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[Preprint]. 2024 Jan 9:2023.07.20.549210.
doi: 10.1101/2023.07.20.549210.

Impaired biogenesis of basic proteins impacts multiple hallmarks of the aging brain

Domenico Di Fraia  1 , Antonio Marino  1 , Jae Ho Lee  2 , Erika Kelmer Sacramento  1 , Mario Baumgart  1 , Sara Bagnoli  3 , Pedro Tomaz da Silva  4   5 , Amit Kumar Sahu  1 , Giacomo Siano  3 , Max Tiessen  1 , Eva Terzibasi-Tozzini  3 , Julien Gagneur  4   6   7 , Judith Frydman  2 , Alessandro Cellerino  1   3 , Alessandro Ori  1
Affiliations

Impaired biogenesis of basic proteins impacts multiple hallmarks of the aging brain

Domenico Di Fraia et al. bioRxiv. .

Abstract

Aging and neurodegeneration entail diverse cellular and molecular hallmarks. Here, we studied the effects of aging on the transcriptome, translatome, and multiple layers of the proteome in the brain of a short-lived killifish. We reveal that aging causes widespread reduction of proteins enriched in basic amino acids that is independent of mRNA regulation, and it is not due to impaired proteasome activity. Instead, we identify a cascade of events where aberrant translation pausing leads to reduced ribosome availability resulting in proteome remodeling independently of transcriptional regulation. Our research uncovers a vulnerable point in the aging brain's biology - the biogenesis of basic DNA/RNA binding proteins. This vulnerability may represent a unifying principle that connects various aging hallmarks, encompassing genome integrity and the biosynthesis of macromolecules.

Keywords: aging; brain; killifish; mitochondria; post-translational modification; proteasome; protein aggregation; proteome; ribosome; translation.

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Conflict of interest statement

Declaration of interest Authors declare no competing interests.

Figures

Figure 1:
Figure 1:. Protein-transcript decoupling affects highly abundant and basic proteins in opposite manners.
A) Characterization of protein-transcript decoupling in the aging brain of killifish. Positive decoupling values indicate increased protein abundance relative to transcripts, while negative decoupling indicates decreased protein abundance compared to transcripts. B) Scatterplot comparing protein and transcript fold changes in aging brain. Color represents decoupling score, red – increased protein abundance relative to transcript, blue – decreased protein abundance compared to transcript. Grey dashed lines indicate equal changes. C) Density distribution of decoupling scores for 39 wph vs. 5 wph comparison. Red: positive decoupling, Blue: negative decoupling. D) Multiple linear regression analysis of decoupling scores based on transcript and protein features. asterisks represent the −log10 P-values of the F-test. E) Added variable plot between features and decoupling scores. F) Multiple linear regression analysis of decoupling scores based on protein amino acid composition. asterisks represent the −log10 P-values of the F-test. G) Transcript and protein fold changes for RNA binding and DNA repair proteins. Two-sample Wilcoxon test H and I) Examples of proteins with negative (H) and positive (I) decoupling (N=3–4). *P ≤ 0.05; **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001. Related to Figure S1 and Table S1.
Figure 2:
Figure 2:. Proteome alterations converges on ribosomes and respiratory chain complexes
A) Overview of the datasets generated at the beginning of this study (wph= weeks post-hatching). B) Heatmap shows correlations of normalized enrichment scores (NES) across datasets (DR=Detergent insolubility, ΔPT=protein-transcript decoupling, AC=Acetylation, PH=Phosphorylation, UB=Ubiquitylation). C) Top-ranking GO terms with strong contributions to PCA analysis. D) Line plots for respiratory chain proteins’ transcript (blue) and protein (red) median abundance across age groups (N=3–4, MANOVA). E) Examples of respiratory chain proteins with positive protein-transcript decoupling. F) Violin plot for detergent insolubility scores of mitochondrial respirasome proteins (N=4, two-sample Wilcoxon test). G) Examples of detergent insolubility profiles for respiratory chain proteins with increased detergent insolubility during aging (N=4, MANOVA). H) Volcano plot for changes in mitochondrial proteome due to aging. Box plot shows the effect of aging on different groups of mitochondrial pathways (N=4, two-sample Wilcoxon test). I) Ribosomal proteins’ transcript and protein abundance across age groups (N=3–4, MANOVA). J) Examples of ribosomal proteins displaying negative protein-transcript decoupling (N=3–4). K) Violin plot for detergent insolubility scores of cytoplasmic ribosomal subunits (N=4, two-sample Wilcoxon test). L) Examples of detergent insolubility profiles for ribosomal proteins with decreased detergent insolubility during aging (N=4, MANOVA). *P ≤ 0.05; **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001. Related to Figure S2:S7 and Tables S2,S3.
Figure 3:
Figure 3:. Effects of four weeks in vivo proteasome impairment on the adult killifish brain.
A) Adult killifish (12 wph, N=10) received weekly intraperitoneal injections of proteasome inhibitor bortezomib or DMSO control. Bottom panel: Gene Set Enrichment Analysis (GSEA) color-coded by normalized enrichment score (NES). Top-right: Chymotrypsin-like proteasome activity quantification (two-sample Wilcoxon test, N=10). B) Barplot: Protein (red) transcript (blue) quantity in proteostasis network proteins in aging and proteasome inhibition (two-sample T-test, N=10). Asterisks indicate Q.value (protein) and Adjusted P.Value (transcript). C) Left: Lysosome (LAMP1) immunofluorescence. Scale bars = 5μm; right: Lysosome morphology analysis (two-sample T-test N=6). D) Left: Proteasome effect on mitochondrial transcripts and proteins (two-sample Wilcoxon test); right: Relative mtDNA copy number was calculated using real-time quantitative PCR with primers for 16S rRNA mitochondrial gene and Cdkn2a/b nuclear gene for normalization (N=10, two-sample Wilcoxon tests). E) Decoupling scores comparison between aging and proteasome impairment for respiratory chain (green) and ribosomal (orange) proteins (Spearman correlation was selected due to the presence of outliers in the distribution). F) Ribosome decoupling comparison between aging and proteasome impairment. G) Oxidative phosphorylation protein decoupling comparison between aging and proteasome impairment. *P≤0.05; **P≤0.01, ***P≤0.001, ****P≤0.0001. Related to Figure S8, Table S5.
Figure 4:
Figure 4:. Increased translation pausing in the aging killifish brain.
A) Experimental Workflow: Ribosome profiling was conducted on brains of Nothobranchius furzeri at different ages—Young (5 weeks post-hatch, wph), Adult (12wph), and Old (39wph). Each age group had two replicates, each consisting of pooled samples from 10–15 animals. B) A 2-D density plot illustrates the connection between age-induced changes in protein abundance (x-axis) and alterations in translation efficiency (y-axis). Different quadrants highlight modes of translation regulation. C) GOEnrichment analysis (ORA) for each quadrant from B. x-axis: −log10(adjusted P-value) Fisher test, Holm correction. D) Differential regulations for key complexes, 26S Proteasome, oxidative phosphorylation, and cytoplasmic ribosomes – Transcriptome (blue), Translation efficiency (purple), and Proteome (red), in Old vs. Young. E) Lineplot showing the normalized ribosome distribution at pausing sites across different age groups. F) Lineplot depicts normalized disome ribosome distribution at disome pausing sites for various age groups. G) Boxplot showing solubility vs. ribosome pausing. x-axis: solubility quantiles (25% of total distribution each), y-axis: log2 fold changes in pausing for significant sites (Adj. P-value < 0.05). Numbers indicate observations. Two-sample Wilcoxon tests H) Peptide motif associated with age-dependent increased pausing (Pause score at 39wph > Pause score at 5wph and 12wph, and Pause score at 39wph > 6). y-axis: relative residue frequencies, x-axis: ribosome positions (E, P, A). I) 2-D density plot showing relation between significant pausing changes (Adj. P-value < 0.05) on y-axis and decoupling metrics (x-axis). Contours: cytoplasmic ribosomes (red), RNA-binding proteins (black), oxidative phosphorylation (white). J) Boxplot showing mRNA half-life estimate changes (methods) between 39 wph and 5 wph. x-axis: selected categories. Asterisks: two-sample Wilcoxon test, Holm correction. *P ≤ 0.05; **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001. Related to Figure S9, Table S6.
Figure 5:
Figure 5:. Reduced ribosome levels can lead to translation reprogramming in the aging brain.
A) Heatmap showing the estimated protein output, modeled as described in Mills and Green 2017 (32). Each column in the heatmap indicates the estimated protein output for a specific ribosome concentration. Transcripts are clustered with a hierarchical clustering using the “ward D2” algorithm on the dissimilarity (1 - Person’s correlation) measure. For display purposes, the heatmap represents 5000 rows randomly sampled from all datasets. In the right panel, an illustrative example of a cluster displaying increased estimated protein output as a function of reduced ribosome levels. For these transcripts, the general ribosome decrease is predicted to relieve trafficking and pausing, leading to overall improved protein production. B) Lineplot showing the estimated protein output for transcript displaying decreased ribosome pausing in the Ribo-Seq data (median per transcript log2 Pausing 39 wph / 5 wph < 0 and Adjusted P-value <=0.15) and increased protein levels relative to the transcript in the decoupling model (orange). The x-axis represents the simulated decreased ribosomal concentration, while the y-axis indicates the estimated protein output, as shown also in A. C) Schematic representation of the translation reprogramming model and its connection with the relevant hallmarks of aging. Aging is associated with increased ribosome collision and pausing on ribosomal proteins, leading to a ~25% reduction of ribosome levels. This generalized decrease of available ribosomes could drive the translation of other high-affinity mRNAs leading to increased protein levels in the aging brain. Related to Table S7.
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