5′-Phosphorylation of Oligonucleotides with Phosphorous Acid in Automated DNA Synthesis

Abstract

Coupling of phosphorous acid in automated DNA synthesis using H-phosphonate methodology leads to 5′-5′ linked dimers and 5′-H-phosphonates. The yield is dependent on the phosphorous acid concentration, chain length of the oligomer, and pore size of the support. 5′-Phosphate oligomers are obtained from the H-phosphonate oligomers by silylation and oxidation.     

CIGB-300, a synthetic peptide-based drug that targets the CK2 phosphoaceptor domain. Translational and clinical research

CK2 represents an oncology target scientifically validated. However, clinical research with inhibitors of the CK2-mediated phosphorylation event is still insufficient to recognize it as a clinically validated target. CIGB-300, an investigational peptide-based drug that targets the phosphoaceptor site, binds to a CK2 substrate array in vitro but mainly to B23/nucleophosmin in vivo. The CIGB-300 proapoptotic effect is preceded by its nucleolar localization, inhibition of the CK2-mediated phosphorylation on B23/nucleophosmin and nucleolar disassembly. Importantly, CIGB-300 shifted a protein array linked to apoptosis, ribosome biogenesis, cell proliferation, glycolisis, and cell motility in proteomic studies which helped to understand its mechanism of action. In the clinical ground, CIGB-300 has proved to be safe and well tolerated in a First-in-Human trial in women with cervical malignancies who also experienced signs of clinical benefit. In a second Phase 1 clinical trial in women with cervical cancer stage IB2/II, the MTD and DLT have been also identified in the clinical setting. Interestingly, in cervical tumors the B23/nucleophosmin protein levels were significantly reduced after CIGB-300 treatment at the nucleus compartment. In addition, expanded use of CIGB-300 in case studies has evidenced antitumor activity when administered as compassional option. Collectively, our data outline important clues on translational and clinical research from this novel peptide-based drug reinforcing its perspectives to treat cancer and paving the way to validate CK2 as a promising target in oncology.     

Raman Spectroscopic Study of Radioresistant Oral Cancer Sublines Established by Fractionated Ionizing Radiation

Radiotherapy is an important treatment modality for oral cancer. However, development of radioresistance is a major hurdle in the efficacy of radiotherapy in oral cancer patients. Identifying predictors of radioresistance is a challenging task and has met with little success. The aim of the present study was to explore the differential spectral profiles of the established radioresistant sublines and parental oral cancer cell lines by Raman spectroscopy. We have established radioresistant sublines namely, 50Gy-UPCI:SCC029B and 70Gy-UPCI:SCC029B from its parental UPCI:SCC029B cell line, by using clinically admissible 2Gy fractionated ionizing radiation (FIR). The developed radioresistant character was validated by clonogenic cell survival assay and known radioresistance-related protein markers like Mcl-1, Bcl-2, Cox-2 and Survivin. Altered cellular morphology with significant increase (p<0.001) in the number of filopodia in radioresistant cells with respect to parental cells was observed. The Raman spectra of parental UPCI:SCC029B, 50Gy-UPCI:SCC029B and 70Gy-UPCI:SCC029B cells were acquired and spectral features indicate possible differences in biomolecules like proteins, lipids and nucleic acids. Principal component analysis (PCA) provided three clusters corresponding to radioresistant 50Gy, 70Gy-UPCI:SCC029B sublines and parental UPCI:SCC029B cell line with minor overlap, which suggest altered molecular profile acquired by the radioresistant cells due to multiple doses of irradiation. The findings of this study support the potential of Raman spectroscopy in prediction of radioresistance and possibly contribute to better prognosis of oral cancer.     

Automated discrimination of brain pathological state attending to complex structural brain network properties: the shiverer mutant mouse case

Neuroimaging classification procedures between normal and pathological subjects are sparse and highly dependent of an expert''s clinical criterion. Here, we aimed to investigate whether possible brain structural network differences in the shiverer mouse mutant, a relevant animal model of myelin related diseases, can reflect intrinsic individual brain properties that allow the automatic discrimination between the shiverer and normal subjects. Common structural networks properties between shiverer (C3Fe.SWV Mbp^shi/Mbp^shi, n = 6) and background control (C3HeB.FeJ, n = 6) mice are estimated and compared by means of three diffusion weighted MRI (DW-MRI) fiber tractography algorithms and a graph framework. Firstly, we found that brain networks of control group are significantly more clustered, modularized, efficient and optimized than those of the shiverer group, which presented significantly increased characteristic path length. These results are in line with previous structural/functional complex brain networks analysis that have revealed topologic differences and brain network randomization associated to specific states of human brain pathology. In addition, by means of network measures spatial representations and discrimination analysis, we show that it is possible to classify with high accuracy to which group each subject belongs, providing also a probability value of being a normal or shiverer subject as an individual anatomical classifier. The obtained correct predictions (e.g., around 91.6–100%) and clear spatial subdivisions between control and shiverer mice, suggest that there might exist specific network subspaces corresponding to specific brain disorders, supporting also the point of view that complex brain network analyses constitutes promising tools in the future creation of interpretable imaging biomarkers.     

TRIF is a critical negative regulator of TLR agonist mediated activation of dendritic cells in vivo

Despite recent advances in developing and licensing adjuvants, there is a great need for more potent formulations to enhance immunogenicity of vaccines. An Eimeria tenella derived antigen (rEA) augments immune responses against several pathogens in animal models and recently was confirmed to be safe for human use. In this study, we have analyzed the molecular mechanisms underlying rEA activity in mice, and confirmed that rEA activates multiple immune cell types, including DCs, macrophages, NK, B, and T cells. The rEA adjuvant also elicits the induction of pleiotropic pro-inflammatory cytokines, responses that completely depend upon the presence of the TLR adaptor protein MyD88. Surprisingly, we also found that the TRIF adaptor protein acts as a potent negative regulator of TLR agonist-triggered immune responses. For example, IL12 production and the induction of co-stimulatory molecule expression by DCs and IFNγ production by NK cells in vivo were significantly increased in rEA-treated TRIF-KO mice. Importantly, however, TRIF suppressive effects were not restricted to rEA-mediated responses, but were apparent in LPS- or ODN2006-activated DCs as well. Taken together, our findings confirm that rEA is a potent adjuvant, triggering robust activation of the innate immune system, in a manner that is augmented by MyD88 and inhibited by TRIF; thereby unveiling the potential complexities of modulating TLR activity to augment vaccine efficacy.     

Efficacy of yeasts in the management of root-knot nematode Meloidogyne incognita,in Flame Seedless grape vines and the consequent effect on the productivity of the vines

Twenty-two yeast strains were screened for their efficacy in suppression of root-knot nematode Meloidogyne incognita on Flame Seedless grapevines. The most effective seven strains were chosen to study their effect on nematode population in vitro and in vivo as well as their impact on crop productivity and fruit quality. Data showed that all treatments significantly reduced the number of juveniles in vitro after 24 and 48 h. The highest percentage of nematode mortality was achieved by the application of Pichia gluilliermondii Moh10, Pachytrichospora transvaalensis Y-1240, Candida albicans Moh Y-5 and Geotichum terrestre Y 2162. Similarly, under greenhouse conditions, all treatments significantly reduced the number of juveniles and disease index compared to the untreated plants during both incubation periods (30 and 50 days). Under field conditions, the tested yeast strains as well as Nemacour (nematocide) resulted in a significant reduction (P<0.05) of populations of Meloidogyne incognita compared to the control in two successive seasons tested. The beneficial impact of application of yeast strains was reflected in crop productivity on Flame Seedless grapevines as well as fruit quality. Percentages of vegetative buds, flower buds, shoot length and leaf number were significantly enhanced as a result of application of the different yeast strains and Nemacour. Bunch weight, berry weight and berry numbers were significantly increased by Pachytrichospora transvaalensis Y-1240, Geotichum terrestre Y-2162 and Candida albicans Moh Y-5. Nemacour and other yeast strains did not show any significant differences compared to the infected vines. Percentage of total soluble solids (TSS%) increased by applying all yeast strains, whereas Nemacour treatment gave the highest value of both TSS percent and percentage of acidity and increased anthocyanins in the skin of berries.     

On evaluating MHC-II binding peptide prediction methods

Choice of one method over another for MHC-II binding peptide prediction is typically based on published reports of their estimated performance on standard benchmark datasets. We show that several standard benchmark datasets of unique peptides used in such studies contain a substantial number of peptides that share a high degree of sequence identity with one or more other peptide sequences in the same dataset. Thus, in a standard cross-validation setup, the test set and the training set are likely to contain sequences that share a high degree of sequence identity with each other, leading to overly optimistic estimates of performance. Hence, to more rigorously assess the relative performance of different prediction methods, we explore the use of similarity-reduced datasets. We introduce three similarity-reduced MHC-II benchmark datasets derived from MHCPEP, MHCBN, and IEDB databases. The results of our comparison of the performance of three MHC-II binding peptide prediction methods estimated using datasets of unique peptides with that obtained using their similarity-reduced counterparts shows that the former can be rather optimistic relative to the performance of the same methods on similarity-reduced counterparts of the same datasets. Furthermore, our results demonstrate that conclusions regarding the superiority of one method over another drawn on the basis of performance estimates obtained using commonly used datasets of unique peptides are often contradicted by the observed performance of the methods on the similarity-reduced versions of the same datasets. These results underscore the importance of using similarity-reduced datasets in rigorously comparing the performance of alternative MHC-II peptide prediction methods.