Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Depletion of eIF4G from yeast cells narrows the range of translational efficiencies genome-wide

ABSTRACT: Investigation of the effects of genetically depleting eIF4G from yeast cells on global translational efficiencies, using gene expresssion microarrays to measure the abundance of mRNA in polysomes relative to total mRNA for ~5900 genes Three biological replicates were examined (designated projects I, II, and III), representing heavy polysomes (HP), light polysomes (LP), and total RNA (T) preparations from three independent pairs of WT and eIF4G degron mutant cultures. Cy3-labeled cDNAs were generated from the 3 HP, 3 LP, and 3 total RNA samples prepared for each strain and the resulting 18 sets of cDNAs were used to probe three (technical) replicate whole-genome microarrays, containing multiple 60-mer oligonucleotides for each gene. The “normalized gene expression summary values” were calculated for each gene from the data obtained from the three technical replicates and used to calculate the translational efficiency (TE) of each gene as the ratio of the intensity values for HP to total RNA (HP/T) or LP to total RNA (LP/T) for each project.

ORGANISM(S): Saccharomyces cerevisiae

SUBMITTER: Eun-Hee Park

PROVIDER: E-GEOD-25721 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Purpose: In this study, we have used a translatomic approach by coupling polysome profiling and deep RNA-sequencing to estimate changes in the translatome of antimony-resistant Leishmania parasites Methods: Leishmania tropica promastigotes were stepwise selected for resistance to trivalent antimony. Two different strains were studied, the L. tropica SbIII-sensitive or wildtype strain (WT) and the derived highly resistant strain (HR). After polysome profiling four types of samples were evaluated by deep RNAseq: total mRNA used as input, monosomes (MS), light polysomes (LP), and heavy polysomes (HP). The DESeq2 algorithm was used for differential expression analysis to identify translational changes at the basal level (HR Vs. WT), translational changes to combat the drug (HR+SbIII Vs. HR), and to compare translatomic Vs. transcriptomic changes (HP Vs. Total input) [see overall design section below]. Results: Differential translational analysis (cutoff of fold change ≥ 1.5 and p-value corrected by Benjamini-Hochberg FDR ≤ 0.05) showed that transcripts composition per polysome fraction was different in the resistant strain. It included several upregulated (Up) and downregulated (Down) transcripts. At the basal level, 2431 different transcripts were differentially translated: monosome (Down: 4, Up: 0), light polysomes (Down: 906, Up: 951), and heavy polysomes (Down: 1096, Up: 1064). Under the antimony challenge, 189 different transcripts were differentially translated: monosome (Down: 0, Up: 2), light polysomes (Down: 9, Up: 57), and heavy polysomes (Down: 30, Up: 134). Overall, most of the changes were identified in polysome fraction when compared with monosomes or total transcriptome. Conclusions: Our study shows evidence that translational control has a main role in coordinating the resistance to antimony in Leishmania parasites. We propose a novel model that establishes translational control as a major driver of antimony-resistant phenotypes in Leishmania parasites.

Project description:A reduction in voluntary feed intake is observed in ruminants consuming nutrient deficient diets, such as those with a low CP or P content, and has been attributed to active metabolic regulation, rather than a physical constraint. The hypothalamus is the key integrator of feed intake regulation in mammals. The objectives of this experiment were to 1) establish a model of metabolic feed intake regulation in ruminants consuming diets of variable CP and P content, and 2) determine key biochemical pathways and influential points of regulation within the hypothalamus. Merino wethers [n = 40; 23.7 ± 1.4 kg liveweight (mean ± SD)] were fed one of five dietary treatments (n = 8/treatment) for 63 days in individual pens. The treatments included targeted combinations of high (H) and low (L) CP (110 and 55 g/kg DM) and high and low P (2.5 and 0.7 g/kg DM) with 9 MJ metabolisable energy (ME) per kg DM which were fed ad libitum (UMEI; unrestricted ME intake) resulting in four experimental diets (HCP-HP-UMEI, LCP-HP-UMEI, HCP-LP-UMEI and LCP-LP-UMEI). An additional nutritional treatment (HCP-HP-RMEI) restricted intake of the HCP-HP diet to an equivalent ME intake of wethers consuming the LCP-LP-UMEI treatment. Wethers offered the LCP-HP-UMEI, HCP-LP-UMEI and LCP-LP-UMEI treatments consumed 42, 32 and 49% less total DM (P ≤ 0.05), respectively than the HCP-HP-UMEI treatment, and this was not attributable to any physical limitation of the rumen. Plasma concentrations of urea nitrogen and inorganic phosphate indicated that these nutrient deficiencies were successfully established. To assess potential mechanisms, RNA-seq was conducted on samples from the arcuate nucleus (ARC), ventromedial hypothalamus and lateral hypothalamus of the wethers, yielding a total of 301, 8 and 148 differentially expressed genes across all pairwise comparisons, respectively. The expression of NPY, AGRP and CARTPT, known for their regulatory role in mammalian feed intake regulation, had a similar transcriptional response in the ARC of wethers consuming nutrient deficient treatments and those consuming a ME restricted treatment, despite these wethers expressing behaviours indicative of satiated and hungry states, respectively. In addition, genes involved with glycolysis (TPI1), the citric acid cycle (CS, OGDH, GLUD1, GOT1) and oxidative phosphorylation (COX5A, ATP5MC1, ATP5F1B, ATP5MC3) were downregulated in the ARC of wethers fed a nutrient deficient (LCP-LP-UMEI) relative to the non-deficient (HCP-HP-UMEI) treatment. In summary, a model for voluntary feed intake restriction was established to determine genome-wide molecular changes in the hypothalamus of young ruminants.

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Depletion of eIF4G from yeast cells narrows the range of translational efficiencies genome-wide

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