Solution scattering study of the <i>Bacillus subtilis</i> PgdS enzyme involved in poly-γ-glutamic acids degradation

<div><p>The PgdS enzyme is a poly-γ-glutamic (γ-PGA) hydrolase, which has potential application for a controllable degradation of γ-PGA by enzymatic depolymerization; however, the structure of PgdS is still unknown. Here, to study in detail the full-length PgdS structure, we analyze the low-resolution architecture of PgdS hydrolase from <i>Bacillus subtilis</i> in solution using small angle X-ray scattering (SAXS) method. Combining with other methods, like dynamic light scattering and mutagenesis analyses, a model for the full length structure and the possible substrate delivery route of PgdS are proposed. The results will provide useful hints for future investigations into the mechanisms of γ-PGA degradation by the PgdS hydrolase and may provide valuable practical information.</p></div

Possible substrate delivery route.

<p>(A) Electrostatic potential properties of PgdS, which are contoured over the range ± 5 kT/e using DelPhi [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195355#pone.0195355.ref038" target="_blank">38</a>] within the PyMOL (<a href="http://www.pymol.sourceforge.net/" target="_blank">http://www.pymol.sourceforge.net/</a>) software (blue represents a positively charged surface region and red represents a negatively charged surface region). The green circles signify the interface between domain 2 and domain 3. (B) The mutation sites in the positively charged surface at the junction of domain 2 and domain 3. (C) Activity of PgdS wild type and mutants.</p

Overall fold of three domains of PgdS from <i>B</i>. <i>subtilis</i> as predicted using secondary and tertiary structure modeling.

<p>The α-helices and β-strands of three domains are colored with different colors (upper). The catalytic core of domain 2 is showed in the lower panel.</p

<i>R</i>_<i>g</i> and <i>D</i>_<i>max</i> distributions of the optimized ensembles for PgdS at various pH analyzed by program EOM.

<p>(A), (B) and (C) represent the distributions of <i>R</i>_<i>g</i> (<i>left</i>) and <i>D</i>_<i>max</i> (<i>right</i>) for PgdS at pH 5.0, pH 6.0 and pH 8.0, respectively.</p

Model reconstructions of PgdS in solution.

<p>(A) Model reconstructions of PgdS in solution. Right: <i>ab initio</i> models from DAMMIF model; left, superposition of DAMMIF and CORAL model. The missing loops are represented as dummy residues colored cyan. Two orientations are shown. The domain 1, 2 and 3 are colored in green, blue and yellow, respectively. The relative positions of the N/C terminal and the functional catalytic core are also labeled. (B) Superimposed of two models of PgdS in a compact state (pH 5.0) and in an extended state (pH 8.0). The domain 2 and domain 3 are colored in grey, whereas domain 1 are colored in yellow (compact state) or blue (extended state).</p

SAXS analyses of PgdS.

<p>SAXS scattering profiles and model reconstructions of PgdS at pH 6.0 (A), 5.0 (B) and 8.0 (C): black circle—experimental intensity; red line—smooth curve back transformed from the <i>p</i>(<i>r</i>) and extrapolated to zero scattering angle; green line—scattering pattern computed from the DAMMIF model; blue line—scattering pattern computed from the SASREF model; cyan line—scattering pattern computed from the CORAL model; magenta line—averaged scattering pattern calculated from the optimized models generated by EOM; lower left panel—distance distribution function <i>p(r)</i> for PgdS in solution. (D) normalized distance distribution functions for PgdS at pH 5.0 (black), pH 6.0 (green) and pH 8.0 (red).</p

Multiple sequence alignment of three domains of PgdS and other NlpC/P60 domains.

<p>Three domains of PgdS, the NlpC/P60 catalytic domains of LytF, LytE and CwlS from <i>B</i>. <i>subtilis</i>, and putative lipoprotein Spr from <i>E</i>. <i>coli</i> (UniProt identifiers, <a href="http://www.uniprot.org/uniprot/P96740" target="_blank">P96740</a>, <a href="http://www.uniprot.org/uniprot/P54421" target="_blank">P54421</a>, <a href="http://www.uniprot.org/uniprot/O07532" target="_blank">O07532</a>, <a href="http://www.uniprot.org/uniprot/O31852" target="_blank">O31852</a> and P0AFV4) are aligned with MUSCLE [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195355#pone.0195355.ref027" target="_blank">27</a>] and edited by hand to match the structural similarity where appropriate by using ALINE [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195355#pone.0195355.ref028" target="_blank">28</a>]. Identical and similar residues are highlighted in black and grey, respectivey. The secondary structure elements base on the domain 2 of PgdS, α-helices and β-strands are marked by red pillar and blue arrow, respectively. The strictly conserved cysteine/histidine/glutamine (asparagine or histidine) catalytic triad are marked with red triangles. Three conserved residues that contribute to the formation of catalytic core are also marked with red circles.</p

A Genome-Wide Regulator–DNA Interaction Network in the Human Pathogen <i>Mycobacterium tuberculosis</i> H37Rv

Transcription regulation translates static genome information to dynamic cell behaviors, making it central to understand how cells interact with and adapt to their environment. However, only a limited number of transcription regulators and their target genes have been identified in the pathogen <i>Mycobacterium tuberculosis</i>, which has greatly impeded our understanding of its pathogenesis and virulence. In this study, we constructed a genome-wide transcription regulatory network of <i>M. tuberculosis</i> H37Rv using a high-throughput bacterial one-hybrid technique. A transcription factor skeleton network was derived on the basis of the identification of more than 5400 protein–DNA interactions. Our findings further highlight the regulatory mechanism of the mammalian cell entry 1 (<i>mce1</i>) module, which includes <i>mce1R</i> and the <i>mce1</i> operon. Mce1R was linked to global negative regulation of cell growth, but was found to be positively regulated by the dormancy response regulator DevR. Expression of the <i>mce1</i> operon was shown to be negatively regulated by the virulence regulator PhoP. These findings provide important new insights into the molecular mechanisms of several <i>mce1</i> module-related hypervirulence phenotypes of the pathogen. Furthermore, a model of <i>mce1</i> module-centered signal circuit for dormancy regulation in <i>M. tuberculosis</i> is proposed and discussed

A Genome-Wide Regulator–DNA Interaction Network in the Human Pathogen <i>Mycobacterium tuberculosis</i> H37Rv

Transcription regulation translates static genome information to dynamic cell behaviors, making it central to understand how cells interact with and adapt to their environment. However, only a limited number of transcription regulators and their target genes have been identified in the pathogen <i>Mycobacterium tuberculosis</i>, which has greatly impeded our understanding of its pathogenesis and virulence. In this study, we constructed a genome-wide transcription regulatory network of <i>M. tuberculosis</i> H37Rv using a high-throughput bacterial one-hybrid technique. A transcription factor skeleton network was derived on the basis of the identification of more than 5400 protein–DNA interactions. Our findings further highlight the regulatory mechanism of the mammalian cell entry 1 (<i>mce1</i>) module, which includes <i>mce1R</i> and the <i>mce1</i> operon. Mce1R was linked to global negative regulation of cell growth, but was found to be positively regulated by the dormancy response regulator DevR. Expression of the <i>mce1</i> operon was shown to be negatively regulated by the virulence regulator PhoP. These findings provide important new insights into the molecular mechanisms of several <i>mce1</i> module-related hypervirulence phenotypes of the pathogen. Furthermore, a model of <i>mce1</i> module-centered signal circuit for dormancy regulation in <i>M. tuberculosis</i> is proposed and discussed

A Genome-Wide Regulator–DNA Interaction Network in the Human Pathogen <i>Mycobacterium tuberculosis</i> H37Rv

Transcription regulation translates static genome information to dynamic cell behaviors, making it central to understand how cells interact with and adapt to their environment. However, only a limited number of transcription regulators and their target genes have been identified in the pathogen <i>Mycobacterium tuberculosis</i>, which has greatly impeded our understanding of its pathogenesis and virulence. In this study, we constructed a genome-wide transcription regulatory network of <i>M. tuberculosis</i> H37Rv using a high-throughput bacterial one-hybrid technique. A transcription factor skeleton network was derived on the basis of the identification of more than 5400 protein–DNA interactions. Our findings further highlight the regulatory mechanism of the mammalian cell entry 1 (<i>mce1</i>) module, which includes <i>mce1R</i> and the <i>mce1</i> operon. Mce1R was linked to global negative regulation of cell growth, but was found to be positively regulated by the dormancy response regulator DevR. Expression of the <i>mce1</i> operon was shown to be negatively regulated by the virulence regulator PhoP. These findings provide important new insights into the molecular mechanisms of several <i>mce1</i> module-related hypervirulence phenotypes of the pathogen. Furthermore, a model of <i>mce1</i> module-centered signal circuit for dormancy regulation in <i>M. tuberculosis</i> is proposed and discussed