TI2BioP — Topological Indices to BioPolymers. A Graphical– Numerical Approach for Bioinformatics

We developed a new graphical–numerical method called TI2BioP (Topological Indices to BioPolymers) to estimate topological indices (TIs) from two-dimensional (2D) graphical approaches for the natural biopolymers DNA, RNA and proteins The methodology mainly turns long biopolymeric sequences into 2D artificial graphs such as Cartesian and four-color maps but also reads other 2D graphs from the thermodynamic folding of DNA/RNA strings inferred from other programs. The topology of such 2D graphs is either encoded by node or adjacency matrixes for the calculation of the spectral moments as TIs. These numerical indices were used to build up alignment-free models to the functional classification of biosequences and to calculate alignment-free distances for phylogenetic purposes. The performance of the method was evaluated in highly diverse gene/protein classes, which represents a challenge for current bioinformatics algorithms. TI2BioP generally outperformed classical bioinformatics algorithms in the functional classification of Bacteriocins, ribonucleases III (RNases III), genomic internal transcribed spacer II (ITS2) and adenylation domains (A-domains) of nonribosomal peptide synthetases (NRPS) allowing the detection of new members in these target gene/protein classes. TI2BioP classification performance was contrasted and supported by predictions with sensitive alignment-based algorithms and experimental outcomes, respectively. The new ITS2 sequence isolated from Petrakia sp. was used in our graphical–numerical approach to estimate alignment-free distances for phylogenetic inferences. Despite TI2BioP having been developed for application in bioinformatics, it can be extended to predict interesting features of other biopolymers than DNA and protein sequences. TI2BioP version 2.0 is freely available from http://ti2biop.sourceforge.net/

Polimorfismos genéticos y cáncer de tiroides

La carcinogénesis es un complejo proceso con base multigénico-ambiental; en el cual intervienen genes de control del ciclo celular, del metabolismo y de reparación del DNA, entre otros de funciones propias del órgano afectado. Los polimorfismos de estos genes, ya sea de manera individual o en combinación, pueden alterar la susceptibilidad a la carcinogénesis. El cáncer de tiroides es la neoplasia maligna más común del sistema endocrino y contribuye en más del 50% a las muertes por cánceres de este origen. Sin embargo, no está esclarecido el mecanismo molecular que subyace en su malignización, ni cuál es la influencia de los distintos polimorfismos genéticos sobre su riesgo. Por ello se estudiaron 14 SNPs que afectan a genes del mecanismo de reparación por escisión de bases (XRCC1 y OGG1), del mecanismo de reparación por recombinación homóloga (XRCC2 y XRCC3), genes propios del tiroides (TG y TSHR) y genes de control del ciclo celular (PTPRJ). Para el genotipado se utilizaron técnicas de PCR RFLP, PCR en tiempo real con sondas FRET y PCR alelo específica con cebadores LNA. Se hizo el estudio de asociación alélica y el análisis de múltiples loci, además se evaluaron las interacciones entre los polimorfismos de los genes en estudio y la modulación del riesgo por efecto de la interacción genotipo-ambiente. Los mejores marcadores de susceptibilidad resultaron ser los SNPs del gen TG, sus alelos variantes analizados solos y en combinación haplotípica aumentaron la susceptibilidad al riesgo. El SNP Arg280His de XRCC1 se asoció con un incremento del riesgo de cáncer de tiroides, sobre todo con su variante papilar; por su parte el SNP del codón 194 de XRCC1 disminuyó la susceptibilidad para este carcinoma. Los haplotipos 399Gln-280His-194Arg y 399Arg-280His-194Arg incrementaron el riesgo e indicaron que los alelos 280His y 194Arg están en desequilibrio de ligamiento. Asimismo, en SNP de región intrónica IVS5-14 de XRCC3 y el del codón 188 de XRCC2 tuvieron efecto protector. El cambio de aminoácido que representa el polimorfismo Thr241Met de XRCC3 es irrelevante sobre el riesgo al cáncer de tiroides, pero el alelo Met en combinación haplotípica con el alelo A-IVS5-14 incrementó el riesgo, posiblemente por estar en desequilibrio con un SNP funcional y de susceptibilidad al cáncer de tiroides. Los resultados en relación a PTPRJ, sin ser significativos, sugieren que la sustitución en el codón 872 de PTPRJ pudiera tener un efecto protector frente al desarrollo del cáncer de tiroides. TSHR y OGG1 no parecen ser relevantes en la carcinogénesis tiroidea. La combinación de algunos genotipos de XRCC1 y de XRCC3, aumentan la susceptibilidad al cáncer y revelan la interacción entre enzimas de dos mecanismos reparadores. Las interacciones de los genotipos de las GST con las enzimas de reparación no tuvieron efecto significativo sobre el riesgo a desarrollar cáncer de tiroides, en tanto la susceptibilidad al cáncer de tiroides se incrementa por el genotipo NAT2 (en concreto por NAT2*5 +/+ y NAT2*6) en individuos donde los mecanismos reparadores están alterados por los polimorfismos en los codones 280 y 399 de XRCC1, y IVS5-14 de XRCC3. El sexo femenino hace más susceptible a los portadores de los genotipos de riesgo y, por el contrario, el hábito de consumir alcohol se relacionó con un efecto protector. No se detectaron interacciones significativas de los diversos genotipos con el hábito de fumar y con la edad.Cancer is the result of complex interactions between inherited and environmental factors. Genetic predisposition to cancer acts via a combination of low and high-penetrance genes and, genetic variation in low-penetrance genes appears to constitute the most essential component of cancer risk heritability. Thyroid cancer is of special concern in the practice of endocrinology because it accounts for more than 90% of all endocrine cancers, and contributes to more than 50% of the deaths derived from this type of tumour. Nevertheless, little information is available about the eventual risk factors associated to thyroid cancer appearance. In this Thesis, we analysed SNPs in several genes, focusing on genes involved in base excision repair: 8-oxoguanine DNA glycosylase (OGG1), and x-ray repair cross-complementing group 1, (XRCC1), genes regulating the homologous recombination pathway of DNA double-strand-break repair (XRCC2 and XRCC3), genes participating in the thyroid hormonal homeostasis (TSHR and TG) or playing important roles in cell growth, differentiation and development (PTPRJ). A case-control study was carried out to determine if there was an association between germ-line variants and thyroid cancer risk. The study was carried out in a group of 455 Caucasians adults, where 248 were confirmed thyroid cancer patients. Peripheral blood lymphocytes and buccal cells were used as a source of genomic DNA. Polymorphisms were genotyped by TrueSNP Allele-Specific PCR in real time PCR, real time PCR with FRET probes, and by PCR-restriction length fragment polymorphism analysis. Analysis of association and analysis of interactions (gene-gene or gene-environment) were based on logistic regression. Linkage disequilibrium (LD) and haplotype analysis was also performed. The statistical analysis of these studies was done using the SPSS statistical packages, and the SNPstas software. Case control association studies demonstrated that an exon 10-12 SNPs cluster and an exon 33 SNP were significantly linked with thyroid cancer risk. Haplotype analyses also showed evidences for linkage with thyroid cancer, suggesting that TG is a strong susceptibility candidate gene for thyroid cancer. Our findings suggest that the XRCC1 Arg399Gln polymorphism is not associated with the risk of thyroid cancer; however, an increased risk was found for His allele of Arg280His at the XRCC1 gene. For the Arg194Trp polymorphism, we observed that Trp allele acts a protective factor. Haplotype analyses also confirmed these findings. A reduced risk was found for those individuals carrying the homozygous variants of Arg188His and IVS5-14 from the XRCC2 and XRCC3 genes, respectively. From our results, there are no significant associations between XRCC3 Thr241Met polymorphism and the overall risk of thyroid cancer, but increased risk was detected when Met allele was in haplotypic combination. On the other hand, no significant associations were detected between Ser326Cys, TSHR-Pro52Thr, TSHR-Asp727Glu and PTPRJ-Asp872Glu genotypes and thyroid cancer risk. Because of the multi-factorial nature of cancer, the interaction between genetic variants, or these with environmental factors, can potentially be quite important in determining the observed phenotype. In our study, we observed that the combination of two risk genotypes of XRCC1 and XRCC3 facilitate the to thyroid cancer progression. In addition, NAT2 status appears to modulate thyroid cancer risk associated with XRCC1 and XRCC3 polymorphisms.Alcohol consumption apparently modify the effect of the polymorphisms on thyroid cancer risk; thus, a decreased risk was found among individuals with the drinking habit carrying the risk alleles of Arg194Trp, Arg399Gln, Arg188His, Arg1980Trp polymorphisms. Finally, any of the interactions between age, smoking and genotypes were found to determine the individual susceptibility to thyroid cancer

Role of glutathione in the regulation of epigenetic mechanisms in disease

Epigenetics is a rapidly growing field that studies gene expression modifications not involving changes in the DNA sequence. Histone H3, one of the basic proteins in the nucleosomes that make up chromatin, is S-glutathionylated in mammalian cells and tissues, making Gamma-L-glutamyl-L-cysteinylglycine, glutathione (GSH), a physiological antioxidant and second messenger in cells, a new post-translational modifier of the histone code that alters the structure of the nucleosome. However, the role of GSH in the epigenetic mechanisms likely goes beyond a mere structural function. Evidence supports the hypothesis that there is a link between GSH metabolism and the control of epigenetic mechanisms at different levels (i.e., substrate availability, enzymatic activity for DNA methylation, changes in the expression of microRNAs, and participation in the histone code). However, little is known about the molecular pathways by which GSH can control epigenetic events. Studying mutations in enzymes involved in GSH metabolism and the alterations of the levels of cofactors affecting epigenetic mechanisms appears challenging. However, the number of diseases induced by aberrant epigenetic regulation is growing, so elucidating the intricate network between GSH metabolism, oxidative stress and epigenetics could shed light on how their deregulation contributes to the development of neurodegeneration, cancer, metabolic pathologies and many other types of diseases.Sin financiación6.020 JCR (2017) Q1, 39/292 Biochemistry and Molecular Biology, 17/143 Endocrinology and MetabolismUE

Emerging Computational Approaches for Antimicrobial Peptide Discovery

In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources

Graph Theory-Based Sequence Descriptors as Remote Homology Predictors

Alignment-free (AF) methodologies have increased in popularity in the last decades as alternative tools to alignment-based (AB) algorithms for performing comparative sequence analyses. They have been especially useful to detect remote homologs within the twilight zone of highly diverse gene/protein families and superfamilies. The most popular alignment-free methodologies, as well as their applications to classification problems, have been described in previous reviews. Despite a new set of graph theory-derived sequence/structural descriptors that have been gaining relevance in the detection of remote homology, they have been omitted as AF predictors when the topic is addressed. Here, we first go over the most popular AF approaches used for detecting homology signals within the twilight zone and then bring out the state-of-the-art tools encoding graph theory-derived sequence/structure descriptors and their success for identifying remote homologs. We also highlight the tendency of integrating AF features/measures with the AB ones, either into the same prediction model or by assembling the predictions from different algorithms using voting/weighting strategies, for improving the detection of remote signals. Lastly, we briefly discuss the efforts made to scale up AB and AF features/measures for the comparison of multiple genomes and proteomes. Alongside the achieved experiences in remote homology detection by both the most popular AF tools and other less known ones, we provide our own using the graphical–numerical methodologies, MARCH-INSIDE, TI2BioP, and ProtDCal. We also present a new Python-based tool (SeqDivA) with a friendly graphical user interface (GUI) for delimiting the twilight zone by using several similar criteria

Epigenetics in spine curvature disorders

Scoliosis is a three-dimensional (3D) structural deformity of the spine with a radiological lateral Cobb angle of ≥ 10°. Several classification systems exist, dividing different types regarding the age of onset or the type of etiology. The minority of cases are secondary to congenital, syndromic of neuromosucular diseases. Most of the cases are classified “idiopathic” due to unknown etiology. These were formally divided by the age of onset into “Infantile Idiopathic Scoliosis” (0–3 years), “Juvenile Idiopathic Scoliosis” (JIS—4–10 years), and “Adolescent Idiopathic Scoliosis” (AIS → 10 years). Since the initiative of the Scoliosis Research Society in 2014 all kind of scoliosis with the onset before the age of 10 years are classified as “Early-Onset-Scoliosis” (EOS) regardless of the etiology. Further types of scoliosis can occur in the adulthood, which are known as “Adolescent Scoliosis in the Adult” and “Degenerative De-novo Scoliosis” (DS) in the adulthood secondary to degenerative disc diseases and/or in combination with osteoporosis and minor compression fractures. The precise molecular mechanisms underlying idiopathic and de-novo scoliosis are mainly unknown, but recent evidence demonstrate the role of epigenetics in the etiology of these conditions. Importantly, early diagnosis and the accurate prediction of curve progression are of special relevance in clinical settings. Therefore, identifying potential progression-relevant biomarkers can substantially improve the clinical management of these patients. This chapter presents the most relevant epigenetic mechanisms and epigenetic biomarkers, which may favor the implementation of precision medicine in scoliosis treatment

Harmonization of QSAR Best Practices and Molecular Docking Provides an Efficient Virtual Screening Tool for Discovering New G-Quadruplex Ligands

Telomeres and telomerase are key players in tumorogenesis. Among the various strategies proposed for telomerase inhibition or telomere uncapping, the stabilization of telomeric G-quadruplex (G4) structures is a very promising one. Additionally, G4 stabilizing ligands also act over tumors mediated by the alternative elongation of telomeres. Accordingly, the discovery of novel compounds able to act on telomeres and/or inhibit the telomerase enzyme by stabilizing DNA telomeric G4 structures as well as the development of approaches efficiently prioritizing such compounds constitute active areas of research in computational medicinal chemistry and anticancer drug discovery. In this direction, we applied a virtual screening strategy based on the rigorous application of QSAR best practices and its harmonized integration with structure-based methods. More than 600,000 compounds from commercial databases were screened, the first 99 compounds were prioritized, and 21 commercially available and structurally diverse candidates were purchased and submitted to experimental assays. Such strategy proved to be highly efficient in the prioritization of G4 stabilizer hits, with a hit rate of 23.5%. The best G4 stabilizer hit found exhibited a shift in melting temperature from FRET assay of +7.3 °C at 5 μM, while three other candidates also exhibited a promising stabilizing profile. The two most promising candidates also exhibited a good telomerase inhibitory ability and a mild inhibition of HeLa cells growth. None of these candidates showed antiproliferative effects in normal fibroblasts. Finally, the proposed virtual screening strategy proved to be a practical and reliable tool for the discovery of novel G4 ligands which can be used as starting points of further optimization campaigns