Inhibition of Interferon-Gamma-Stimulated Melanoma Progression by Targeting Neuronal Nitric Oxide Synthase (nNOS)

Interferon-gamma (IFN-γ) is shown to stimulate melanoma development and progression. However, the underlying mechanism has not been completely defined. Our study aimed to determine the role of neuronal nitric oxide synthase (nNOS)-mediated signaling in IFN-γ-stimulated melanoma progression and the anti-melanoma effects of novel nNOS inhibitors. Our study shows that IFN-γ markedly induced the expression levels of nNOS in melanoma cells associated with increased intracellular nitric oxide (NO) levels. Co-treatment with novel nNOS inhibitors effectively alleviated IFN-γ-activated STAT1/3. Further, reverse phase protein array (RPPA) analysis demonstrated that IFN-γ induced the expression of HIF1α, c-Myc, and programmed death-ligand 1 (PD-L1), in contrast to IFN-α. Blocking the nNOS-mediated signaling pathway using nNOS-selective inhibitors was shown to effectively diminish IFN-γ-induced PD-L1 expression in melanoma cells. Using a human melanoma xenograft mouse model, the in vivo studies revealed that IFN-γ increased tumor growth compared to control, which was inhibited by the co-administration of nNOS inhibitor MAC-3-190. Another nNOS inhibitor, HH044, was shown to effectively inhibit in vivo tumor growth and was associated with reduced PD-L1 expression levels in melanoma xenografts. Our study demonstrates the important role of nNOS-mediated NO signaling in IFN-γ-stimulated melanoma progression. Targeting nNOS using highly selective small molecular inhibitors is a unique and effective strategy to improve melanoma treatment

Feature selection using a one dimensional naïve Bayes' classifier increases the accuracy of support vector machine classification of CDR3 repertoires.

MOTIVATION: Somatic DNA recombination, the hallmark of vertebrate adaptive immunity, has the potential to generate a vast diversity of antigen receptor sequences. How this diversity captures antigen specificity remains incompletely understood. In this study we use high throughput sequencing to compare the global changes in T cell receptor β chain complementarity determining region 3 (CDR3β) sequences following immunization with ovalbumin administered with complete Freund's adjuvant (CFA) or CFA alone. RESULTS: The CDR3β sequences were deconstructed into short stretches of overlapping contiguous amino acids. The motifs were ranked according to a one-dimensional Bayesian classifier score comparing their frequency in the repertoires of the two immunization classes. The top ranking motifs were selected and used to create feature vectors which were used to train a support vector machine. The support vector machine achieved high classification scores in a leave-one-out validation test reaching  : >90% in some cases. SUMMARY: The study describes a novel two-stage classification strategy combining a one-dimensional Bayesian classifier with a support vector machine. Using this approach we demonstrate that the frequency of a small number of linear motifs three amino acids in length can accurately identify a CD4 T cell response to ovalbumin against a background response to the complex mixture of antigens which characterize Complete Freund's Adjuvant. AVAILABILITY AND IMPLEMENTATION: The sequence data is available at www.ncbi.nlm.nih.gov/sra/?term¼SRP075893 The Decombinator package is available at github.com/innate2adaptive/Decombinator The R package e1071 is available at the CRAN repository https://cran.r-project.org/web/packages/e1071/index.html CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online

Specificity, Privacy, and Degeneracy in the CD4 T Cell Receptor Repertoire Following Immunization.

T cells recognize antigen using a large and diverse set of antigen-specific receptors created by a complex process of imprecise somatic cell gene rearrangements. In response to antigen-/receptor-binding-specific T cells then divide to form memory and effector populations. We apply high-throughput sequencing to investigate the global changes in T cell receptor sequences following immunization with ovalbumin (OVA) and adjuvant, to understand how adaptive immunity achieves specificity. Each immunized mouse contained a predominantly private but related set of expanded CDR3β sequences. We used machine learning to identify common patterns which distinguished repertoires from mice immunized with adjuvant with and without OVA. The CDR3β sequences were deconstructed into sets of overlapping contiguous amino acid triplets. The frequencies of these motifs were used to train the linear programming boosting (LPBoost) algorithm LPBoost to classify between TCR repertoires. LPBoost could distinguish between the two classes of repertoire with accuracies above 80%, using a small subset of triplet sequences present at defined positions along the CDR3. The results suggest a model in which such motifs confer degenerate antigen specificity in the context of a highly diverse and largely private set of T cell receptors

A Small Peptide Increases Drug Delivery in Human Melanoma Cells

Melanoma is the most fatal type of skin cancer and is notoriously resistant to chemotherapies. The response of melanoma to current treatments is difficult to predict. To combat these challenges, in this study, we utilize a small peptide to increase drug delivery to melanoma cells. A peptide library array was designed and screened using a peptide array-whole cell binding assay, which identified KK-11 as a novel human melanoma-targeting peptide. The peptide and its D-amino acid substituted analogue (VPWxEPAYQrFL or D-aa KK-11) were synthesized via a solid-phase strategy. Further studies using FITC-labeled KK-11 demonstrated dose-dependent uptake in human melanoma cells. D-aa KK-11 significantly increased the stability of the peptide, with 45.3% remaining detectable after 24 h with human serum incubation. Co-treatment of KK-11 with doxorubicin was found to significantly enhance the cytotoxicity of doxorubicin compared to doxorubicin alone, or sequential KK-11 and doxorubicin treatment. In vivo and ex vivo imaging revealed that D-aa KK-11 distributed to xenografted A375 melanoma tumors as early as 5 min and persisted up to 24 h post tail vein injection. When co-administered, D-aa KK-11 significantly enhanced the anti-tumor activity of a novel nNOS inhibitor (MAC-3-190) in an A375 human melanoma xenograft mouse model compared to MAC-3-190 treatment alone. No apparent systemic toxicities were observed. Taken together, these results suggest that KK-11 may be a promising human melanoma-targeted delivery vector for anti-melanoma cargo

Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires

The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity in order to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic and (iv) machine learning methods applied to dissect, quantify and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology towards coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.Comment: 27 pages, 2 figure

Towards sustainability: An assessment of an urbanisation bubble in China using a hierarchical - stochastic multicriteria acceptability analysis - Choquet integral method

Urbanisation bubbles have become an increasingly serious problem. Attention has been paid to the speed of urbanisation; however, the issue of quality has been neglected, particularly in the case of China. Therefore, the aim of this research is to evaluate China’s urbanisation bubbles by employing a hierarchical - stochastic multicriteria acceptability analysis (SMAA) - Choquet integral method. In order to highlight regional disparities, we measure the urbanisation bubbles at a provincial level. Our study aggregates the urbanisation bubble indices using the Choquet integral preference model, and considers the interactions between various indicators. Furthermore, robust ordinal regression and SMAA are applied to resolve the robustness issues associated with the entire set of weights assigned to the urbanisation bubble composite indicator. In addition, by employing a multiple criteria hierarchy process, the study aggregates urbanisation bubble indices not only at the comprehensive level, but also at the intermediate levels of the hierarchy. Our findings suggest that the ranking of urbanisation bubbles is positively related to the level of regional development. This study contributes to the evaluation of regional urbanisation and sustainable development

LIF-Dependent Signaling: New Pieces in the Lego

LIF, a member of the IL6 family of cytokine, displays pleiotropic effects on various cell types and organs. Its critical role in stem cell models (e.g.: murine ES, human mesenchymal cells) and its essential non redundant function during the implantation process of embryos, in eutherian mammals, put this cytokine at the core of many studies aiming to understand its mechanisms of action, which could benefit to medical applications. In addition, its conservation upon evolution raised the challenging question concerning the function of LIF in species in which there is no implantation. We present the recent knowledge about the established and potential functions of LIF in different stem cell models, (embryonic, hematopoietic, mesenchymal, muscle, neural stem cells and iPSC). We will also discuss EVO-DEVO aspects of this multifaceted cytokine

A review on resilience assessment of energy systems

Energy systems are regularly subject to major disruptions affecting economic activities, operation of infrastructure and the society as a whole. Resilience assessment comprises the pre-event oriented classical risk assessment as a central element, but it goes beyond that because it also includes and evaluates post-event strategies to improve the functioning of the system during its future operation. First, an overview of resilience definitions used across various scientific disciplines is presented, followed by an in-depth analysis of resilience assessment and quantification for energy systems. The relevant literature is classified by approach and according to four key functions of resilience: resist, restabilize, rebuild, and reconfigure. Findings show that irrespective of the research field, a resilient system always operates with an aim to minimize the potential consequences resulting from a disruptive event and to efficiently recover from a potential system performance loss.ISSN:2378-9697ISSN:2378-968