Effect of a siderophore producer on animal cell apoptosis: a possible role as anti-cancer agent.

Iron plays an essential role in the proliferation of aggressive tumors therefore it represents an ideal target for cancer therapy. Cell free supernatants from a siderophore producing actinobacterium previously isolated from Thailand were tested against six human cancer cell lines including malignant melanoma A 375 (ATCC no.: CRL-1619), colorectal adenocarcinoma SW620 (ATCC no.: CCL-227), gastric carcinoma Katob III (ATCC no.: HTB-103), liver hepatoblastoma HepG2 (ATCC no.: HB-8065), breast carcinoma BT474 (ATCC no.: HTB-20) and Acute T cell leukemia Jurkat (ATCC no.: CRL-2063). Following treatment of cells with the bacterial culture supernatant the cell viability of A375 cells was dramatically decreased with cell survival of less than 34 % within 48 h. The rest of the cell lines were largely unaffected. Therefore it is suggested that the actinobacterium produced a cytotoxic compound responsible for the cell death by inducing apoptotic activity. We further speculate that this compound was desferioxamine E as the bacterium is known to produce this compound under the culture conditions used

Production of a monoclonal antibody against aflatoxin M1 and its application for detection of aflatoxin M1 in fortified milk

AbstractAflatoxin M1 (AFM1) is a toxic metabolite of the fungal product aflatoxin found in milk. For food safety concern, maximum residual limits of AFM1 in milk and dairy products have been differently enforced in many countries. A suitable detection method is required to screen a large number of product samples for the AFM1 contamination. In this study, monoclonal antibodies (MAbs) against AFM1 were generated using a conventional somatic cell fusion technique. After screening, five MAbs (AFM1-1, AFM1-3, AFM1-9, AFM1-11, and AFM1-17) were obtained that showed cross-reactivity with aflatoxin B1 (AFB1) and aflatoxin G1 (AFG1) but with no other tested compounds. An indirect competitive enzyme-linked immunosorbent assay (ELISA) using a partially purified MAb and antigen-coated plates yielded the best sensitivity with the 50% inhibition concentration (IC50) and the limit of detection (LOD) values of 0.13 ng/mL and 0.04 ng/mL, respectively. This indirect competitive ELISA was used to quantify the amount of fortified AFM1 in raw milk. The precision and accuracy in terms of % coefficient of variation (CV) and % recovery of the detection was investigated for both intra- (n = 6) and inter- (n = 12) variation assays. The % CV was found in the range of 3.50–15.8% and 1.32–7.98%, respectively, while the % recovery was in the range of 92–104% and 100–103%, respectively. In addition, the indirect ELISA was also used to detect AFM1 fortified in processed milk samples. The % CV and % recovery values were in the ranges of 0.1–33.0% and 91–109%, respectively. Comparison analysis between the indirect ELISA and high performance liquid chromatography was also performed and showed a good correlation with the R2 of 0.992 for the concentration of 0.2–5.0 ng/mL. These results indicated that the developed MAb and ELISA could be used for detection of AFM1 in milk samples

A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature

The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein–protein interactions (PPIs) reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study shows that three kernels are clearly superior to the other methods

PIM2 Induced COX-2 and MMP-9 Expression in Macrophages Requires PI3K and Notch1 Signaling

Activation of inflammatory immune responses during granuloma formation by the host upon infection of mycobacteria is one of the crucial steps that is often associated with tissue remodeling and breakdown of the extracellular matrix. In these complex processes, cyclooxygenase-2 (COX-2) plays a major role in chronic inflammation and matrix metalloproteinase-9 (MMP-9) significantly in tissue remodeling. In this study, we investigated the molecular mechanisms underlying Phosphatidyl-myo-inositol dimannosides (PIM2), an integral component of the mycobacterial envelope, triggered COX-2 and MMP-9 expression in macrophages. PIM2 triggers the activation of Phosphoinositide-3 Kinase (PI3K) and Notch1 signaling leading to COX-2 and MMP-9 expression in a Toll-like receptor 2 (TLR2)-MyD88 dependent manner. Notch1 signaling perturbations data demonstrate the involvement of the cross-talk with members of PI3K and Mitogen activated protein kinase pathway. Enforced expression of the cleaved Notch1 in macrophages induces the expression of COX-2 and MMP-9. PIM2 triggered significant p65 nuclear factor -κB (NF-κB) nuclear translocation that was dependent on activation of PI3K or Notch1 signaling. Furthermore, COX-2 and MMP-9 expression requires Notch1 mediated recruitment of Suppressor of Hairless (CSL) and NF-κB to respective promoters. Inhibition of PIM2 induced COX-2 resulted in marked reduction in MMP-9 expression clearly implicating the role of COX-2 dependent signaling events in driving the MMP-9 expression. Taken together, these data implicate PI3K and Notch1 signaling as obligatory early proximal signaling events during PIM2 induced COX-2 and MMP-9 expression in macrophages

Notch signaling in T helper cell subsets: Instructor or unbiased amplifier?

For protection against pathogens, it is essential that naïve CD4+ T cells differentiate into specific effector T helper (Th) cell subsets following activation by antigen presented by dendritic cells (DCs). Next to T cell receptor and cytokine signals, membrane-bound Notch ligands have an important role in orchestrating Th cell differentiation. Several studies provided evidence that DC activation is accompanied by surface expression of Notch ligands. Intriguingly, DCs that express the delta-like or Jagged Notch ligands gain the capacity to instruct Th1 or Th2 cell polarization, respectively. However, in contrast to this model it has also been hypothesized that Notch signaling acts as a general amplifier of Th cell responses rather than an instructive director of specific T cell fates. In this alternative model, Notch enhances proliferation, cytokine production, and anti-apoptotic signals or promotes co-stimulatory signals in T cells. An instructive role for Notch ligand expressing DCs in the induction of Th cell differentiation is further challenged by evidence for the involvement of Notch signaling in differentiation of Th9, Th17, regulatory T cells, and follicular Th cells. In this review, we will discuss the two opposing models, referred to as the "instructive" and the "unbiased amplifier" model. We highlight both the function of different Notch receptors on CD4+ T cells and the impact of Notch ligands on antigen-presenting cells

Presenilin 2 Is the Predominant γ-Secretase in Microglia and Modulates Cytokine Release

Presenilin 1 (PS1) and Presenilin 2 (PS2) are the enzymatic component of the γ-secretase complex that cleaves amyloid precursor protein (APP) to release amyloid beta (Aβ) peptide. PS deficiency in mice results in neuroinflammation and neurodegeneration in the absence of accumulated Aβ. We hypothesize that PS influences neuroinflammation through its γ-secretase action in CNS innate immune cells. We exposed primary murine microglia to a pharmacological γ-secretase inhibitor which resulted in exaggerated release of TNFα and IL-6 in response to lipopolysaccharide. To determine if this response was mediated by PS1, PS2 or both we used shRNA to knockdown each PS in a murine microglia cell line. Knockdown of PS1 did not lead to decreased γ-secretase activity while PS2 knockdown caused markedly decreased γ-secretase activity. Augmented proinflammatory cytokine release was observed after knockdown of PS2 but not PS1. Proinflammatory stimuli increased microglial PS2 gene transcription and protein in vitro. This is the first demonstration that PS2 regulates CNS innate immunity. Taken together, our findings suggest that PS2 is the predominant γ-secretase in microglia and modulates release of proinflammatory cytokines. We propose PS2 may participate in a negative feedback loop regulating inflammatory behavior in microglia

Gene regulatory network reveals oxidative stress as the underlying molecular mechanism of type 2 diabetes and hypertension

<p>Abstract</p> <p>Background</p> <p>The prevalence of diabetes is increasing worldwide. It has been long known that increased rates of inflammatory diseases, such as obesity (OBS), hypertension (HT) and cardiovascular diseases (CVD) are highly associated with type 2 diabetes (T2D). T2D and/or OBS can develop independently, due to genetic, behavioral or lifestyle-related variables but both lead to oxidative stress generation. The underlying mechanisms by which theses complications arise and manifest together remain poorly understood. Protein-protein interactions regulate nearly every living process. Availability of high-throughput genomic data has enabled unprecedented views of gene and protein co-expression, co-regulations and interactions in cellular systems.</p> <p>Methods</p> <p>The present work, applied a systems biology approach to develop gene interaction network models, comprised of high throughput genomic and PPI data for T2D. The genes differentially regulated through T2D were 'mined' and their 'wirings' were studied to get a more complete understanding of the overall gene network topology and their role in disease progression.</p> <p>Results</p> <p>By analyzing the genes related to T2D, HT and OBS, a highly regulated gene-disease integrated network model has been developed that provides useful functional linkages among groups of genes and thus addressing how different inflammatory diseases are connected and propagated at genetic level. Based on the investigations around the 'hubs' that provided more meaningful insights about the cross-talk within gene-disease networks in terms of disease phenotype association with oxidative stress and inflammation, a hypothetical co-regulation disease mechanism model been proposed. The results from this study revealed that the oxidative stress mediated regulation cascade is the common mechanistic link among the pathogenesis of T2D, HT and other inflammatory diseases such as OBS.</p> <p>Conclusion</p> <p>The findings provide a novel comprehensive approach for understanding the pathogenesis of various co-associated chronic inflammatory diseases by combining the power of pathway analysis with gene regulatory network evaluation.</p