A co-formulation of interferons alpha2b and gamma distinctively targets cell cycle in the glioblastoma-derived cell line U-87MG.

BACKGROUND: HeberFERON is a co-formulation of α2b and γ interferons, based on their synergism, which has shown its clinical superiority over individual interferons in basal cell carcinomas. In glioblastoma (GBM), HeberFERON has displayed promising preclinical and clinical results. This led us to design a microarray experiment aimed at identifying the molecular mechanisms involved in the distinctive effect of HeberFERON compared to the individual interferons in U-87MG model. METHODS: Transcriptional expression profiling including a control (untreated) and three groups receiving α2b-interferon, γ-interferon and HeberFERON was performed using an Illumina HT-12 microarray platform. Unsupervised methods for gene and sample grouping, identification of differentially expressed genes, functional enrichment and network analysis computational biology methods were applied to identify distinctive transcription patterns of HeberFERON. Validation of most representative genes was performed by qPCR. For the cell cycle analysis of cells treated with HeberFERON for 24 h, 48 and 72 h we used flow cytometry. RESULTS: The three treatments show different behavior based on the gene expression profiles. The enrichment analysis identified several mitotic cell cycle related events, in particular from prometaphase to anaphase, which are exclusively targeted by HeberFERON. The FOXM1 transcription factor network that is involved in several cell cycle phases and is highly expressed in GBMs, is significantly down regulated. Flow cytometry experiments corroborated the action of HeberFERON on the cell cycle in a dose and time dependent manner with a clear cellular arrest as of 24 h post-treatment. Despite the fact that p53 was not down-regulated, several genes involved in its regulatory activity were functionally enriched. Network analysis also revealed a strong relationship of p53 with genes targeted by HeberFERON. We propose a mechanistic model to explain this distinctive action, based on the simultaneous activation of PKR and ATF3, p53 phosphorylation changes, as well as its reduced MDM2 mediated ubiquitination and export from the nucleus to the cytoplasm. PLK1, AURKB, BIRC5 and CCNB1 genes, all regulated by FOXM1, also play central roles in this model. These and other interactions could explain a G2/M arrest and the effect of HeberFERON on the proliferation of U-87MG. CONCLUSIONS: We proposed molecular mechanisms underlying the distinctive behavior of HeberFERON compared to the treatments with the individual interferons in U-87MG model, where cell cycle related events were highly relevant.

Targeting CK2 mediated signaling to impair/tackle SARS-CoV-2 infection: a computational biology approach.

BACKGROUND: Similarities in the hijacking mechanisms used by SARS-CoV-2 and several types of cancer, suggest the repurposing of cancer drugs to treat Covid-19. CK2 kinase antagonists have been proposed for cancer treatment. A recent study in cells infected with SARS-CoV-2 found a significant CK2 kinase activity, and the use of a CK2 inhibitor showed antiviral responses. CIGB-300, originally designed as an anticancer peptide, is an antagonist of CK2 kinase activity that binds to the CK2 phospho-acceptor sites. Recent preliminary results show the antiviral activity of CIGB-300 using a surrogate model of coronavirus. Here we present a computational biology study that provides evidence, at the molecular level, of how CIGB-300 may interfere with the SARS-CoV-2 life cycle within infected human cells. METHODS: Sequence analyses and data from phosphorylation studies were combined to predict infection-induced molecular mechanisms that can be interfered by CIGB-300. Next, we integrated data from multi-omics studies and data focusing on the antagonistic effect on the CK2 kinase activity of CIGB-300. A combination of network and functional enrichment analyses was used. RESULTS: Firstly, from the SARS-CoV studies, we inferred the potential incidence of CIGB-300 in SARS-CoV-2 interference on the immune response. Afterwards, from the analysis of multiple omics data, we proposed the action of CIGB-300 from the early stages of viral infections perturbing the virus hijacking of RNA splicing machinery. We also predicted the interference of CIGB-300 in virus-host interactions that are responsible for the high infectivity and the particular immune response to SARS-CoV-2 infection. Furthermore, we provided evidence of how CIGB-300 may participate in the attenuation of phenotypes related to muscle, bleeding, coagulation and respiratory disorders. CONCLUSIONS: Our computational analysis proposes putative molecular mechanisms that support the antiviral activity of CIGB-300.

Safety and Therapeutic Profile of a GnRH-Based Vaccine Candidate Directed to Prostate Cancer. A 10-Year Follow-Up of Patients Vaccinated With Heberprovac.

Heberprovac is a GnRH based vaccine candidate containing 2.4 mg of the GnRHm1-TT peptide as the main active principle; 245 µg of the very small size proteoliposomes adjuvant (VSSP); and 350 µL of Montanide ISA 51 VG oil adjuvant. The aim of this study was to assess the safety and tolerance of the Heberprovac in advanced prostate cancer patients as well as its capacity to induce anti-GnRH antibodies, the subsequent effects on serum levels of testosterone and PSA and the patient overall survival. The study included eight patients with histologically-proven advanced prostate cancer with indication for hormonal therapy, who received seven intramuscular immunizations with Heberprovac within 18 weeks. Anti-GnRH antibody titers, testosterone and PSA levels, as well as clinical parameters were recorded and evaluated. The vaccine was well tolerated. Significant reductions in serum levels of testosterone and PSA were seen after four immunizations. Castrate levels of testosterone were observed in all patients at the end of the immunization schedule, which remained at the lowest level for at least 20 months. In a 10-year follow-up three out of six patients who completed the entire trial survived. In contrast only one out eight patients survived in the same period in a matched randomly selected group receiving standard anti-hormonal treatment. Heberprovac vaccination showed a good security profile, as well as immunological, biochemical and, most importantly, clinical benefit. The vaccinated group displayed survival advantage compared with the reference group that received standard treatment. These results warrant further clinical trials with Heberprovac involving a larger cohort.

Gradual reduction of testosterone using a gonadotropin-releasing hormone vaccination delays castration resistance in a prostate cancer model.

In a previous study aimed to design a novel prostate cancer vaccine, the authors of the present study demonstrated the advantage of combining the adjuvants Montanide ISA 51 with very small size proteoliposomes (VSSP) to promote a significant humoral immune response to gonadotropin-releasing hormone (GnRH) in healthy animals. The present study compared the efficacy of this vaccine formulation versus the standard treatment currently available in terms of preventing the development of tumors in DD/S mice injected with Shionogi carcinoma (SC) 115 cells. The results demonstrated that 5 non-vaccinated control mice exhibited a fast tumor growth, and succumbed to the disease within 19-31 days. Mice immunized with the GnRH/Montanide ISA 51/VSSP vaccine exhibited a moderate decline in testosterone levels that was associated with a decrease in anti-GnRH antibody titers, which lead to a sustained tumor growth inhibition. In total, 2 mice in the immunized group exhibited complete remission of the tumor for the duration of the present study. In addition, castrated mice, which were used as a control for standard hormonal therapy, exhibited an accelerated decrease in tumor size. However, tumor relapse was observed between days 50 and 54, and between days 65 and 85, following the injection of SC 155 cells. Therefore, these mice were sacrificed at day 90. The present study concludes that the slow and moderate reduction of testosterone levels observed using the GnRH-based vaccine may delay the appearance of castration resistance in a Shionogi prostate cancer model. These findings suggest that this vaccine may be used to delay castration resistance in patients with prostate cancer.

Computational proteomics pitfalls and challenges: HavanaBioinfo 2012 workshop report.

The workshop "Bioinformatics for Biotechnology Applications (HavanaBioinfo 2012)", held December 8-11, 2012 in Havana, aimed at exploring new bioinformatics tools and approaches for large-scale proteomics, genomics and chemoinformatics. Major conclusions of the workshop include the following: (i) development of new applications and bioinformatics tools for proteomic repository analysis is crucial; current proteomic repositories contain enough data (spectra/identifications) that can be used to increase the annotations in protein databases and to generate new tools for protein identification; (ii) spectral libraries, de novo sequencing and database search tools should be combined to increase the number of protein identifications; (iii) protein probabilities and FDR are not yet sufficiently mature; (iv) computational proteomics software needs to become more intuitive; and at the same time appropriate education and training should be provided to help in the efficient exchange of knowledge between mass spectrometrists and experimental biologists and bioinformaticians in order to increase their bioinformatics background, especially statistics knowledge.

A methodology based on molecular interactions and pathways to find candidate genes associated to diseases: its application to schizophrenia and Alzheimer's disease.

Experimental techniques for the identification of genes associated with diseases are expensive and have certain limitations. In this scenario, computational methods are useful tools to identify lists of promising genes for further experimental verification. This paper describes a flexible methodology for the in silico prediction of genes associated with diseases combining the use of available tools for gene enrichment analysis, gene network generation and gene prioritization. A set of reference genes, with a known association to a disease, is used as bait to extract candidate genes from molecular interaction networks and enriched pathways. In a second step, prioritization methods are applied to evaluate the similarities between previously selected candidates and the set of reference genes. The top genes obtained by these programs are grouped into a single list sorted by the number of methods that have selected each gene. As a proof of concept, top genes reported a few years ago in SzGene and AlzGene databases were used as references to predict genes associated to schizophrenia and Alzheimer's disease, respectively. In both cases, we were able to predict a statistically significant amount of genes belonging to the updated lists.

BisoGenet: a new tool for gene network building, visualization and analysis.

BACKGROUND: The increasing availability and diversity of omics data in the post-genomic era offers new perspectives in most areas of biomedical research. Graph-based biological networks models capture the topology of the functional relationships between molecular entities such as gene, protein and small compounds and provide a suitable framework for integrating and analyzing omics-data. The development of software tools capable of integrating data from different sources and to provide flexible methods to reconstruct, represent and analyze topological networks is an active field of research in bioinformatics. RESULTS: BisoGenet is a multi-tier application for visualization and analysis of biomolecular relationships. The system consists of three tiers. In the data tier, an in-house database stores genomics information, protein-protein interactions, protein-DNA interactions, gene ontology and metabolic pathways. In the middle tier, a global network is created at server startup, representing the whole data on bioentities and their relationships retrieved from the database. The client tier is a Cytoscape plugin, which manages user input, communication with the Web Service, visualization and analysis of the resulting network. CONCLUSION: BisoGenet is able to build and visualize biological networks in a fast and user-friendly manner. A feature of Bisogenet is the possibility to include coding relations to distinguish between genes and their products. This feature could be instrumental to achieve a finer grain representation of the bioentities and their relationships. The client application includes network analysis tools and interactive network expansion capabilities. In addition, an option is provided to allow other networks to be converted to BisoGenet. This feature facilitates the integration of our software with other tools available in the Cytoscape platform. BisoGenet is available at http://bio.cigb.edu.cu/bisogenet-cytoscape/.

Gonadotrophin releasing hormone-based vaccine, an effective candidate for prostate cancer and other hormone-sensitive neoplasms.

Prostate growth, development, functions, and neoplastic transformation is androgen dependent. Estrogens have similar effects in the ovary and breast. Previous studies using gonadotrophin releasing hormone (GnRH/LHRH) vaccines have shown the usefulness of immunization against this hormone in prostate (PC) and breast cancer (BC). We have synthesized a peptide mutated at position 6 and attached to the 830-844 tetanic toxoid (TT) helper T cell sequence in the same synthesis process. After repeated pig immunizations, we have demonstrated a vaccine that significantly decreased testes size (p < 0.001), prostate (p < 0.01), seminal vesicles (p < 0.01), and testosterone (T) castration [0.05 nM ml(-1) (p < 0. 01)]. Similar results were obtained in adult male and female healthy dogs and Macaca fascicularis models. These data indicate that this GnRHm1-TT vaccine is safe and able to induce significant tumor growth inhibition in the Dunning R3327-H rat androgen responsive prostate tumor model. In these rats, the immunization induced high anti-GnRH titers concomitant with T castration reduction (p < 0.01) in 90% of the animals tested. In addition, 70% of the responders exhibited tumor growth inhibition (p = 0.02) and a survival rate approximately three times longer that those of untreated rats. These data indicate that GnRHm1-TT vaccine may be a potential candidate in the treatment of PC, BC, and other hormone-dependent cancers.

Prioritization of gene regulatory interactions from large-scale modules in yeast.

BACKGROUND: The identification of groups of co-regulated genes and their transcription factors, called transcriptional modules, has been a focus of many studies about biological systems. While methods have been developed to derive numerous modules from genome-wide data, individual links between regulatory proteins and target genes still need experimental verification. In this work, we aim to prioritize regulator-target links within transcriptional modules based on three types of large-scale data sources. RESULTS: Starting with putative transcriptional modules from ChIP-chip data, we first derive modules in which target genes show both expression and function coherence. The most reliable regulatory links between transcription factors and target genes are established by identifying intersection of target genes in coherent modules for each enriched functional category. Using a combination of genome-wide yeast data in normal growth conditions and two different reference datasets, we show that our method predicts regulatory interactions with significantly higher predictive power than ChIP-chip binding data alone. A comparison with results from other studies highlights that our approach provides a reliable and complementary set of regulatory interactions. Based on our results, we can also identify functionally interacting target genes, for instance, a group of co-regulated proteins related to cell wall synthesis. Furthermore, we report novel conserved binding sites of a glycoprotein-encoding gene, CIS3, regulated by Swi6-Swi4 and Ndd1-Fkh2-Mcm1 complexes. CONCLUSION: We provide a simple method to prioritize individual TF-gene interactions from large-scale transcriptional modules. In comparison with other published works, we predict a complementary set of regulatory interactions which yields a similar or higher prediction accuracy at the expense of sensitivity. Therefore, our method can serve as an alternative approach to prioritization for further experimental studies.

Immunotherapy of prostate cancer in a murine model using a novel GnRH based vaccine candidate.

Previous studies with gonadotrophin releasing hormone (GnRH/LHRH) vaccines have shown the usefulness of immunization against this hormone in prostate cancer. To this end, we have generated a completely synthetic peptide modified at position 6 and attached to the 830-844 tetanic toxoid (TT) helper T cell sequence. Through this work we have demonstrated that the GnRHm1-TT molecule was highly immunogenic when it is formulated as an oil-based emulsion adjuvated with Montanide ISA 51. That results correlated directly with testosterone reduction and tumor growth inhibition of the Dunning R3327-H androgen responsive prostate tumor model in rats. GnRHm1-TT, proved to be safe and useful for future clinical trials.

Correlating protein-DNA and protein-protein interaction networks.

Here, we search protein-DNA binding data for prevalent pairs and higher-order tuples of co-occurring transcription factors (TF) in Saccharomyces cerevisiae. While the identification of such modules is dependent on uncertainties of genome-wide data sets, we find several biologically meaningful examples, which allow putative annotation of yet unclassified genes. For the frequently occurring transcriptional module Mcm1-Fkh2-Ndd1, we identified several new target genes involved in cell-cycle control and filament formation. Using large-scale protein interaction data, we demonstrate a significant correlation between co-occurrence of TF binding sites and the vicinity in the protein interaction network. In particular we find that directly interacting transcription factors and those which are members of a protein complex are more likely to occur together as putative DNA-binding modules.