Restriction-modification systems in Mycoplasma spp

Restriction and Modification (R-M) systems are present in all Mycoplasma species sequenced so far. The presence of these genes poses barriers to gene transfer and could protect the cell against phage infections. The number and types of R-M genes between different Mycoplasma species are variable, which is characteristic of a polymorphism. The majority of the CDSs code for Type III R-M systems and particularly for methyltransferase enzymes, which suggests that functions other than the protection against the invasion of heterologous DNA may exist. A possible function of these enzymes could be the protection against the invasion of other but similar R-M systems. In Mycoplasma hyopneumoniae strain J, three of the putative methyltransferase genes were clustered in a region forming a genomic island. Many R-M CDSs were mapped in the vicinity of transposable elements suggesting an association between these genes and reinforcing the idea of R-M systems as mobile selfish DNA. Also, many R-M genes present repeats within their coding sequences, indicating that their expression is under the control of phase variation mechanisms. Altogether, these data suggest that R-M systems are a remarkable characteristic of Mycoplasma species and are probably involved in the adaptation of these bacteria to different environmental conditions.236244Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

The Omicron lineages BA.1 and BA.2 (Betacoronavirus SARS-CoV-2) have repeatedly entered Brazil through a single dispersal hub

Brazil currently ranks second in absolute deaths by COVID-19, even though most of its population has completed the vaccination protocol. With the introduction of Omicron in late 2021, the number of COVID-19 cases soared once again in the country. We investigated in this work how lineages BA.1 and BA.2 entered and spread in the country by sequencing 2173 new SARS-CoV-2 genomes collected between October 2021 and April 2022 and analyzing them in addition to more than 18,000 publicly available sequences with phylodynamic methods. We registered that Omicron was present in Brazil as early as 16 November 2021 and by January 2022 was already more than 99% of samples. More importantly, we detected that Omicron has been mostly imported through the state of São Paulo, which in turn dispersed the lineages to other states and regions of Brazil. This knowledge can be used to implement more efficient non-pharmaceutical interventions against the introduction of new SARS-CoV variants focused on surveillance of airports and ground transportation

From sequence to dynamics: the effects of transcription factor and polymerase concentration changes on activated and repressed promoters

<p>Abstract</p> <p>Background</p> <p>The fine tuning of two features of the bacterial regulatory machinery have been known to contribute to the diversity of gene expression within the same regulon: the sequence of Transcription Factor (TF) binding sites, and their location with respect to promoters. While variations of binding sequences modulate the strength of the interaction between the TF and its binding sites, the distance between binding sites and promoters alter the interaction between the TF and the RNA polymerase (RNAP).</p> <p>Results</p> <p>In this paper we estimated the dissociation constants (<it>K</it>_<it>d</it>) of several <it>E. coli </it>TFs in their interaction with variants of their binding sequences from the scores resulting from aligning them to Positional Weight Matrices. A correlation coefficient of 0.78 was obtained when pooling together sites for different TFs. The theoretically estimated <it>K</it>_{<it>d </it>}values were then used, together with the dissociation constants of the RNAP-promoter interaction to analyze activated and repressed promoters. The strength of repressor sites -- i.e., the strength of the interaction between TFs and their binding sites -- is slightly higher than that of activated sites. We explored how different factors such as the variation of binding sequences, the occurrence of more than one binding site, or different RNAP concentrations may influence the promoters' response to the variations of TF concentrations. We found that the occurrence of several regulatory sites bound by the same TF close to a promoter -- if they are bound by the TF in an independent manner -- changes the effect of TF concentrations on promoter occupancy, with respect to individual sites. We also found that the occupancy of a promoter will never be more than half if the RNAP concentration-to-<it>K</it>_{<it>p </it>}ratio is 1 and the promoter is subject to repression; or less than half if the promoter is subject to activation. If the ratio falls to 0.1, the upper limit of occupancy probability for repressed drops below 10%; a descent of the limits occurs also for activated promoters.</p> <p>Conclusion</p> <p>The number of regulatory sites may thus act as a versatility-producing device, in addition to serving as a source of robustness of the transcription machinery. Furthermore, our results show that the effects of TF concentration fluctuations on promoter occupancy are constrained by RNAP concentrations.</p

Erratum to: Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction

MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets. By comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools. The extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool

Genomic and evolutionary comparisons of diazotrophic and pathogenic bacteria of the order Rhizobiales

<p>Abstract</p> <p>Background</p> <p>Species belonging to the Rhizobiales are intriguing and extensively researched for including both bacteria with the ability to fix nitrogen when in symbiosis with leguminous plants and pathogenic bacteria to animals and plants. Similarities between the strategies adopted by pathogenic and symbiotic Rhizobiales have been described, as well as high variability related to events of horizontal gene transfer. Although it is well known that chromosomal rearrangements, mutations and horizontal gene transfer influence the dynamics of bacterial genomes, in Rhizobiales, the scenario that determine pathogenic or symbiotic lifestyle are not clear and there are very few studies of comparative genomic between these classes of prokaryotic microorganisms trying to delineate the evolutionary characterization of symbiosis and pathogenesis.</p> <p>Results</p> <p>Non-symbiotic nitrogen-fixing bacteria and bacteria involved in bioremediation closer to symbionts and pathogens in study may assist in the origin and ancestry genes and the gene flow occurring in Rhizobiales. The genomic comparisons of 19 species of Rhizobiales, including nitrogen-fixing, bioremediators and pathogens resulted in 33 common clusters to biological nitrogen fixation and pathogenesis, 15 clusters exclusive to all nitrogen-fixing bacteria and bacteria involved in bioremediation, 13 clusters found in only some nitrogen-fixing and bioremediation bacteria, 01 cluster exclusive to some symbionts, and 01 cluster found only in some pathogens analyzed. In BBH performed to all strains studied, 77 common genes were obtained, 17 of which were related to biological nitrogen fixation and pathogenesis. Phylogenetic reconstructions for Fix, Nif, Nod, Vir, and Trb showed possible horizontal gene transfer events, grouping species of different phenotypes.</p> <p>Conclusions</p> <p>The presence of symbiotic and virulence genes in both pathogens and symbionts does not seem to be the only determinant factor for lifestyle evolution in these microorganisms, although they may act in common stages of host infection. The phylogenetic analysis for many distinct operons involved in these processes emphasizes the relevance of horizontal gene transfer events in the symbiotic and pathogenic similarity.</p

A Cellular Automata-Based Mathematical Model for Thymocyte Development

Intrathymic T cell development is an important process necessary for the normal formation of cell-mediated immune responses. Importantly, such a process depends on interactions of developing thymocytes with cellular and extracellular elements of the thymic microenvironment. Additionally, it includes a series of oriented and tunely regulated migration events, ultimately allowing mature cells to cross endothelial barriers and leave the organ. Herein we built a cellular automata-based mathematical model for thymocyte migration and development. The rules comprised in this model take into account the main stages of thymocyte development, two-dimensional sections of the normal thymic microenvironmental network, as well as the chemokines involved in intrathymic cell migration. Parameters of our computer simulations with further adjusted to results derived from previous experimental data using sub-lethally irradiated mice, in which thymus recovery can be evaluated. The model fitted with the increasing numbers of each CD4/CD8-defined thymocyte subset. It was further validated since it fitted with the times of permanence experimentally ascertained in each CD4/CD8-defined differentiation stage. Importantly, correlations using the whole mean volume of young normal adult mice revealed that the numbers of cells generated in silico with the mathematical model fall within the range of total thymocyte numbers seen in these animals. Furthermore, simulations made with a human thymic epithelial network using the same mathematical model generated similar profiles for temporal evolution of thymocyte developmental stages. Lastly, we provided in silico evidence that the thymus architecture is important in the thymocyte development, since changes in the epithelial network result in different theoretical profiles for T cell development/migration. This model likely can be used to predict thymocyte evolution following therapeutic strategies designed for recovery of the thymus in diseases coursing with thymus involution, such as some primary immunodeficiencies, acute infections, and malnutrition

An MLSA-based online scheme for the rapid identification of Stenotrophomonas isolates

An online scheme to assign Stenotrophomonas isolates to genomic groups was developed using the multilocus sequence analysis (MLSA), which is based on the DNA sequencing of selected fragments of the housekeeping genes ATP synthase alpha subunit (atpA), the recombination repair protein (recA), the RNA polymerase alpha subunit (rpoA) and the excision repair beta subunit (uvrB). This MLSA-based scheme was validated using eight of the 10 Stenotrophomonas species that have been previously described. The environmental and nosocomial Stenotrophomonas strains were characterised using MLSA, 16S rRNA sequencing and DNA-DNA hybridisation (DDH) analyses. Strains of the same species were found to have greater than 95% concatenated sequence similarity and specific strains formed cohesive readily recognisable phylogenetic groups. Therefore, MLSA appeared to be an effective alternative methodology to amplified fragment length polymorphism fingerprint and DDH techniques. Strains of Stenotrophomonas can be readily assigned through the open database resource that was developed in the current study (www.steno.lncc.br/)

BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments

Advances in sequencing techniques have led to exponential growth in biological data, demanding the development of large-scale bioinformatics experiments. Because these experiments are computation- and data-intensive, they require high-performance computing (HPC) techniques and can benefit from specialized technologies such as Scientific Workflow Management Systems (SWfMS) and databases. In this work, we present BioWorkbench, a framework for managing and analyzing bioinformatics experiments. This framework automatically collects provenance data, including both performance data from workflow execution and data from the scientific domain of the workflow application. Provenance data can be analyzed through a web application that abstracts a set of queries to the provenance database, simplifying access to provenance information. We evaluate BioWorkbench using three case studies: SwiftPhylo, a phylogenetic tree assembly workflow; SwiftGECKO, a comparative genomics workflow; and RASflow, a RASopathy analysis workflow. We analyze each workflow from both computational and scientific domain perspectives, by using queries to a provenance and annotation database. Some of these queries are available as a pre-built feature of the BioWorkbench web application. Through the provenance data, we show that the framework is scalable and achieves high-performance, reducing up to 98% of the case studies execution time. We also show how the application of machine learning techniques can enrich the analysis process