Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR27067511_1.fastq.gz | Fastqsanger.gz | |||
| SRR27067511_2.fastq.gz | Fastqsanger.gz | |||
| SRR27067512_1.fastq.gz | Fastqsanger.gz | |||
| SRR27067512_2.fastq.gz | Fastqsanger.gz | |||
| SRR27067513_1.fastq.gz | Fastqsanger.gz |
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Project description:Ribosome biogenesis is essential for protein synthesis in gene expression. Yeast eIF5B has been shown biochemically to facilitate 18S rRNA 3’ end maturation during late-40S ribosomal subunit assembly and gate the transition from translation initiation to elongation. But the effects of eIF5B have not been studied at the genome-wide level in any organism, and 18S rRNA 3’ end maturation is poorly understood in plants. Arabidopsis HOT3/eIF5B1 was found to promote development and heat-stress acclimation by translational regulation, but its molecular function remained unknown. Here, we show that HOT3 is a late-stage ribosome biogenesis factor that facilitates 18S rRNA 3’ end processing and is a translation initiation factor that globally impacts the transition from initiation to elongation. By developing and implementing 18S-ENDseq, we revealed previously unknown events in 18S rRNA 3’ end maturation or metabolism. We quantitatively defined new processing hotspots and identified adenylation as the prevalent non-templated RNA modification at the 3’ ends of pre-18S rRNAs. Aberrant 18S rRNA maturation in hot3 further activated RNAi to generate RDR1- and DCL2/4-dependent risiRNAs mainly from a 3’ portion of 18S rRNA. We further showed that risiRNAs in hot3 were predominantly localized in ribosome-free fractions not responsible for the 18S rRNA maturation or translation initiation defects in hot3. Our study uncovered the molecular function of HOT3/eIF5B1 in 18S rRNA maturation at the late-40S assembly stage and revealed the regulatory crosstalk among ribosome biogenesis, mRNA translation initiation, and siRNA biogenesis in plants.
2023-04-19 | GSE223171 | GEO
Project description:Abstract - 18S nonfunctional rRNA decay (NRD) detects and eliminates translationally nonfunctional 18S rRNA. While this process is critical for ribosome quality control, the mechanisms underlying nonfunctional 18S rRNA turnover remain elusive, particularly in mammals. Here, we show that mammalian 18S NRD initiates through the integrated stress response (ISR) via GCN2. Nonfunctional 18S rRNA induces translational arrest at start sites. Biochemical analyses demonstrate that ISR activation limits translation initiation and attenuates collisions between scanning 43S preinitiation complexes and stalled nonfunctional ribosomes. The ISR promotes 18S NRD and 40S ribosomal protein turnover by RNF10-mediated ubiquitination. Ultimately, RIOK3 binds the resulting ubiquitinated 40S subunits and facilitates 18S rRNA decay. Overall, mammalian 18S NRD acts through GCN2, followed by ubiquitin-dependent 18S rRNA degradation involving the ubiquitin E3 ligase RNF10 and the atypical protein kinase RIOK3. These findings establish a dynamic feedback mechanism by which the GCN2-RNF10-RIOK3 axis surveils ribosome functionality at the translation initiation step.
2024-12-09 | MSV000096620 | MassIVE
Project description:Araguaia River Metagenome - 18S Metagenome
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Project description:Today, many contaminants of emerging concern can be measured in waters across the United States, including the tributaries of the Great Lakes. However, just because the chemicals can be measured does not mean that they necessarily result in harm to fish and other aquatic species. Complicating risk assessment in these waters is the fact that aquatic species are encountering the chemicals as mixtures, which may have additive or synergistic risks that cannot be calculated using single chemical hazard and concentration-response information. We developed an in vitro effects-based screening approach to help us predict potential liver toxicity and cancer in aquatic organisms using water from specific Great Lakes tributaries: St. Louis River (MN), Bad River (WI), Fox River (WI), Manitowoc River (WI), Milwaukee River (WI), Indiana Harbor Canal (IN), St. Joseph River (MI), Grand River (MI), Clinton River (MI), River Rouge (MI), Maumee River (OH), Vermilion River (OH), Cuyahoga River (OH), Genesee River (NY), and Oswego River (NY). We exposed HepG2 cells for 48hrs to medium spiked with either field collected water (final concentration of environmental samples in the exposure medium were 75% of the field-collected water samples) or purified water. Using a deep neural network we clustered our collection sites from each tributary based on water chemistry. We also performed high throughput transcriptomics on the RNA obtained from the HepG2 cells. We used the transcriptomics data with our Bayesian Inferene for Sustance and Chemical Toxicity (BISCT) Bayesian Network for Steatosis to predict the probability of the field samples yielding a gene expression pattern consistent with predicting steatosis as an outcome. Surprisingly, we found that the probability of steatosis did not correspond to the surface water chemistry clustering. Our analysis suggests that chemical signatures are not informative in predicting biological effects. Furthermore, recent reports published after we obtained our samples, suggest that chemical levels in the sediment may be more relevant for predicting potential biological effects in the fish species developing tumors in the Great Lakes basin.
2022-12-01 | GSE146903 | GEO