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.

Synergic effect of anticancer peptide CIGB-552 and Cisplatin in lung cancer models.

BACKGROUND: The antitumor peptide CIGB-552 is a new targeted anticancer therapy which molecular mechanism is associated with the inhibition of the transcription factor NF-kB, mediated by COMMD1 protein stabilization. In this study, we examined the antiproliferative capacity of CIGB-552 in combination with chemotherapeutic agents in lung cancer models. METHODS AND RESULTS: We combined of CIGB-552 and the antineoplastic agent Cisplatin (CDDP) in concomitant and pre-treatment scenary in a dose matrix approach. This study was performed in the non-small cell lung cancer cell lines NCI-H460, A549 and in a mouse model of TC-1 lung cancer. Our results demonstrate a clear synergic effect between 37.5 µM of CIGB-552 and 5 µM of CDDP under concomitant scheme, on proliferation inhibition, cell cycle arrest, apoptosis induction and oxidative stress response. The effect of CIGB-552 (1 mg/kg) and CDDP (0.4 mg/kg) administrated as a combined therapy was demonstrated in vivo in a TC-1 mouse model where the combination achieved an effective antitumor response, without any deterioration signs or side effects. CONCLUSIONS: These findings demonstrate the efficacy of the concomitant combination of both drugs in preclinical studies and support the use of this therapy in clinical trials. This study is the first evidence of synergistic effect of the combination of  the antitumoral peptide CIGB-552 and CDDP.

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.

A self-consistent probabilistic formulation for inference of interactions.

Large molecular interaction networks are nowadays assembled in biomedical researches along with important technological advances. Diverse interaction measures, for which input solely consisting of the incidence of causal-factors, with the corresponding outcome of an inquired effect, are formulated without an obvious mathematical unity. Consequently, conceptual and practical ambivalences arise. We identify here a probabilistic requirement consistent with that input, and find, by the rules of probability theory, that it leads to a model multiplicative in the complement of the effect. Important practical properties are revealed along these theoretical derivations, that has not been noticed before.

Proteomic Study to Survey the CIGB-552 Antitumor Effect.

CIGB-552 is a cell-penetrating peptide that exerts in vitro and in vivo antitumor effect on cancer cells. In the present work, the mechanism involved in such anticancer activity was studied using chemical proteomics and expression-based proteomics in culture cancer cell lines. CIGB-552 interacts with at least 55 proteins, as determined by chemical proteomics. A temporal differential proteomics based on iTRAQ quantification method was performed to identify CIGB-552 modulated proteins. The proteomic profile includes 72 differentially expressed proteins in response to CIGB-552 treatment. Proteins related to cell proliferation and apoptosis were identified by both approaches. In line with previous findings, proteomic data revealed that CIGB-552 triggers the inhibition of NF-κB signaling pathway. Furthermore, proteins related to cell invasion were differentially modulated by CIGB-552 treatment suggesting new potentialities of CIGB-552 as anticancer agent. Overall, the current study contributes to a better understanding of the antitumor action mechanism of CIGB-552.

Synergistic interactions of the anti-casein kinase 2 CIGB-300 peptide and chemotherapeutic agents in lung and cervical preclinical cancer models.

CIGB-300 is a novel clinical-stage synthetic peptide that impairs the casein kinase 2 (CK2)-mediated phosphorylation of B23/nucleophosmin in different experimental settings and cancer models. As a single agent, CIGB-300 induces apoptosis in vitro and in vivo and modulates an array of proteins that are mainly involved in drug resistance, cell proliferation and apoptosis, as determined by proteomic analysis. However, the clinical oncology practice and cumulative knowledge on tumor biology suggest that drug combinations are more likely to cope with tumor complexity compared to single agents. In this study, we investigated the antiproliferative effect of CIGB-300 when combined with different anticancer drugs, such as cisplatin (alkylating), paclitaxel (antimitotic), doxorubicin (antitopoisomerase II) or 5-fluorouracil (DNA/RNA antimetabolite) in cell lines derived from lung and cervical cancer. Of note, using a Latin square design and subsequent analysis by CalcuSyn software, we observed that paclitaxel and cisplatin exhibited the best synergistic/additive profile when combined with CIGB-300, according to the combination and dose reduction indices. Such therapeutically favorable profiles may be explained by a direct cytotoxic effect and also by the observed cell cycle impairment following incubation of tumor cells with selected drug combinations. Importantly, on in vivo dose-finding schedules in human cervical tumors xenografted in nude mice, we observed that concomitant administration of CIGB-300 and cisplatin increased mice survival compared to single-agent treatment. Collectively, these findings provide a rationale for combining the anti-CK2 CIGB-300 peptide with currently available anticancer agents in the clinical setting and indicate platins and taxanes as compounds with major perspectives.

Computation of isotopic peak center-mass distribution by Fourier transform.

We derive a new efficient algorithm for the computation of the isotopic peak center-mass distribution of a molecule. With the use of Fourier transform techniques, the algorithm accurately computes the total abundance and average mass of all the isotopic species with the same number of nucleons. We evaluate the performance of the method with 10 benchmark proteins and other molecules; results are compared with BRAIN, a recently reported polynomial method. The new algorithm is comparable to BRAIN in accuracy and superior in terms of speed and memory, particularly for large molecules. An implementation of the algorithm is available for download.

Introducing an Asp-Pro linker in the synthesis of random one-bead-one-compound hexapeptide libraries compatible with ESI-MS analysis.

A random hexapeptide library (one-bead-one-compound), containing sixteen amino acids (16(6) different sequences) was synthesized on a Tentagel resin previously modified with a dipeptide linker (Asp-Pro). This peptide bond is highly susceptible to cleavage under mild acidic conditions in a salt-free solution prepared with H(2)(16)O/H(2)(18)O (60/40% v/v). In the hydrolysis, hexapeptides are released with an additional Asp residue partially labeled with (18)O at the C-terminus. These conditions are fully compatible with ESI-MS analysis and facilitate sequencing by MS, as N- and C-terminal ions can be easily differentiated in MS/MS spectra. The peptides were sequenced manually and also with de novo sequencing programs, and identifying them in a database containing all possible heptapeptide sequences or in a filtered database. The proposed strategy is also compatible with stepwise Edman degradation using either intact beads or the released free peptides.

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.

Charge state-selective separation of peptides by reversible modification of amino groups and strong cation-exchange chromatography: evaluation in proteomic studies using peptide-centric database searches.

Here we describe an integrated approach for the selective separation of peptides from complex mixtures using strong cation-exchange chromatography. The procedure exploits the charge differences produced by reversible modification of primary amino groups in peptides, enabling their separation into three major fractions: 1) neutral peptides 2) peptides with one positive charge and 3) peptides with 2 or more positive charges. The procedure demonstrated an excellent selectivity which allowed restricted MS/MS ion searches with peptide-centric databases.

Mass spectrum patterns of 18O-tagged peptides labeled by enzyme-catalyzed oxygen exchange.

(18)O-labeling of peptides is a technique widely and routinely applied in the protein chemistry laboratories. The rate of (18)O incorporation at the carboxyl terminus of peptides via enzyme-catalyzed oxygen exchange fluctuates from peptide to peptide. This fluctuation is mostly attributed to enzyme-substrate different affinity. The final distributions of the (18)O(0)-, (18)O(1)-, and (18)O(2)-tagged peptides remain unpredictable though usually constrained to binomial proportions. It is proved here that this constraint can sometimes be a poor model. A more general model is then derived which predicts linear paths for digestion in H(2)(18)O-enriched water while confining binomial proportions to postdigestion labeling. Both subderived models are simple in structure and relevant for the current software development in the analysis of quantitative shotgun proteomics data. Accuracy and time dependency are examined and compared with actual labeled-digest data.

Selective isolation-detection of two different positively charged peptides groups by strong cation exchange chromatography and matrix-assisted laser desorption/ionization mass spectrometry: application to proteomics studies.

We report here a procedure for the independent analysis of two groups of peptides by liquid chromatography-matrix-assisted laser desorption/ionization mass spectrometry (LC-MALDI MS/MS), using a selective isolation-detection procedure. In this procedure all primary amino groups of tryptic peptides derived from mouse liver proteins are blocked, restricting their positive charge, at acidic pH, to the presence of histidine and arginine residues. After strong cation exchange chromatography, multiply charged peptides (R + H > 1) are retained on the column and separated with high selectivity from singly (R + H = 1) and neutral peptides (R + H = 0) which are together collected in the flow-through. Using LC-MALDI-MS/MS analysis, the retained fraction displayed a 94% of enrichment of multiply charged peptides while in the flow-through; peptides with at least one arginine or histidine residue were exclusively identified, which suggests that MS detection in this fraction is restricted only to those peptides with ionizable side chains, arginine and histidine amino acids.

Proteomic profile regulated by the anticancer peptide CIGB-300 in non-small cell lung cancer (NSCLC) cells.

CIGB-300 is a proapoptotic peptide-based drug that abrogates the CK2-mediated phosphorylation. This peptide has antineoplastic effect on lung cancer cells in vitro and in vivo. To understand the mechanisms involved on such anticancer activity, the NCI-H125 cell line proteomic profile after short-term incubation (45 min) with CIGB-300 was investigated. As determined by 2-DE or 2D-LC-MS/MS, 137 proteins changed their abundances more than 2-fold in response to the CIGB-300 treatment. The expression levels of proteins related to ribosome biogenesis, metastasis, cell survival and proliferation, apoptosis, and drug resistance were significantly modulated by the presence of CIGB-300. The protein translation process was the most affected (23% of the identified proteins). From the proteome analysis of the NCI-H125 cell line, novel potentialities for CIGB-300 as anticancer agent were evidenced.

Computation of the isotopic distribution in two dimensions.

A new algorithm for the calculation of the isotopic distribution is described here. The extreme levels of detail, the coarser structures (approximately 1 Da) and the sharper irregular details (approximately millidaltons), get split in separate dimensions. Consequently, dense sampling can be concentrated in the close informative surrounding of the isotopic peaks. The one-dimensional isotopic distribution is reconstructed by allotting the abundances along the mass axis. Performance is evaluated with small and relatively large compounds (300 Da to 90 kDa) of diverse composition including challenging polyisotopic elements. The superiority over well-established methods in terms of accuracy, speed, and memory resources is clearly demonstrated for high and ultrahigh resolution ranges by a program implementing the algorithm.

Efficient packing Fourier-transform approach for ultrahigh resolution isotopic distribution calculations.

Fine isotopic structure patterns resolvable by ultrahigh-resolution mass spectrometers are diagnostic of the elemental composition of moderately large compounds. Despite the proven performance of Fourier transforms algorithms to calculate accurate high resolution isotopic distribution, its application to finer ultrahigh resolving power exhibits limited performance. Fast Fourier transforms algorithm requires sampling the relevant range at equally spaced mass values, but ultrahigh resolution mass spectrum displays highly localized complex patterns (peaks) separated in between by relatively large unstructured intervals. Computational efforts consumed on those uninformative intervals are a waste of resources. A fast and memory efficient procedure is introduced in this paper to calculate the isotopic distribution of a single relatively high-mass molecule at ultrahigh resolution by Fourier transforms approaches. The whole isotopic distribution is packed closer to the monoisotopic peak without distorting the actual scale of the peak fine structure. This packing procedure reduced 8 to 32 times the computation resources in comparison to the same calculation performed without packing. The procedure can be readily implemented in existing software.

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/.

Proteomics based on peptide fractionation by SDS-free PAGE.

Here we demonstrate the usefulness of peptide fractionation by SDS-free polyacrylamide gel electrophoresis and its applicability to proteomics studies. In the absence of SDS, the driving force for the electrophoretic migration toward the anode is supplied by negatively charged acidic amino acid residues and other residues as phosphate, sulfate and sialic acid, while the resulting mobility depends on both the charge and the molecular mass of the peptides. A straightforward method was achieved for SDS-PAGE of proteins, enzyme digestion, peptide transfer and fractionation by SDS-free PAGE, which was named dual-fractionation polyacrylamide gel electrophoresis (DF-PAGE). This method increases the number of identified proteins 2.5-fold with respect to the proteins identified after direct analysis, and more than 80% of assigned peptides were found in unique SDS-free gel slices. A vast majority of identified peptides (93%) have p I values below 7.0, and 7% have p I values between 7.0 and 7.35. Peptide digests that were derived from complex protein mixtures were in consequence simplified as peptides that are positively charged are not recovered in the present conditions. The analysis of a membrane protein extract from Neisseria meningitidis by this approach allowed the identification of 97 proteins, including low-abundance components.

HLA class I polymorphism in the Cuban population.

The extreme polymorphism found at some of the human leukocyte antigen (HLA) system loci makes it an invaluable tool for population genetic analyses. In the present study the genetic polymorphism of the Cuban population was estimated at HLA-A, -B, and -Cw loci by DNA typing. HLA class I allele and haplotype diversity were determined in 390 unrelated Cuban individuals (188 whites and 202 mulattos) from all over the country. In whites 19, 27, and 14 allele families for the HLA-A, -B, and -Cw loci, respectively, were identified. In mulattos, for the same loci, 20, 18, and 14 allele families were identified. Allele and haplotypes frequencies, comparisons with other worldwide populations based on genetic distances, neighbor-joining dendrograms, and correspondence analyses were estimated. Most of the identified allele groups and haplotypes are also common to sub-Saharan African and Europeans populations. However, Amerindian and Asian alleles were also detected at lower frequencies. The results clearly reveal the high diversity and interethnic admixture of the studied population. Our results provide useful information for the further studies of the Cuban population evolution and disease association in terms of HLA class I genes.

Selective isolation of multiple positively charged peptides for 2-DE-free quantitative proteomics.

A method for quantitative proteomic analysis based on the selective isolation of multiply charged peptides (RH peptides) containing arginine and histidine residues is described. Two pools of proteins are digested in tandem with lysyl-endopeptidase and trypsin and the primary amino groups of proteolytic peptides are separately labeled with d3- and d0-acetic anhydride. This reaction has a dual purpose: (i) to allow the relative protein quantification in two different conditions and (ii) to restrict the positive charges of peptides to the presence of arginine and histidine. The N-acylated peptides are separated by cation-exchange chromatography into two groups, neutral and singly charged peptides (R+H1) are retained into the column and can be eluted in batch or further fractionated using a saline gradient before LC-MS/MS analysis. In silico analysis revealed that the selective isolation of RH peptides considerably simplifies the complex mixture of peptides (three RH peptides/protein) and at the same time they represent 84% of the whole proteomes. The selectivity, and recovery of the method were evaluated with model proteins and with a complex mixture of proteins extracted from Vibrio cholerae.

Selective isolation of lysine-free tryptic peptides delimited by arginine residues: A new tool for proteome analysis.

Tryptic digestion of biotinylated Lys-C peptides followed by affinity chromatography allows the selective isolation of lysine-free tryptic peptides delimited by arginine residues (RRnK peptides). In silico analysis revealed that RRnK peptides represent 87% of the whole proteomes and their specific isolation simplifies the complex peptide mixture (5 peptides per protein). The good recoveries and high selectivity obtained in the isolation of RRnK peptides anticipate the applicability of this method in 2DE-free quantitative proteome analyses.