Small RNA-seq
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BACKGROUND: Limited accessibility to intestinal epithelial tissue in wild animals and humans makes it challenging to study patterns of intestinal gene regulation, and hence to monitor physiological status and health in field conditions. To explore solutions to this limitation, we have used a noninvasive approach via fecal RNA-seq, for the quantification of gene expression markers in gastrointestinal cells of free-range primates and a forager human population. Thus, a combination of poly(A) mRNA enrichment and rRNA depletion methods was used in tandem with RNA-seq to quantify and compare gastrointestinal gene expression patterns in fecal samples of wild Gorilla gorilla gorilla (n = 9) and BaAka hunter-gatherers (n = 10) from The Dzanga Sangha Protected Areas, Central African Republic. RESULTS: Although only a small fraction (< 4.9%) of intestinal mRNA signals was recovered, the data was sufficient to detect significant functional differences between gorillas and humans, at the gene and pathway levels. These intestinal gene expression differences were specifically associated with metabolic and immune functions. Additionally, non-host RNA-seq reads were used to gain preliminary insights on the subjects' dietary habits, intestinal microbiomes, and infection prevalence, via identification of fungi, nematode, arthropod and plant RNA. CONCLUSIONS: Overall, the results suggest that fecal RNA-seq, targeting gastrointestinal epithelial cells can be used to evaluate primate intestinal physiology and gut gene regulation, in samples obtained in challenging conditions in situ. The approach used herein may be useful to obtain information on primate intestinal health, while revealing preliminary insights into foraging ecology, microbiome, and diet.
- MeSH
- feces * MeSH
- gastrointestinální trakt metabolismus MeSH
- Gorilla gorilla genetika MeSH
- lidé MeSH
- messenger RNA genetika MeSH
- poly A genetika MeSH
- sekvenování transkriptomu * MeSH
- stanovení celkové genové exprese * MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
Marine oomycetes have recently been shown to be concurrently infected by (-)ssRNA viruses of the order Bunyavirales. In this work, even higher virus variability was found in a single isolate of Phytophthora condilina, a recently described member of Phytophthora phylogenetic Clade 6a, which was isolated from brackish estuarine waters in southern Portugal. Using total and small RNA-seq the full RdRp of 13 different potential novel bunya-like viruses and two complete toti-like viruses were detected. All these viruses were successfully confirmed by reverse transcription polymerase chain reaction (RT-PCR) using total RNA as template, but complementarily one of the toti-like and five of the bunya-like viruses were confirmed when dsRNA was purified for RT-PCR. In our study, total RNA-seq was by far more efficient for de novo assembling of the virus sequencing but small RNA-seq showed higher read numbers for most viruses. Two main populations of small RNAs (21 nts and 25 nts-long) were identified, which were in accordance with other Phytophthora species. To the best of our knowledge, this is the first study using small RNA sequencing to identify viruses in Phytophthora spp.
- MeSH
- fylogeneze MeSH
- genom virový MeSH
- malá nekódující RNA genetika MeSH
- otevřené čtecí rámce MeSH
- Phytophthora virologie MeSH
- RNA virová genetika MeSH
- RNA-viry klasifikace genetika izolace a purifikace MeSH
- sekvenční analýza DNA MeSH
- sekvenční analýza RNA * MeSH
- sekvenování transkriptomu MeSH
- virové nemoci virologie MeSH
- výpočetní biologie MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Geografické názvy
- Portugalsko MeSH
Pre-mRNA splicing represents an important regulatory layer of eukaryotic gene expression. In the simple budding yeast Saccharomyces cerevisiae, about one-third of all mRNA molecules undergo splicing, and splicing efficiency is tightly regulated, for example, during meiotic differentiation. S. cerevisiae features a streamlined, evolutionarily highly conserved splicing machinery and serves as a favourite model for studies of various aspects of splicing. RNA-seq represents a robust, versatile, and affordable technique for transcriptome interrogation, which can also be used to study splicing efficiency. However, convenient bioinformatics tools for the analysis of splicing efficiency from yeast RNA-seq data are lacking. We present a complete workflow for the calculation of genome-wide splicing efficiency in S. cerevisiae using strand-specific RNA-seq data. Our pipeline takes sequencing reads in the FASTQ format and provides splicing efficiency values for the 5' and 3' splice junctions of each intron. The pipeline is based on up-to-date open-source software tools and requires very limited input from the user. We provide all relevant scripts in a ready-to-use form. We demonstrate the functionality of the workflow using RNA-seq datasets from three spliceosome mutants. The workflow should prove useful for studies of yeast splicing mutants or of regulated splicing, for example, under specific growth conditions.
Meiotic maturation of oocyte relies on pre-synthesised maternal mRNA, the translation of which is highly coordinated in space and time. Here, we provide a detailed polysome profiling protocol that demonstrates a combination of the sucrose gradient ultracentrifugation in small SW55Ti tubes with the qRT-PCR-based quantification of 18S and 28S rRNAs in fractionated polysome profile. This newly optimised method, named Scarce Sample Polysome Profiling (SSP-profiling), is suitable for both scarce and conventional sample sizes and is compatible with downstream RNA-seq to identify polysome associated transcripts. Utilising SSP-profiling we have assayed the translatome of mouse oocytes at the onset of nuclear envelope breakdown (NEBD)-a developmental point, the study of which is important for furthering our understanding of the molecular mechanisms leading to oocyte aneuploidy. Our analyses identified 1847 transcripts with moderate to strong polysome occupancy, including abundantly represented mRNAs encoding mitochondrial and ribosomal proteins, proteasomal components, glycolytic and amino acids synthetic enzymes, proteins involved in cytoskeleton organization plus RNA-binding and translation initiation factors. In addition to transcripts encoding known players of meiotic progression, we also identified several mRNAs encoding proteins of unknown function. Polysome profiles generated using SSP-profiling were more than comparable to those developed using existing conventional approaches, being demonstrably superior in their resolution, reproducibility, versatility, speed of derivation and downstream protocol applicability.
- MeSH
- jaderný obal genetika metabolismus MeSH
- meióza genetika MeSH
- myši MeSH
- oocyty růst a vývoj metabolismus MeSH
- polyribozomy genetika MeSH
- proteiny vázající RNA genetika MeSH
- RNA messenger skladovaná genetika MeSH
- RNA ribozomální 18S genetika MeSH
- RNA ribozomální 28S genetika MeSH
- sekvenování transkriptomu MeSH
- vývojová regulace genové exprese genetika MeSH
- zvířata MeSH
- Check Tag
- myši MeSH
- ženské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
Small RNA-sequencing (RNA-Seq) is being increasingly used for profiling of circulating microRNAs (miRNAs), a new group of promising biomarkers. Unfortunately, small RNA-Seq protocols are prone to biases limiting quantification accuracy, which motivated development of several novel methods. Here, we present comparison of all small RNA-Seq library preparation approaches that are commercially available for quantification of miRNAs in biofluids. Using synthetic and human plasma samples, we compared performance of traditional two-adaptor ligation protocols (Lexogen, Norgen), as well as methods using randomized adaptors (NEXTflex), polyadenylation (SMARTer), circularization (RealSeq), capture probes (EdgeSeq), or unique molecular identifiers (QIAseq). There was no single protocol outperforming others across all metrics. Limited overlap of measured miRNA profiles was documented between methods largely owing to protocol-specific biases. Methods designed to minimize bias largely differ in their performance, and contributing factors were identified. Usage of unique molecular identifiers has rather negligible effect and, if designed incorrectly, can even introduce spurious results. Together, these results identify strengths and weaknesses of all current methods and provide guidelines for applications of small RNA-Seq in biomarker research.
Nucleoside-containing metabolites such as NAD+ can be incorporated as 5' caps on RNA by serving as non-canonical initiating nucleotides (NCINs) for transcription initiation by RNA polymerase (RNAP). Here, we report CapZyme-seq, a high-throughput-sequencing method that employs NCIN-decapping enzymes NudC and Rai1 to detect and quantify NCIN-capped RNA. By combining CapZyme-seq with multiplexed transcriptomics, we determine efficiencies of NAD+ capping by Escherichia coli RNAP for ∼16,000 promoter sequences. The results define preferred transcription start site (TSS) positions for NAD+ capping and define a consensus promoter sequence for NAD+ capping: HRRASWW (TSS underlined). By applying CapZyme-seq to E. coli total cellular RNA, we establish that sequence determinants for NCIN capping in vivo match the NAD+-capping consensus defined in vitro, and we identify and quantify NCIN-capped small RNAs (sRNAs). Our findings define the promoter-sequence determinants for NCIN capping with NAD+ and provide a general method for analysis of NCIN capping in vitro and in vivo.
- MeSH
- DNA řízené RNA-polymerasy metabolismus MeSH
- endoribonukleasy metabolismus MeSH
- Escherichia coli genetika metabolismus MeSH
- exprese genu genetika MeSH
- genetická transkripce genetika MeSH
- NAD metabolismus MeSH
- nukleotidy genetika MeSH
- počátek transkripce fyziologie MeSH
- promotorové oblasti (genetika) genetika MeSH
- RNA čepičky genetika MeSH
- transkriptom genetika MeSH
- vysoce účinné nukleotidové sekvenování metody MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
Genomic regions that encode small RNA genes exhibit characteristic patterns in their sequence, secondary structure, and evolutionary conservation. Convolutional Neural Networks are a family of algorithms that can classify data based on learned patterns. Here we present MuStARD an application of Convolutional Neural Networks that can learn patterns associated with user-defined sets of genomic regions, and scan large genomic areas for novel regions exhibiting similar characteristics. We demonstrate that MuStARD is a generic method that can be trained on different classes of human small RNA genomic loci, without need for domain specific knowledge, due to the automated feature and background selection processes built into the model. We also demonstrate the ability of MuStARD for inter-species identification of functional elements by predicting mouse small RNAs (pre-miRNAs and snoRNAs) using models trained on the human genome. MuStARD can be used to filter small RNA-Seq datasets for identification of novel small RNA loci, intra- and inter- species, as demonstrated in three use cases of human, mouse, and fly pre-miRNA prediction. MuStARD is easy to deploy and extend to a variety of genomic classification questions. Code and trained models are freely available at gitlab.com/RBP_Bioinformatics/mustard.
- MeSH
- algoritmy MeSH
- genomika metody MeSH
- lidé MeSH
- malá jadérková RNA genetika MeSH
- mikro RNA genetika MeSH
- myši MeSH
- nekódující RNA genetika MeSH
- neuronové sítě MeSH
- software MeSH
- výpočetní biologie metody MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- myši MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Non-small cell lung carcinoma (NSCLC) represents the majority of lung cancer cases, comprising approximately 85 % of the total. The five-year survival rate for NSCLC patients remains discouragingly low. Recently, immunotherapy has emerged as a promising approach. Nevertheless, only a minority of patients experience considerable benefits from these treatments. This highlights the critical need for effective biomarkers that can predict both patient prognosis and response to immunotherapy. CD8+ T cells play a crucial role in cancer immunotherapy. Their presence within tumours is generally indicative of a favourable prognosis and increased efficacy of immunotherapy. This study was undertaken to identify and authenticate a novel biomarker signature based on CD8+ T-cell marker genes, to prognosticate therapeutic responses in individuals afflicted with NSCLC. This in-depth study was based on a total of 1,200 samples, which included four NSCLC specimens analysed through single-cell RNA sequencing (scRNA-seq), 1,000 NSCLC samples obtained from The Cancer Genome Atlas (TCGA) and 196 NSCLC specimens collected from the GSE37745 cohort. In patients with NSCLC, those presenting a favourable risk profile demonstrated notable elevations in specific immune cells while concurrently exhibiting reductions in other types. CD8+ T cells, with their established role in inducing apoptosis in cancer cells, have emerged as crucial predictors and modulators of treatment strategies for NSCLC patients. The combination of single-cell and bulk RNA sequencing has produced a biomarker signature, emphasizing the CD8+ T cells' crucial role in NSCLC prognosis and treatment.
- MeSH
- CD8-pozitivní T-lymfocyty * imunologie MeSH
- imunoterapie * metody MeSH
- lidé středního věku MeSH
- lidé MeSH
- nádorové biomarkery MeSH
- nádory plic * imunologie terapie MeSH
- nemalobuněčný karcinom plic * imunologie terapie MeSH
- prognóza MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
