CDBProm: the Comprehensive Directory of Bacterial Promoters.

The decreasing cost of whole genome sequencing has produced high volumes of genomic information that require annotation. The experimental identification of promoter sequences, pivotal for regulating gene expression, is a laborious and cost-prohibitive task. To expedite this, we introduce the Comprehensive Directory of Bacterial Promoters (CDBProm), a directory of in-silico predicted bacterial promoter sequences. We first identified that an Extreme Gradient Boosting (XGBoost) algorithm would distinguish promoters from random downstream regions with an accuracy of 87%. To capture distinctive promoter signals, we generated a second XGBoost classifier trained on the instances misclassified in our first classifier. The predictor of CDBProm is then fed with over 55 million upstream regions from more than 6000 bacterial genomes. Upon finding potential promoter sequences in upstream regions, each promoter is mapped to the genomic data of the organism, linking the predicted promoter with its coding DNA sequence, and identifying the function of the gene regulated by the promoter. The collection of bacterial promoters available in CDBProm enables the quantitative analysis of a plethora of bacterial promoters. Our collection with over 24 million promoters is publicly available at https://aw.iimas.unam.mx/cdbprom/.

Differentially regulated targets in the fast-acting antidepressant effect of (R)-ketamine: A systems biology approach.

Approximately two-thirds of patients with major depressive disorder (MDD) fail to respond to conventional antidepressants, suggesting that additional mechanisms are involved in the MDD pathophysiology. In this scenario, the glutamatergic system represents a promising therapeutic target for treatment-resistant depression. To our knowledge, this is the first study using semantic approach with systems biology to identify potential targets involved in the fast-acting antidepressant effects of ketamine and its enantiomers as well as identifying specific targets of (R)-ketamine. We performed a systematic review, followed by a semantic analysis and functional gene enrichment to identify the main biological processes involved in the therapeutic effects of these agents. Protein-protein interaction networks were constructed, and the genes exclusively regulated by (R)-ketamine were explored. We found that the regulation of α-Amino-3-Hydroxy-5-Methyl-4-Isoxazolepropionic Acid (AMPA) receptor and N-methyl-d-aspartate (NMDA) receptor subunits-Postsynaptic Protein 95 (PSD-95), Brain Derived Neurotrophic Factor (BDNF), and Tyrosine Receptor Kinase B (TrkB) are shared by the three-antidepressant agents, reinforcing the central role of the glutamatergic system and neurogenesis on its therapeutic effects. Differential regulation of Transforming Growth Factor Beta 1 (TGF-ß1) receptors-Mitogen-Activated Protein Kinases (MAPK's), Receptor Activator of Nuclear Factor-Kappa Beta Ligand (RANKL), and Serotonin Transporter (SERT) seems to be particularly involved in (R)-ketamine antidepressant effects. Our data helps further studies investigating the relationship between these targets and the mechanisms of (R)-ketamine and searching for other therapeutic compounds that share the regulation of these specific biomolecules. Ultimately, this study could contribute to improve the fast management of depressive-like symptoms with less detrimental side effects than ketamine and (S)-ketamine.

Identification of novel biomarkers with potential for diagnosis and prognosis of gastric cancer: a bioinformatics approach

INTRODUCTION: Gastric cancer (GC) is the fifth most diagnosed neoplasia and the third leading cause of cancer-related deaths. A substantial number of patients exhibit an advanced GC stage once diagnosed. Therefore, the search for biomarkers contributes to the improvement and development of therapies. OBJECTIVE: This study aimed to identify potential GC biomarkers making use of in silico tools. METHODS: Gastric tissue microarray data available in Gene Expression Omnibus and The Cancer Genome Atlas Program was extracted. We applied statistical tests in the search for differentially expressed genes between tumoral and non-tumoral adjacent tissue samples. The selected genes were submitted to an in-house tool for analyses of functional enrichment, survival rate, histological and molecular classifications, and clinical follow-up data. A decision tree analysis was performed to evaluate the predictive power of the potential biomarkers. RESULTS: In total, 39 differentially expressed genes were found, mostly involved in extracellular structure organization, extracellular matrix organization, and angiogenesis. The genes SLC7A8, LY6E, and SIDT2 showed potential as diagnostic biomarkers considering the differential expression results coupled with the high predictive power of the decision tree models. Moreover, GC samples showed lower SLC7A8 and SIDT2 expression, whereas LY6E was higher. SIDT2 demonstrated a potential prognostic role for the diffuse type of GC, given the higher patient survival rate for lower gene expression. CONCLUSION: Our study outlines novel biomarkers for GC that may have a key role in tumor progression. Nevertheless, complementary in vitro analyses are still needed to further support their potential.

A Survey of Biological Data in a Big Data Perspective.

The amount of available data is continuously growing. This phenomenon promotes a new concept, named big data. The highlight technologies related to big data are cloud computing (infrastructure) and Not Only SQL (NoSQL; data storage). In addition, for data analysis, machine learning algorithms such as decision trees, support vector machines, artificial neural networks, and clustering techniques present promising results. In a biological context, big data has many applications due to the large number of biological databases available. Some limitations of biological big data are related to the inherent features of these data, such as high degrees of complexity and heterogeneity, since biological systems provide information from an atomic level to interactions between organisms or their environment. Such characteristics make most bioinformatic-based applications difficult to build, configure, and maintain. Although the rise of big data is relatively recent, it has contributed to a better understanding of the underlying mechanisms of life. The main goal of this article is to provide a concise and reliable survey of the application of big data-related technologies in biology. As such, some fundamental concepts of information technology, including storage resources, analysis, and data sharing, are described along with their relation to biological data.

Analysis of carbohydrate-active enzymes and sugar transporters in Penicillium echinulatum: A genome-wide comparative study of the fungal lignocellulolytic system.

Penicillium echinulatum 2HH is an ascomycete well known for its production of cellulolytic enzymes. Understanding lignocellulolytic and sugar uptake systems is essential to obtain efficient fungi strains for the production of bioethanol. In this study we performed a genome-wide functional annotation of carbohydrate-active enzymes and sugar transporters involved in the lignocellulolytic system of P. echinulatum 2HH and S1M29 strains (wildtype and mutant, respectively) and eleven related fungi. Additionally, signal peptide and orthology prediction were carried out. We encountered a diverse assortment of cellulolytic enzymes in P. echinulatum, especially in terms of ß-glucosidases and endoglucanases. Other enzymes required for the breakdown of cellulosic biomass were also found, including cellobiohydrolases, lytic cellulose monooxygenases and cellobiose dehydrogenases. The S1M29 mutant, which is known to produce an increased cellulase activity, and the 2HH wild type strain of P. echinulatum did not show significant differences between their enzymatic repertoire. Nevertheless, we unveiled an amino acid substitution for a predicted intracellular ß-glucosidase of the mutant, which might contribute to hyperexpression of cellulases through a cellodextrin induction pathway. Most of the P. echinulatum enzymes presented orthologs in P. oxalicum 114-2, supporting the presence of highly similar cellulolytic mechanisms and a close phylogenetic relationship between these fungi. A phylogenetic analysis of intracellular ß-glucosidases and sugar transporters allowed us to identify several proteins potentially involved in the accumulation of intracellular cellodextrins. These may prove valuable targets in the genetic engineering of P. echinulatum focused on industrial cellulases production. Our study marks an important step in characterizing and understanding the molecular mechanisms employed by P. echinulatum in the enzymatic hydrolysis of lignocellulosic biomass.

Analysis of the nucleotide content of Escherichia coli promoter sequences related to the alternative sigma factors.

Promoters are DNA sequences located upstream of the transcription start site of genes. In bacteria, the RNA polymerase enzyme requires additional subunits, called sigma factors (σ) to begin specific gene transcription in distinct environmental conditions. Currently, promoter prediction still poses many challenges due to the characteristics of these sequences. In this paper, the nucleotide content of Escherichia coli promoter sequences, related to five alternative σ factors, was analyzed by a machine learning technique in order to provide profiles according to the σ factor which recognizes them. For this, the clustering technique was applied since it is a viable method for finding hidden patterns on a data set. As a result, 20 groups of sequences were formed, and, aided by the Weblogo tool, it was possible to determine sequence profiles. These found patterns should be considered for implementing computational prediction tools. In addition, evidence was found of an overlap between the functions of the genes regulated by different σ factors, suggesting that DNA structural properties are also essential parameters for further studies.