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Triatoma rubrofasciata (T. rubrofasciata), one kind of triatomine insects, is the vector of Trypanosoma cruzi (T. cruzi), which lead to American trypanosomiasis. Although the gut microbiome may play an essential role in the development and susceptibility of triatomine, there is limited research on the gut microbiota of T. rubrofasciata. To elucidate the effect of the vector's developmental stages and environmental conditions on the gut microbiome, we employed 16S rRNA gene sequencing to profile the gut bacterial community diversity and composition of T. rubrofasciata. Significant shifts were observed in the overall gut microbe diversity and composition across the development of T. rubrofasciata and specific bacteria were detected in different stages. Serratia and Burkholderia-Caballeronia-Paraburkholderia were dominant in the 1st nymphal stage, while the abundance of Staphylococcus was low in the 1st nymphal stage. Oceanicaulis were undetectable in the adult stage and Odoribacter peaked in the 2nd nymphal stage. Moreover, Staphylococcus was correlated negatively with Serratia. Likewise, the total gut microbiota diversity and composition of T. rubrofasciata differentiated significantly by environmental conditions. The ingestion of a bloodmeal increased alpha diversity of gut bacterial communities, and Staphylococcus was more abundant in laboratory-reared bugs whereas Enterococcus enriched in wild-caught bugs. Furthermore, Pantoea was negatively correlated with Staphylococcus, and positively related to Bacillus only. The phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) algorithm showed obvious metagenomic functional differences by environmental conditions, and Chagas disease relevant pathway was enriched in wild-caught T. rubrofasciata.
Pubmed ID: 33224899 RIS Download
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NIH genetic sequence database that provides annotated collection of all publicly available DNA sequences for almost 280 000 formally described species (Jan 2014) .These sequences are obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects, including whole-genome shotgun (WGS) and environmental sampling projects. Most submissions are made using web-based BankIt or standalone Sequin programs, and GenBank staff assigns accession numbers upon data receipt. It is part of International Nucleotide Sequence Database Collaboration and daily data exchange with European Nucleotide Archive (ENA) and DNA Data Bank of Japan (DDBJ) ensures worldwide coverage. GenBank is accessible through NCBI Entrez retrieval system, which integrates data from major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of GenBank database are available by FTP.
View all literature mentionsDatabase that provides access to the current and comprehensive 16S rRNA gene sequence alignment for browsing, blasting, probing, and downloading. The data and tools can assist the researcher in choosing phylogenetically specific probes, interpreting microarray results, and aligning/annotating novel sequences. The 16S rRNA gene database provides chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies. ARB users can use Greengenes to update local databases.
View all literature mentionsHigh quality ribosomal RNA databases providing comprehensive, quality checked and regularly updated datasets of aligned small (16S/18S, SSU) and large subunit (23S/28S, LSU) ribosomal RNA (rRNA) sequences for all three domains of life (Bacteria, Archaea and Eukarya). Supplementary services include a rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. The extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches. Alignment tool, SINA, is available for download as well as available for use online.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 23,2023.Software package for comparison and analysis of microbial communities, primarily based on high-throughput amplicon sequencing data, but also supporting analysis of other types of data. QIMME analyzes and transforms raw sequencing data generated on Illumina or other platforms to publication quality graphics and statistics.
View all literature mentionsA commercial organization which provides assay technologies to isolate DNA, RNA, and proteins from any biological sample. Assay technologies are then used to make specific target biomolecules, such as the DNA of a specific virus, visible for subsequent analysis.
View all literature mentionsSoftware Java pipeline for trimming tasks for Illumina paired end and single ended data. Flexible Trimmer for Illumina Sequence Data. Pair aware preprocessing tool optimized for Illumina next generation sequencing data. Includes several processing steps for read trimming and filtering. Operating systems Unix/Linux, Mac OS, Windows.
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Algorithm for high-dimensional biomarker discovery and explanation that identifies genes, pathways, or taxa characterizing the differences between two or more biological conditions. The algorithm identifies features that are statistically different among biological classes, then performs additional tests to assess whether these differences are consistent with respect to expected biological behavior. Statistical significance and biological relevance are emphasized.
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