were monitored at 205 nm. The data were fit to the Michaelis-Menten equation. The kinetic parameters are as follows. With Fe(II): V_max = 36.50 ± 1.27 μM/s; k_cat = 18.25 s^-1; <i>K</i>_m = 8.68 ± 0.73 mM; <i>k</i>_cat/<i>K</i>_m = 2.10 x 10³ M^-1s^-1; With Mn(II): V_max = 34.66 ± 1.64 μM/s; k_cat = 17.33 s^-1; <i>K</i>_m = 4.50 ± 0.75 mM; <i>k</i>_cat/<i>K</i>_m = 3.85x10³ M^-1s^-1.</p

Cupin motifs and metal binding residues of DddW.

<p>(A) Sequence alignment of cupin regions of selected DddW, DddQ and DddL proteins using sequences deposited at NCBI and CLUSTAL 2.1 for the alignment. The two conserved cupin motifs 1 (GX₅HXHX_3,4EX₆G) and 2 (GX₅PXGX₂HX₃N), containing residues that bind metal ions and are catalytically important are highlighted in green. Tyr residues playing catalytic role in <i>Ruegeria lacuscaerulensis</i> DddQ are marked cyan and other conserved residues in the cupin motifs are colored yellow. The sequences are from: W1 = DddW, <i>Ruegeria pomeroyi</i> DSS-3 (SPO0453); W2 = DddW, <i>Roseobacter sp</i>. MED193, (MED193_09710); Q1 = DddQ, <i>Ruegeria pomeroyi</i> DSS-3 (SPO1596); Q2 = DddQ, <i>Ruegeria lacuscaerulensis</i> (ITI-1157); L1 = DddL, <i>Sulfitobacter sp</i>. EE-36 (EE36_11918); L2 = DddL, <i>Rhodobacter sphaeroides</i> 2.4.1 (RSP_1433); L3 = DddL, <i>Roseibacterium elongatum</i> DSM 19469 (roselon_02436); L4 = DddL, <i>Caenispirillum salinarum</i> (C882_2645). (B) Homology model of <i>Ruegeria pomeroyi</i> DddW (grey) (generated using Phyre 2 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127288#pone.0127288.ref052" target="_blank">52</a>]) superimposed on the Zn(II)-bound structure of <i>Ruegeria lacuscaerulensis</i> DddQ (cyan) (PDB 4LA2). The homology model of DddW shows the catalytic residues H81, H83, E87, and H121. Most of these residues of DddW (H83, E87, and H121) superimpose well on the zinc-coordinating DddQ residues (H125, E129, and H163). While Tyr usually is not involved in metal ion binding in cupin proteins, the DddQ structure shows a Zn-coordinated Tyr residue (Tyr131) and this Tyr superimposes on Tyr89 of DddW. The side chain residues are shown in ball and stick with oxygens in red, nitrogens in blue, zinc in slate, and carbons are similar to protein backbone.</p