Fine mechanisms of the interaction of silver nanoparticles with the cells of Salmonella typhimurium and Staphylococcus aureus

Silver nanoparticles possess antibacterial effect for various bacteria; however mechanisms of the interaction between Ag-NPs and bacterial cells remain unclear. The aim of our study was to obtain direct evidence of Ag-NPs penetration into cells of Gram-negative bacterium S. typhimurium and Gram-positive bacterium S. aureus, and to study cell responses to Ag-NPs. The Ag-NPs (most 8–10 nm) were obtained by gas-jet method. S. typhimurium (7.81 × 10⁷ CFU), or S. aureus (8.96 × 10⁷ CFU) were treated by Ag-NPs (0.05 mg/l of silver) in orbital shaker at 190 rpm, 37 °C. Bacteria were sampled at 0.5, 1, 1.5, 2, 5 and 23 h of the incubation for transmission electron microscopy of ultrathin sections. The Ag-NPs adsorbed on outer membrane of S. typhimurium and cell wall of S. auereus; penetrated and accumulated in cells without aggregation and damaging of neighboring cytoplasm. In cells of S. aureus Ag-NPs bound with DNA fibers. Cell responses to Ag-NPs differed morphologically in S. typhimurium and S. aureus, and mainly were presented by damage of cell structures. The cytoplasm of S. aureus became amorphous, while S. typhimurium showed lumping and lysis of cytoplasm which led to formation of “empty” cells. Other difference was fast change of cell shape in S. typhimurium, and late deformation of S. aureus cells. The obtained results showed how different could be responses induced by the same NPs in relatively simple prokaryotic cells. Evidently, Ag-NPs directly interact with macromolecular structures of living cells and are exert an active influence on their metabolism.     

Perturbation of the mutated EGFR interactome identifies vulnerabilities and resistance mechanisms

We hypothesized that elucidating the interactome of epidermal growth factor receptor (EGFR) forms that are mutated in lung cancer, via global analysis of protein–protein interactions, phosphorylation, and systematically perturbing the ensuing network nodes, should offer a new, more systems‐level perspective of the molecular etiology. Here, we describe an EGFR interactome of 263 proteins and offer a 14‐protein core network critical to the viability of multiple EGFR‐mutated lung cancer cells. Cells with acquired resistance to EGFR tyrosine kinase inhibitors (TKIs) had differential dependence of the core network proteins based on the underlying molecular mechanisms of resistance. Of the 14 proteins, 9 are shown to be specifically associated with survival of EGFR‐mutated lung cancer cell lines. This included EGFR, GRB2, MK12, SHC1, ARAF, CD11B, ARHG5, GLU2B, and CD11A. With the use of a drug network associated with the core network proteins, we identified two compounds, midostaurin and lestaurtinib, that could overcome drug resistance through direct EGFR inhibition when combined with erlotinib. Our results, enabled by interactome mapping, suggest new targets and combination therapies that could circumvent EGFR TKI resistance.     

Base-Calling Algorithm with Vocabulary (BCV) Method for Analyzing Population Sequencing Chromatograms

Sanger sequencing is a common method of reading DNA sequences. It is less expensive than high-throughput methods, and it is appropriate for numerous applications including molecular diagnostics. However, sequencing mixtures of similar DNA of pathogens with this method is challenging. This is important because most clinical samples contain such mixtures, rather than pure single strains. The traditional solution is to sequence selected clones of PCR products, a complicated, time-consuming, and expensive procedure. Here, we propose the base-calling with vocabulary (BCV) method that computationally deciphers Sanger chromatograms obtained from mixed DNA samples. The inputs to the BCV algorithm are a chromatogram and a dictionary of sequences that are similar to those we expect to obtain. We apply the base-calling function on a test dataset of chromatograms without ambiguous positions, as well as one with 3–14% sequence degeneracy. Furthermore, we use BCV to assemble a consensus sequence for an HIV genome fragment in a sample containing a mixture of viral DNA variants and to determine the positions of the indels. Finally, we detect drug-resistant Mycobacterium tuberculosis strains carrying frameshift mutations mixed with wild-type bacteria in the pncA gene, and roughly characterize bacterial communities in clinical samples by direct 16S rRNA sequencing.     

Inter‐Fullerene Electronic Coupling Controls the Efficiency of Photoinduced Charge Generation in Organic Bulk Heterojunctions

Photoinduced charge generation (PCG) dynamics are notoriously difficult to correlate with specific molecular properties in device relevant polymer:fullerene organic photovoltaic blend films due to the highly complex nature of the solid state blend morphology. Here, this study uses six judiciously selected trifluoromethylfullerenes blended with the prototypical polymer poly(3‐hexylthiophene) and measure the PCG dynamics in 50 fs–500 ns time scales with time‐resolved microwave conductivity and femtosecond transient absorption spectroscopy. The isomeric purity and thorough chemical characterization of the fullerenes used in this study allow for a detailed correlation between molecular properties, driving force, local intermolecular electronic coupling and, ultimately, the efficiency of PCG yield. The findings show that the molecular design of the fullerene not only determines inter‐fullerene electronic coupling, but also influences the decay dynamics of free holes in the donor phase even when the polymer microstructure remains unchanged.     

Signal Intensities in Preoperative MRI Do Not Reflect Proliferative Activity in Meningioma

BACKGROUND: Identification of high-grade meningiomas in preoperative magnetic resonance imaging (MRI) is important for optimized surgical strategy and best possible resection. Numerous studies investigated subjectively determined morphological features as predictors of tumor biology in meningiomas. The aim of this study was to identify the predictive value of more reliable, quantitatively measured signal intensities in MRI for differentiation of high- and low-grade meningiomas and identification of meningiomas with high proliferation rates, respectively. PATIENTS AND METHODS: Sixty-six patients (56 World Health Organization [WHO] grade I, 9 WHO grade II, and 1 WHO grade I) were included in the study. Preoperative MRI signal intensities (fluid-attenuated inversion recovery [FLAIR], T1 precontrast, and T1 postcontrast as genuine and normalized values) were correlated with Ki-67 expression in tissue sections of resected meningiomas. Differences between the groups (analysis of variance) and Spearman rho correlation were computed using SPSS 22. RESULTS: Mean values of genuine signal intensities of meningiomas in FLAIR, T1 native, and T1 postcontrast were 323.9 ± 59, 332.8 ± 67.9, and 768.5 ± 165.3. Mean values of normalized (to the contralateral white matter) signal intensities of meningiomas in FLAIR, T1 native, and T1 postcontrast were 1.5 ± 0.3, 0.8 ± 0.1, and 1.9 ± 0.4. There was no significant correlation between MRI signal intensities and WHO grade or Ki-67 expression. Signal intensities did not differ significantly between WHO grade I and II/III meningiomas. Ki-67 expression was significantly increased in high-grade meningiomas compared with low-grade meningiomas (P < 0.01). Objectively measured values of MRI signal intensities (FLAIR, T1 precontrast, and T1 postcontrast enhancement) did not distinguish between high-grade and low-grade meningiomas. Furthermore, there was no association between MRI signal intensities and Ki-67 expression representing proliferative activity.Meningiomas are among the most common brain tumors. Their incidence is about 1%, and they account for almost one third of all primary intracranial masses. The majority of meningiomas are very slowly growing and nonsymptomatic or minimally symptomatic entities, discovered as incidental findings on neuroimaging [1]. The World Health Organization (WHO) classification system distinguishes 3 histological grades and 15 subtypes and is a well-accepted tool for prediction of prognosis. Although most meningiomas are benign masses, certain histological subtypes reveal very high recurrence rates despite the tumors’ seemingly total removal. Grade II (atypical) and grade III (anaplastic) meningiomas are associated with an increased risk of recurrence, are more aggressive, and show invasive behavior [2]. Grade I meningiomas are generally considered as benign tumors, but recent studies indicate substantial neurological deficits and impaired long-term survival due to tumor recurrence and stroke despite their low histopathological grading in a considerable proportion of cases [3], [4]. Increased mitotic activity (more than 4 mitoses per 10 high-power fields) and elevated Ki-67 expression (Ki-67 index of more than 5% of nuclei) are reliable histopathological markers for tumor recurrence [2].Because histopathological grading alone does not predict outcome satisfyingly, numerous studies investigated the value of preoperative magnetic resonance imaging (MRI) for prognostics. For example, Liu et al. demonstrated that hyperintensity on diffusion-weighted imaging, heterogeneous gadolinium enhancement, disruption of the arachnoid at brain tumor interface, T2 hyperintense peritumoral edema, and irregular tumor shape were independent predictors of non–grade I meningioma [5]. Other works produced comparable results, although some of these studies underline the importance of positive capsular enhancement [6], [7], whereas others emphasize the predictive value of peritumoral edema [5], [8]. All the above-cited works investigated morphological features of meningiomas summarized in subjective scoring systems, but not one of the studies objectively analyzed values of SIs in commonly used preoperative MRI sequences.Therefore, the aim of this study was to investigate the predictive value of genuine and normalized SIs of standardized preoperative MRI (T1 pre- and postcontrast, T2, and fluid-attenuated inversion recovery [FLAIR]) as in vivo predictors of proliferative activity of meningiomas.     

Mice with low levels of Shc proteins display reduced glycolytic and increased gluconeogenic activities in liver

Shc proteins play a role in energy metabolism through interaction with the insulin receptor. The aim of this study was to determine whether Shc proteins influence liver glycolysis and gluconeogenesis under both fed and fasted states. Decreased glycolytic and increased gluconeogenic and transamination enzyme activities were observed in ShcKO versus WT mice. Levels of key regulatory metabolites, such as fructose-2,6-bisphosphate, matched the activity of metabolic pathways. Protein levels of glycolytic and gluconeogenic enzymes were not different. pAMPK protein levels increased with fasting and were higher in ShcKO versus WT mice. Therefore, Shc proteins play a role in shifting the metabolism from glucose oxidation to gluconeogenesis and lipid catabolism and should be considered as regulators of fuel selection. Fuel selection and utilization could play a critical role in healthy aging. Characterization of metabolic events in ShcKO mice would help to elucidate how metabolism is influenced by these proteins.