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. 2026 May 8;14:RP103427. doi: 10.7554/eLife.103427

Mettl5 coordinates protein production and degradation of PERIOD to regulate sleep in Drosophila

Xiaoyu Wu 1,†, Xingzhuo Yang 1,†, Tiantian Fu 1,†, Yikang Rong 2, Juan Du 1,✉
Editors: Jiwon Shim3, Utpal Banerjee4
PMCID: PMC13155753  PMID: 42100920

Abstract

Sleep plays a critical role in animal physiology, primarily governed by the brain, and its disruption is prevalent in various brain disorders. Mettl5 is associated with intellectual disability (ID), which often includes sleep disturbances. However, the mechanism underlying these sleep disruptions in ID remains poorly understood. In this study, we investigated the sleep phenotypes resulting from Drosophila Mettl5 mutations. Rescue experiments revealed that Mettl5 functions predominantly within neurons and glia marked by Mettl5-Gal4 to regulate sleep. Previous work established that Mettl5 forms a complex with Trmt112 to influence rRNA methylation. Notably, a mutation in Trmt112 recapitulated these sleep disturbances, implicating translational regulation by the Mettl5/Trmt112 complex. Subsequent RNA-seq and Ribo-seq analyses of Mettl51bp mutants uncovered downstream effects, including altered expression of proteasome components and clock genes. Rescue experiments confirmed that the net increase in PERIOD protein underlies the sleep phenotype. This study illuminates the interplay between ribosome function, clock genes, and the proteasome in sleep regulation, highlighting the integrated roles of protein synthesis and degradation. These findings could potentially provide an example for in vivo study of rRNA methylation function, expand our understanding of protein homeostasis in sleep, and offer insights into the sleep phenotypes associated with ID.

Research organism: D. melanogaster

Introduction

Sleep is essential for animal physiology, and understanding its molecular mechanisms has significant implications for both basic research and clinical applications. Although previous studies have identified key sleep regulators (Du et al., 2021), fundamental questions remain regarding the roles of cellular processes such as protein synthesis and degradation in sleep regulation.

Growing evidence suggests a dynamic interplay between sleep regulation and protein homeostasis. Protein synthesis is particularly active during sleep (Lyons et al., 2023), while sleep deprivation affects translational initiation (Costa et al., 2019). The proteasome, a critical mediator of ubiquitin-dependent protein degradation, regulates synapse homeostasis by modulating ribosomal components (Costa et al., 2019), thereby influencing synaptic function. Intriguingly, proteasome components exhibit oscillating expression patterns at the transcriptional level in human cells (Desvergne et al., 2016). A recent study in Drosophila showed that mutation in a proteasome component alters sleep patterns (Fernández-Cruz et al., 2020). Despite these advances, the precise mechanisms by which protein synthesis and degradation influence sleep, and how these processes interact, remain unresolved.

While several factors influencing proteasome assembly and subunit transcription have been identified (Kapetanou et al., 2022), the translational control of proteasome subunits remains poorly understood. Additionally, proteasome inhibition has been shown to alter ribosome function (Galimberti et al., 2016; Costa et al., 2019; Palanca et al., 2014), raising the question of whether the proteasome itself is regulated by the status of the translation machinery. Exploring this relationship could provide key insights into the interplay between proteasome activity and translation.

In Drosophila, Mettl5 exhibits 18S ribosomal RNA N6-methyladenosine (m6A) methyltransferase activity (Leismann et al., 2020) and interacts with Trmt112 to facilitate this function, a mechanism conserved in humans (Leismann et al., 2020; van Tran et al., 2019). Although Mettl5 depletion abolishes m6A modification on 18S rRNA without impairing rRNA maturation, it affects fly orientation behavior. In mammalian cells, Mettl5-mediated 18S rRNA m6A modification regulates stem cell fate determination and neural function (Wang et al., 2022). However, it remains unclear whether Mettl5 modulates global or transcript-specific translation profiles in vivo, particularly in tissues rather than cell lines. Moreover, while Mettl5 is linked to intellectual disability (ID) with comorbid sleep disturbances, its mechanistic role in these disorders is unknown. Testing whether Mettl5 loss affects behaviors like sleep in Drosophila could clarify this connection.

We discovered that Mettl5 regulates sleep in Drosophila. To investigate the underlying mechanism, we performed RNA-seq and Ribo-seq on Mettl5 mutants, revealing dysregulation of multiple clock genes and proteasome components. This suggests Mettl5 coordinates protein production and degradation, which are crucial for protein homeostasis. Follow-up experiments confirmed that the protein level of Period was upregulated in Mettl5 mutants, contributing to the sleep phenotype. This study highlights that ribosome defects can perturb proteasome function, uncovering a mechanism that couples protein degradation with synthesis. Additionally, by mapping the genome-wide downstream gene profile of an rRNA methylation modifier, this study offers insights into the gene-specific roles of ribosome function.

Results

Mettl5 regulates sleep in Drosophila

Mettl5 contains a predicted N6 adenine-specific nucleic acids methyltransferase domain. In order to study the function of this gene in sleep regulation, we generated CRISPR–Cas9 knockout mutants. We created two alleles. Mettl51bp, which deleted 1 bp in the coding region, results in a truncated version (Figure 1A) and downregulation of Mettl5 at the mRNA level (Figure 1B). Another allele, Mettl59bp, which results in three amino acids deleted version of Mettl5 (Figure 1A), does not cause significant change at the mRNA level (Figure 1B). We found that heterozygous Mettl5¹ᵇᵖ mutants exhibited significantly reduced nighttime sleep (Figure 1C–G), particularly during early night (ZT12–ZT16; Figure 1C). Quantitative analysis revealed increased wakefulness at ZT14 in mutants (Figure 1H). ‘awake %’ was used to indicate the percentage of awake fruit fly population at specific time points (e.g., ZT14). The quantitative nighttime sleep latency measurements indicated a delayed sleep start in mutants (Figure 1—figure supplement 1G). In addition, Mettl5 expression was significantly upregulated during the sleep recovery period after mechanical sleep deprivation (Figure 1I, J). Mettl5 mutant displayed significantly increased sleep rebound in 24 hr after sleep deprivation (Figure 1K, L), indicating its effects on sleep homeostasis. Moreover, results of sleep arousal assay at ZT19 indicated that the percentage of aroused flies is significantly more than the control group (Figure 1M). These results demonstrate that the truncated Mettl5 mutation causes sleep deficits, establishing Mettl5 as a novel sleep regulator in Drosophila.

Figure 1. Mettl5 is a regulator of Drosophila sleep.

(A) Diagram illustrating CRISPR–Cas knockout of 1 or 9 bases in the Mettl5 gene. The corresponding protein sequence is listed with the predicted N6 adenine-specific nucleic acids methyltransferase domain highlighted in the red box. (B) Relative expression of Mettl5 mRNA in homozygous Mettl51bp and Mettl59bp mutant male flies compared to control flies. (C) Sleep curve throughout the day for Mettl5 mutant male flies (blue) and control flies (black). (D) Total sleep of Mettl5 mutant male flies and control flies in 24 hr. (E) Total sleep of Mettl5 mutant male flies and control flies within day and night, respectively. (F) Sleep bout duration of Mettl5 mutant male flies and control flies. (G) Number of sleep bouts of Mettl5 mutant male flies and control flies. (H) Percentage of awake for Mettl5 mutant flies and control flies. (I) Sleep curve is tracked throughout the entire day prior to sleep deprivation and during the daytime sleep rebound period. (J) Mettl5 mRNA expression level at different time points. W (wake), SD (sleep deprivation), SR (sleep recovery). (K) Sleep curve is tracked throughout the entire day prior to sleep deprivation and during the daytime sleep rebound period in Mettl5 mutant male flies (blue) and control flies (black). (L) Response to sleep deprivation and performance measures in Mettl5 mutants and controls. Black bars represent the amount of sleep lost during the 24-hr sleep deprivation period, blue bars indicate the amount of sleep regained, whereas the red bars indicate the proportion of sleep recovered (right y-axis). (M) Sleep arousal of Mettl51bp male flies and control flies at ZT19. (N) Sleep curve throughout the day for the following genotypes: w1118 (black), Mettl51bp/+ (blue), and Mettl5-Gal4, UAS-Mettl5, Mettl51bp/+ (pink). (O) Total sleep of the indicated genotypes. (P) Sleep bout duration of the indicated genotypes. (Q) Number of sleep bouts of the indicated genotypes. (R) Percentage of awake for the indicated genotypes. For * stands for p < 0.05, ** stands for p < 0.01, *** stands for p < 0.001, ns stands for not significant. For letter-based annotations, groups with no significant differences share the same letter; groups with significant differences are assigned new letters.

Figure 1.

Figure 1—figure supplement 1. Mettl5 functions in a portion of neurons and glia.

Figure 1—figure supplement 1.

(A–C) Colocalization of Mettl5-YFP with antibodies staining ELAV. Scale bar: 50 μm (main figures) and 20 μm (zoomed regions). (D–F) Colocalization of Mettl5-YFP with antibodies staining REPO. Scale bar: 50 μm (main figures) and 20 μm (zoomed regions). (G) Sleep latency. (H) Climbing index of Mettl51bp male flies and control flies. (I) Sleep curves throughout the day for Mettl5-RNAi and control flies. (J) Total sleep for Mettl5-RNAi and control flies. (K) Sleep bout duration of Mettl5-RNAi and control flies. (L) Number of sleep bouts of Mettl5-RNAi and control flies. For statistical significance, * stands for p < 0.05, ** stands for p < 0.01, *** stands for p < 0.001, ns stands for not significant.
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To confirm the specificity of the sleep phenotype in Mettl51bp, we performed genetic rescue experiments. Introduction of a single wild-type Mettl5 copy completely rescued both the reduced sleep amount and increased wakefulness at ZT14 (Figure 1N–R), demonstrating that these phenotypes specifically result from Mettl5 deficiency.

The observed expression pattern of Mettl5 further supports its sleep regulatory function. Using Mettl5-Gal4 reporter lines, we found expression in both neurons (colocalizing with Elav staining; Figure 1—figure supplement 1A–C) and glial cells (colocalizing with REPO staining; Figure 1—figure supplement 1D–F). Behavioral analyses revealed additional mutant phenotypes consistent with sleep dysregulation. We tested the climbing ability of Mettl5 mutation, which showed an increase (Figure 1—figure supplement 1H). RNAi knocking down of Mettl5 showed a consistent phenotype of downregulated sleep amount during the nighttime (Figure 1—figure supplement 1I–L).

Mettl5 regulates Drosophila sleep through its methyltransferase activity

Previous studies established that Mettl5 interacts with Trmt112 to regulate 18S rRNA m6A modification (Leismann et al., 2020; van Tran et al., 2019). Consistent with these findings, our LC–MS/MS analysis revealed significantly reduced m6A levels in both total RNA and 18S rRNA from heterozygous Mettl51bp (Figure 2A, B). To determine whether Mettl5’s sleep regulatory function depends on its methyltransferase activity, we performed two key experiments. First, we examined the phenotype of Trmt112 knockdown. We found that the Mettl5-Gal4 driven Trmt112 RNAi resulted in a similar phenotype to Mettl51bp (Figure 2C–G), suggesting that Mettl5 regulates sleep through its 18S rRNA m6A modification activity. The efficiency of the Trmt112 RNAi line was tested in a previous study (López-Varea et al., 2021). More importantly, we performed a rescue experiment with a mutated form of Mettl5 that lacks the NPPF amino acids required for its methyltransferase activity (Figure 2A; Iyer et al., 2016). We observed that this mutant Mettl5 failed to rescue the sleep phenotype caused by Mettl51bp (Figure 2H–L). Together, these results demonstrate that Mettl5’s methyltransferase activity is essential for its role in sleep regulation, likely through its function in 18S rRNA m6A modification.

Figure 2. Mettl5 regulation of Drosophila sleep was dependent on its methyltransferase activity.

Figure 2.

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(A) The m6A level in the total RNA of Mettl5 mutant male flies. (B) The m6A level in the 18S rRNA of Mettl5 mutant male flies. (C) The sleep curve throughout the day shows the sleep pattern of induced Trmt112 RNAi male flies and control flies. (D) Total sleep of induced Trmt112 RNAi male flies and control flies. (E) Sleep bout duration in induced Trmt112 RNAi male flies and control flies. (F) Number of sleep bouts in induced Trmt112 RNAi male flies and control flies. (G) Percentage of awake in Trmt112 RNAi and control flies. (H) Sleep curve throughout the day for Mettl5 mutant male flies, induced Mettl5m overexpression male flies, and control flies. (I) Total sleep of Mettl5 mutant male flies, induced Mettl5m overexpression male flies, and control flies. (J) Sleep bout duration in Mettl5 mutant male flies, induced Mettl5 overexpression male flies, and control flies. (K) Number of sleep bouts in Mettl5 mutant male flies, induced Mettl5 overexpression male flies, and control flies. (L) Percentage of awake in Mettl5 mutant male flies, induced Mettl5 overexpression male flies, and control flies. For * stands for p < 0.05, ** stands for p < 0.01, *** stands for p < 0.001, ns stands for not significant. For letter-based annotations, groups with no significant differences share the same letter. Groups with significant differences are assigned new letters.

RNA-seq and Ribo-seq revealed the downstream gene profile of Mettl5

To better understand the downstream events of Mettl5, we performed RNA-seq and Ribo-seq to assess transcriptomic and translational changes in Mettl51bp. Principal Coordinates Analysis of RNA-seq and Ribo-seq datasets revealed clear separation between mutant and control groups (Figure 3—figure supplement 1A, B, D, E). High reproducibility was found among biological replicates (Figure 3—figure supplement 1C, F), confirming data quality. Transcriptome analysis identified 1053 significantly differentially expressed genes (|log2(fold change)| ≥1 and p.adj <0.05), comprising 217 upregulated and 836 downregulated transcripts (Figure 3A, B). Parallel ribosome profiling revealed 299 translationally regulated genes (|log2(fold change)| ≥0.265 and p.adj <0.05), with 149 upregulated and 150 downregulated targets compared to the controls (Figure 3C). Heatmaps of the top 100 differentially expressed genes from both RNA-seq and Ribo-seq analyses were generated according to the rlog transformed values (Figure 3—figure supplement 2A, B).

Figure 3. RNA-seq and Ribo-seq analysis revealed changes in the gene profile of Mettl51bp.

(A) Venn diagram depicting the number of significant differentially expressed genes revealed by RNA-seq and Ribo-seq. (B) Volcano plot representing the differentially expressed genes identified by RNA-seq. Genes that met the criteria of |log2(fold change)| ≥1 and p.adjust <0.05 were considered significantly expressed, marked in orange for downregulation and green for upregulation, comparing with the controls. (C) Volcano plot representing the differentially expressed genes identified by Ribo-seq. Candidates that satisfied the criteria of |log2(fold change)| ≥0.265 and p.adjust <0.05 were regarded as significantly expressed, marked in red for downregulation and blue for upregulation, respectively. Gene set enrichment analysis of differentially expressed genes revealed by RNA-seq (D–G) and Ribo-seq (H–K). All the plots are generated using the KEGG gene set database. The bar chart at the bottom of each panel shows the distribution of target genes for each pathway according to their rank position. Each vertical line represents a gene. Genes on the left show positive correlation with Mettl51bp, while genes on the right show negative correlation with Mettl51bp. The green line indicates the enrichment score (ES), and NES stands for normalized enrichment score. (L) Distribution of the differentially expressed genes revealed by both RNA-seq and Ribo-seq. (M) Cumulative distribution of translation efficiency (TE) frequencies among w1118 and Mettl51bp. (N, O) Gene Ontology (GO) and KEGG enrichment of significantly changed TE-related genes between w1118 and Mettl51bp. The color of the bar indicates the enrichment p.adjust value. (P) KEGG network showing the top 10 pathways and associated genes. The size of the dots represents the number of genes in the pathway.

Figure 3.

Figure 3—figure supplement 1. Quality control of RNA-seq and Ribo-seq samples.

Figure 3—figure supplement 1.

(A, D) Meta analysis of gene expression data obtained from RNA-seq and Ribo-seq. (B, E) Principal Coordinates Analysis (PCoA) of different samples for RNA-seq and Ribo-seq. (C, F) Correlation heatmap of normalized RNA and ribosome-protected fragments (RPFs) values between every pair of samples. The color and size of dots represent the Pearson correlation coefficient.
Figure 3—figure supplement 2. Heatmap of differentially expressed genes between Mettl51bp and w1118.

Figure 3—figure supplement 2.

Heatmap showing the top 100 significantly changed genes identified by RNA-seq (A) and Ribo-seq (B) in Mettl51bp andw1118. Red indicates increased expression, while blue indicates decreased expression.
Figure 3—figure supplement 3. Enrichment analysis of differentially expressed genes between Mettl51bp and w1118.

Figure 3—figure supplement 3.

Gene Ontology (GO) analysis performed on the differentially expressed genes revealed by RNA-seq (A, C) and Ribo-seq (B, D). The bubble chart shows the top 15 most significantly enriched categories under the themes of biological processes, cellular components, and molecular functions. The circle color indicates the enrichment p.adjust, and the dot size indicates the number of differentially expressed genes in the functional class or pathway.
Figure 3—figure supplement 4. Simplified Gene Ontology (GO) enrichment analysis for differentially expressed genes in RNA-seq and Ribo-seq.

Figure 3—figure supplement 4.

GO enrichments for biological process (A, D), cellular components (B, E), and molecular function (C, F) were shown. The bottom right clusters, which lack word cloud annotations, include all other small clusters with fewer than five terms. The color shades represent the similarity of GO terms based on binary cut clustering.
Figure 3—figure supplement 5. Gene set enrichment analysis (GSEA) for differentially expressed genes found in RNA-seq and Ribo-seq.

Figure 3—figure supplement 5.

GSEA were performed on the differentially expressed genes identified in RNA-seq (A, B) and Ribo-seq (C, D). The top 5 terms or pathways found in GSEA based on Gene Ontology (GO) (A, C) and KEGG (B, D) databases are shown. Genes were sorted according to their signed NES values. The circle color indicates the enrichment p.adjust, and the dot size indicates the number of differentially expressed genes in the respective functional class or pathway.
Figure 3—figure supplement 6. Gene Ontology (GO) and KEGG enrichment analysis of genes that were significantly changed at both transcriptional and translational levels.

Figure 3—figure supplement 6.

(A, B) Top 10 GO and KEGG terms for genes with the same direction of change at transcriptional and translational levels. (C, D) Top 10 GO and KEGG terms for genes with the opposite direction of change at transcriptional and translational levels. (E, F) Top 10 GO terms for genes that were only changed at the transcriptional level; (G, H) Top 10 GO terms for genes that were only changed at the translational level. The circle color indicates the enrichment p.adjust, and the dot size indicates the number of differentially expressed genes in the respective functional class or pathway. Changes with |log2(fold change)| ≥0.265 of translational levels and |log2(fold change)| ≥1 of transcriptional levels were considered significant.
Figure 3—figure supplement 7. Translation efficiency (TE) correlation and P-site offsets.

Figure 3—figure supplement 7.

(A–F) TE correlation between the three biological replicates of two groups showing high reproducibility. Different colors represent the density distribution of different translated genes. (G–L) Metagene analysis of individual 28 nt reads mapped to their 5′ ends to determine P-site offsets. The distribution of 28 nt ribosome-protected fragment (RPF) reads around the ribosomal P-site in w1118 (G–I) and Mettl5 (J–L). The horizontal axis represents the distance from the start codon or stop codon, and the vertical axis represents the read counts. The P-site positions are colored according to the reading frame.
Figure 3—figure supplement 8. Global scan of ribosome-protected fragments (RPFs) distribution.

Figure 3—figure supplement 8.

Distribution of RPFs periodicity among translation start codons and translation end codons in w1118 (A–C) and Mettl51bp (D–F). Distribution of RPFs on different coding frames, including frame 0, frame 1, and frame 2, for the 5′-UTR, CDS, and 3′-UTR in w1118 (G–I) and Mettl51bp (J–L). The plot illustrates the distribution of RPFs across different coding frames (frame 0, frame 1, and frame 2) on the x-axis, with the y-axis representing the percentage of RPFs in each coding frame.
Figure 3—figure supplement 9. Ribo-seq has revealed that the Mettl51bp leads to changes in global translation features.

Figure 3—figure supplement 9.

(A) The distribution of ribosome-protected fragment (RPF) length among different samples in Mettl51bp and w1118 was compared. (B) A comparison was made between Mettl51bp and w1118 regarding the statistics on active translated open reading frames (ORFs). The distribution of different types of ORFs based on their relative location to associated coding sequences (CDS) was illustrated in the comparison between Mettl51bp and w1118. The categories include ‘annotated’ (ORFs that overlap with annotated CDS and have the same stop codon as the annotated CDS), ‘uORF’ (ORFs located upstream of annotated CDS, not overlapping annotated CDS), ‘dORF’ (ORFs located downstream of annotated CDS, not overlapping annotated CDS), ‘Overlap_uORF’ (ORFs located upstream of annotated CDS, overlapping annotated CDS), ‘Overlap_dORF’ (ORFs located downstream of annotated CDS, overlapping annotated CDS), ‘Internal’ (ORFs located within annotated CDS but in a different frame relative to the annotated CDS), and ‘novel’ (ORFs located in non-coding genes or non-coding transcripts of coding genes). The percentages of each ORF category were compared between Mettl51bp and w1118, providing insights into the differences in ORF distribution between the two analyzed samples. (C) A comparison was made between Mettl51bp and w1118 regarding the length of translated and untranslated uORFs. (D) The count of dORFs was compared between Mettl51bp and w1118. (E) The count of translated uORFs was compared between Mettl51bp and w1118. (F, G) Motif analysis was performed separately on the translated and untranslated uORFs between w1118 and Mettl51bp. (H) The correlation of codon occupancy (A-site) between Mettl51bp and w1118 was analyzed. The cumulative changes in RPFs for codon UCC (I), GAC (J), GAU (K), and amino acid ASP (L) were compared between Mettl51bp and w1118. The statistical analysis was performed using the Kolmogorov–Smirnov test (KS test). (M–O) A metagene plot was generated for RPFs on the whole CDS region and around the CDS start and end regions in group case study. The CDS was divided into 100 equal bins.
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To explore the biological implication of differentially expressed genes at transcriptional and translational levels, we performed Gene Ontology (GO) enrichment analysis. At the transcriptional level (Figure 3—figure supplement 3A, C), the most significantly enriched biological processes included cellular response to chemical stimulus, small molecule biosynthetic process, and fatty acid metabolic process (Figure 3—figure supplement 3A), with supporting gene networks (Figure 3—figure supplement 3C). Translational-level analysis (Figure 3—figure supplement 3B, D) revealed prominent enrichment for organic acid and amino acid metabolic processes, lipid catabolism, cellular respiration, and transport mechanisms. Notably, we identified strong associations with circadian regulation, including circadian regulation of gene expression and entrainment of the circadian clock.

The GO results usually contain a long list of enriched terms that have highly redundant information and are difficult to summarize. So, we performed a simplified enrichment analysis that visualizes the summaries of clusters by word cloud for the GO enrichment result at transcriptional and translational levels. For transcriptionally significant DEGs (Figure 3—figure supplement 4A), major enriched processes included metabolic and stimulus–response pathways, wing disc and imaginal system development, ion-channel homeostasis and transport, and cytokinesis/cell cycle transitions. Translationally regulated genes additionally showed enrichment for sleep–wake cycles, circadian behavior, and neuronal cell death (Figure 3—figure supplement 4D). Cellular component analysis highlighted chromosomal structures, membrane-bound complexes, and vesicles (Figure 3—figure supplement 4B), while molecular functions predominantly involved enzymatic activities, ion/protein binding, receptor activities, and transcription processes (Figure 3—figure supplement 4C). The strong concordance between RNA-seq and Ribo-seq enrichment profiles (Figure 3—figure supplement 4E–F) further validated these findings.

Our gene set enrichment analysis (GSEA) identified pathways enriched in differentially expressed genes at both transcriptional and translational levels. To capture subtle but biologically important changes that might be excluded by stringent statistical thresholds, we analyzed gene sets ranked by log2 fold change without p-value filtering. The GSEA–GO and GSEA–KEGG results, sorted by normalized enrichment score, revealed several key pathways (Figure 3—figure supplement 5A–D). Notably, the proteasome pathway showed significant suppression at both transcriptional and translational levels (p.adjust <0.05) (Figure 3E, I). Transcriptional-level analysis highlighted disruptions in arginine and proline metabolism and insect hormone biosynthesis (p.adjust <0.05) (Figure 3D, G). While, while translational changes predominantly affected fatty acid degradation and phosphonate/phosphinate metabolism (p.adjust <0.05) (Figure 3H–K).

To better understand the correlation between transcription and translation levels, we performed correlation analysis of differentially expressed genes based on fold change of DEGs (Figure 3L). 977 genes showed opposing trends between transcription and translation, while only 24 genes exhibited concordant changes. We identified 240 translation-specific and 3309 transcription-specific DEGs, with each cluster undergoing separate enrichment analysis (Figure 3—figure supplement 6A–H). Notably, core clock genes (Clk, tim, etc.) displayed significant changes at both levels (Figure 3L).

To isolate translational effects, we calculated translation efficiency (TE) differences between Mettl51bp and w1118 controls (Figure 3M). Among 1,204 genes with significantly altered TE, GO enrichment highlighted amino acid metabolism and small molecule biosynthesis (Figure 3N), while KEGG analysis emphasized glycine/serine/threonine metabolism and one-carbon pool by folate pathways (Figure 3O, P).

Ribo-seq revealed that Mettl51bp led to changes of some global translation features

Using Ribo-seq, we compared global translation features between the w1118 and Mettl51b groups. The TE correlation coefficients among the three biological replicates of each genotype ranged from 0.87 to 0.93, demonstrating high reproducibility within genotypes (Figure 3—figure supplement 7A–F). We found that the length of ribosome-protected RNA fragments (RPFs) was approximately 28 nt (Figure 3—figure supplement 9A). The RPFs exhibited a significant 3-nt periodicity. Metagene analysis of individual 28 nt reads revealed the distribution of RPFs across the gene locus in w1118 and Mettl51bp, respectively. The starting point of translation is 12 nt upstream of the start codon and gradually disappears 15 nt from the stop codon (Figure 3—figure supplement 7G–L). RPFs on the metagene plot distribution around the translation start and translation stop site (Figure 3—figure supplement 8A–F), different coding frames on CDS, 3′-UTR and 5′-UTR, respectively, in two groups of samples all showed the periodicity (Figure 3—figure supplement 8G–L). As expected, all plots show an enrichment of P-sites in the first frame on the coding sequence but not the UTRs, in accord with ribosome protected fragments from protein coding mRNAs (Figure 3—figure supplement 8G–L).

Analysis of open reading frame (ORF) types revealed alterations in their distribution. Compared to w1118, Mettl51bp exhibited an increased proportion of overlapping downstream ORFs (dORFs) and fewer non-overlapping dORFs (Figure 3—figure supplement 9B). In both groups, translated upstream ORFs (uORFs) were significantly shorter than untranslated uORFs (p < 0.05) (Figure 3—figure supplement 9C). Additionally, Mettl51bp displayed reduced read counts for both dORFs and uORFs (Figure 3—figure supplement 9D, E). Based on translation potential, uORFs were classified as either translated or untranslated, and motif analysis was performed separately for each category (Figure 3—figure supplement 9F, G).

Mutation of Mettl5 altered codon preference

Comparison of codon occupancy (A-site) between the two groups revealed that Mettl51bp preferentially used GAC and GAU, whereas w1118 favored UCC (Figure 3—figure supplement 9H). This trend was further supported by the cumulative frequency distribution of these codons (Figure 3—figure supplement 9I–K). Since GAC and GAU both encode aspartate (Asp), we analyzed Asp amino acid occupancy. Intriguingly, Asp was significantly enriched in w1118 during translation (Figure 3—figure supplement 9L), suggesting that Mettl51bp may exhibit altered translational regulation (see Discussion for details).

Metagene analysis of RPFs revealed distinct translation patterns between Mettl51bp and w1118. The coding sequence (CDS) and flanking regions were segmented into 100 equal bins, and average RPF density was computed for each bin. The resulting plots illustrate differences in ribosome occupancy between Mettl51bp and w1118 along the CDS (Figure 3—figure supplement 9M), near the translation start site (Figure 3—figure supplement 9N) and around the translation termination site (Figure 3—figure supplement 9O). Notably, Mettl51bp and w1118 exhibited divergent ribosome occupancy patterns, particularly along the CDS and near the start codon (Figure 3—figure supplement 9M, N), suggesting potential differences in translation dynamics and initiation efficiency.

Mettl5 regulates the clock gene regulatory loop

Our findings demonstrate that Mettl51bp disrupts the core clock gene regulatory loop controlling circadian rhythm. We observed significant alterations in both transcriptional and translational levels of multiple clock genes, with cry and Clk showing upregulation while tim, per, vri, and pdp1 were downregulated at both levels (Figure 4A, B). Notably, per, vri, and pdp1 exhibited particularly pronounced downregulation in translation efficiency (Figure 4C). These changes occurred without affecting clock neuron morphology at different time points (Figure 4—figure supplement 1).

Figure 4. Clock genes expression mediated the sleep phenotype caused by Mettl5 mutation.

(A–C) Fold changes in clock genes with significant expression level differences between w1118 and Mettl51bp were observed in RNA-seq, Ribo-seq, and translation efficiency analyses. (D) The gene expression levels of per at four different time points of w1118 and Mettl51bp. (E) Representative western blot analysis of PER protein levels in w1118 and Mettl51bp/+fly heads collected at four distinct time points (ZT0, ZT6, ZT12, and ZT18). Brackets indicate different phosphorylation states of PER: hyper-phosphorylated (Hyper), intermediate (Inper), and hypo-phosphorylated (Hypo). β-Tubulin was used as a loading control. (F) Quantification of total PER protein levels relative to β-tubulin. Data are presented as mean ± SEM from three independent biological replicates (n = 3). Statistical significance was determined by unpaired Student’s t-test at each time point. * stands for p < 0.05; ns stands for not significant. (G) Representative image of PER protein immunofluorescence staining at ZT0 in the small ventral lateral neurons (small LNvs). (H) Statistical analysis of the immunofluorescence intensity for PER in small LNvs. (I) Sleep curve throughout the day for Mettl51bp, per01, double mutant and control flies. (J) Total sleep for Mettl51bp, per01, double mutant and control flies. (K) Percentage of awake time in Mettl51bp flies, partially rescued by double mutant flies. (L) Sleep curve throughout the day for Mettl51bp, per01, double mutant, and control flies. (M) Total sleep for Mettl51bp, per01, double mutant, and control flies. (N) Percentage of awake time in Mettl51bp flies, partially rescued by double mutant flies. (O) Fold changes in proteasome subunits with significant expression level differences between w1118 and Mettl51bp were observed in RNA-seq, Ribo-seq, and translation efficiency analyses. For statistical significance, * stands for p < 0.05, ** stands for p < 0.01, *** stands for p < 0.001, ns stands for not significant.

Figure 4—source data 1. PDF file containing original western blots for Figure 4E, indicating the relevant bands, phosphorylation states, and genotypes.
Figure 4—source data 2. Original files for western blot analysis displayed in Figure 4E.

Figure 4.

Figure 4—figure supplement 1. Morphology of clock neurons in Mettl5 mutant.

Figure 4—figure supplement 1.

Ventral lateral neurons labeled by PDF staining in Mettl51bp and control flies across different time points.Scale bar: 50 μm.
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The observed expression patterns revealed an unexpected regulatory relationship: while the canonical clock circuitry positions per downstream of Clk, our finding that Clk was upregulated while per was downregulated suggests per may actually function upstream of Clk in Mettl5-mediated regulation. Surprisingly, despite the transcriptional downregulation of per (Figure 4D), we detected increased PER protein levels through both immunostaining and western blot analyses (Figure 4E–H). Detection of the PER protein at different time points indicated that it was increased at both ZT0 and ZT18 (Figure 4E, F, Figure 4—source data 1, Figure 4—source data 2). This apparent contradiction aligns with the observed circadian phenotype, as Mettl5 mutants showed significantly longer period lengths (Table 1), mirroring effects seen when PER stabilization results from reduced kinase activity as previously reported (Philpott et al., 2023). Genetic epistasis experiments further supported this model, with clock gene mutants modifying Mettl5 mutant phenotypes that suggest both Clk and per downstream of Mettl5 (Figure 4I–N, Table 1). Secondary effects may exist for the significant increase in daytime sleep in the double mutants. Together, these results indicate that Mettl51bp affects circadian regulation through mechanisms that extend beyond transcriptional control, likely involving post-translational regulation of PER protein stability.

Table 1. Circadian rhythm phenotypes of various mutants.

Genotype NumTotal %Rhythmic Period Signif vs w1118 (Period) Power Signif vs w1118 (Power)
w1118 32 92.6 23.9 ± 0.05 127.9 ± 7.62
Mettl51bp/+ 32 92.3 28.3 ± 0.4 *** 114.3 ± 6.76 ns
Mettl51bp/+; UAS-Mettl5/Mettl5-Gal4 32 96.9 24 ± 0.02 ns 127.1 ± 5.42 ns
Mettl51bp/ClkJRK 44 29.5 24 ± 0.04 ns 68.5 ± 8.85 ***
Mettl51bp/Per01 54 9.3 24.2 ± 0.2 ns 43.8 ± 13.41 ***
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To investigate the factors mediating PER protein level changes in Mettl5 mutants, we examined the ubiquitin–proteasome pathway, which plays a well-documented role in Period protein degradation (Grima et al., 2002; Ko et al., 2002; Chiu et al., 2008). Notably, recent evidence indicates that m6A regulates the ubiquitin–proteasome system in other biological contexts (Sun et al., 2023). Our integrated analysis of RNA-seq and Ribo-seq data revealed significant downregulation of multiple proteasome pathway components in Mettl51bp mutants at both transcriptional and translational levels (Figures 3E, I, 4O), suggesting impaired protein degradation capacity. Based on these findings, we propose a model where Mettl5 regulates circadian function through three interconnected mechanisms: first, by directly modulating proteasome components to control PER protein stability post-translationally; second, by transcriptionally and translationally regulating per and other Clk expression. In this model, Mettl51bp-induced proteasome downregulation leads to PER accumulation, which is responsible for the phenotypes (Figure 5).

Figure 5. A working model illustrating the role of Mettl5 in Drosophila sleep was presented.

Figure 5.

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Mettl51bp alters axon complexity

Previous studies have established a strong correlation between sleep homeostasis and synaptic complexity (Bushey et al., 2011). Additionally, synaptogenesis has been shown to enhance proteasome activity in axons (Costa et al., 2019). Given our observations of impaired sleep rebound following deprivation and altered expression of proteasome subunits in Mettl51bp mutants, we sought to examine potential effects on synaptic complexity.

To assess synaptic complexity, we adapted an established quantification method (Bushey et al., 2011) using Syt-GFP, a marker that colocalizes with endogenous synaptic vesicles, to visualize presynaptic morphology changes. Control experiments with UAS-Fmr1 and Fmr1 mutations successfully replicated the expected decrease and increase in syt-eGFP signal, respectively (Figure 6A–D). Strikingly, Mettl51bp mutants exhibited significantly increased syt-eGFP fluorescence in presynaptic terminals (Figure 6E–M), indicating altered synaptic complexity.

Figure 6. The axon complexity was found to be affected by Mettl51bp.

Figure 6.

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(A–C) Representative confocal micrographs of small ventral lateral neuron (s-LNv) axonal terminals across different genotypes. Presynaptic structures were visualized using syt-eGFP (green), which colocalizes with endogenous synaptic vesicles: (A) control, (B) Fmr1 overexpression, and (C) Fmr1 null mutant. (D) Quantification of s-LNv axonal terminal volumes corresponding to genotypes in A–C. (E–H) Representative images of axonal terminal morphology in control flies at four time points: ZT0, ZT6, ZT12, and ZT18. (I–L) Representative images of axonal terminal morphology in Mettl5 mutant flies at ZT0, ZT6, ZT12, and ZT18. (M) Quantitative comparison of axonal terminal volumes between control and Mettl5 mutant flies at different time points. Scale bar: 50 μm. For statistical significance, * stands for p < 0.05, ** stands for p < 0.01, *** stands for p < 0.001, ns stands for not significant.

Discussion

Our study reveals that Mettl5, a known rRNA methyltransferase, modulates sleep through its RNA methylation activity. Through integrated RNA-seq and Ribo-seq analyses of Mettl51bp mutants, we identified Mettl5’s downstream targets at both transcriptional and translational levels. Further investigation demonstrated that Mettl5 influences sleep regulation by affecting two key pathways: the circadian clock gene network and the proteasome system. These findings provide novel mechanistic insights into sleep control, highlighting the coordinated role of protein synthesis and degradation in this process. Notably, our Ribo-seq analysis revealed that Mettl51bp alters fundamental translation features, including uORF translation efficiency and codon preference, suggesting rRNA methylation plays a regulatory role in these processes.

This discovery has important clinical implications, as METTL5, the human ortholog of Mettl5, is associated with ID when mutated (Richard et al., 2019). Our work expands the understanding of Mettl5’s molecular function and may inform potential therapeutic strategies for ID. The clinical relevance of our findings is underscored by reports of sleep disturbances, particularly reduced sleep duration, in ID patients—a phenotype that parallels our observations in Mettl51bp mutants. The mechanistic framework established in this study could explain these clinical sleep abnormalities. However, further validation in vertebrate models is needed to determine whether this regulatory mechanism is evolutionarily conserved and applicable to human sleep disorders.

As shown in Table 1, the Mettl51bp/+ mutant exhibits a robust long-period phenotype, with circadian rhythms significantly extended to 28.3 ± 0.4 hr compared to the wild-type’s 23.9 ± 0.05 hr. This prolonged period perfectly aligns with the observed behavioral phenotypes, including delayed nighttime sleep onset, later daytime waking, and the overall shift in sleep profile. This is indeed quite similar to a previous report on PERIOD3 variant (Zhang et al., 2016). We think that the prolonged circadian period contributes to the observed sleep phenotype. However, since total sleep time was significantly reduced in the mutant, we cannot attribute the phenotype solely to period lengthening. Furthermore, our 24-hr PER expression analysis in Mettl5 mutants revealed elevated PER protein levels at ZT1 and ZT18, while ZT6 and ZT12 showed no significant changes, with no apparent phase shift. These findings collectively suggest that the phenotype primarily results from PER protein stabilization and accumulation.

We found that Mettl5 heterozygotes showed significant reductions in total RNA and 18S rRNA methylation levels, contrasting with mouse studies where heterozygous knockouts maintained normal 18S rRNA m6A methylation (Sepich-Poore et al., 2022). This discrepancy may stem from either a fundamental difference in rRNA methylation regulation between Drosophila and mice, or distinct biological consequences of knockdown versus knockout approaches, as complete gene elimination often triggers compensatory mechanisms (Teng et al., 2013; Rossi et al., 2015; Vu et al., 2015; Ma et al., 2019; El-Brolosy et al., 2019).

Our study uncovers a previously unrecognized connection between circadian clock genes and proteasome function. While previous work demonstrated that the circadian clock rhythmically regulates proteasome components in Drosophila fat bodies under dietary restriction (Hwangbo et al., 2023), we now show that Mettl5 modulates clock protein synthesis and degradation in clock neurons by influencing proteasome activity. This regulation likely occurs through Mettl5-mediated ribosomal methylation in clock neurons, which impacts the proteasome degradation pathway (Costa et al., 2019), ultimately altering clock protein dynamics.

Mettl5 represents a novel integrator of rRNA methylation and proteasome function, providing a mechanism to balance protein synthesis and degradation. Interestingly, we observed specific effects on both transcriptional and translational outputs, with particular proteasome subunits showing differential regulation. This specificity may arise from either selective translational control by Mettl5 or additional layers of regulation through protein–protein interactions. These findings suggest an intricate regulatory network coordinating these processes, though further studies are needed to elucidate the underlying mechanisms.

Our results reveal complex relationships between clock genes and sleep regulation. While cyc loss-of-function alleles show enhanced sleep rebound in females (Shaw et al., 2002), Mettl51bp mutants with elevated PER protein exhibit reduced rebound. Notably, Mettl5’s regulatory effects display tissue specificity—although absent in canonical clock neurons, Mettl5-Gal4 is expressed in distinct neurons and glia that appear crucial for sleep regulation. This expression pattern, combined with Clock’s broader distribution (Patop et al., 2023), suggests specialized circuits for sleep homeostasis that warrant further investigation.

Evidence indicates that sleep functions in development, metabolism, and neuronal plasticity (Anafi et al., 2019). Sleep exerts effects on neuronal plasticity by modifying synapses. The synaptic homeostasis theory proposes that sleep has a role in downscaling synaptic strength (Tononi and Cirelli, 2006). Indeed, synapse markers progressively decrease during sleep in both mammals and Drosophila brains (López-Varea et al., 2021; Vyazovskiy et al., 2008; Gilestro et al., 2009; Liu et al., 2010). More importantly, evidence indicates that sleep need and protein levels in synapses are tightly linked. Studies of the presynaptic active zone have shown that synaptic plasticity regulates sleep homeostasis (Huang et al., 2020). Genome-wide proteomic studies of synapses in mouse brains indicate that the synaptic proteins peak around dusk and dawn (Noya et al., 2019; Brüning et al., 2019). Sleep deprivation experiments have demonstrated that the sleep drive is significantly dependent on the cycling of proteins and phosphoproteins in synapses, in contrast to mRNAs (Noya et al., 2019; Brüning et al., 2019). These results suggest that gene expression regulation at the protein level is crucial for sleep. Consistently, we detected changes in the quantity of axons in Mettl51bp, suggesting a possible regulation of neural circuits by Mettl5.

The mechanism by which METTL5 regulates translation warrants further investigation. Previous studies have demonstrated that METTL5 influences translation (Rong et al., 2020; Peng et al., 2022), but whether the mechanisms identified here are conserved across other systems remains an intriguing question. In our analysis, we observed increased usage of aspartate (Asp) codons in Mettl5 mutants. Notably, prior work has linked codon usage to PER protein function—specifically, a codon-optimized version of PER failed to rescue circadian rhythmicity in per mutant flies, unlike the wild-type version (Fu et al., 2016). Further analysis revealed that PER protein levels were elevated in these mutants, suggesting that codon optimization enhances PER expression (Figure 2B in Fu et al., 2016). Strikingly, when we examined the codon-optimized region from Fu et al., 2016, we found that GAC (Asp) was highly enriched, raising the possibility that Mettl5 mutation affects PER protein accumulation by altering GAC codon usage. Additional experiments will be needed to validate this hypothesis. Furthermore, we detected changes in uORFs in Mettl5 mutants, but their relationship to translational regulation requires further exploration.

Our study demonstrates that ribosomal components can exert gene-specific regulatory functions, building upon previous work showing context-dependent ribosome specialization (Simsek et al., 2017). In Mettl5 mutants, we observed distinct alterations in the translational efficiency of specific genes. These effects could potentially arise through two non-exclusive mechanisms: (1) Mettl5-mediated rRNA modifications may modulate ribosomal binding affinity for particular mRNA sequences, or (2) these modifications might contribute to the formation of specialized ribosome populations that preferentially translate specific subsets of mRNAs. Future studies will be required to distinguish between these possibilities and fully elucidate the underlying molecular mechanisms.

Materials and methods

Drosophila strains

Fly stocks used in this study were maintained under standard culture conditions. We used the w1118 as the control strain. The following flies were obtained from Bloomington Stock Center: w1118 (Bl: 5905), Mettl5-Gal4 (Bl: 19514), Repo-Gal4 (Bl: 7415), nSyb-Gal4 (Bl: 51941), pdf-Gal4 (Bl: 41286), ClkJRK (Bl: 80927), per 01 (Bl: 80917), Fmr1 Δ50M (Bl: 6930), UAS-Trmt112RNAi (VDRC: 101515), UAS-DenMark, UAS-syt.eGFP (Bl: 33064), UAS-GFPstinger (from Yi Rao’s lab), UAS-Fmr1 (Bl: 6931), and UAS-Mettl5 (FlyORF: F000760) are from Fly ORF collection. UAS-Mettl5m-3HA (generated in this study).

Mettl51bp and Mettl59bp mutants were generated by the CRISPR/Cas system as described previously (Cheng et al., 2020). The target gRNA was designed with an online tool: http://tools.flycrispr.molbio.wisc.edu/targetFinder/. A target sequence was chosen that has the sequence of 5′- GAGTGGGGTACTGTTCCAACAGG with the PAM sequence in bold. Mutations were verified by genomic PCR and sequencing.

Sleep behavior assays

All sleep assays were conducted in a controlled environment incubator maintained at 25 ± 1°C with 60% ± 5% relative humidity. We maintained a 12:12 light-dark cycle with lights on at ZT0 (06:30) and off at ZT12 (18:30). Fly activity was monitored using the Drosophila Activity Monitoring (DAM) System (Trikinetics, Waltham, MA). Following a 2-day acclimation period, we recorded locomotor activity at 1 min intervals for 3 consecutive days. Data analysis was performed using pysolo (Gilestro and Cirelli, 2009), with sleep defined as ≥5 min of continuous inactivity.

Mechanical sleep deprivation was performed using the SNAP method to keep flies awake for 12 hr overnight (Shaw et al., 2002). Sleep deprivation is applied by mechanical stimuli using a timer-controlled rotating shaker. The intensity of stimuli was 1500 rpm once every minute (2 s/1 min) (Shimizu et al., 2008). Baseline sleep was established from 24-hr recordings prior to deprivation. Sleep loss and recovery were quantified according to previous publication (Cirelli et al., 2005).

Regarding the ‘awake %’ metric, it indicates that at specific time points (e.g., ZT14), the percentage of awake fruit fly population at that moment. At ZT19, we evaluated arousal thresholds by administering a standardized gentle mechanical stimulus. Responsiveness was determined by monitoring activity for 1-min post-stimulation. Flies showing no activity during this window were scored as non-responsive. We calculated arousal percentages from the proportion of flies that awakened in response to stimulation.

Circadian rhythm of individual male flies was measured using the DAM System (Trikinetics). Male flies were loaded individually into glass tubes with a length of 65 mm and an inner diameter of 5 mm. The tubes contained standard cornmeal fly food at one end and were sealed with a cotton stopper at the other end. The flies were entrained to a 12-hr light/12-hr dark cycle for 3 days and then released to constant darkness for at least 6 days to measure their rhythmicity. Data analysis is done on a Macintosh computer running the FaasX (Fly activity analysis suite) software.

Statistical analysis

All statistical analyses were performed using GraphPad Prism 5 software. For sleep parameter comparisons, we used nonparametric tests including the two-tailed Mann–Whitney test for pairwise comparisons and one-way ANOVA with Tukey’s post hoc test for multiple comparisons. qPCR data were analyzed using unpaired Student’s t-tests. The specific statistical test used for each experiment is indicated in the corresponding figure. In all analyses, a p-value of less than 0.05 was considered statistically significant.

Quantitative PCR

Total RNA was extracted from cells and tissues using the TRNzol Universal Reagent (Tiangen #DP4-02). For cDNA synthesis, we employed the PrimeScript RT reagent Kit with gDNA Eraser (TAKARA #RR047A) following the manufacturer’s protocol. Quantitative PCR was carried out using SuperReal PreMix Plus (SYBR Green) (Tiangen #DP4-02). RP49 served as the endogenous reference gene for normalization across samples. All experiments included three independent biological replicates to ensure reproducibility. The primers used in this experiment are RP49-F: CGGTTACGGATCGAACAAGC; RP49-R: CTTGCGCTTCTTGGAGGAGA; Mettl5-F: CGGTTTCTGGAGGTGGC; Mettl5-R: GCTGGCGTCGATGTTGTAC.

Imaging and analysis of axon volume

We quantified small LNvs axon volume using ImageJ’s Object Counter 3D plugin to measure pixel counts from raw images. Image processing involved applying a standardized threshold that clearly visualized intact axons in control samples. For consistent measurements, we specifically analyzed the axon span between the first axonal bifurcation and the terminal tip, as indicated by the white rectangular markers in Figure 6.

Sample collection and library construction for Ribo-seq and RNAseq

For sample collection, w1118 and Mettl51bp Drosophila were harvested at ZT15, immediately transferred to centrifuge tubes, and flash-frozen in liquid nitrogen. Fly heads were subsequently separated and collected while frozen, with three biological replicates prepared for each genotype. Each sample was equally divided for parallel RNA-seq and Ribo-seq analyses. For Ribo-seq library preparation, samples were lysed in buffer containing 50 mg/ml cycloheximide (Novogen, China) to preserve ribosome positioning, followed by RNase I digestion to generate RPFs. Monosomes were isolated using MicroSpin S-400 HR size-exclusion chromatography, followed by rRNA depletion and PAGE purification to select 20–38 nt RPFs. Purified fragments underwent end repair, adapter ligation, reverse transcription, and PCR amplification before Illumina PE150 sequencing. For RNA-seq, total RNA was extracted using TRIzol reagent, with cDNA libraries prepared and sequenced using Illumina PE150 by Novogen. Raw sequencing data in FASTQ format were processed to remove adapter sequences, reads containing N bases, and low-quality reads, while simultaneously calculating Q20/Q30 scores and GC content to generate clean reads for downstream analysis.

RNA-seq analysis

We performed genome alignment and transcriptome analysis using the following pipeline: First, we built a Hisat2 index (v2.0.5) for the Drosophila melanogaster reference genome (dm6 assembly). Clean paired-end reads were then aligned to this reference using Hisat2 (v2.0.5) (Kim et al., 2019). The resulting alignments were processed using StringTie (v1.3.3b) (Pertea et al., 2015) for reference-based transcript assembly. For gene-level quantification, we used FeatureCounts (v1.5.0) (Liao et al., 2014) to count reads mapped to each annotated gene. Differential expression analysis between w1118 and Mettl51bp was conducted with DESeq2 (v1.20.0) (Pertea et al., 2015), with genes meeting both criteria (p.adjust <0.05 and absolute log2 (fold change) ≥1) considered statistically significant.

Ribo-seq analysis

We implemented a comprehensive Ribo-seq analysis pipeline beginning with quality filtering using Bowtie (Liao et al., 2014) to remove reads aligning to non-coding RNAs (rRNA, tRNA, snoRNA, snRNA from FlyBase Release 6.13) with a 2-mismatch allowance (-v 2). The remaining RPFs were mapped to the Drosophila melanogaster genome (FlyBase Release 6.13) using STAR (v2.7.3a) (Dobin et al., 2013), followed by transcript-level alignment to protein-coding sequences using Bowtie (v1.2.2) (Langmead et al., 2009) with parameters ‘-a -v 2’. CDS-aligned RPFs were quantified using featureCounts (Subread v1.6.3) and normalized as RPKM. Differential expression analysis was performed with DESeq2 (v1.14.1) (Love et al., 2014) using thresholds of |log2FC| ≥0.265 and p.adj <0.05, while translation efficiency differences were assessed using RiboDiff. Data quality was verified through riboWaltz (v1.1.0) (Lauria et al., 2018) for 3-nt periodicity and reading frame analysis, with Ribocode (Xiao et al., 2018) employed for P-site positioning and uORF motif analysis. Functional enrichment analyses (GO, KEGG, and GSEA) were conducted using clusterProfiler (v4.5.2.002) (Wu et al., 2021), with results visualized through ggplot2-generated plots and GO term simplification performed using SimplifyEnrichment (Gu and Hübschmann, 2023).

Immunofluorescence experiments

We performed immunofluorescence staining on 7- to 15-day-old adult flies (unless otherwise specified). Flies were anesthetized with CO2 and dissected in ice-cold 0.03% PBST (1× PBS with 0.03% Triton X-100; Sigma, T9284). Samples were fixed in 2% paraformaldehyde for 55 min at room temperature (RT), followed by four 15 min washes in 0.03% PBST at RT. After blocking overnight at 4°C in 10% Normal Goat Serum (NGS; in 1× PBS with 2% Triton), samples were incubated with primary antibodies for 24 hr at 4°C. Primary antibodies included rat anti-Elav (DSHB, 9F8A9; 1:200) and mouse anti-Repo (DSHB, 8D12; 1:200), diluted in antibody buffer (1.25% PBST, 1% NGS). Following four 15 min washes in 1× PBS with 1% Triton at RT, samples were incubated overnight at 4°C with secondary antibodies: Alexa Fluor 568 (Thermo Fisher, A11004; 1:200) and Alexa Fluor 647 (Thermo Fisher, A21247; 1:200). After four additional 15 min washes in 1× PBS with 1% Triton at RT, samples were mounted using DAPI-containing antifade mounting medium (Solarbio, S2110).

Images were acquired using a Leica SP8 confocal microscope with LAS X software, applying auto Z-brightness correction when needed for signal uniformity. Images were processed in Adobe Photoshop CS6 and figures assembled in Adobe Illustrator 2020. PDF immunofluorescence followed the same protocol. All experiments included ≥3 biological replicates, each containing ≥10 flies. Fluorescence intensity was quantified using ImageJ.

Western blotting

Protein samples were prepared by homogenizing approximately 30 fly heads in RIPA lysis buffer (150 mM NaCl, 1.0% NP-40, 0.5% sodium deoxycholate, 0.1% SDS, 50 mM Tris-HCl, pH 8.0) supplemented with protease (CW2200S) and phosphatase (CW2383S) inhibitor cocktails according to manufacturer specifications. Lysates were mixed with 2× SDS loading buffer, boiled at 100°C for 5 min, and immediately cooled on ice.

For immunoblotting, membranes were probed with rabbit anti-PER primary antibody (1:5000 dilution; kindly provided by Dr. Jeffrey Price’s laboratory, University of Missouri-Kansas City) overnight at 4°C, followed by incubation with HRP-conjugated goat anti-rabbit IgG secondary antibody (1:1500; ABclonal, AS014) for 4 hr at RT. Protein signals were detected using ECL substrate (ABclonal, RM00021P) and imaged with an Amersham ImageQuant 800 system (GE Healthcare). Band intensities were quantified using ImageJ software, with three biological replicates performed for statistical reliability.

LC–MS/MS analysis of m6A levels

Total RNA was isolated from cells and tissues using TRNzol Universal Reagent (Tiangen, #DP4-02). For 18S rRNA purification, we separated total RNA by polyacrylamide gel electrophoresis followed by gel extraction. Our analysis utilized ribonucleoside standards including adenosine (rA) and N6-methyladenosine (N6 mA), with a mobile phase consisting of methanol: ddH2O (vol/vol).

For each biological replicate, 1 μg of total RNA or purified 18S rRNA was digested to single nucleosides using Nucleoside Digestion Mix (NEB, #M0649). Proteins were precipitated by adding a 4:1 methanol: digest ratio and incubating at –20°C for 2 hr. Quantitative analysis was performed using multiple reaction monitoring with the following transitions: 268.10275 → 136.0621 (rA), 282.11835 → 150.0774 (N6 mA).

Negative geotaxis RING assay

Climbing assays were performed following a 12-hr recovery period after CO2 anesthesia, using 200 flies per genotype distributed across 10 vials (20 flies/vial) marked at 90 mm height. Each assay consisted of three trials separated by 15-min intervals, initiated by sharply tapping vials three times to induce negative geotaxis, with the number of flies reaching the 90 mm mark within 10 s recorded per trial. Five groups were tested per genotype (15 trials total). All assays were video recorded under standardized conditions with the camera positioned 20 cm from vials and uniform backlighting provided by a white open-faced box to ensure consistent imaging quality.

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 32070492 and 32122017) to Juan Du. We would also like to acknowledge the 2115 Talent Development Program of China Agricultural University.

Funding Statement

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Contributor Information

Juan Du, Email: dujuan9981@cau.edu.cn.

Jiwon Shim, Seoul National University, Republic of Korea.

Utpal Banerjee, University of California, Los Angeles, United States.

Funding Information

This paper was supported by the following grants:

  • National Natural Science Foundation of China 32070492 to Juan Du.

  • National Natural Science Foundation of China 32122017 to Juan Du.

Additional information

Competing interests

No competing interests declared.

Author contributions

Formal analysis, Investigation, Methodology, Writing – original draft.

Data curation, Software, Formal analysis, Investigation, Methodology, Writing – original draft.

Investigation.

Resources, Writing – original draft.

Conceptualization, Resources, Formal analysis, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing.

Additional files

MDAR checklist

Data availability

The RNA-seq and Ribo-seq data from this publication have been deposited to the NCBI bioproject database https://www.ncbi.nlm.nih.gov/bioproject/ and assigned the identifier PRJNA994860.

The following dataset was generated:

Yang X. 2024. RNA-seq and Ribo-seq revealed the downstream events in Mettl5 mutation. NCBI BioProject. PRJNA994860

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eLife Assessment

Jiwon Shim 1

The authors present useful findings demonstrating that the RNA modification enzyme Mettl5 regulates sleep in Drosophila. Through transcriptome- and proteome-wide analyses, the authors identified downstream targets affected in heterozygous mutants and proposed that Mettl5 regulates the translation and degradation of clock genes to maintain normal sleep function. Through additional analyses, the authors provided solid evidence supporting this model.

Reviewer #1 (Public review):

Anonymous

Summary:

Here the authors attempted to test whether the function of Mettl5 in sleep regulation was conserved in Drosophila, and if so, by which molecular mechanisms. To do so they performed sleep analysis, as well as RNA-seq and ribo-seq in order to identify the downstream targets. They found that the loss of one copy of Mettl5 affects sleep, and that its catalytic activity is important for this function. Transcriptional and proteomic analyses show that multiple pathways were altered, including the clock signaling pathway and the proteasome. Based on these changes the authors propose that Mettl5 modulate sleep through regulation of the clock genes, both at the level of their production and degradation, possibly by altering the usage of Aspartate codon.

Comments on revisions:

The authors addressed all my comments satisfactorily.

Reviewer #3 (Public review):

Anonymous

Xiaoyu Wu and colleagues examined a potential role in sleep of a Drosophila ribosomal RNA methyltransferase, mettl5. Based on sleep defects reported in CRISPR generated mutants, the authors performed both RNA-seq and Ribo-seq analyses of head tissue from mutants and compared to control animals collected at the same time point. A major conclusion was that the mutant showed altered expression of circadian clock genes, and that the altered expression of the period gene in particular accounted for the sleep defect reported in the mettl5 mutant. In this revision, the authors have added a more thorough analysis of clock gene expression and show that PER protein levels are increased relative to wild type animals a specific times of day, indicating increased stability of the protein. Given that PER inhibits its own transcription, the per RNA is low in the mutants. The revised manuscript included efforts toward a more detailed understanding of how clock gene expression was altered in the mutants, as well as other clarification of sleep phenotypes.

Comments on revisions:

All critiques have been addressed by the authors; the manuscript is much improved from its original submission. Thank you.

eLife. 2026 May 8;14:RP103427. doi: 10.7554/eLife.103427.4.sa3

Author response

Xiaoyu Wu 1, Xingzhuo Yang 2, Tiantian Fu 3, Yikang Rong 4, Juan Du 5

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Here, the authors attempted to test whether the function of Mettl5 in sleep regulation was conserved in Drosophila, and if so, by which molecular mechanisms. To do so they performed sleep analysis, as well as RNA-seq and ribo-seq in order to identify the downstream targets. They found that the loss of one copy of Mettl5 affects sleep, and that its catalytic activity is important for this function. Transcriptional and proteomic analyses show that multiple pathways were altered, including the clock signaling pathway and the proteasome. Based on these changes the authors propose that Mettl5 modulate sleep through regulation of the clock genes, both at the level of their production and degradation, possibly by altering the usage of Aspartate codon.

Comments on revised version:

The authors satisfactorily addressed my comments, even though the precise mechanism by which Mettl5 regulates translation of clock genes remains to be firmly demonstrated.

Reviewer #3 (Public review):

Xiaoyu Wu and colleagues examined a potential role in sleep of a Drosophila ribosomal RNA methyltransferase, mettl5. Based on sleep defects reported in CRISPR generated mutants, the authors performed both RNA-seq and Ribo-seq analyses of head tissue from mutants and compared to control animals collected at the same time point. A major conclusion was that the mutant showed altered expression of circadian clock genes, and that the altered expression of the period gene in particular accounted for the sleep defect reported in the mettl5 mutant. In this revision, the authors have added a more thorough analysis of clock gene expression and show that PER protein levels are increased relative to wild type animals a specific times of day, indicating increased stability of the protein. Given that PER inhibits its own transcription, the per RNA is low in the mutants. Efforts toward a more detailed understanding of how clock gene expression was altered in the mutants, as well as other clarification of sleep phenotypes throughout is appreciated. As noted above, a strength of this work is its relevance to a human developmental disorder as well as the transcriptomic and ribosomal profiling of the mutant. However, there still remain some minor weaknesses in the manuscript. This reviewer is not in agreement with the interpretation of the epigenetic experiments. Specifically, co-expression of Clk[jrk] or per [01] with the mettl5 mutant recovered the nighttime sleep phenotype, but was additive to the daytime sleep phenotype such that double mutants showed higher sleep. This effect should be acknowledged and discussed. Overall, this is an interesting paper that indicates a molecular link between mettl5 and the circadian clock in regulation of sleep.

Recommendations for the authors:

Reviewer #3 (Recommendations for the authors):

The authors misunderstood my original comment for Fig 1A. Please provide an explanation for the significance of the boxed region. There is little or no detail in the legend to help guide the reader.

The information has been added to the figure legends for Figure 1A.

Efforts toward improving analysis of circadian genes as well as sleep phenotypes (sleep onset time, rebound, etc) is much appreciated, thank you. However, Figure S1H and G panel labels are mixed up; please label in the order that they appear and that they correspond to the main text. Why is Figure S1H labeled "ZT 14"?

Sleep latency is defined as the time from preparing to sleep to actually falling asleep. In this study, it specifically refers to the time taken for each individual fly to reach the sleep phenotype (i.e., 25 minutes of continuous sleep). We noted that this label was misleading, as the actual time to reach the sleep phenotype varied among individual flies. Therefore, in the revised figures, we have removed the ZT14 label. In addition, we have corrected the labeling of Figures S1G and S1H to ensure they appear in the correct order and correspond accurately to the descriptions in the main text.

Unfortunately, based on Fig S1A-C, I am not convinced that mettl5 localizes to neurons, as there are no cells that show double labelling. This figure does not support the statement: "we found expression in both neurons (colocalizing with ELAV staining: Figure S1A-C) (lines 91-92), and "Mettl5-Gal4 is expressed in distinct neurons and glia that appear crucial for sleep regulation." (line 297). What "distinct" sleep related neurons were labeled? The staining in Fig S1A shows a different distribution from that in Fig S1D, and so it's possible this was a technical issue. Is there a better example?

Thank you for your careful review and valuable comments. We agree that the colocalization of METTL5 with the neuronal marker ELAV is relatively sparse. However, as indicated by the arrows in Fig S1A–C, we did observe a few cells showing clear double labeling. These examples support the presence of METTL5 expression in neurons, albeit at a low frequency.

In Figure 4G-H, please indicate the time of day of tissue collection.

In Figure 4G-H, the tissue was collected at ZT0. We have now indicated this time point in the figure and legend to clarify the experimental timing.

As noted in the public comment, I remain in disagreement with the assessment that "the double mutant showed the similar phenotype as downstream genes". The striking significant increase in daytime sleep in the double mutants remains unexplained. No further experiments are necessary, but this should be acknowledged in the text. Instead of an epistatic effect, given that overall sleep is high in the double mutants, another possible explanation is that the flies are sick and so are less active and sleeping more.

Thank you for your suggestion. This has been acknowledged in the text. “Genetic epistasis experiments further supported this model, with clock gene mutants modified Mettl5 mutant phenotypes that suggesting both Clock and Per downstream of Mettl5 (Figure 4I-N, Table 1). Secondary effect may exist for the significant increase in daytime sleep in the double mutants.”

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Data Citations

    1. Yang X. 2024. RNA-seq and Ribo-seq revealed the downstream events in Mettl5 mutation. NCBI BioProject. PRJNA994860

    Supplementary Materials

    Figure 4—source data 1. PDF file containing original western blots for Figure 4E, indicating the relevant bands, phosphorylation states, and genotypes.
    elife-103427-fig4-data1.zip (329KB, zip)
    Figure 4—source data 2. Original files for western blot analysis displayed in Figure 4E.
    elife-103427-fig4-data2.zip (2.9MB, zip)
    MDAR checklist
    elife-103427-mdarchecklist1.docx (90.2KB, docx)

    Data Availability Statement

    The RNA-seq and Ribo-seq data from this publication have been deposited to the NCBI bioproject database https://www.ncbi.nlm.nih.gov/bioproject/ and assigned the identifier PRJNA994860.

    The following dataset was generated:

    Yang X. 2024. RNA-seq and Ribo-seq revealed the downstream events in Mettl5 mutation. NCBI BioProject. PRJNA994860

    Articles from eLife are provided here courtesy of eLife Sciences Publications, Ltd

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