Histomorphological evaluation of maternal and neonatal distal airspaces after maternal intake of nanoparticulate titanium dioxide: an experimental study in Wistar rats

This study was performed to determine the histomorphological alterations occurring in maternal and neonatal pulmonary distal airspaces of Wistar rats after maternal administration of titanium dioxide nanoparticles (TiO₂ NPs). Thirty adult pregnant rats (150–250 g) and their offspring were used in this study. Pregnant rats were randomly divided into control (n = 15) and TiO₂ NP-treated (n = 15) groups. A suspension of TiO₂ NPs in phosphate-buffered saline was given orally to the treated group (0.1 ml/10 g body weight once daily) from days 6 to 12 of gestation. At term, maternal and neonatal lungs were collected and processed for energy-dispersive X-ray (EDX) and histological analysis. The mean linear intercept (MLI) and airspace wall thickness were measured by a stereological procedure with image analysis to assess alveolarization. EDX analysis demonstrated the presence of TiO₂ in maternal and neonatal lungs. The lungs of TiO₂ NP-treated mothers revealed evidence of pneumocytic apoptosis, abnormal lamellar inclusions, and macrophage and inflammatory cell infiltrates. Significant thinning of alveolar septa was detected in the treated rats (p < 0.001), but the MLI was constant in both groups (p = 0.207). Neonatal lungs from treated mothers revealed deficient septation, thickened mesenchyme between the saccules, pneumocytic apoptosis, atypical lamellar inclusions, and macrophage infiltration. The thickness of the primary septa was significantly increased (p = 0.001) with no significant change in MLI (p = 0.579) compared with the control group. In conclusion, TiO₂ NPs were detected in maternal and neonatal lungs after oral intake by pregnant rats. The pulmonary response manifested as inflammatory lesions and delayed saccular development in neonates.     

RNABindRPlus: A Predictor that Combines Machine Learning and Sequence Homology-Based Methods to Improve the Reliability of Predicted RNA-Binding Residues in Proteins

Protein-RNA interactions are central to essential cellular processes such as protein synthesis and regulation of gene expression and play roles in human infectious and genetic diseases. Reliable identification of protein-RNA interfaces is critical for understanding the structural bases and functional implications of such interactions and for developing effective approaches to rational drug design. Sequence-based computational methods offer a viable, cost-effective way to identify putative RNA-binding residues in RNA-binding proteins. Here we report two novel approaches: (i) HomPRIP, a sequence homology-based method for predicting RNA-binding sites in proteins; (ii) RNABindRPlus, a new method that combines predictions from HomPRIP with those from an optimized Support Vector Machine (SVM) classifier trained on a benchmark dataset of 198 RNA-binding proteins. Although highly reliable, HomPRIP cannot make predictions for the unaligned parts of query proteins and its coverage is limited by the availability of close sequence homologs of the query protein with experimentally determined RNA-binding sites. RNABindRPlus overcomes these limitations. We compared the performance of HomPRIP and RNABindRPlus with that of several state-of-the-art predictors on two test sets, RB44 and RB111. On a subset of proteins for which homologs with experimentally determined interfaces could be reliably identified, HomPRIP outperformed all other methods achieving an MCC of 0.63 on RB44 and 0.83 on RB111. RNABindRPlus was able to predict RNA-binding residues of all proteins in both test sets, achieving an MCC of 0.55 and 0.37, respectively, and outperforming all other methods, including those that make use of structure-derived features of proteins. More importantly, RNABindRPlus outperforms all other methods for any choice of tradeoff between precision and recall. An important advantage of both HomPRIP and RNABindRPlus is that they rely on readily available sequence and sequence-derived features of RNA-binding proteins. A webserver implementation of both methods is freely available at http://einstein.cs.iastate.edu/RNABindRPlus/.     

Network mechanisms of grid cells

One of the major breakthroughs in neuroscience is the emerging understanding of how signals from the external environment are extracted and represented in the primary sensory cortices of the mammalian brain. The operational principles of the rest of the cortex, however, have essentially remained in the dark. The discovery of grid cells, and their functional organization, opens the door to some of the first insights into the workings of the association cortices, at a stage of neural processing where firing properties are shaped not primarily by the nature of incoming sensory signals but rather by internal self-organizing principles. Grid cells are place-modulated neurons whose firing locations define a periodic triangular array overlaid on the entire space available to a moving animal. The unclouded firing pattern of these cells is rare within the association cortices. In this paper, we shall review recent advances in our understanding of the mechanisms of grid-cell formation which suggest that the pattern originates by competitive network interactions, and we shall relate these ideas to new insights regarding the organization of grid cells into functionally segregated modules.     

Effects of Thermal Curing Conditions on Drug Release from Polyvinyl Acetate–Polyvinyl Pyrrolidone Matrices

This study aimed to investigate the effects of dry and humid heat curing on the physical and drug release properties of polyvinyl acetate–polyvinyl pyrrolidone matrices. Both conditions resulted in increased tablet hardness; tablets stored under humid conditions showed high plasticity and deformed during hardness testing. Release from the matrices was dependent on the filler's type and level. Release profiles showed significant changes, as a result of exposure to thermal stress, none of the fillers used stabilized matrices against these changes. Density of neat polymeric compacts increased upon exposure to heat; the effect of humid heat was more evident than dry heat. Thermograms of samples cured under dry heat did not show changes, while those of samples stored under high humidity showed significant enlargement of the dehydration endotherm masking the glass transition of polyvinyl acetate. The change of the physical and release properties of matrices could be explained by the hygroscopic nature of polyvinyl pyrrolidone causing water uptake; absorbed water then acts as a plasticizer of polyvinyl acetate promoting plastic flow, deformation, and coalescence of particles, and altering the matrices internal structure. Results suggest that humid heat is more effective as a curing environment than dry heat for polyvinyl acetate–polyvinyl pyrrolidone matrices.     

A stable analogue of glucose-dependent insulinotropic polypeptide, GIP(LysPAL16), enhances functional differentiation of mouse embryonic stem cells into cells expressing islet-specific genes and hormones

Embryonic stem (ES) cells can be differentiated into insulin-producing cells by conditioning the culture media. However, the number of insulin-expressing cells and amount of insulin released is very low. Glucose-dependent insulinotropic polypeptide (GIP) enhances the growth and differentiation of pancreatic beta-cells. This study examined the potential of the stable analogue GIP(LysPAL16) to enhance the differentiation of mouse ES cells into insulin-producing cells using a five-stage culturing strategy. Semi-quantitative PCR indicated mRNA expression of islet development markers (nestin, Pdx1, Nkx6.1, Oct4), mature pancreatic beta-cell markers (insulin, glucagon, Glut2, Sur1, Kir6.1) and the GIP receptor gene GIP-R in undifferentiated (stage 1) cells, with increasing levels in differentiated stages 4 and 5. IAPP and somatostatin genes were only expressed in differentiated stages. Immunohistochemical studies confirmed the presence of insulin, glucagon, somatostatin and IAPP in differentiated ES cells. After supplementation with GIP(LysPAL16), ES cells at stage 4 released insulin in response to secretagogues and glucose in a concentration-dependent manner, with 35-100% increases in insulin release. Cellular C-peptide content also increased by 45% at stages 4 and 5. We conclude that the stable GIP analogue enhanced differentiation of mouse ES cells towards a phenotype expressing specific beta-cell genes and releasing insulin.     

Evaluation of bio-aerosols at an animal feed manufacturing industry: A case study

Both total and biological particles (totalculturable bacteria, Gram negative bacteria,mold and actinomycetes) were measured at ananimal feed manufacturing industry. Suspendedparticle concentration ranged from 1.72 to2.3 mg m^–3 with a mean value of1.97 mg m^–3. Airborne microorganisms weredetected in lower concentrations than thoseassociated with suspended dust andfeed-materials. Bacterial concentrations weretwo to three times higher than concentrationsof mold and actinomycetes. Bacterialconcentrations averaged4.86 × 10³ cfu m^–3; 2.6 × 10⁴cfu m^–3 and 3.96 × 10⁷ cfu g^–1 inair, associated with suspended dust andfeed-materials, respectively, whereas moldconcentrations averaged 7.33 × 10²cfu m^–3; 1.97 × 10³ cfu m^–3 and7 × 10⁵ cfu g^–1 of the correspondingenvironments, respectively. Enterobacterspp and Klebsiella spp were the mostabundant Gram negative bacteria, whereas Bacillus species. were the most dominant Grampositive bacteria. Aspergllius niger,other Aspergillus species and Penicillium were the dominant mold isolates.Acremonium was only detected in feedmaterials, whereas Aspergillus fumigatuswas only detected in air. The animal feedindustry environment has a significantbio-contamination and many of microorganismsimplicated in respiratory problems weredetected in this environment.     

Polyphosphate Kinase 2: A Novel Determinant of Stress Responses and Pathogenesis in Campylobacter jejuni

Background

Inorganic polyphosphate (poly P) plays an important role in stress tolerance and virulence in many bacteria. PPK1 is the principal enzyme involved in poly P synthesis, while PPK2 uses poly P to generate GTP, a signaling molecule that serves as an alternative energy source and a precursor for various physiological processes. Campylobacter jejuni, an important cause of foodborne gastroenteritis in humans, possesses homologs of both ppk1 and ppk2. ppk1 has been previously shown to impact the pathobiology of C. jejuni.

Methodology/Principal Findings

Here, we demonstrate for the first time that the deletion of ppk2 in C. jejuni resulted in a significant decrease in poly P-dependent GTP synthesis, while displaying an increased intracellular ATP:GTP ratio. The Δppk2 mutant exhibited a significant survival defect under osmotic, nutrient, aerobic, and antimicrobial stresses and displayed an enhanced ability to form static biofilms. However, the Δppk2 mutant was not defective in poly P and ppGpp synthesis suggesting that PPK2-mediated stress tolerance is not ppGpp-mediated. Importantly, the Δppk2 mutant was significantly attenuated in invasion and intracellular survival within human intestinal epithelial cells as well as in chicken colonization.

Conclusions/Significance

Taken together, we have highlighted the role of PPK2 as a novel pathogenicity determinant that is critical for C. jejuni survival, adaptation, and persistence in the host environments. PPK2 is absent in humans and animals; therefore, can serve as a novel target for therapeutic intervention of C. jejuni infections.     

Numerical modeling of ferrous-ion oxidation rate in Acidithiobacillus ferrooxidans ATCC 23270: optimization of culture conditions through statistically designed experiments

Statistically designed experimental strategy has been performed in order to evaluate and optimize nutritional and environmental parameters that affect ferrous ion oxidation rate in Acidithiobacillus ferrooxidans ATCC 23270. Plackett-Burman design was carried out to evaluate efficiently the biological significance of 10 culture conditions influencing ferrous-ion oxidation rate of A. ferrooxidans grown for 5 days in shake-flask batch mode on the newly modified 9-K media. Among ten fermentation factors examined, the most significant variables influencing ferrous-ion oxidation rate were statistically elucidated to be pH and calcium nitrate as positive contributors, whereas trace metals solution and potassium chloride were the most significant negative contributors. The optimal levels of the most significant three nutritional factors were further predicted from a polynomial model created from the data obtained from three level factorial design, a Box-Behnken design. Predicted optimal ferrous-ion oxidation rate Q(Fe2+) was recorded to be 0.148 (g Fe2+/l/hr). On verifying the predicted value, an experiment was performed under optimal predicted conditions and showed an actual experimental Q(Fe2+) of 0.152 g/l/hr, which was 2.7% over the predicted value. Our optimized medium formula gave overall five folds increase in ferrous-ion oxidation rates over the previously published data of standard 9-K medium on batch culture of A. ferrooxidans ATCC 23270 with higher mu(max) (hr(-1)) of 0.177 which was achieved within 75 h incubation in shake-flask culture.     

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

ABSTRACT: BACKGROUND: RNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition 'code' that mediates interactions between proteins and RNA is not yet understood. Success in deciphering this code would dramatically impact the development of new therapeutic strategies for intervening in devastating diseases such as AIDS and cancer. Because of the high cost of experimental determination of protein-RNA interfaces, there is an increasing reliance on statistical machine learning methods for training predictors of RNA-binding residues in proteins. However, because of differences in the choice of datasets, performance measures, and data representations used, it has been difficult to obtain an accurate assessment of the current state of the art in protein-RNA interface prediction. RESULTS: We provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Naive Bayes (NB) and Support Vector Machine (SVM)) using three different data representations in which features are encoded using either sequence- or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix (PSSM)-based representation (PSSMSeq) outperform those that use an amino acid identity based representation (IDSeq) or a smoothed PSSM (SmoPSSMSeq); (ii) Structure-based classifiers that use smoothed PSSM representation (SmoPSSMStr) outperform those that use PSSM (PSSMStr) as well as sequence identity based representation (IDStr). PSSMSeq classifiers, when tested on an independent test set of 44 proteins, achieve performance that is comparable to that of three state-of-the-art structure-based predictors (including those that exploit geometric features) in terms of Matthews Correlation Coefficient (MCC), although the structure-based methods achieve substantially higher Specificity (albeit at the expense of Sensitivity) compared to sequence-based methods. We also find that the expected performance of the classifiers on a residue level can be markedly different from that on a protein level. Our experiments show that the classifiers trained on three different non-redundant protein-RNA interface datasets achieve comparable cross-validation performance. However, we find that the results are significantly affected by differences in the distance threshold used to define interface residues. CONCLUSIONS: Our results demonstrate that protein-RNA interface residue predictors that use a PSSM-based encoding of sequence windows outperform classifiers that use other encodings of sequence windows. While structure-based methods that exploit geometric features can yield significant increases in the Specificity of protein-RNA interface residue predictions, such increases are offset by decreases in Sensitivity. These results underscore the importance of comparing alternative methods using rigorous statistical procedures, multiple performance measures, and datasets that are constructed based on several alternative definitions of interface residues and redundancy cutoffs as well as including evaluations on independent test sets into the comparisons.     

Human Decidua Basalis mesenchymal stem/stromal cells reverse the damaging effects of high level of glucose on endothelial cells in vitro

Recently, we reported the therapeutic potential of mesenchymal stem/stromal cells (MSCs) from the maternal decidua basalis tissue of human term placenta (DBMSCs) to treat inflammatory diseases, such as atherosclerosis and cancer. DMSCs protect endothelial cell functions from the negative effects of oxidative stress mediators including hydrogen peroxide (H₂O₂) and monocytes. In addition, DBMSCs induce the generation of anti-cancer immune cells known as M1 macrophages. Diabetes is another inflammatory disease where endothelial cells are injured by H₂O₂ produced by high level of glucose (hyperglycaemia), which is associated with development of thrombosis. Here, we investigated the ability of DBMSCs to reverse the damaging effects of high levels of glucose on endothelial cells. DBMSCs and endothelial cells were isolated from human placental and umbilical cord tissues, respectively. Endothelial cells were incubated with glucose in presence of DBMSCs, and their functions were evaluated. The effect of DBMSCs on glucose- treated endothelial cell expression of genes was also determined. DBMSCs reversed the effects of glucose on endothelial cell functions including proliferation, migration, angiogenesis and permeability. In addition, DBMSCs modified the expression of several genes mediating essential endothelial cell functions including survival, apoptosis, permeability and angiogenesis. We report the first evidence that DBMSCs protect the functions of endothelial cells from the damaging effects of glucose. Based on these results, we establish that DBMSCs are promising therapeutic agents to repair glucose-induced endothelial cell injury in diabetes. However, these finding must be investigated further to determine the pathways underlying the protective role of DBMSCs on glucose-stimulated endothelial cell Injury.