Ag(III)···Ag(III) Argentophilic Interaction in a Cofacial Corrole Dyad

Metallophilic interactions between closed-shell metal centers are exemplified by d10 ions, with Au(I) aurophilic interactions as the archetype. Such an interaction extends to d8 species, and examples involving Au(III) are prevalent. Conversely, Ag(III) argentophilic interactions are uncommon. Here, we identify argentophilic interactions in silver corroles, which are authentic Ag(III) species. The crystal structure of a monomeric silver corrole is a dimer in the solid state, and the macrocycle exhibits an atypical domed conformation. In order to evaluate whether this represents an authentic metallophilic interaction or a crystal-packing artifact, the analogous cofacial or “pacman” corrole was prepared. The conformation of the monomer was recapitulated in the silver pacman corrole, exhibiting a short 3.67 Å distance between metal centers and a significant compression of the xanthene backbone. Theoretical calculations support the presence of a rare Ag(III)···Ag(III) argentophilic interaction in the pacman complex

Solution Structure and Molecular Determinants of Hemoglobin Binding of the First NEAT Domain of IsdB in Staphylococcus aureus

The human pathogen Staphylococcus aureus acquires heme iron from hemoglobin (Hb) via the action of a series of iron-regulated surface determinant (Isd) proteins. The cell wall anchored IsdB protein is recognized as the predominant Hb receptor, and is comprised of two NEAr transporter (NEAT) domains that act in concert to bind, extract, and transfer heme from Hb to downstream Isd proteins. Structural details of the NEAT 2 domain of IsdB have been investigated, but the molecular coordination between NEAT 2 and NEAT 1 to extract heme from hemoglobin has yet to be characterized. To obtain a more complete understanding of IsdB structure and function, we have solved the 3D solution structure of the NEAT 1 domain of IsdB (IsdB^N1) spanning residues 125–272 of the full-length protein by NMR. The structure reveals a canonical NEAT domain fold and has particular structural similarity to the NEAT 1 and NEAT 2 domains of IsdH, which also interact with Hb. IsdB^N1 is also comprised of a short N-terminal helix, which has not been previously observed in other NEAT domain structures. Interestingly, the Hb binding region (loop 2 of IsdB^N1) is disordered in solution. Analysis of Hb binding demonstrates that IsdB^N1 can bind metHb weakly and the affinity of this interaction is further increased by the presence of IsdB linker domain. IsdB^N1 loop 2 variants reveal that phenylalanine 164 (F164) of IsdB is necessary for Hb binding and rapid heme transfer from metHb to IsdB. Together, these findings provide a structural role for IsdB^N1 in enhancing the rate of extraction of metHb heme by the IsdB NEAT 2 domain

Variable importance in projection (VIP) plot displays the top 15 most important metabolite features identified by PLS-DA.

<p>Colored boxes on right indicate relative concentration of corresponding metabolite for samples biopsied from the bottom and top of chronic wounds. VIP is a weighted sum of squares of the PLS-DA loadings taking into account the amount of explained Y-variable in each dimension.</p

Details of clinical subjects and chronic pressure ulcer wounds.

<p>*CP = cerebral palsy,</p><p>^ŧMR = mental retardation,</p><p>^§DVT = deep vein thrombosis,</p><p>^¶HSV = herpes simplex virus,</p><p>** = urinary tract infection</p><p>Details of clinical subjects and chronic pressure ulcer wounds.</p

Mean abundance of major bacterial phyla for samples (n = 8) from clinical pressure ulcer wounds (n = 4).

<p>Mean abundance of major bacterial phyla for samples (n = 8) from clinical pressure ulcer wounds (n = 4).</p

Putative metabolic pathways associated with the wound environment of chronic pressure ulcers.

<p>Significantly contributing pathway nodes include glyoxylate and dicarboxylate metabolism. Highlighted metabolites indicated hits from the metabolic profiling and are coded according to p-value. Pathway maps are generated using the KEGG reference map (<a href="http://www.kegg.jp/kegg/pathway.html" target="_blank">http://www.kegg.jp/kegg/pathway.html</a>).</p

Correlation between the bacterial microbiome and metabolome in chronic pressure ulcer wounds.

<p>Nonparametric Spearman rank correlation was used to quantify the association between the relative abundance of bacterial genera and metabolite concentration in chronic pressure ulcer wounds. Major genera observed across wound samples are shown (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126735#pone.0126735.g003" target="_blank">Fig 3</a>) with phylum Firmicutes (purple), phylum Proteobacteria (red), and phylum Actinobacteria (green) clustered together. Correlation coefficient threshold of significance is set at 0.700 and p-values ≤ 0.05.</p

Putative metabolic pathways associated with the wound environment of chronic pressure ulcers.

<p>Metabolome summary of pathway analysis. Mapping of the relative concentration of metabolites to the metabolome indicates impact contribution of metabolic pathways. Node color indicates significance based on p-value and node size indicates significance of pathway impact. Significantly impacted pathways include (a) inositol phosphate metabolism, (b) glyoxylate and dicarboxylate metabolism, (c) alanine, aspartate, and glutamate metabolism, (d) arginine and proline metabolism, (e) glycine, serine, and threonine metabolism, (f) pyruvate metabolism, and (g) citric acid cycle (TCA). The metabolic pathways are arranged according to the scores from the enrichment analysis (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126735#pone.0126735.g004" target="_blank">Fig 4</a>) (Y-axis) and from the topology analysis (X-axis).</p

Box-whisker plot graphs of metabolites with significant concentration differences profiled from top and bottom sections of chronic pressure ulcer wounds.

<p>Important metabolites were selected by volcano plot which is a combination of fold change analysis (FC≥2.0) and t-test analysis (p≤0.05). Box-whisker plots are calculated from normalized concentrations (y-axis).</p

Scores plot of 3D PLS-DA statistically clusters chronic wound samples based on depth of biopsy.

<p>Red triangles indicates sections from the bottom of the wound biopsy and green crosses indicates sections from the top of the wound biopsy. 49.6% of the variance observed in the matrix of metabolite profiles is explained by the first 3 components.</p