J. P. Florido

Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns

MOTIVATION Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. RESULTS The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal-Wallis test (P < 0.01). This algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P < 0.05), whereas it shows results not significantly different to 3D-COFFEE (P > 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments. AVAILABILITY The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.

267 Spanish Exomes Reveal Population-Specific Differences in Disease-Related Genetic Variation

Recent results from large-scale genomic projects suggest that allele frequencies, which are highly relevant for medical purposes, differ considerably across different populations. The need for a detailed catalog of local variability motivated the whole-exome sequencing of 267 unrelated individuals, representative of the healthy Spanish population. Like in other studies, a considerable number of rare variants were found (almost one-third of the described variants). There were also relevant differences in allelic frequencies in polymorphic variants, including ∼10,000 polymorphisms private to the Spanish population. The allelic frequencies of variants conferring susceptibility to complex diseases (including cancer, schizophrenia, Alzheimer disease, type 2 diabetes, and other pathologies) were overall similar to those of other populations. However, the trend is the opposite for variants linked to Mendelian and rare diseases (including several retinal degenerative dystrophies and cardiomyopathies) that show marked frequency differences between populations. Interestingly, a correspondence between differences in allelic frequencies and disease prevalence was found, highlighting the relevance of frequency differences in disease risk. These differences are also observed in variants that disrupt known drug binding sites, suggesting an important role for local variability in population-specific drug resistances or adverse effects. We have made the Spanish population variant server web page that contains population frequency information for the complete list of 170,888 variant positions we found publicly available (http://spv.babelomics.org/), We show that it if fundamental to determine population-specific variant frequencies to distinguish real disease associations from population-specific polymorphisms.

Melatonin protects rats from radiotherapy-induced small intestine toxicity

Radiotherapy-induced gut toxicity is among the most prevalent dose-limiting toxicities following radiotherapy. Prevention of radiation enteropathy requires protection of the small intestine. However, despite the prevalence and burden of this pathology, there are currently no effective treatments for radiotherapy-induced gut toxicity, and this pathology remains unclear. The present study aimed to investigate the changes induced in the rat small intestine after external irradiation of the tongue, and to explore the potential radio-protective effects of melatonin gel. Male Wistar rats were subjected to irradiation of their tongues with an X-Ray YXLON Y.Tu 320-D03 irradiator, receiving a dose of 7.5 Gy/day for 5 days. For 21 days post-irradiation, rats were treated with 45 mg/day melatonin gel or vehicle, by local application into their mouths. Our results showed that mitochondrial oxidative stress, bioenergetic impairment, and subsequent NLRP3 inflammasome activation were involved in the development of radiotherapy-induced gut toxicity. Oral treatment with melatonin gel had a protective effect in the small intestine, which was associated with mitochondrial protection and, consequently, with a reduced inflammatory response, blunting the NF-κB/NLRP3 inflammasome signaling activation. Thus, rats treated with melatonin gel showed reduced intestinal apoptosis, relieving mucosal dysfunction and facilitating intestinal mucosa recovery. Our findings suggest that oral treatment with melatonin gel may be a potential preventive therapy for radiotherapy-induced gut toxicity in cancer patients.

Optimization of multiple sequence alignment methodologies using a multiobjective evolutionary algorithm based on NSGA-II

Multiple sequence alignment (MSA) is one of the most studied approach in Bioinformatics to carry out other outstanding tasks like structural predictions, biological function analysis or next-generation sequencing. However, MSA algorithms do not achieve consistent results in all cases, as alignments become difficult when sequences have low similarity. In other words, each algorithm is focused in specific features of sequences and their results depend on them. For this reason, each approach could align better those sections of sequences that include such features, obtaining partially optimal solutions. In this work, a multiobjective evolutionary algorithm based on NSGA-II will be implemented in order to assemble previously aligned sequences, trying to avoid suboptimal alignments.

Melatonin enhances neural stem cell differentiation and engraftment by increasing mitochondrial function

Neural stem cells (NSCs) are regarded as a promising therapeutic approach to protecting and restoring damaged neurons in neurodegenerative diseases (NDs) such as Parkinsons disease and Alzheimers disease (PD and AD, respectively). However, new research suggests that NSC differentiation is required to make this strategy effective. Several studies have demonstrated that melatonin increases mature neuronal markers, which reflects NSC differentiation into neurons. Nevertheless, the possible involvement of mitochondria in the effects of melatonin during NSC differentiation has not yet been fully established. We therefore tested the impact of melatonin on NSC proliferation and differentiation in an attempt to determine whether these actions depend on modulating mitochondrial activity. We measured proliferation and differentiation markers, mitochondrial structural and functional parameters as well as oxidative stress indicators and also evaluated cell transplant engraftment. This enabled us to show that melatonin (25 μM) induces NSC differentiation into oligodendrocytes and neurons. These effects depend on increased mitochondrial mass/DNA/complexes, mitochondrial respiration, and membrane potential as well as ATP synthesis in NSCs. It is also interesting to note that melatonin prevented oxidative stress caused by high levels of mitochondrial activity. Finally, we found that melatonin enriches NSC engraftment in the ND mouse model following transplantation. We concluded that a combined therapy involving transplantation of NSCs pretreated with pharmacological doses of melatonin could efficiently restore neuronal cell populations in PD and AD mouse models depending on mitochondrial activity promotion.

Predicting the accuracy of multiple sequence alignment algorithms by using computational intelligent techniques

Multiple sequence alignments (MSAs) have become one of the most studied approaches in bioinformatics to perform other outstanding tasks such as structure prediction, biological function analysis or next-generation sequencing. However, current MSA algorithms do not always provide consistent solutions, since alignments become increasingly difficult when dealing with low similarity sequences. As widely known, these algorithms directly depend on specific features of the sequences, causing relevant influence on the alignment accuracy. Many MSA tools have been recently designed but it is not possible to know in advance which one is the most suitable for a particular set of sequences. In this work, we analyze some of the most used algorithms presented in the bibliography and their dependences on several features. A novel intelligent algorithm based on least square support vector machine is then developed to predict how accurate each alignment could be, depending on its analyzed features. This algorithm is performed with a dataset of 2180 MSAs. The proposed system first estimates the accuracy of possible alignments. The most promising methodologies are then selected in order to align each set of sequences. Since only one selected algorithm is run, the computational time is not excessively increased.

Combination of melatonin and rapamycin for head and neck cancer therapy: Suppression of AKT/mTOR pathway activation, and activation of mitophagy and apoptosis via mitochondrial function regulation

Head and neck squamous cell carcinoma (HNSCC) clearly involves activation of the Akt mammalian target of rapamycin (mTOR) signalling pathway. However, the effectiveness of treatment with the mTOR inhibitor rapamycin is often limited by chemoresistance. Melatonin suppresses neoplastic growth via different mechanisms in a variety of tumours. In this study, we aimed to elucidate the effects of melatonin on rapamycin‐induced HNSCC cell death and to identify potential cross‐talk pathways. We analysed the dose‐dependent effects of melatonin in rapamycin‐treated HNSCC cell lines (Cal‐27 and SCC‐9). These cells were treated with 0.1, 0.5 or 1 mmol/L melatonin combined with 20 nM rapamycin. We further examined the potential synergistic effects of melatonin with rapamycin in Cal‐27 xenograft mice. Relationships between inhibition of the mTOR pathway, reactive oxygen species (ROS), and apoptosis and mitophagy reportedly increased the cytotoxic effects of rapamycin in HNSCC. Our results demonstrated that combined treatment with rapamycin and melatonin blocked the negative feedback loop from the specific downstream effector of mTOR activation S6K1 to Akt signalling, which decreased cell viability, proliferation and clonogenic capacity. Interestingly, combined treatment with rapamycin and melatonin‐induced changes in mitochondrial function, which were associated with increased ROS production, increasing apoptosis and mitophagy. This led to increase cell death and cellular differentiation. Our data further indicated that melatonin administration reduced rapamycin‐associated toxicity to healthy cells. Overall, our findings suggested that melatonin could be used as an adjuvant agent with rapamycin, improving effectiveness while minimizing its side effects.

GENERATING BALANCED LEARNING AND TEST SETS FOR FUNCTION APPROXIMATION PROBLEMS

In function approximation problems, one of the most common ways to evaluate a learning algorithm consists in partitioning the original data set (input/output data) into two sets: learning, used for building models, and test, applied for genuine out-of-sample evaluation. When the partition into learning and test sets does not take into account the variability and geometry of the original data, it might lead to non-balanced and unrepresentative learning and test sets and, thus, to wrong conclusions in the accuracy of the learning algorithm. How the partitioning is made is therefore a key issue and becomes more important when the data set is small due to the need of reducing the pessimistic effects caused by the removal of instances from the original data set. Thus, in this work, we propose a deterministic data mining approach for a distribution of a data set (input/output data) into two representative and balanced sets of roughly equal size taking the variability of the data set into consideration with the purpose of allowing both a fair evaluation of learnings accuracy and to make reproducible machine learning experiments usually based on random distributions. The sets are generated using a combination of a clustering procedure, especially suited for function approximation problems, and a distribution algorithm which distributes the data set into two sets within each cluster based on a nearest-neighbor approach. In the experiments section, the performance of the proposed methodology is reported in a variety of situations through an ANOVA-based statistical study of the results.

Using machine learning techniques and genomic/proteomic information from known databases for defining relevant features for PPI classification

In modern proteomics, prediction of protein-protein interactions (PPIs) is a key research line, as these interactions take part in most essential biological processes. In this paper, a new approach is proposed to PPI data classification based on the extraction of genomic and proteomic information from well-known databases and the incorporation of semantic measures. This approach is carried out through the application of data mining techniques and provides very accurate models with high levels of sensitivity and specificity in the classification of PPIs. The well-known support vector machine paradigm is used to learn the models, which will also return a new confidence score which may help expert researchers to filter out and validate new external PPIs. One of the most-widely analyzed organisms, yeast, will be studied. We processed a very high-confidence dataset by extracting up to 26 specific features obtained from the chosen databases, half of them calculated using two new similarity measures proposed in this paper. Then, by applying a filter-wrapper algorithm for feature selection, we obtained a final set composed of the eight most relevant features for predicting PPIs, which was validated by a ROC analysis. The prediction capability of the support vector machine model using these eight features was tested through the evaluation of the predictions obtained in a set of external experimental, computational, and literature-collected datasets.

Melatonin-Induced Oncostasis, Mechanisms and Clinical Relevance

Melatonin is a natural substance ubiquitously distributed and present in almost all living species, from unicellular organisms to humans. Melatonin is synthesized not only in the pineal gland but also in most tissues in the body where it may have a cytoprotective function via paracrine or autocrine effects. Melatonin is effective in suppressing neoplastic growth in a variety of tumors. The mechanisms involved include antiproliferative effects via modulation of cell cycle, ability to induce apoptosis in cancer cells, anti-angiogenic and antimetastatic effects, anti-estrogenic activity, the capacity to decrease telomerase activity, immune modulation, and direct and indirect antioxidant effects. Besides these oncostatic properties, melatonin deserves to be considered in the treatment of cancer for two other reasons. First, because its hypnotic-chronobiotic properties, melatonin use that can allow the clinician to effectively address sleep disturbances, a major co-morbidity in cancer. Second, because melatonin’s anxiolytic and antidepressant effects, it has a possible application in two other major co-morbidities seen in cancer patients, i.e. depression and anxiety. This report summarizes the possible mechanisms involved in melatonin oncostasis and reviews what is known about the clinical application of melatonin as an adjuvant therapy in cancer patients.