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Ive attached the homework questions to be answered based on the article that is attached as well. Please follow the instructions and no plagiarism utf_8__hw1_bio308_sp20.docx trna_abundance_regulation_as1.pdf Unformatted Attachment Preview Name: _____________________ Biology 308 Homework Assignment 1 (10 points) Word documents only. No PDF. Due Date: Wednesday January 15th by 11:59PM on Blackboard Directions: Use the article (when applicable) Cells Alter their tRNA Abundance to Selectively Regulate Protein Synthesis During Stress Conditions. Please do not share your answers with other students. Discussion is important, and can be helpful, but not providing your answer to another student. If you need clarification (not an answer), then schedule an office hours appointment on Monday, or see me after lab on Tuesday. 1. What would be the difference between nuclear-encoded, mitochondrial-encoded, or chloroplast-encoded genes? (1 point) 2. The article mentions the following: “…the anticodon demand placed by the mRNA must be balanced by the tRNA supply of the cell”. What is your interpretation of this statement in your own words? How is this important for cell homeostasis with regard to translation? (2 points) 3. Please include a brief definition of each word below (maximum 30 words). It is acceptable to search for the definition of the word on Google, but unacceptable to use that definition, as your answer. I want your understanding of each word, as your answer. (2 points) a. Oxidativeb. Osmoticc. Temperatured. Diauxic4. Investigators measured tRNA abundance under different stress conditions. The 4 stress conditions are listed below. Please provide a brief explanation (70 words or less) on what the findings were for each condition with the abundance of tRNAs. Note* I will only grade the first 70 words. (3 points) a. Oxidativeb. Osmoticc. Temperatured. Diauxic5. In the discussion section of the article, they state the following: “…we suggest that changes in the tRNA pool ensure that newly synthesized stress-related transcripts are selectively translated with higher efficiency by the ribosome compared to the already present mRNA, thereby leading to a selective increase in the abundance of the required proteins.” How does Figure 5 help support this excerpt? Use the information from Figure 5 to make your explanation valid. Maximum 50 words. (2 points) SCIENCE SIGNALING | RESEARCH ARTICLE STRESS RESPONSES Cells alter their tRNA abundance to selectively regulate protein synthesis during stress conditions Marc Torrent1,2*†, Guilhem Chalancon1*, Natalia S. de Groot1‡, Arthur Wuster1§, M. Madan Babu1† Decoding the information in mRNA during protein synthesis relies on tRNA adaptors, the abundance of which can affect the decoding rate and translation efficiency. To determine whether cells alter tRNA abundance to selectively regulate protein expression, we quantified changes in the abundance of individual tRNAs at different time points in response to diverse stress conditions in Saccharomyces cerevisiae. We found that the tRNA pool was dynamic and rearranged in a manner that facilitated selective translation of stress-related transcripts. Through genomic analysis of multiple data sets, stochastic simulations, and experiments with designed sequences of proteins with identical amino acids but altered codon usage, we showed that changes in tRNA abundance affected protein expression independently of factors such as mRNA abundance. We suggest that cells alter their tRNA abundance to selectively affect the translation rates of specific transcripts to increase the amounts of required proteins under diverse stress conditions. Translation of mRNAs into proteins is a central step during gene expression. The information in mRNA, encoded by 61 different nucleotide triplets (codons), is decoded into a protein that is composed of 20 different amino acids. In Saccharomyces cerevisiae, 42 nuclear-encoded transfer RNAs (tRNAs) (1) recognize the 61 codons and bring the corresponding amino acids to the ribosome to facilitate protein synthesis through the formation of peptide bonds. To ensure effective protein synthesis and cellular homeosta­ sis, the anticodon demand placed by the mRNA must be balanced by the tRNA supply of the cell (2–6). An imbalance between mRNA codon usage and cognate tRNAs can affect the polypeptide elongation rate in ribosomes and induce pauses during translation that may have wide implications for homeostasis, protein quality control, and disease (7). These pauses may be due to changes in tRNA abundance (8, 9) or modifications in certain bases (such as those in the anticodon stem) (10–12). Despite their central role in translation, tRNAs are seen primarily as adaptor molecules with the function of ensuring correct translation (13). However, this view has been expanded by findings that demonstrate the tissue-specific expression of tRNA molecules (14) and changes in global tRNA abundance and modification during the cell cycle, development, and disease (15–18), among others (13, 19–23). In yeast, stress-responsive genes are highly expressed but are unexpectedly enriched in codons that use rare tRNAs (24). We hypothesized that a dynamic tRNA pool might regulate efficient and selective translation of certain genes during stress conditions. 1 Laboratory of Molecular Biology, Medical Research Council, Francis Crick Avenue, CB2 0QH Cambridge, UK. 2Systems Biology of Infection Lab, Department of Biochemistry and Molecular Biology, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain. *Joint first authors. †Corresponding author. Email: marc.torrent@uab.cat (M.T.); madanm@mrc-lmb. cam.ac.uk (M.M.B.) ‡Present address: Center for Genomic Regulation, Parc de Recerca Biomèdica de Barcelona, Aiguader 88, 08003 Barcelona, Spain. §Present address: Department of Human Genetics and Department of Bioinformatics and Computational Biology, Genentech Inc., South San Francisco, CA 94080, USA. Torrent et al., Sci. Signal. 11, eaat6409 (2018) 4 September 2018 RESULTS Quantifying changes in tRNA abundance under diverse stress conditions We first quantified changes in abundance of each of the 42 nuclear-­ encoded tRNAs during adaptation to different stress conditions in the yeast S. cerevisiae using reverse transcription quantitative polymerase chain reaction (RT-qPCR) (Fig. 1A; table S1; fig. S1, A and B; and data file S1). There are several approaches for quantifying tRNA abundance, which include tRNA microarrays (25), Northern blot (26), and sequencing-based methods (22). Each of these approaches has their strengths and limitations based on various considerations such as detection sensitivity, scalability, and the ability to resolve the identity of tRNAs and discriminate cleaved tRNA fragments from mature tRNA, as well as effects of tRNA modifications. Although the efficiency of reverse transcription may vary due to nucleotide modifications in tRNAs, this approach has been validated as a reliable method to quantify mature tRNAs (22, 27). Measurement of the relative tRNA abundance profiles under four different stress conditions (oxidative stress, osmotic stress, temperature stress, and diauxic shift) at three different time points (20, 60, and 120 min) revealed that tRNA abundances changed substantially (about 2 to 5 log2 fold change; Fig. 1B) in a reproducible manner (fig. S2). Analysis of the changes in relative abundance of the individual tRNAs immediately upon stress revealed that decreasing the abundance of existing tRNA molecules (possibly through rapid degradation) could be a mechanism that changed relative tRNA abundance under different stress conditions (except under temperature stress) (Fig. 1C). The abundance of some tRNA molecules increased for all stress conditions except during oxidative stress, suggesting that active transcription could be another mechanism that regulates tRNA abundance during stress. At 120 min after stress, a higher proportion of the tRNA molecules showed decreased abundance, suggesting that repression of transcription- or degradation-based mechanisms might be a prevalent mechanism to alter tRNA abundance upon prolonged exposure to different stress conditions (Fig. 1C). Patterns of changes in tRNA abundance during stress Using t-distributed stochastic neighborhood embedding [t-SNE (28)] and K-means clustering, we analyzed the patterns of tRNA expression 1 of 9 Downloaded from http://stke.sciencemag.org/ on January 10, 2020 INTRODUCTION Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY). SCIENCE SIGNALING | RESEARCH ARTICLE observations indicate that under stress conditions, the tRNA pool is rearranged to adopt a complex structure that may influence translation. On one hand, the abundance of C1 tRNAs either increased or remained stable during stress compared to normal conditions (Fig. 2B). This result is consist­ ent with the notion that a decrease in the abundance of these essential tRNAs would likely negatively affect cellular B fitness (21). On the other hand, the abundance of C2 tRNAs marginally decreased or remained stable and that of C3 tRNAs decreased under all stress conditions and at all time points (Fig. 2B). Because C1 and C3 tRNAs primarily decode nonoptimal codons, we reasoned that the differences in their abundance might have a role in protein production by controlling the rate at which transcripts with nonoptimal codons are translated. The tRNAs coding for Glu, Cys, and Gly showed reduced abun­­­ dance under all stress conditions (Fig. 1B). These are the three amino acids that are required for the nonribosomal synthesis C of the antioxidant glutathione (GSH), which is required for adaptation to stress conditions (29). One possible explanation is that a feedback mechanism between the nonribosomal GSH biosynthesis pathway and ribosomal protein translation could have evolved to ensure that the precursor amino acids are available in higher abundance for GSH production under stress conditions. From a kinetic perspective, our results showed that after 20 min of exposure to stress, changes in tRNA abundances were Fig. 1. Quantification of change in abundance of yeast tRNAs during stress. (A) Yeast cells were grown in YPD most different within and across stress (yeast extract, peptone, and dextrose) at 30°C and then challenged with four different stress conditions: (i) temperature stress by increasing incubation temperature from 30° to 37°C, (ii) osmotic stress by increasing sorbitol concentration conditions, as indicated by their average from 0 to 1 M, (iii) oxidative stress by increasing H2O2 concentration from 0 to 0.5 mM, and (iv) diauxic shift by correlation, which was calculated as the changing the carbon source of the media from 2\% glucose to 2\% ethanol. For each condition, change in abundance average Pearson correlation on the off-­ of individual tRNAs was measured by qPCR with respect to normal, nonstressed conditions at three different time diagonal elements (Fig. 2C). After 60 min points: 20, 60, and 120 min (all in biological triplicates). (B) Hierarchical clustering of tRNAs based on their relative of exposure, we found a better correlation changes in abundance over time (average fold change of n = 3 biological replicates). (C) Pie charts of the proportion (Fig. 2C); however, the correlation was the of up- and down-regulated tRNAs under different stress conditions. DNase I, deoxyribonuclease I; cDNA, complehighest for prolonged stress (t = 120 min) mentary DNA; RNase A, ribonuclease A. (Fig. 2C). The observed changes in tRNA fold change during stress suggested a across the 12 conditions and time points and found that tRNAs can biphasic behavior during adaptation to stress (fig. S3): An immediate be segregated into three clusters (C1, C2, and C3) (Fig. 2A). The transient response (at 20 min) with stress-specific variations, followed distribution of tRNAs among the three clusters corresponded to key by a long-term adapted response (at 120 min) in which the tRNA functional features. C1 contained four of five tRNAs that are coded pool is remodeled to a similar extent under all stress conditions by single essential genes and six of seven tRNAs that are unique but altered relative to the nonstress condition. acceptors of their amino acid (Fig. 2A). Almost all tRNAs in C3 (7 of 8) and more than half of the tRNAs in C1 (9 of 16) contained Genome-scale analysis of adaptation to tRNA nonoptimal anticodons (namely, those that can form a wobble abundance changes codon-anticodon pair with low affinity). In contrast, C2 was de- Given that the tRNA abundance influences the decoding rate of codons pleted of tRNAs that carry nonoptimal anticodons (4 of 14). These during translation (7, 30), our observations implied that the rate of A 4 September 2018 2 of 9 Downloaded from http://stke.sciencemag.org/ on January 10, 2020 Torrent et al., Sci. Signal. 11, eaat6409 (2018) SCIENCE SIGNALING | RESEARCH ARTICLE A t-SNE clustering of tRNA based on differential expression 300 Single-gene coded essential tRNA CUG-Gln AGA-Ser 200 C UGC-Ala C1 Unique tRNA for its amino acid CCU-Arg Nonessential isoacceptor tRNA UUG-Gln GUC-Asp 20 min (µ r20 = 0.29) GUU-Asn CGU-Thr GAA-Phe UUU-Lys CCG-Arg GUG-His 100 Temperature 0.05 0.39 1 0.77 Diauxic 0.77 1 Osmotic AAU-Ile Osmotic UAA-Leu GAG-Leu AGG-Pro 0.34 Diauxic C2 Oxidative 0.39 −0.04 0.34 UGU-Thr CCA-Trp 0 0.05 −0.04 1 Temperature GUA-Tyr 0.30 Oxidative Dimension 2 UGA-Ser 1 0.30 1 0.70 0.39 0.17 Oxidative 0.70 1 0.46 0.19 Temperature 0.39 0.46 1 0.62 Diauxic 0.17 0.19 0.62 1 Osmotic GCC-Gly UCU-Arg AAC-Val AGC-Ala 100- AGU-Thr GCA-Cys C3 CUU-Lys ACG-Arg UUC-Glu 60 min (µ r60 = 0.42) CGA-Ser UAC-Val CCC-Gly UGG-Pro CUC-Glu CAC-Val 200- UCC-Gly GCU-Ser UAU-Ile Fold change (log2) 1 0.76 0.85 0.82 Diauxic 0.76 1 0.86 0.86 Temperature 0.85 0.86 1 0.94 Oxidative 0.82 0.86 0.94 1 Temperature stress 20 min 120 min 2.5 120 min (µ r120 = 0.85) 0.0 −2.5 Osmotic −5.0 Effect size C1 C2 C3 0.48 0.72 ** *** C1 C2 C3 0.53 0.61 ** ** C1 C2 C3 0.47 0.59 ** ** C1 C2 C3 0.24 0.78 *** C1 C2 C3 0.49 0.48 ** * C1 C2 C3 0.29 0.77 *** C1 C2 C3 0.21 0.19 C1 C2 C3 0.18 0.77 *** Fig. 2. Analysis of tRNA expression patterns. (A) Multivariate analysis of the tRNA expression patterns using t-SNE and K-means clustering. Three groups of tRNAs (C1, C2, and C3) are highlighted. All tRNAs except tRNALeu(CAA), tRNALeu(UAG) (which were outliers, assignable to C1), and the initiator tRNAMet(CAU) (assignable to C3) were included for the visualization. (B) Fold change distribution of tRNA abundance in each group (dot plots). Horizontal dashed lines indicate threshold values for up-regulation (+50\%) and down-regulation (−50\%). The effect sizes (rank-biserial correlation using Wendt’s criterion) of comparisons between C1-C2 and C2-C3 are indicated for each stress condition. P values are computed using Mann-Whitney U test, *P < 0.05, **P < 0.01, ***P < 0.001. (C) Pairwise correlation matrices showing the similarity of changes in tRNA abundance across the four stress conditions after 20, 60, and 120 min. Each cell shows the Pearson’s r correlation coefficient, with negative and positive values colored in shades of red and blue, respectively. r represents global r values for each time point. protein synthesis of individual genes might be selectively affected during stress. To investigate this notion, we first measured the extent to which the codon usage of all yeast genes was adapted to the tRNA abundance under normal and each of the four stress conditions. This was quantified by computing the tRNA adaptation index under normal conditions (n-tAI) and a newly developed metric, stress-adjusted tAI for each of the four stress conditions (s-tAI) (Fig. 3A, and data files S2 and S3), using the experimental measurements described above. For each stress condition, we identified genes that were better adapted, not affected, and less adapted to the experimentally measured tRNA pool by obtaining a z score of the rank change of their n-tAI and s-tAI values [Fig. 3A (orange, gray, and red data points) and data file S3]. We found that the genes with better codon adaptation to the tRNA pool under the different stress conditions were specifically enriched for functions related to external stimulus responses (Fig. 3A and fig. S4). Genes whose codon adaptation was not substantially altered were enriched in ribosomal proteins and translation machinery. Finally, the ones whose codon usage patterns were less well adapted were enriched in anabolic functions such as amino acid Torrent et al., Sci. Signal. 11, eaat6409 (2018) 4 September 2018 and lipid biosynthesis enzymes and tricarboxylic acid cycle (Fig. 3A, fig. S4, and data file S4). Although this observation suggested that the translation efficiency (TE) of certain genes might be selectively altered, we still observed a global positive correlation between tRNA supply and codon demand under all stress conditions, suggesting that overall translation was unlikely to be substantially compromised (fig. S5, A and B). Alteration in translation rate and protein abundance during stress To assess whether the genes that were identified as better adapted to the tRNA pool during stress, based on the changes in s-tAI ranks (Fig. 3A), showed a gain in TE, we analyzed experimentally derived ribosome footprinting data measured under oxidative stress (31, 32). The median log2 TE (or the amount of footprint normalized to mRNA abundance) of genes with better codon adaptation under oxidative stress was substantially higher compared to that of genes whose adaptation remains unaffected (Fig. 3B). Genes that were less well adapted had a lower median log2 TE compared to genes whose adaptation remained stable during oxidative stress (Fig. 3B). 3 of 9 Downloaded from http://stke.sciencemag.org/ on January 10, 2020 Osmoticstress stress oxidative diauxic shift 20 min 120 min Osmotic Oxidative stress oxidative diauxic stress shift 20 min 120 min Oxidative Diauxicshift shift diauxic 20 min 120 min Osmotic B Diauxic 300 Temperature 200 Temperature 100 Dimension 1 Diauxic 0- Oxidative 100 SCIENCE SIGNALING | RESEARCH ARTICLE Oxidative stress 1 2 Temperature stress 1 2 3 ILV6, CYS4, HIS4, ... TCA cycle, oxidative phosphorylation Legend: 3 3 Genes with large gain in tAI (z score increased by 1.28-fold) Other genes (z score changed within 1.28-fold) Genes with large loss in tAI (z score reduced by 1.28-fold) P < 2x 10–16P = 0.020 0.22 0.05 0.5 P = 4.20 x 10–7 P = 8.65 x 10 P = 0.368 P = 4.64 x 10–8 P = 0.363 P = 4.84 x 10–12 0 2 RPL4A, RPL13A, RPL16B, ... Protein translation machinery High mRNA abundance –6 –0.5 3 P = 2.75 x 10 Medium mRNA abundance –5 P = 0.319 P = 2.64 x 10 –6 –1.0 s-tAI (z score) 3 P < 2 x 10 –16 Fold change in protein abundance (log2) 1 Low mRNA abundance 0.26 0.25 0.22 –1.5 GAA1, ERF2, LAS21, ... DNA recombination 1 NTG1, MMS4, SLX1, ... Response to external stimulus 1 4 2 2 C B Exemplars 0 Osmotic stress −4 Diauxic shift Ribosome profiling (oxidative stress, t = 30 min) Fold change in translation efficiency (log2) A D (...) Ct \%nt n-tAI s-tAI 100 76 0.41 0.25 0.33 0.27 0.40 100 74 0.28 0.13 0.25 0.13 0.28 100 79 0.46 0.24 0.34 0.31 0.42 100 77 0.42 0.23 0.33 0.26 0.42 Diauxic Oxidative Osmotic Temp. stress stress shift stress 0.6 r = 0.68 P = 0.0037 0.4 0.2 0.0 0.0 0.1 0.2 0.3 0.4 0.5 s-tAI Fig. 3. Changes in the tAI during stress. (A) n-tAI (x axis) and s-tAI (y axis) were both normalized to range between 0 and 1. Dots indicate individual genes. Orange genes have increased their z score of shift in rank more than 1.28 times in terms of tRNA adaptation (10\% of genes that are likely to b ... Purchase answer to see full attachment