Inhibition of hydrogen sulfide production by gene silencing attenuates inflammatory activity of LPS-activated RAW264.7 cells

Hydrogen sulfide is an inflammatory mediator and is produced by the activity of the enzyme cystathionine γ-lyase (CSE) in macrophages. Previously, pharmacological inhibition of CSE has been reported to have conflicting results, and this may be due to the lack of specificity of the pharmacological agents. Therefore, this study used a very specific approach of small interfering RNA (siRNA) to inhibit the production of the CSE in an in vitro setting. We found that the activation of macrophages by lipopolysaccharide (LPS) resulted in higher levels of CSE mRNA and protein as well as the increased production of proinflammatory cytokines and nitric oxide (NO). We successfully used siRNA to specifically reduce the levels of CSE mRNA and protein in activated macrophages. Furthermore, the levels of proinflammatory cytokines in LPS-activated macrophages were significantly lower in siRNA-transfected cells compared to those in untransfected controls. However, the production levels of NO by the transfected cells were higher, suggesting that CSE activity has an inhibitory effect on NO production. These findings suggest that the CSE enzyme has a crucial role in the activation of macrophages, and its activity has an inhibitory effect on NO production by these cells.     

Genome-Wide Analysis of Alternative Splicing in Zea mays during Maize Iranian Mosaic Virus Infection

Plant Molecular Biology Reporter - Maize Iranian mosaic virus (MIMV) infects several gramineous plants and is an economically important nucleorhabdovirus in Iran. Maize responds to MIMV infection...     

Signaling Crosstalk of FHIT,p53, and p38 in etoposide-induced apoptosis in MCF-7 cells

Fragile histidine trail (FHIT) is a tumor suppressor in response to DNA damage which has been deleted in various tumors. However, the signaling mechanisms and interactions of FHIT with regard to apoptotic proteins including p53 and p38 in the DNA damage-induced apoptosis are not well described. In the present study, we used etoposide-induced DNA damage in MCF-7 as a model to address these crosstalks. The time course study showed that the expression of FHIT, p53, and p38MAPK started after 1 hour following etoposide treatment. FHIT overexpression led to increase p53 expression, p38 activation, and augmented apoptosis following etoposide-induced DNA damage compared to wild-type cells. However, FHIT knockdown blocked p53 expression, delayed p38 activation, and completely inhibited etoposide-induced apoptosis. Inhibition of p38 activity prevented induction of p53, FHIT, and apoptosis in this model. Thus, activation of p38 upon etoposide treatment leads to increase in FHIT and p53 expression. In p53 knockdown MCF-7, the FHIT induction was hampered but p38 activation was induced in lower doses of etoposide. In p53 knockdown cells, inhibition of p38 induced FHIT expression and apoptosis. Our data demonstrated that the exposure of MCF-7 cells to etoposide increases apoptosis through a mechanism involving the activation of the p38-FHIT-p53 pathway. Moreover, our findings suggest signaling interaction for these pathways may represent a promising therapy for breast cancer.     

Salicylic acid improves podophyllotoxin production in cell cultures of Linum album by increasing the expression of genes related with its biosynthesis

Treatment of Linum album cell cultures with 10 μM salicylic acid (SA) for 3 days improved podophyllotoxin (PTOX) production up to 333 μg/g dry weight (DW): over three times that of the control cultures. qPCR analyses showed that in SA-treated cells, the expression of the genes coding for phenylalanine ammonia-lyase (PAL), cinnamoyl-CoA reductase (CCR) and cinnamyl-alcohol dehydrogenase (CAD), all involved in the first steps of PTOX biosynthesis, also increased reaching a peak 8–12 h after the treatment. Expression of the pinoresinol-lariciresinol reductase gene (PLR), which is involved in one of the last biosynthetic steps, was not affected by SA. The selective action of SA on these genes can be applied to control the biotechnological production of this anticancer agent.     

Aqueous humor nitric oxide in patients with central retinal vein occlusion

PurposeIt has been suggested that nitric oxide (NO) has a role in ischemic retinopathies. Since retinal ischemia may develop in retinal vein occlusion, we investigated the presence of nitric oxide in the pathogenesis of central retinal vein occlusion (CRVO).MethodsEighteen consecutive patients with CRVO were included in this study. Aqueous humor specimens were obtained within 21 days of diagnosis. Samples of aqueous humor were also collected from 20 control patients undergoing cataract surgery. For each sample after reduction of nitrate to nitrite with vanadium chloride (VCl₃), we used spectrophotometric method for simultaneous detection of nitrate and nitrite (NO_x).ResultsMean level of aqueous humor NO_x in CRVO and control group was 94.1 ± 23.2 μmol/l and 55.6 ± 11.0 μmol/l, respectively. The difference between two groups was statistically significant (p < 0.0001).ConclusionsOur results may support involvement of nitric oxide in the pathogenesis of CRVO.     

Imaging of human glioma cells by means of a Syndecan-4 directed DOTA-conjugate

The extracellular glycoprotein Tenascin-C (TN-C) is highly upregulated in gliomas. Therefore, many chemotherapies with radiolabeled antibodies against TN-C have been performed. However, TN-Cs binding partner Syndecan-4 did not play any role as a therapeutic or imaging target in gliomas. We constructed an imaging compound containing the magnetic resonance imaging (MRI) contrast agent gadolinium (Gd)-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA), the fluorescence dye sulforhodamine and a synthetic Syndecan-4-specific 21 amino acid peptide derived from TN-C. Magnetic resonance relaxometry, confocal laser scanning microscopy, and flow cytometry showed that the Syndecan-4-DOTA-Rhodamine conjugate was taken up into the cytoplasm of human U373 glioma cells without any cytotoxic effects. Competition experiments indicate that this uptake was receptor-mediated. This conjugate might be used for future MRI studies of brain tumors after systemic or intraoperative local application.     

Aggregation and Amyloid Fibril Formation Induced by Chemical Dimerization of Recombinant Prion Protein in Physiological-like Conditions

Prion diseases are caused by the conversion of a cellular protein (PrP^C) into a misfolded, aggregated isoform (PrP^Res). Misfolding of recombinant PrP^C in the absence of PrP^Res template, cellular factors, denaturing agents, or at neutral pH has not been achieved. A number of studies indicate that dimerization of PrP^C may be a key step in the aggregation process. In an effort to understand the molecular event that may activate misfolding of PrP^C in more relevant physiological conditions, we tested if enforced dimerization of PrP^C may induce a conformational change reminiscent of the conversion of PrP^C to PrP^Res. We used a well described inducible dimerization strategy whereby a chimeric PrP^C composed of a modified FK506-binding protein (Fv) fused with PrP^C and termed Fv-PrP is incubated in the presence of a monomeric FK506 or dimerizing AP20187 ligand. Addition of AP20187 but not FK506 to recombinant Fv-PrP (rFv-PrP) in physiological-like conditions resulted in a rapid conformational change characterized by an increase in β-sheet structure and simultaneous aggregation of the protein. Aggregates were partially resistant to proteinase K and induced the conversion of soluble rFv-PrP in serial seeding experiments. As judged from thioflavin T binding and electron microscopy, aggregates converted to amyloid fibers. Aggregates were toxic to cultured cells, whereas soluble rFv-PrP and amyloid fibers were harmless. This study strongly supports the proposition that dimerization of PrP^C is a key pathological primary event in the conversion of PrP^C and may initiate the pathogenesis of prion diseases.     

In vitro machine learning-based CAR T immunological synapse quality measurements correlate with patient clinical outcomes

The human immune system consists of a highly intelligent network of billions of independent, self-organized cells that interact with each other. Machine learning (ML) is an artificial intelligence (AI) tool that automatically processes huge amounts of image data. Immunotherapies have revolutionized the treatment of blood cancer. Specifically, one such therapy involves engineering immune cells to express chimeric antigen receptors (CAR), which combine tumor antigen specificity with immune cell activation in a single receptor. To improve their efficacy and expand their applicability to solid tumors, scientists optimize different CARs with different modifications. However, predicting and ranking the efficacy of different "off-the-shelf" immune products (e.g., CAR or Bispecific T-cell Engager [BiTE]) and selection of clinical responders are challenging in clinical practice. Meanwhile, identifying the optimal CAR construct for a researcher to further develop a potential clinical application is limited by the current, time-consuming, costly, and labor-intensive conventional tools used to evaluate efficacy. Particularly, more than 30 years of immunological synapse (IS) research data demonstrate that T cell efficacy is not only controlled by the specificity and avidity of the tumor antigen and T cell interaction, but also it depends on a collective process, involving multiple adhesion and regulatory molecules, as well as tumor microenvironment, spatially and temporally organized at the IS formed by cytotoxic T lymphocytes (CTL) and natural killer (NK) cells. The optimal function of cytotoxic lymphocytes (including CTL and NK) depends on IS quality. Recognizing the inadequacy of conventional tools and the importance of IS in immune cell functions, we investigate a new strategy for assessing CAR-T efficacy by quantifying CAR IS quality using the glass-support planar lipid bilayer system combined with ML-based data analysis. Previous studies in our group show that CAR-T IS quality correlates with antitumor activities in vitro and in vivo. However, current manually quantified IS quality data analysis is time-consuming and labor-intensive with low accuracy, reproducibility, and repeatability. In this study, we develop a novel ML-based method to quantify thousands of CAR cell IS images with enhanced accuracy and speed. Specifically, we used artificial neural networks (ANN) to incorporate object detection into segmentation. The proposed ANN model extracts the most useful information to differentiate different IS datasets. The network output is flexible and produces bounding boxes, instance segmentation, contour outlines (borders), intensities of the borders, and segmentations without borders. Based on requirements, one or a combination of this information is used in statistical analysis. The ML-based automated algorithm quantified CAR-T IS data correlates with the clinical responder and non-responder treated with Kappa-CAR-T cells directly from patients. The results suggest that CAR cell IS quality can be used as a potential composite biomarker and correlates with antitumor activities in patients, which is sufficiently discriminative to further test the CAR IS quality as a clinical biomarker to predict response to CAR immunotherapy in cancer. For translational research, the method developed here can also provide guidelines for designing and optimizing numerous CAR constructs for potential clinical development.Trial Registration: ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00881920","term_id":"NCT00881920"}}NCT00881920.     

A multibody modelling approach to determine load sharing between passive elements of the lumbar spine

The human spinal segment is an inherently complex structure, a combination of flexible and semi-rigid articulating elements stabilised by seven principal ligaments. An understanding of how mechanical loading is shared among these passive elements of the segment is required to estimate tissue failure stresses. A 3D rigid body model of the complete lumbar spine has been developed to facilitate the prediction of load sharing across the passive elements. In contrast to previous multibody models, this model includes a non-linear, six degrees of freedom intervertebral disc, facet bony articulations and all spinal ligaments. Predictions of segmental kinematics and facet joint forces, in response to pure moment loading (flexion–extension), were compared to published in vitro data. On inclusion of detailed representation of the disc and facets, the multibody model fully captures the non-linear flexibility response of the spinal segment, i.e. coupled motions and a mobile instantaneous centre of rotation. Predicted facet joint forces corresponded well with reported values. For the loading case considered, the model predicted that the ligaments are the main stabilising elements within the physiological motion range; however, the disc resists a greater proportion of the applied load as the spine is fully flexed. In extension, the facets and capsular ligaments provide the principal resistance. Overall patterns of load distribution to the spinal ligaments are in agreement with previous predictions; however, the current model highlights the important role of the intraspinous ligament in flexion and the potentially high risk of failure. Several important refinements to the multibody modelling of the passive elements of the spine have been described, and such an enhanced passive model can be easily integrated into a full musculoskeletal model for the prediction of spinal loading for a variety of daily activities.     

Environmental factors–ecological species group relationships in the Surash lowland‐mountain forests in northern Iran

Identification of the primary factors that influence the ecological distribution of species groups is important to managers of lowland‐mountain forests in northern Iran. The aim of this study was to identify main ecological species groups, describe the site conditions associated with these species groups and the relationships between environmental factors and the distribution of ecological species groups using multi‐variate analysis (Detrended correspondence analysis (DCA) and Canonical correspondence analysis (CCA)). For this purpose, 50 relevés (400 m² each) were sampled using the Braun‐Blanquet method. Vegetation was classified into three ecological species groups using a modified two‐way indicator species analysis (TWINSPAN). In each relevé, environmental factors (topographic and soil variables) were measured and analysed using one‐way ANOVA and Pearson r statistics. Further, species diversity indices were determined for the identified ecological species groups. Our results show that the environmental factors, e.g. elevation, slope, slope aspect, soil texture, pH and organic matter, were the most important factors explaining the distribution of the three ecological species groups in the study area. The diversity of the ecological species groups decreased with elevation. The results provide an ecological basis for forest management and for developing strategies for forest conservation in the study area.