Ablation of EIF5A2 induces tumor vasculature remodeling and improves tumor response to chemotherapy via regulation of matrix metalloproteinase 2 expression

Hepatocellular carcinoma (HCC) is a highly vascularized tumor with poor clinical outcome. Our previous work has shown that eukaryotic initiation factor 5A2 (EIF5A2) over-expression enhances HCC cell metastasis. In this study, EIF5A2 was identified to be an independent risk factor for poor disease-specific survival among HCC patients. Both in vitro and in vivo assays indicated that ablation of endogenous EIF5A2 inhibited tumor angiogenesis by reducing matrix metalloproteinase 2 (MMP-2) expression. Given that MMP-2 degrades collagen IV, a main component of the vascular basement membrane (BM), we subsequently investigated the effect of EIF5A2 on tumor vasculature remodeling using complementary approaches, including fluorescent immunostaining, transmission electron microscopy, tumor perfusion assays and tumor hypoxia assays. Taken together, our results indicate that EIF5A2 silencing increases tumor vessel wall continuity, increases blood perfusion and improves tumor oxygenation. Additionally, we found that ablation of EIF5A2 enhanced the chemosensitivity of HCC cells to 5-Fluorouracil (5-FU). Finally, we demonstrated that EIF5A2 might exert these functions by enhancing MMP-2 activity via activation of p38 MAPK and JNK/c-Jun pathways. Conclusion: This study highlights an important role of EIF5A2 in HCC tumor vessel remodeling and indicates that EIF5A2 represents a potential therapeutic target in the treatment of HCC.published_or_final_versio

Oral Treatment with γ-Aminobutyric Acid Improves Glucose Tolerance and Insulin Sensitivity by Inhibiting Inflammation in High Fat Diet-Fed Mice

Adipocyte and β-cell dysfunction and macrophage-related chronic inflammation are critical for the development of obesity-related insulin resistance and type 2 diabetes mellitus (T2DM), which can be negatively regulated by Tregs. Our previous studies and those of others have shown that activation of γ-aminobutyric acid (GABA) receptors inhibits inflammation in mice. However, whether GABA could modulate high fat diet (HFD)-induced obesity, glucose intolerance and insulin resistance has not been explored. Here, we show that although oral treatment with GABA does not affect water and food consumption it inhibits the HFD-induced gain in body weights in C57BL/6 mice. Furthermore, oral treatment with GABA significantly reduced the concentrations of fasting blood glucose, and improved glucose tolerance and insulin sensitivity in the HFD-fed mice. More importantly, after the onset of obesity and T2DM, oral treatment with GABA inhibited the continual HFD-induced gain in body weights, reduced the concentrations of fasting blood glucose and improved glucose tolerance and insulin sensitivity in mice. In addition, oral treatment with GABA reduced the epididymal fat mass, adipocyte size, and the frequency of macrophage infiltrates in the adipose tissues of HFD-fed mice. Notably, oral treatment with GABA significantly increased the frequency of CD4+Foxp3+ Tregs in mice. Collectively, our data indicated that activation of peripheral GABA receptors inhibited the HFD-induced glucose intolerance, insulin resistance, and obesity by inhibiting obesity-related inflammation and up-regulating Treg responses in vivo. Given that GABA is safe for human consumption, activators of GABA receptors may be valuable for the prevention of obesity and intervention of T2DM in the clinic

Balancing Selection at the Tomato RCR3 Guardee Gene Family Maintains Variation in Strength of Pathogen Defense

Coevolution between hosts and pathogens is thought to occur between interacting molecules of both species. This results in the maintenance of genetic diversity at pathogen antigens (or so-called effectors) and host resistance genes such as the major histocompatibility complex (MHC) in mammals or resistance (R) genes in plants. In plant-pathogen interactions, the current paradigm posits that a specific defense response is activated upon recognition of pathogen effectors via interaction with their corresponding R proteins. According to the''Guard-Hypothesis,'' R proteins (the ``guards'') can sense modification of target molecules in the host (the ``guardees'') by pathogen effectors and subsequently trigger the defense response. Multiple studies have reported high genetic diversity at R genes maintained by balancing selection. In contrast, little is known about the evolutionary mechanisms shaping the guardee, which may be subject to contrasting evolutionary forces. Here we show that the evolution of the guardee RCR3 is characterized by gene duplication, frequent gene conversion, and balancing selection in the wild tomato species Solanum peruvianum. Investigating the functional characteristics of 54 natural variants through in vitro and in planta assays, we detected differences in recognition of the pathogen effector through interaction with the guardee, as well as substantial variation in the strength of the defense response. This variation is maintained by balancing selection at each copy of the RCR3 gene. Our analyses pinpoint three amino acid polymorphisms with key functional consequences for the coevolution between the guardee (RCR3) and its guard (Cf-2). We conclude that, in addition to coevolution at the ``guardee-effector'' interface for pathogen recognition, natural selection acts on the ``guard-guardee'' interface. Guardee evolution may be governed by a counterbalance between improved activation in the presence and prevention of auto-immune responses in the absence of the corresponding pathogen

Capsid and Infectivity in Virus Detection

The spectacular achievements and elegance of viral RNA analyses have somewhat obscured the importance of the capsid in transmission of viruses via food and water. The capsid’s essential roles are protection of the RNA when the virion is outside the host cell and initiation of infection when the virion contacts a receptor on an appropriate host cell. Capsids of environmentally transmitted viruses are phenomenally durable. Fortuitous properties of the capsid include antigenicity, isoelectric point(s), sometimes hemagglutination, and perhaps others. These can potentially be used to characterize capsid changes that cause or accompany loss of viral infectivity and may be valuable in distinguishing native from inactivated virus when molecular detection methods are used

Control and Characterization of Individual Grains and Grain Boundaries in Graphene Grown by Chemical Vapor Deposition

The strong interest in graphene has motivated the scalable production of high quality graphene and graphene devices. Since large-scale graphene films synthesized to date are typically polycrystalline, it is important to characterize and control grain boundaries, generally believed to degrade graphene quality. Here we study single-crystal graphene grains synthesized by ambient CVD on polycrystalline Cu, and show how individual boundaries between coalescing grains affect graphene's electronic properties. The graphene grains show no definite epitaxial relationship with the Cu substrate, and can cross Cu grain boundaries. The edges of these grains are found to be predominantly parallel to zigzag directions. We show that grain boundaries give a significant Raman "D" peak, impede electrical transport, and induce prominent weak localization indicative of intervalley scattering in graphene. Finally, we demonstrate an approach using pre-patterned growth seeds to control graphene nucleation, opening a route towards scalable fabrication of single-crystal graphene devices without grain boundaries.Comment: New version with additional data. Accepted by Nature Material

Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms

Background: Predicting protein function has become increasingly demanding in the era of next generation sequencing technology. The task to assign a curator-reviewed function to every single sequence is impracticable. Bioinformatics tools, easy to use and able to provide automatic and reliable annotations at a genomic scale, are necessary and urgent. In this scenario, the Gene Ontology has provided the means to standardize the annotation classification with a structured vocabulary which can be easily exploited by computational methods.Results: Argot2 is a web-based function prediction tool able to annotate nucleic or protein sequences from small datasets up to entire genomes. It accepts as input a list of sequences in FASTA format, which are processed using BLAST and HMMER searches vs UniProKB and Pfam databases respectively; these sequences are then annotated with GO terms retrieved from the UniProtKB-GOA database and the terms are weighted using the e-values from BLAST and HMMER. The weighted GO terms are processed according to both their semantic similarity relations described by the Gene Ontology and their associated score. The algorithm is based on the original idea developed in a previous tool called Argot. The entire engine has been completely rewritten to improve both accuracy and computational efficiency, thus allowing for the annotation of complete genomes.Conclusions: The revised algorithm has been already employed and successfully tested during in-house genome projects of grape and apple, and has proven to have a high precision and recall in all our benchmark conditions. It has also been successfully compared with Blast2GO, one of the methods most commonly employed for sequence annotation. The server is freely accessible at http://www.medcomp.medicina.unipd.it/Argot2Journal Articleinfo:eu-repo/semantics/publishe

Insight into glucocorticoid receptor signalling through interactome model analysis

Glucocorticoid hormones (GCs) are used to treat a variety of diseases because of their potent anti-inflammatory effect and their ability to induce apoptosis in lymphoid malignancies through the glucocorticoid receptor (GR). Despite ongoing research, high glucocorticoid efficacy and widespread usage in medicine, resistance, disease relapse and toxicity remain factors that need addressing. Understanding the mechanisms of glucocorticoid signalling and how resistance may arise is highly important towards improving therapy. To gain insight into this we undertook a systems biology approach, aiming to generate a Boolean model of the glucocorticoid receptor protein interaction network that encapsulates functional relationships between the GR, its target genes or genes that target GR, and the interactions between the genes that interact with the GR. This model named GEB052 consists of 52 nodes representing genes or proteins, the model input (GC) and model outputs (cell death and inflammation), connected by 241 logical interactions of activation or inhibition. 323 changes in the relationships between model constituents following in silico knockouts were uncovered, and steady-state analysis followed by cell-based microarray genome-wide model validation led to an average of 57% correct predictions, which was taken further by assessment of model predictions against patient microarray data. Lastly, semi-quantitative model analysis via microarray data superimposed onto the model with a score flow algorithm has also been performed, which demonstrated significantly higher correct prediction ratios (average of 80%), and the model has been assessed as a predictive clinical tool using published patient microarray data. In summary we present an in silico simulation of the glucocorticoid receptor interaction network, linked to downstream biological processes that can be analysed to uncover relationships between GR and its interactants. Ultimately the model provides a platform for future development both by directing laboratory research and allowing for incorporation of further components, encapsulating more interactions/genes involved in glucocorticoid receptor signalling

Observation of a ppb mass threshoud enhancement in \psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p}) decay

The decay channel $\psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p})$ is studied using a sample of $1.06\times 10^8$ $\psi^\prime$ events collected by the BESIII experiment at BEPCII. A strong enhancement at threshold is observed in the $p\bar{p}$ invariant mass spectrum. The enhancement can be fit with an $S$-wave Breit-Wigner resonance function with a resulting peak mass of $M=1861^{+6}_{-13} {\rm (stat)}^{+7}_{-26} {\rm (syst)} {\rm MeV/}c^2$ and a narrow width that is $\Gamma<38 {\rm MeV/}c^2$ at the 90% confidence level. These results are consistent with published BESII results. These mass and width values do not match with those of any known meson resonance.Comment: 5 pages, 3 figures, submitted to Chinese Physics

p53 modeling as a route to mesothelioma patients stratification and novel therapeutic identification

Background Malignant pleural mesothelioma (MPM) is an orphan disease that is difficult to treat using traditional chemotherapy, an approach which has been effective in other types of cancer. Most chemotherapeutics cause DNA damage leading to cell death. Recent discoveries have highlighted a potential role for the p53 tumor suppressor in this disease. Given the pivotal role of p53 in the DNA damage response, here we investigated the predictive power of the p53 interactome model for MPM patients’ stratification. Methods We used bioinformatics approaches including omics type analysis of data from MPM cells and from MPM patients in order to predict which pathways are crucial for patients’ survival. Analysis of the PKT206 model of the p53 network was validated by microarrays from the Mero-14 MPM cell line and RNA-seq data from 71 MPM patients, whilst statistical analysis was used to identify the deregulated pathways and predict therapeutic schemes by linking the affected pathway with the patients’ clinical state. Results In silico simulations demonstrated successful predictions ranging from 52 to 85% depending on the drug, algorithm or sample used for validation. Clinical outcomes of individual patients stratified in three groups and simulation comparisons identified 30 genes that correlated with survival. In patients carrying wild-type p53 either treated or not treated with chemotherapy, FEN1 and MMP2 exhibited the highest inverse correlation, whereas in untreated patients bearing mutated p53, SIAH1 negatively correlated with survival. Numerous repositioned and experimental drugs targeting FEN1 and MMP2 were identified and selected drugs tested. Epinephrine and myricetin, which target FEN1, have shown cytotoxic effect on Mero-14 cells whereas marimastat and batimastat, which target MMP2 demonstrated a modest but significant inhibitory effect on MPM cell migration. Finally, 8 genes displayed correlation with disease stage, which may have diagnostic implications. Conclusions Clinical decisions related to MPM personalized therapy based on individual patients’ genetic profile and previous chemotherapeutic treatment could be reached using computational tools and the predictions reported in this study upon further testing in animal models