Genome-scale reconstruction of Paenarthrobacter aurescens TC1 metabolic model towards the study of atrazine bioremediation

Abstract Atrazine is an herbicide and a pollutant of great environmental concern that is naturally biodegraded by microbial communities. Paenarthrobacter aurescens TC1 is one of the most studied degraders of this herbicide. Here, we developed a genome scale metabolic model for P. aurescens TC1, iRZ1179, to study the atrazine degradation process at organism level. Constraint based flux balance analysis and time dependent simulations were used to explore the organism’s phenotypic landscape. Simulations aimed at designing media optimized for supporting growth and enhancing degradation, by passing the need in strain design via genetic modifications. Growth and degradation simulations were carried with more than 100 compounds consumed by P. aurescens TC1. In vitro validation confirmed the predicted classification of different compounds as efficient, moderate or poor stimulators of growth. Simulations successfully captured previous reports on the use of glucose and phosphate as bio-stimulators of atrazine degradation, supported by in vitro validation. Model predictions can go beyond supplementing the medium with a single compound and can predict the growth outcomes for higher complexity combinations. Hence, the analysis demonstrates that the exhaustive power of the genome scale metabolic reconstruction allows capturing complexities that are beyond common biochemical expertise and knowledge and further support the importance of computational platforms for the educated design of complex media. The model presented here can potentially serve as a predictive tool towards achieving optimal biodegradation efficiencies and for the development of ecologically friendly solutions for pollutant degradation

Nano-Drugs Based on Nano Sterically Stabilized Liposomes for the Treatment of Inflammatory Neurodegenerative Diseases

<div><p>The present study shows the advantages of liposome-based nano-drugs as a novel strategy of delivering active pharmaceutical ingredients for treatment of neurodegenerative diseases that involve neuroinflammation. We used the most common animal model for multiple sclerosis (MS), mice experimental autoimmune encephalomyelitis (EAE). The main challenges to overcome are the drugs’ unfavorable pharmacokinetics and biodistribution, which result in inadequate therapeutic efficacy and in drug toxicity (due to high and repeated dosage). We designed two different liposomal nano-drugs, i.e., nano sterically stabilized liposomes (NSSL), remote loaded with: (a) a “water-soluble” amphipathic weak acid glucocorticosteroid prodrug, methylprednisolone hemisuccinate (MPS) or (b) the amphipathic weak base nitroxide, Tempamine (TMN). For the NSSL-MPS we also compared the effect of passive targeting alone and of active targeting based on short peptide fragments of ApoE or of β-amyloid. Our results clearly show that for NSSL-MPS, active targeting is not superior to passive targeting. For the NSSL-MPS and the NSSL-TMN it was demonstrated that these nano-drugs ameliorate the clinical signs and the pathology of EAE. We have further investigated the MPS nano-drug’s therapeutic efficacy and its mechanism of action in both the acute and the adoptive transfer EAE models, as well as optimizing the perfomance of the TMN nano-drug. The highly efficacious anti-inflammatory therapeutic feature of these two nano-drugs meets the criteria of disease-modifying drugs and supports further development and evaluation of these nano-drugs as potential therapeutic agents for diseases with an inflammatory component.</p></div

Small angle X-ray scattering (SAXS) measurements of NSSL-TMN.

<p><b>(A)</b> Radially integrated background-subtracted scattering data (symbols) of DMPC:DPPC NSSL with and without drug, at 4 and 37°C, as indicated in the figure. Note that the curves are shifted in the intensity axis only for clarity of presentation. The solid curves are the corresponding form-factor models of a stack of infinite slabs with a Gaussian electron density profile along the vertical direction. <b>(B)</b> The electron density profiles of the DMPC:DPPC NSSL bilayers (with and without drug at 4 and 37°C) along the normal direction. The density profiles are obtained by fitting the scattering data to the models (see A) with the software X+, choosing a Gaussian electron density profile for the liposome membrane [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130442#pone.0130442.ref040" target="_blank">40</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130442#pone.0130442.ref041" target="_blank">41</a>]. The profile is almost symmetric and very slightly affected by the temperature or the presence of the drug. The arrows point to the profile of the inner and outer PEG layers. <b>(C)</b> The integrated scattering patterns as a function of the magnitude of the scattering vector, q, for EPC liposomes. Note that the curves are shifted in the intensity axis for clarity of presentation. The scattering curves of the EPC NSSL with and without drug, at 4 and 37°C are very similar. These curves are analyzed using the software X+, as in (A). The liposome bilayer is described by a Gaussian electron density profile. <b>(D)</b> The electron density profile in the direction normal to the membrane, calculated using the software X+, is presented for EPC NSSL, with and without drug at 4 and 37°C. The density profile of the membrane is almost unaffected by the temperature or the presence of the drug. Notice that this profile is asymmetric, suggesting that the inner and the outer PEG layers (pointed by an arrow) of the liposome are different.</p

Nano-Drugs Based on Nano Sterically Stabilized Liposomes for the Treatment of Inflammatory Neurodegenerative Diseases - Fig 1

<p><b>(A)</b> DSC measurements. Samples of SUVs (DMPC:DPPC:Chol:PEG-DSPE, DMPC:DPPC:PEG-DSPE, DMPC:DPPC, DMPC:PEG-DSPE, DPPC:PEG-DSPE, DMPC, DPPC) in saline, and saline in the reference cell, were scanned in the range 10°-80°C, at the heating rate of 1°C/min. <b>(B)</b> Zooming in: Samples of SUVs DMPC:DPPC:Chol:PEG-DSPE, DMPC:DPPC:PEG-DSPE.</p

Comparison of the therapeutic efficacy of EPC-based NSSL-TMN and DMPC:DPPC-based NSSL-TMN in acute EAE mice model.

<p>^a Significant difference from the control group P<0.0001</p><p>^b Significant difference from the EPC NSSL-TMN treated group P<0.001.</p><p>Comparison of the therapeutic efficacy of EPC-based NSSL-TMN and DMPC:DPPC-based NSSL-TMN in acute EAE mice model.</p

Comparison of the therapeutic efficacy of passively targeted NSSL-MPS and actively targeted peptide conjugated NSSL-MPS in the acute EAE mice model.

<p>^a Significant difference from the control group P<0.00001</p><p>^b Significant difference from the control group P<0.001</p><p>^c Significant difference from the β-amyloid NSSL-MPS group P<0.0005</p><p>^d Significant difference from the β-amyloid NSSL-MPS group P<0.0001</p><p>^e Significant difference from the control group P<0.005</p><p>^f Significant difference from the control group P<0.0001.</p><p>Comparison of the therapeutic efficacy of passively targeted NSSL-MPS and actively targeted peptide conjugated NSSL-MPS in the acute EAE mice model.</p

Comparison of the therapeutic efficacy of NSSL-MPS and free MPS in the adoptive transfer EAE mice model.

<p>^a Significant difference from the control group P<0.000001</p><p>^b Significant difference from the NSSL-MPS group P<0.0005</p><p>^c Significant difference from the control group P<0.00005</p><p>^d Significant difference from the NSSL-MPS group P<0.005.</p><p>Comparison of the therapeutic efficacy of NSSL-MPS and free MPS in the adoptive transfer EAE mice model.</p