Differences in Bacterial Small RNAs in Stool Samples from Hypercholesterolemic and Normocholesterolemic Subjects.

Cholesterol metabolism is important at the physiological level as well as in several diseases, with small RNA being an element to consider in terms of its epigenetic control. Thus, the aim of this study was to identify differences between bacterial small RNAs present at the gut level in hypercholesterolemic and normocholesterolemic individuals. Twenty stool samples were collected from hypercholesterolemic and normocholesterolemic subjects. RNA extraction and small RNA sequencing were performed, followed by bioinformatics analyses with BrumiR, Bowtie 2, BLASTn, DESeq2, and IntaRNA, after the filtering of the reads with fastp. In addition, the prediction of secondary structures was obtained with RNAfold WebServer. Most of the small RNAs were of bacterial origin and presented a greater number of readings in normocholesterolemic participants. The upregulation of small RNA ID 2909606 associated with Coprococcus eutactus (family Lachnospiraceae) was presented in hypercholesterolemic subjects. In addition, a positive correlation was established between small RNA ID 2149569 from the species Blautia wexlerae and hypercholesterolemic subjects. Other bacterial and archaeal small RNAs that interacted with the LDL receptor (LDLR) were identified. For these sequences, the prediction of secondary structures was also obtained. There were significant differences in bacterial small RNAs associated with cholesterol metabolism in hypercholesterolemic and normocholesterolemic participants.

OUTBREAK: a user-friendly georeferencing online tool for disease surveillance.

The current COVID-19 pandemic has already claimed more than 3.7 million victims and it will cause more deaths in the coming months. Tools that track the number and locations of cases are critical for surveillance and help in making policy decisions for controlling the outbreak. However, the current surveillance web-based dashboards run on proprietary platforms, which are often expensive and require specific computational knowledge. We developed a user-friendly web tool, named OUTBREAK, that facilitates epidemic surveillance by showing in an animated graph the timeline and geolocations of cases of an outbreak. It permits even non-specialist users to input data most conveniently and track outbreaks in real-time. We applied our tool to visualize the SARS 2003, MERS, and COVID19 epidemics, and provided them as examples on the website. Through the zoom feature, it is also possible to visualize cases at city and even neighborhood levels. We made the tool freely available at https://outbreak.sysbio.tools/ . OUTBREAK has the potential to guide and help health authorities to intervene and minimize the effects of outbreaks.

StructRNAfinder: an automated pipeline and web server for RNA families prediction.

BACKGROUND: The function of many noncoding RNAs (ncRNAs) depend upon their secondary structures. Over the last decades, several methodologies have been developed to predict such structures or to use them to functionally annotate RNAs into RNA families. However, to fully perform this analysis, researchers should utilize multiple tools, which require the constant parsing and processing of several intermediate files. This makes the large-scale prediction and annotation of RNAs a daunting task even to researchers with good computational or bioinformatics skills. RESULTS: We present an automated pipeline named StructRNAfinder that predicts and annotates RNA families in transcript or genome sequences. This single tool not only displays the sequence/structural consensus alignments for each RNA family, according to Rfam database but also provides a taxonomic overview for each assigned functional RNA. Moreover, we implemented a user-friendly web service that allows researchers to upload their own nucleotide sequences in order to perform the whole analysis. Finally, we provided a stand-alone version of StructRNAfinder to be used in large-scale projects. The tool was developed under GNU General Public License (GPLv3) and is freely available at http://structrnafinder.integrativebioinformatics.me . CONCLUSIONS: The main advantage of StructRNAfinder relies on the large-scale processing and integrating the data obtained by each tool and database employed along the workflow, of which several files are generated and displayed in user-friendly reports, useful for downstream analyses and data exploration.

CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses.

BACKGROUND: The analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions. RESULTS: In this paper, we present a fast and easy-to-use Bioconductor package named CEMiTool that unifies the discovery and the analysis of co-expression modules. Using the same real datasets, we demonstrate that CEMiTool outperforms existing tools, and provides unique results in a user-friendly html report with high quality graphs. Among its features, our tool evaluates whether modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group, as well as it integrates transcriptomic data with interactome information, identifying the potential hubs on each network. We successfully applied CEMiTool to over 1000 transcriptome datasets, and to a new RNA-seq dataset of patients infected with Leishmania, revealing novel insights of the disease's physiopathology. CONCLUSION: The CEMiTool R package provides users with an easy-to-use method to automatically implement gene co-expression network analyses, obtain key information about the discovered gene modules using additional downstream analyses and retrieve publication-ready results via a high-quality interactive report.

Bacterial networks in Atlantic salmon with Piscirickettsiosis.

An unbalanced composition of gut microbiota in fish is hypothesized to play a role in promoting bacterial infections, but the synergistic or antagonistic interactions between bacterial groups in relation to fish health are not well understood. We report that pathogenic species in the Piscirickettsia, Aeromonas, Renibacterium and Tenacibaculum genera were all detected in the digesta and gut mucosa of healthy Atlantic salmon without clinical signs of disease. Although Piscirickettsia salmonis (and other pathogens) occurred in greater frequencies of fish with clinical Salmonid Rickettsial Septicemia (SRS), the relative abundance was about the same as that observed in healthy fish. Remarkably, the SRS-positive fish presented with a generalized mid-gut dysbiosis and positive growth associations between Piscirickettsiaceae and members of other taxonomic families containing known pathogens. The reconstruction of metabolic phenotypes based on the bacterial networks detected in the gut and mucosa indicated the synthesis of Gram-negative virulence factors such as colanic acid and O-antigen were over-represented in SRS positive fish. This evidence indicates that cooperative interactions between organisms of different taxonomic families within localized bacterial networks might promote an opportunity for P. salmonis to cause clinical SRS in the farm environment.

Salmo salar Skin and Gill Microbiome during Piscirickettsia salmonis Infection.

Maintaining the high overall health of farmed animals is a central tenant of their well-being and care. Intense animal crowding in aquaculture promotes animal morbidity especially in the absence of straightforward methods for monitoring their health. Here, we used bacterial 16S ribosomal RNA gene sequencing to measure bacterial population dynamics during P. salmonis infection. We observed a complex bacterial community consisting of a previously undescribed core pathobiome. Notably, we detected Aliivibrio wodanis and Tenacibaculum dicentrarchi on the skin ulcers of salmon infected with P. salmonis, while Vibrio spp. were enriched on infected gills. The prevalence of these co-occurring networks indicated that coinfection with other pathogens may enhance P. salmonis pathogenicity.

Characterization of microbial communities from gut microbiota of hypercholesterolemic and control subjects.

Introduction: In recent years, several studies have evidenced the importance of the microbiome to host physiology as metabolism regulator, along with its potential role in triggering various diseases. In this study, we analyzed the gut microbiota in hypercholesterolemic (cases) and normocholesterolemic (controls) individuals to identify characteristic microbial signature for each condition. Methods: Stool samples were obtained from 57 adult volunteers (27 hypercholesterolemic and 30 controls). The taxonomic profiling of microbial communities was performed using high-throughput sequencing of 16S rRNA V3-V4 amplicons, followed by data analysis using Quantitative Insights Into Microbial Ecology 2 (QIIME2) and linear discriminant analysis (LDA) effect size (LEfSe). Results: Significant differences were observed in weight, height, body mass index (BMI) and serum levels of triglycerides, total cholesterol and low-density lipoprotein cholesterol (LDL-C) between the groups (p<0.05). LEfSe showed differentially abundant prokaryotic taxa (α=0.05, LDA score > 2.0) in the group of hypercholesterolemic individuals (Methanosphaera, Rothia, Chromatiales, Clostridiales, Bacillaceae and Coriobacteriaceae) and controls (Faecalibacterium, Victivallis and Selenomonas) at various taxonomic levels. In addition, through the application of Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2), the predominance of pathways related to biosynthesis in hypercholesterolemic patients was established, compared to controls in which degradation pathways were predominant. Finally, in the analysis of co-occurrence networks, it was possible to identify associations between the microorganisms present in both studied groups. Conclusion: Our results point out to unique microbial signatures, which likely play a role on the cholesterol metabolism in the studied population.

RNAmining: A machine learning stand-alone and web server tool for RNA coding potential prediction.

Non-coding RNAs (ncRNAs) are important players in the cellular regulation of organisms from different kingdoms. One of the key steps in ncRNAs research is the ability to distinguish coding/non-coding sequences. We applied seven machine learning algorithms (Naive Bayes, SVM, KNN, Random Forest, XGBoost, ANN and DL) through 15 model organisms from different evolutionary branches. Then, we created a stand-alone and web server tool (RNAmining) to distinguish coding and non-coding sequences, selecting the algorithm with the best performance (XGBoost). Firstly, we used coding/non-coding sequences downloaded from Ensembl (April 14th, 2020). Then, coding/non-coding sequences were balanced, had their tri-nucleotides counts analysed and we performed a normalization by the sequence length. Thus, in total we built 180 models. All the machine learning algorithms tests were performed using 10-folds cross-validation and we selected the algorithm with the best results (XGBoost) to implement at RNAmining. Best F1-scores ranged from 97.56% to 99.57% depending on the organism. Moreover, we produced a benchmarking with other tools already in literature (CPAT, CPC2, RNAcon and Transdecoder) and our results outperformed them, opening opportunities for the development of RNAmining, which is freely available at https://rnamining.integrativebioinformatics.me/.

Novel molecular insights and public omics data in pulmonary hypertension.

Pulmonary hypertension is a rare disease with high morbidity and mortality which mainly affects women of reproductive age. Despite recent advances in understanding the pathogenesis of pulmonary hypertension, the high heterogeneity in the presentation of the disease among different patients makes it difficult to make an accurate diagnosis and to apply this knowledge to effective treatments. Therefore, new studies are required to focus on translational and personalized medicine to overcome the lack of specificity and efficacy of current management. Here, we review the majority of public databases storing 'omics' data of pulmonary hypertension studies, from animal models to human patients. Moreover, we review some of the new molecular mechanisms involved in the pathogenesis of pulmonary hypertension, including non-coding RNAs and the application of 'omics' data to understand this pathology, hoping that these new approaches will provide insights to guide the way to personalized diagnosis and treatment.

Predicting RNA Families in Nucleotide Sequences Using StructRNAfinder.

Noncoding RNA (ncRNA) research is already a routine in every genomics or transcriptomics initiatives. According to their functions, ncRNAs can be grouped into several different RNA families, which can be represented by conserved primary sequences, secondary structures, or covariance models (CMs). CMs are very sensitive in predicting RNA families in nucleotide sequences and have been widely used in characterizing the repertoire of ncRNAs in organisms from all domains of life. However, the large-scale prediction and annotation of ncRNAs require multiple tools along the process, imposing a great obstacle for researchers with lesser computational or bioinformatics background. StructRNAfinder emerged as an automated tool to avoid these bottlenecks, by performing the automatic identification and complete annotation of regulatory RNA families derived directly from nucleotide sequences. In this chapter, we provide a complete tutorial for both stand-alone and web server versions of StructRNAfinder. This will help users to install the tool and to perform predictions of RNA families in any genome or transcriptome sequences dataset.