Characterization of Morreton virus as an oncolytic virotherapy platform for liver cancers.

BACKGROUND: Morreton virus (MORV) is an oncolytic Vesiculovirus , genetically distinct from vesicular stomatitis virus (VSV). AIM: To report that MORV induced potent cytopathic effects (CPEs) in cholangiocarcinoma (CCA) and hepatocellular carcinoma (HCC) in vitro models. APPROACH AND RESULTS: In preliminary safety analyses, high intranasal doses (up to 10 10 50% tissue culture infectious dose [TCID 50 ]) of MORV were not associated with significant adverse effects in immune competent, non-tumor-bearing mice. MORV was shown to be efficacious in a Hep3B hepatocellular cancer xenograft model but not in a CCA xenograft HuCCT1 model. In an immune competent, syngeneic murine CCA model, single intratumoral treatments with MORV (1 × 10 7 TCID 50 ) triggered a robust antitumor immune response leading to substantial tumor regression and disease control at a dose 10-fold lower than VSV (1 × 10 8 TCID 50 ). MORV led to increased CD8 + cytotoxic T cells without compensatory increases in tumor-associated macrophages and granulocytic or monocytic myeloid-derived suppressor cells. CONCLUSIONS: Our findings indicate that wild-type MORV is safe and can induce potent tumor regression via immune-mediated and immune-independent mechanisms in HCC and CCA animal models without dose limiting adverse events. These data warrant further development and clinical translation of MORV as an oncolytic virotherapy platform.

Empirical comparison of web-based antimicrobial peptide prediction tools.

MOTIVATION: Antimicrobial peptides (AMPs) are innate immune molecules that exhibit activities against a range of microbes, including bacteria, fungi, viruses and protozoa. Recent increases in microbial resistance against current drugs has led to a concomitant increase in the need for novel antimicrobial agents. Over the last decade, a number of AMP prediction tools have been designed and made freely available online. These AMP prediction tools show potential to discriminate AMPs from non-AMPs, but the relative quality of the predictions produced by the various tools is difficult to quantify. RESULTS: We compiled two sets of AMP and non-AMP peptides, separated into three categories-antimicrobial, antibacterial and bacteriocins. Using these benchmark data sets, we carried out a systematic evaluation of ten publicly available AMP prediction methods. Among the six general AMP prediction tools-ADAM, CAMPR3(RF), CAMPR3(SVM), MLAMP, DBAASP and MLAMP-we find that CAMPR3(RF) provides a statistically significant improvement in performance, as measured by the area under the receiver operating characteristic (ROC) curve, relative to the other five methods. Surprisingly, for antibacterial prediction, the original AntiBP method significantly outperforms its successor, AntiBP2 based on one benchmark dataset. The two bacteriocin prediction tools, BAGEL3 and BACTIBASE, both provide very good performance and BAGEL3 outperforms its predecessor, BACTIBASE, on the larger of the two benchmarks. CONTACT: gaberemu@ngha.med.sa or william-noble@uw.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DAMPD: a manually curated antimicrobial peptide database.

The demand for antimicrobial peptides (AMPs) is rising because of the increased occurrence of pathogens that are tolerant or resistant to conventional antibiotics. Since naturally occurring AMPs could serve as templates for the development of new anti-infectious agents to which pathogens are not resistant, a resource that contains relevant information on AMP is of great interest. To that extent, we developed the Dragon Antimicrobial Peptide Database (DAMPD, http://apps.sanbi.ac.za/dampd) that contains 1232 manually curated AMPs. DAMPD is an update and a replacement of the ANTIMIC database. In DAMPD an integrated interface allows in a simple fashion querying based on taxonomy, species, AMP family, citation, keywords and a combination of search terms and fields (Advanced Search). A number of tools such as Blast, ClustalW, HMMER, Hydrocalculator, SignalP, AMP predictor, as well as a number of other resources that provide additional information about the results are also provided and integrated into DAMPD to augment biological analysis of AMPs.

Multi-modal efficacy of a chimeric vesiculovirus expressing the Morreton glycoprotein in sarcoma.

Vesiculoviruses are attractive oncolytic virus platforms due to their rapid replication, appreciable transgene capacity, broad tropism, limited preexisting immunity, and tumor selectivity through type I interferon response defects in malignant cells. We developed a synthetic chimeric virus (VMG) expressing the glycoprotein (G) from Morreton virus (MorV) and utilizing the remaining structural genes from vesicular stomatitis virus (VSV). VMG exhibited in vitro efficacy by inducing oncolysis in a broad range of sarcoma subtypes across multiple species. Notably, all cell lines tested showed the ability of VMG to yield productive infection with rapid replication kinetics and induction of apoptosis. Furthermore, pilot safety evaluations of VMG in immunocompetent, non-tumor-bearing mice showed an absence of toxicity with intranasal doses as high as 1e10 50% tissue culture infectious dose (TCID50)/kg. Locoregional administration of VMG in vivo resulted in tumor reduction in an immunodeficient Ewing sarcoma xenograft at doses as low as 2e5 TCID50. In a murine syngeneic fibrosarcoma model, while no tumor inhibition was achieved with VMG, there was a robust induction of CD8+ T cells within the tumor. The studies described herein establish the promising potential for VMG to be used as a novel oncolytic virotherapy platform with anticancer effects in sarcoma.

Repurposing live attenuated trivalent MMR vaccine as cost-effective cancer immunotherapy.

It has long been known that oncolytic viruses wield their therapeutic capability by priming an inflammatory state within the tumor and activating the tumor immune microenvironment, resulting in a multifaceted antitumor immune response. Vaccine-derived viruses, such as measles and mumps, have demonstrated promising potential for treating human cancer in animal models and clinical trials. However, the extensive cost of manufacturing current oncolytic viral products makes them far out of reach for most patients. Here by analyzing the impact of intratumoral (IT) administrations of the trivalent live attenuated measles, mumps, and rubella viruses (MMR) vaccine, we unveil the cellular and molecular basis of MMR-induced anti-cancer activity. Strikingly, we found that IT delivery of low doses of MMR correlates with tumor control and improved survival in murine hepatocellular cancer and colorectal cancer models via increased tumor infiltration of CD8+ granzyme B+ T-cells and decreased macrophages. Moreover, our data indicate that MMR activates key cellular effectors of the host's innate and adaptive antitumor immunity, culminating in an immunologically coordinated cancer cell death. These findings warrant further work on the potential for MMR to be repurposed as safe and cost-effective cancer immunotherapy to impact cancer patients globally.

Automated Hippocampal Subfield Volumetric Analyses in Atypical Alzheimer's Disease.

BACKGROUND: Posterior cortical atrophy (PCA) and logopenic progressive aphasia (LPA) are two of the most common variants of atypical Alzheimer's disease (AD). Both PCA and LPA are associated with relative sparing of hippocampus compared to neocortex, although hippocampal atrophy is observed. It is unclear whether regional patterns of hippocampal subfield involvement differ between PCA and LPA, and whether they differ from typical AD. OBJECTIVE: To assess volume of specific subfields of the hippocampus in PCA, LPA, and typical AD. METHODS: Fifty-nine patients with PCA and 77 patients with LPA were recruited and underwent T1-weighted MRI and Pittsburgh Compound B (PiB) PET at Mayo Clinic. Thirty-six probable AD patients and 100 controls were identified from the Alzheimer's Disease Neuroimaging Initiative. Hippocampal subfield volumes were calculated using Freesurfer, and volumes were compared between PCA, LPA, AD, and controls using Kruskal-Wallis and Dunn tests. RESULTS: The LPA and PCA groups both showed the most striking abnormalities in CA4, presubiculum, molecular layer of the hippocampus, molecular and granule cell layers of the dentate gyrus, and the hippocampal-amygdala transition area, although atrophy was left-sided in LPA. PCA showed smaller volume of right presubiculum compared to LPA, with trends for smaller volumes of right parasubiculum and fimbria. LPA showed a trend for smaller volumes of left CA1 compared to PCA. The AD group showed smaller volumes of the right subiculum, CA1, and presubiculum compared to LPA. CONCLUSION: Patterns of hippocampal subfield atrophy differ across the different syndromic variants of AD.

Peptide-Protein Interaction Studies of Antimicrobial Peptides Targeting Middle East Respiratory Syndrome Coronavirus Spike Protein: An In Silico Approach.

There is no effective therapeutic or vaccine for Middle East Respiratory Syndrome and this study attempts to find therapy using peptide by establishing a basis for the peptide-protein interactions through in silico docking studies for the spike protein of MERS-CoV. The antimicrobial peptides (AMPs) were retrieved from the antimicrobial peptide database (APD3) and shortlisted based on certain important physicochemical properties. The binding mode of the shortlisted peptides was measured based on the number of clusters which forms in a protein-peptide docking using Piper. As a result, we identified a list of putative AMPs which binds to the spike protein of MERS-CoV, which may be crucial in providing the inhibitory action. It is observed that seven putative peptides have good binding score based on cluster size cutoff of 208. We conclude that seven peptides, namely, AP00225, AP00180, AP00549, AP00744, AP00729, AP00764, and AP00223, could possibly have binding with the active site of the MERS-CoV spike protein. These seven AMPs could serve as a therapeutic option for MERS and enhance its treatment outcome.

Current treatment options and the role of peptides as potential therapeutic components for Middle East Respiratory Syndrome (MERS): A review.

Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is a highly pathogenic respiratory virus with mechanisms that may be driven by innate immune responses. Despite the effort of scientific studies related to this virus, Middle East Respiratory Syndrome (MERS) is still a public health concern. MERS-CoV infection has a high mortality rate, and to date, no therapeutic or vaccine has been discovered, that is effective in treating or preventing the disease. In this review, we summarize our understanding of the molecular and biological events of compounds acting as MERS-CoV inhibitors, the outcomes of existing therapeutic options and the various drugs undergoing clinical trials. Currently, several therapeutic options have been employed, such as convalescent plasma (CP), intravenous immunoglobulin (IVIG), monoclonal antibodies and repurposing of existing clinically approved drugs. However, these therapeutic options have drawbacks, thus the need for an alternative approach. The requirement for effective therapeutic treatment has brought the necessity for additional MERS treatments. We suggest that antimicrobial peptides (AMPs) may be used as alternative therapeutic agents against MERS-CoV infection. In addition, we propose the feasibility of developing effective agents by repurposing the existing and clinically approved anti-coronavirus and anti-viral peptide drugs.

A Model of Exposure to Extreme Environmental Heat Uncovers the Human Transcriptome to Heat Stress.

The molecular mechanisms by which individuals subjected to environmental heat stress either recover or develop heat-related complications are not well understood. We analysed the changes in blood mononuclear gene expression patterns in human volunteers exposed to extreme heat in a sauna (temperature of 75.7 ± 0.86 °C). Our analysis reveals that expression changes occur rapidly with no significant increase in core temperature and continue to amplify one hour after the end of heat stress. The reprogramed transcriptome was predominantly inhibitory, as more than two-thirds of the expressed genes were suppressed. The differentially expressed genes encoded proteins that function in stress-associated pathways; including proteostasis, energy metabolism, cell growth and proliferation, and cell death, and survival. The transcriptome also included mitochondrial dysfunction, altered protein synthesis, and reduced expression of genes -related to immune function. The findings reveal the human transcriptomic response to heat and highlight changes that might underlie the health outcomes observed during heat waves.

Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer.

PURPOSE: There has been considerable interest in using whole-genome expression profiles for the classification of colorectal cancer (CRC). The selection of important features is a crucial step before training a classifier. METHODS: In this study, we built a model that uses support vector machine (SVM) to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300, and 500 genes most relevant to CRC using the minimum-redundancy-maximum-relevance (mRMR) technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function [RBF], and sigmoid). RESULTS: The best model, which used 30 genes and RBF kernel, outperformed other combinations; it had an accuracy of 84% for both ten fold and leave-one-out cross validations in discriminating the cancer samples from the normal samples. With this 30 genes set from mRMR, six classifiers were trained using random forest (RF), Bayes net (BN), multilayer perceptron (MLP), naïve Bayes (NB), reduced error pruning tree (REPT), and SVM. Two hybrids, mRMR + SVM and mRMR + BN, were the best models when tested on other datasets, and they achieved a prediction accuracy of 95.27% and 91.99%, respectively, compared to other mRMR hybrid models (mRMR + RF, mRMR + NB, mRMR + REPT, and mRMR + MLP). Ingenuity pathway analysis was used to analyze the functions of the 30 genes selected for this model and their potential association with CRC: CDH3, CEACAM7, CLDN1, IL8, IL6R, MMP1, MMP7, and TGFB1 were predicted to be CRC biomarkers. CONCLUSION: This model could be used to further develop a diagnostic tool for predicting CRC based on gene expression data from patient samples.