Ribo-Seq and RNA-Seq of TMA46 ( DFRP1) and GIR2 ( DFRP2) knockout yeast strains.
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ABSTRACT:
SUBMITTER: Egorov AA
PROVIDER: S-EPMC8637242 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Ribosome profiling (Ribo-Seq) is a powerful method to study translation in bacteria. However, Ribo-Seq signal can be observed across RNAs that one would not expect to be bound by ribosomes. For example, Escherichia coli Ribo-Seq libraries also capture reads from most noncoding RNAs (ncRNAs). While some of these ncRNAs may overlap coding regions, this alone does not explain the majority of observed signal across ncRNAs. These fragments of ncRNAs in Ribo-Seq data pass all size selection steps of the Ribo-Seq protocol and survive hours of micrococcal nuclease (MNase) treatment. In this work, we specifically focus on Ribo-Seq signal across ncRNAs and provide evidence to suggest that RNA structure, as opposed to ribosome binding, protects them from degradation and allows them to persist in the Ribo-Seq sequencing library preparation. By inspecting these "contaminant reads" in bacterial Ribo-Seq, we show that data previously disregarded in bacterial Ribo-Seq experiments may, in fact, be used to gain partial information regarding the in vivo secondary structure of ncRNAs.IMPORTANCE Structured ncRNAs are pivotal mediators of bioregulation in bacteria, and their functions are often reliant on their specific structures. Here, we first inspect Ribo-Seq reads across noncoding regions, identifying contaminant reads in these libraries. We observe that contaminant reads in bacterial Ribo-Seq experiments that are often disregarded, in fact, strongly overlap with structured regions of ncRNAs. We then perform several bioinformatic analyses to determine why these contaminant reads may persist in Ribo-Seq libraries. Finally, we highlight some structured RNA contaminants in Ribo-Seq and support the hypothesis that structures in the RNA protect them from MNase digestion. We conclude that researchers should be cautious when interpreting Ribo-Seq signal as coding without considering signal distribution. These findings also may enable us to partially resolve RNA structures, identify novel structured RNAs, and elucidate RNA structure-function relationships in bacteria at a large scale and in vivo through the reanalysis of existing Ribo-Seq data sets.
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Project description:Codon usage is biased between lowly and highly expressed genes in a genome-specific manner. This universal bias has been well assessed in some unicellular species, but remains problematic to assess in more complex species. We propose a new method to compute codon usage bias based on genome wide translational data. A new technique based on sequencing of ribosome protected mRNA fragments (Ribo-seq) allowed us to rank genes and compute codon usage bias with high precision for a great variety of species, including mammals. Genes ranking using Ribo-Seq data confirms the influence of the tRNA pool on codon usage bias and shows a decreasing bias in multicellular species. Ribo-Seq analysis also makes possible to detect preferred codons without information on genes function.
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Project description:Peripheral blood is a highly accessible biofluid providing a rich source of information about human physiology and health status. However, for studies of the blood transcriptome with RNA sequencing (RNA-Seq) techniques, high levels of hemoglobin mRNAs (hgbRNA) present in blood can occupy valuable sequencing space, impacting detection and quantification of non-hgbRNAs. In this study, we evaluated two methods for preparing ribosomal RNA (rRNA)-depleted sequencing libraries for RNA-Seq of whole blood, one of which is also designed to deplete hgbRNAs. Two experiments were performed: one evaluating library performance across 6 human blood samples and the other examining library reproducibility and performance in a two-subject subset. We find that addition of hgbRNA depletion to the rRNA-depletion protocol for library preparation from blood RNA effectively reduces highly abundant hgbRNA reads; however, it does not result in a statistically significant increase in differentially expressed genes in our patient-control study. Bioinformatic removal of globin gene counts in non-hgbRNA depleted libraries provides improvement in overall performance of these libraries. We conclude that use of a standard ribosomal RNA depletion method for library preparation coupled with bioinformatic removal of globin gene counts is sufficient for reproducible and sensitive measurement of both coding and noncoding RNAs in the blood transcriptome.
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Project description:Ribosome Profiling (Ribo-seq) has revolutionized the study of RNA translation by providing information on ribosome positions across all translated RNAs with nucleotide-resolution. Yet several technical limitations restrict the sequencing depth of such experiments, the most common of which is the overabundance of rRNA fragments. Various strategies can be employed to tackle this issue, including the use of commercial rRNA depletion kits. However, as they are designed for more standardized RNAseq experiments, they may perform suboptimally in Ribo-seq. In order to overcome this, it is possible to use custom biotinylated oligos complementary to the most abundant rRNA fragments, however currently no computational framework exists to aid the design of optimal oligos. Here, we first show that a major confounding issue is that the rRNA fragments generated via Ribo-seq vary significantly with differing experimental conditions, suggesting that a "one-size-fits-all" approach may be inefficient. Therefore we developed Ribo-ODDR, an oligo design pipeline integrated with a user-friendly interface that assists in oligo selection for efficient experiment-specific rRNA depletion. Ribo-ODDR uses preliminary data to identify the most abundant rRNA fragments, and calculates the rRNA depletion efficiency of potential oligos. We experimentally show that Ribo-ODDR designed oligos outperform commercially available kits and lead to a significant increase in rRNA depletion in Ribo-seq. Ribo-ODDR is freely accessible at https://github.com/fallerlab/Ribo-ODDR. Supplementary data are available at Bioinformatics online.
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Project description:Glucocorticoids such as dexamethasone (Dex) are widely used to treat both acute and chronic inflammatory conditions. They regulate immune responses by dampening cell-mediated immunity in a glucocorticoid receptor (GR)-dependent manner, by suppressing the expression of pro-inflammatory cytokines and chemokines and by stimulating the expression of anti-inflammatory mediators. Despite its evident clinical benefit, the mechanistic underpinnings of the gene regulatory networks transcriptionally controlled by GR in a context-specific manner remain mysterious. Next generation sequencing methods such mRNA sequencing (RNA-seq) and Ribosome profiling (ribo-seq) provide tools to investigate the transcriptional and post-transcriptional mechanisms that govern gene expression. Here, we integrate matched RNA-seq data with ribo-seq data from human acute monocytic leukemia (THP-1) cells treated with the TLR4 ligand lipopolysaccharide (LPS) and with Dex, to investigate the global transcriptional and translational regulation (translational efficiency, ΔTE) of Dex-responsive genes. We find that the expression of most of the Dex-responsive genes are regulated at both the transcriptional and the post-transcriptional level, with the transcriptional changes intensified on the translational level. Overrepresentation pathway analysis combined with STRING protein network analysis and manual functional exploration, identified these genes to encode immune effectors and immunomodulators that contribute to macrophage-mediated immunity and to the maintenance of macrophage-mediated immune homeostasis. Further research into the translational regulatory network underlying the GR anti-inflammatory response could pave the way for the development of novel immunomodulatory therapeutic regimens with fewer undesirable side effects.
| S-EPMC9582734 | biostudies-literature
Project description:Characterizing a protein's function often requires a description of the cellular state in its absence. Multiplexing in mass spectrometry-based proteomics has now achieved the ability to globally measure protein expression levels in yeast from 10 cell states simultaneously. We applied this approach to quantify expression differences in wild type and nine deubiquitylating enzyme (DUB) knockout strains with the goal of creating "information networks" that might provide deeper, mechanistic insights into a protein's biological role. In total, more than 3700 proteins were quantified with high reproducibility across three biological replicates (30 samples in all). DUB mutants demonstrated different proteomics profiles, consistent with distinct roles for each family member. These included differences in total ubiquitin levels and specific chain linkages. Moreover, specific expression changes suggested novel functions for several DUB family members. For instance, the ubp3Δ mutant showed large expression changes for members of the cytochrome C oxidase complex, consistent with a role for Ubp3 in mitochondrial regulation. Several DUBs also showed broad expression changes for phosphate transporters as well as other components of the inorganic phosphate signaling pathway, suggesting a role for these DUBs in regulating phosphate metabolism. These data highlight the potential of multiplexed proteome-wide analyses for biological investigation and provide a framework for further study of the DUB family. Our methods are readily applicable to the entire collection of yeast deletion mutants and may help facilitate systematic analysis of yeast and other organisms.
| S-EPMC4802358 | biostudies-literature
Project description:Ribosome profiling has been used to predict thousands of short open reading frames (sORFs) in eukaryotic cells, but it suffers from substantial levels of noise. PRICE (https://github.com/erhard-lab/price) is a computational method that models experimental noise to enable researchers to accurately resolve overlapping sORFs and noncanonical translation initiation. We experimentally validated translation using major histocompatibility complex class I (MHC I) peptidomics and observed that sORF-derived peptides efficiently enter the MHC I presentation pathway and thus constitute a substantial fraction of the antigen repertoire.
| S-EPMC6152898 | biostudies-literature
Project description:Purpose: We develop a multivariate polynomial temporal genetic association (MPTGA) approach for detecting temporal genetic loci of quantitative traits monitored over time in a population. To dissect causal regulators under each temporal genetic locus, we develop a temporal genetic causality test (TGCT) for inferring causal relationships between traits linked to the locus. We applied MPTGA and TGCT to simulated data sets and a yeast population that was molecularly profiled over six time points in response to treatment with the drug rapamycin. We demonstrate significantly increased power to detect genetic loci modulating gene expression traits over time in this population and to resolve the causal regulators of dynamic expression quantitative loci hot spots, thereby increasing the power to elucidate regulatory networks. We experimentally validated a teQTL (temporal expression Quantitative Trait Locus) hot spot locus interacting with rapamycin treatment over time, which could only be identified using our temporal genetic association procedure. We experimentally validated RRD1, a cis regulated gene in this locus, as the causal regulator for this teQTL hot spot. Methods: The wild type strain BY4730 and RRD1 knockout strain YSC6273-201925697 were obtained from Thermo Scientific Open Biosystems. Yeast was grown in YPD medium to log-phase in shaken flasks at 30°C. Total RNA was extracted as described previously. For rapamycin treatment, 100nM rapamycin (Cayman Chemical, Ann Arbor, MI) was added to the medium after yeast grew to log-phase. After culture for 50 minutes, total RNA was extracted the same as above. All experiments were repeated 3 times on three different days. Approximately 250 ng of total RNA per sample were used for library construction by the TruSeq RNA Sample Prep Kit (Illumina) and sequenced using the Illumina HiSeq 2500 instrument with 100nt single read setting according to the manufacturer's instructions. Sequence reads were aligned to yeast genome assembly using Tophat . Total 6,932 yeast transcripts were quantified using Cufflinks, and 5,542 of them overlap with transcripts on Yeast Genome 2.0 Arrays from Affymatrix, which was used for generating the yeast F2 time course data. The 5,542 transcripts were used in further analysis. Differentially expressed genes were defined by CuffDiff. At q-value <0.01, 64 and 581 were in RRD1 ko signature without rapamycin (RRD1 ko no treatment vs. wild type no treatment) and RRD1 ko signature with rapamycin (RRD1 ko with rapamycin vs. wild type with rapamycin), respectively. Conclusions: Integration of temporal and genetic data has the potential to enhance the power to resolve causal relationships and to provide a more accurate view of regulatory networks in complex biological systems.
2018-12-21 | GSE86786 | GEO
Project description:The ribosome profiling technique (Ribo-seq) allows the selective sequencing of translated RNA regions. Recently, the analysis of genomic sequences associated to Ribo-seq reads has been widely employed to assess their coding potential. These analyses led to the identification of differentially translated transcripts under different experimental conditions, and/or ribosome pausing on codon motifs. In the context of the ever-growing need for tools analyzing Ribo-seq reads, we have developed 'RiboProfiling', a new Bioconductor open-source package. 'RiboProfiling' provides a full pipeline to cover all key steps for the analysis of ribosome footprints. This pipeline has been implemented in a single R workflow. The package takes an alignment (BAM) file as input and performs ribosome footprint quantification at a transcript level. It also identifies footprint accumulation on particular amino acids or multi amino-acids motifs. Report summary graphs and data quantification are generated automatically. The package facilitates quality assessment and quantification of Ribo-seq experiments. Its implementation in Bioconductor enables the modeling and statistical analysis of its output through the vast choice of packages available in R. This article illustrates how to identify codon-motifs accumulating ribosome footprints, based on data from Escherichia coli.
| S-EPMC4918025 | biostudies-other
Project description:Comprehensive analysis of coding and non -coding transcriptome using ribo-depleted total RNA-seq and poly A selected RNA-seq of MCF-7 cells grown in hypoxia and normoxia. Breast cancer cell line (MCF-7) is cultured in normoxic condition (21% O2) and hypoxic condition (1%O2) for 24 hours. Expression of HIF-1alpha and/or HIF-2alpha subunits was suppressed using siRNAs in hypoxic MCF-7 cells. Total RNA was isolated from both hypoxia and normoxia conditions were subjected for ribosomal depleted stand specific RNA-seq and poly A selected RNA-seq
| S-ECPF-MTAB-1994 | biostudies-other