Insights into the microRNA landscape of Rhodnius prolixus, a vector of Chagas disease.

The growing interest in microRNAs (miRNAs) over recent years has led to their characterization in numerous organisms. However, there is currently a lack of data available on miRNAs from triatomine bugs (Reduviidae: Triatominae), which are the vectors of the protozoan parasite Trypanosoma cruzi, the causative agent of Chagas disease. A comprehensive understanding of the molecular biology of vectors provides new insights into insect-host interactions and insect control approaches, which are key methods to prevent disease incidence in endemic areas. In this work, we describe the miRNome profiles from gut, hemolymph, and salivary gland tissues of the Rhodnius prolixus triatomine. Small RNA sequencing data revealed abundant expression of miRNAs, along with tRNA- and rRNA-derived fragments. Fifty-two mature miRNAs, previously reported in Ecdysozoa, were identified, including 39 ubiquitously expressed in the three tissues. Additionally, 112, 73, and 78 novel miRNAs were predicted in the gut, hemolymph, and salivary glands, respectively. In silico prediction showed that the top eight most highly expressed miRNAs from salivary glands potentially target human blood-expressed genes, suggesting that R. prolixus may modulate the host's gene expression at the bite site. This study provides the first characterization of miRNAs in a Triatominae species, shedding light on the role of these crucial regulatory molecules.

Draft Genome Sequence of the Wild Edible Mushroom Pleurotus ostreatoroseus DPUA 1720.

The draft whole-genome sequence of the mushroom Pleurotus ostreatoroseus DPUA 1720 (38,588,587 bp) is presented here. This report contributes to the prospective research for bioactive compounds in the genus Pleurotus.

An Integrative Sialomic Analysis Reveals Molecules From Triatoma sordida (Hemiptera: Reduviidae).

Triatomines have evolved salivary glands that produce versatile molecules with various biological functions, including those leading their interactions with vertebrate hosts' hemostatic and immunological systems. Here, using high-throughput transcriptomics and proteomics, we report the first sialome study on the synanthropic triatomine Triatoma sordida. As a result, 57,645,372 reads were assembled into 26,670 coding sequences (CDS). From these, a total of 16,683 were successfully annotated. The sialotranscriptomic profile shows Lipocalin as the most abundant protein family within putative secreted transcripts. Trialysins and Kazal-type protease inhibitors have high transcript levels followed by ubiquitous protein families and enzyme classes. Interestingly, abundant trialysin and Kazal-type members are highlighted in this triatomine sialotranscriptome. Furthermore, we identified 132 proteins in T. sordida salivary gland soluble extract through LC-MS/MS spectrometry. Lipocalins, Hemiptera specific families, CRISP/Antigen-5 and Kazal-type protein inhibitors proteins were identified. Our study provides a comprehensive description of the transcript and protein compositions of the salivary glands of T. sordida. It significantly enhances the information in the Triatominae sialome databanks reported so far, improving the understanding of the vector's biology, the hematophagous behaviour, and the Triatominae subfamily's evolution.

Discovering Selected Antibodies From Deep-Sequenced Phage-Display Antibody Library Using ATTILA.

Phage display is a powerful technique to select high-affinity antibodies for different purposes, including biopharmaceuticals. Next-generation sequencing (NGS) presented itself as a robust solution, making it possible to assess billions of sequences of the variable domains from selected sublibraries. Handling this process, a central difficulty is to find the selected clones. Here, we present the AutomaTed Tool For Immunoglobulin Analysis (ATTILA), a new tool to analyze and find the enriched variable domains throughout a biopanning experiment. The ATTILA is a workflow that combines publicly available tools and in-house programs and scripts to find the fold-change frequency of deeply sequenced amplicons generated from selected VH and VL domains. We analyzed the same human Fab library NGS data using ATTILA in 5 different experiments, as well as on 2 biopanning experiments regarding performance, accuracy, and output. These analyses proved to be suitable to assess library variability and to list the more enriched variable domains, as ATTILA provides a report with the amino acid sequence of each identified domain, along with its complementarity-determining regions (CDRs), germline classification, and fold change. Finally, the methods employed here demonstrated a suitable manner to combine amplicon generation and NGS data analysis to discover new monoclonal antibodies (mAbs).

Bioinformatics Workflows With NoSQL Database in Cloud Computing.

Scientific workflows can be understood as arrangements of managed activities executed by different processing entities. It is a regular Bioinformatics approach applying workflows to solve problems in Molecular Biology, notably those related to sequence analyses. Due to the nature of the raw data and the in silico environment of Molecular Biology experiments, apart from the research subject, 2 practical and closely related problems have been studied: reproducibility and computational environment. When aiming to enhance the reproducibility of Bioinformatics experiments, various aspects should be considered. The reproducibility requirements comprise the data provenance, which enables the acquisition of knowledge about the trajectory of data over a defined workflow, the settings of the programs, and the entire computational environment. Cloud computing is a booming alternative that can provide this computational environment, hiding technical details, and delivering a more affordable, accessible, and configurable on-demand environment for researchers. Considering this specific scenario, we proposed a solution to improve the reproducibility of Bioinformatics workflows in a cloud computing environment using both Infrastructure as a Service (IaaS) and Not only SQL (NoSQL) database systems. To meet the goal, we have built 3 typical Bioinformatics workflows and ran them on 1 private and 2 public clouds, using different types of NoSQL database systems to persist the provenance data according to the Provenance Data Model (PROV-DM). We present here the results and a guide for the deployment of a cloud environment for Bioinformatics exploring the characteristics of various NoSQL database systems to persist provenance data.