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The microbiomes of tropical corals are actively studied using 16S rRNA gene amplicons to understand microbial roles in coral health, metabolism, and disease resistance. However, due to the prokaryotic origins of mitochondria, primers targeting bacterial and archaeal 16S rRNA genes may also amplify homologous 12S mitochondrial rRNA genes from the host coral, associated microbial eukaryotes, and encrusting organisms. Standard microbial bioinformatics pipelines attempt to identify and remove these sequences by comparing them to reference taxonomies. However, commonly used tools have severely under-annotated mitochondrial sequences in 1440 coral microbiomes from the Global Coral Microbiome Project, preventing annotation of over 95% of reads in some samples. This issue persists when using Greengenes or SILVA prokaryotic reference taxonomies, and in other hosts, including 16S studies of vertebrates, and of marine sponges. Worse, mitochondrial under-annotation varies between coral families and across coral compartments, biasing comparisons of - and -diversity. By supplementing existing reference taxonomies with over 3000 animal mitochondrial rRNA gene sequences, we resolved roughly 97% of unique unclassified sequences as mitochondrial. These additional sequences did not cause a false elevation in mitochondrial annotations in mock communities with known compositions. We recommend using these extended taxonomies for coral microbiome analysis and whenever eukaryotic contamination may be a concern.
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Transcriptome analysis provides genome annotation and expression profiles in the central nervous system of Lymnaea stagnalis at different ages
Abstract Background The pond snail, Lymnaea stagnalis ( L. stagnalis ), has served as a valuable model organism for neurobiology studies due to its simple and easily accessible central nervous system (CNS). L. stagnalis has been widely used to study neuronal networks and recently gained popularity for study of aging and neurodegenerative diseases. However, previous transcriptome studies of L. stagnalis CNS have been exclusively carried out on adult L. stagnalis only. As part of our ongoing effort studying L. stagnalis neuronal growth and connectivity at various developmental stages, we provide the first age-specific transcriptome analysis and gene annotation of young (3 months), adult (6 months), and old (18 months) L. stagnalis CNS. Results Using the above three age cohorts, our study generated 55–69 millions of 150 bp paired-end RNA sequencing reads using the Illumina NovaSeq 6000 platform. Of these reads, ~ 74% were successfully mapped to the reference genome of L. stagnalis . Our reference-based transcriptome assembly predicted 42,478 gene loci, of which 37,661 genes encode coding sequences (CDS) of at least 100 codons. In addition, we provide gene annotations using Blast2GO and functional annotations using Pfam for ~ 95% of these sequences, contributing to the largest number of annotated genes in L. stagnalis CNS so far. Moreover, among 242 previously cloned L. stagnalis genes, we were able to match ~ 87% of them in our transcriptome assembly, indicating a high percentage of gene coverage. The expressional differences for innexins, FMRFamide, and molluscan insulin peptide genes were validated by real-time qPCR. Lastly, our transcriptomic analyses revealed distinct, age-specific gene clusters, differentially expressed genes, and enriched pathways in young, adult, and old CNS. More specifically, our data show significant changes in expression of critical genes involved in transcription factors, metabolisms (e.g. cytochrome P450), extracellular matrix constituent, and signaling receptor and transduction (e.g. receptors for acetylcholine, N-Methyl-D-aspartic acid, and serotonin), as well as stress- and disease-related genes in young compared to either adult or old snails. Conclusions Together, these datasets are the largest and most updated L. stagnalis CNS transcriptomes, which will serve as a resource for future molecular studies and functional annotation of transcripts and genes in L. stagnalis .
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- Award ID(s):
- 1916563
- PAR ID:
- 10376900
- Date Published:
- Journal Name:
- BMC Genomics
- Volume:
- 22
- Issue:
- 1
- ISSN:
- 1471-2164
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1186/s12864-021-07946-y
