LKRSDH-dependent histone modifications of insulin-like peptide sites contribute to age-related circadian rhythm changes

Lv, Pengfei; Yang, Xingzhuo; Du, Juan

doi:10.1038/s41467-024-47740-4

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Published: 18 April 2024

LKRSDH-dependent histone modifications of insulin-like peptide sites contribute to age-related circadian rhythm changes

Nature Communications volume 15, Article number: 3336 (2024) Cite this article

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Abstract

To understand aging impact on the circadian rhythm, we screened for factors influencing circadian changes during aging. Our findings reveal that LKRSDH mutation significantly reduces rhythmicity in aged flies. RNA-seq identifies a significant increase in insulin-like peptides (dilps) in LKRSDH mutants due to the combined effects of H3R17me2 and H3K27me3 on transcription. Genetic evidence suggests that LKRSDH regulates age-related circadian rhythm changes through art4 and dilps. ChIP-seq analyzes whole genome changes in H3R17me2 and H3K27me3 histone modifications in young and old flies with LKRSDH mutation and controls. The results reveal a correlation between H3R17me2 and H3K27me3, underscoring the role of LKRSDH in regulating gene expression and modification levels during aging. Overall, our study demonstrates that LKRSDH-dependent histone modifications at dilps sites contribute to age-related circadian rhythm changes. This data offers insights and a foundational reference for aging research by unveiling the relationship between LKRSDH and H3R17me2/H3K27me3 histone modifications in aging.

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Introduction

Circadian rhythm is a universal phenomenon in living organisms that helps them better adapt to their environment. Since the discovery of the first gene regulating circadian rhythm in 1972¹, Drosophila has become a model system for studying circadian rhythm. In Drosophila, the core regulatory loop that controls circadian rhythm consists of CLOCK (CLK) and CYCLE (CYC), which form a heterodimer complex and activate rhythmic transcription of clock-controlled genes², as well as PERIOD (PER) and TIMELESS (TIM), which are the main repressors of CLK/CYC activity³. Studies have shown that this mechanism is conserved in vertebrates³.

Age has an impact on circadian rhythm. In Drosophila, aged flies exhibit weakened sleep/activity rhythm, as studies have shown^4,5,6. Electrophysiological recordings of large ventral lateral neurons in Drosophila have demonstrated a significant decrease in input resistance with age. Furthermore, aging in Drosophila leads to a reduction in circadian rhythm and the daily plasticity in the architecture of small ventral lateral neurons⁷. In addition, the oscillations of the clock genes per, tim and their proteins are attenuated in the heads of aged flies compared to young flies^5,8. Similar changes have been observed in the clock-associated genes Pdp1ε, vri, and cry^5,8. In mammals, it has been also observed that the amplitude of circadian rhythm tends to decrease with age^9,10. By facilitating SIRT1-dependent deacetylation of the core clock protein PER2, nicotinamide adenine dinucleotide (NAD + ) has the potential to rejuvenate both circadian gene expression and behavior in aged mice¹¹. A genome-wide association study has also indicated that the muscle circadian clock is important for longevity¹². The identification of more factors that regulate changes in the circadian rhythm during the aging process plays a crucial role in unraveling the mechanisms underlying this phenomenon.

Multiple studies have provided evidence for the existence of molecular connections between aging and circadian rhythm. For example, mutations in genes associated with circadian rhythm have been identified to impact the aging process in Drosophila^13,14,15. In vertebrates, the core circadian system is intricately linked with various aging-associated signaling pathways, notably including mTOR, SIRT1, and AMPK which are nutrient sensors¹⁶. Gaining a comprehensive understanding of the regulatory mechanisms underlying longevity and age-related changes in circadian rhythm will enable us to establish the connection between these two processes.

Chromatin features undergo changes during the aging process. DNA modifications, levels of histone expression, histone methylation and histone acetylation all exhibit alterations throughout aging^17,18,19. Genomic instability increases with age, resulting from elevated rates of mutations, transposon instability and transcriptional instability¹⁷. Epigenetic regulation is also important in governing circadian rhythm^20,21,22,23. Recent research has highlighted that age-related changes in the epigenetic regulation of the clock gene Per1 contribute to certain facets of cognitive decline²⁴. However, the specific regulators responsible for these epigenetic features involved in aging process have yet to be identified.

Histone arginine methylation is regulated by methyltransferases and cofactors. H3R17 is a histone arginine site that can be methylated by coactivator-associated arginine methyltransferase 1 (CARM1)²⁵. It has been observed that the stability of CARM1 in the aging heart of mammals is significantly reduced²⁶, suggesting a potential involvement of histone arginine methylation in the aging process. dLKR/SDH, which is an evolutionarily conserved protein from Arabidopsis to humans²⁷, is a dual-activity enzyme with an N-terminal LKR domain and a C-terminal SDH domain. In Drosophila, LKRSDH interacts with histone H3 to inhibit the ecdysone-induced expression of cell death genes by blocking histone H3R17me2, a modification catalyzed by the Drosophila arginine methyltransferase ART4 (ortholog of CARM1)²⁸. It is still an open question whether histone arginine methylation plays a role in regulating aging.

To investigate age-dependent circadian rhythm changes, we conduct a screening of Drosophila mutants, specifically targeting genes involved in the regulation of histone methylation. The mutants are obtained from the Bloomington Stock Center after creating a list of genes related to histone methylation based on a previous publication (Supplementary file 2 in ref. ²⁹). The results show that the mutation of LKRSDH (short for Lysine ketoglutarate reductase/saccharopine dehydrogenase), an inhibitor of histone H3R17me2, leads to significant decrease in rhythmicity among aged flies compared to controls. Combinational analysis of genome-wide differentially expressed genes in LKRSDH mutants, as revealed by RNA-seq and ChIP-seq data, indicates that a significant upregulation of insulin-like peptides, resulting from the combined effects of H3R17me2 and H3K27me3 on transcription. Further genetic evidence indicates that the regulation of age-related changes in circadian rhythm by LKRSDH is mediated by art4 and dilps. A combination of RNA-seq and ChIP-seq is applied to identify the whole genome changes in H3R17me2 and H3K27me3 histone modifications in young and old flies with LKRSDH mutations, as well as in controls. The results provides insights into the relationship between LKRSDH and histone modifications of H3R17me2 and H3K27me3 during the aging process.

Results

LKRSDH regulates the circadian rhythm of aged Drosophila

As Drosophila melanogaster undergoes the aging process, there is a gradual weakening observed in its circadian rhythm^5,6. To investigate the influence of epigenetic factors on circadian rhythm during aging, we performed a screen for mutants of epigenetic regulators that had a significant impact on circadian rhythm of older flies. Our findings revealed that the rhythmicity of the 40-day-old LKRSDH^MB01942 homozygous allele was more severely dampened compared to the 40-day-old w¹¹¹⁸ control group (Fig. 1a and Supplementary Fig. 1a–h, w¹¹¹⁸ and LKRSDH^MB01942 homozygous allele at 40 day). These results strongly indicate the involvement of LKRSDH in the regulation of the circadian rhythm in aging flies. Real time RT-PCR analysis confirmed that the expression of LKRSDH was significantly down-regulated in the LKRSDH^MB01942 homozygous allele (Supplementary Fig. 1i). Western blot analysis also showed a down-regulation of LKRSDH protein levels in both heterozygous and homozygous alleles (Supplementary Fig. 1j). The antibody used were also validated by a tagged LKRSDH protein (Supplementary Fig. 1k). In comparison to the w¹¹¹⁸ control, the aged LKRSDH^MB01942 homozygous allele exhibited a more significant decline in power values (Fig. 1b,w¹¹¹⁸ and LKRSDH^MB01942 homozygous allele at 40 day). Notably, the characteristic morning-evening activity peak pattern was almost entirely absent in the aged LKRSDH^MB01942 homozygous allele (Supplementary Fig. 1d, h). In contrast, LKRSDH^MB01942 homozygous allele did not have a significant effect on the circadian rhythm parameters (percentage of rhythmicity, tau and power) of 3-day-old Drosophila (Fig. 1a, b, w¹¹¹⁸ and LKRSDH^MB01942 at 3 day). To further validate our findings, we utilized three independent LKRSDH RNAis driven by the LKRSDH-Gal4 lines, which resulted in similar phenotypes (Fig. 1a, b). These findings provide evidence for an age-dependent impact of LKRSDH on the circadian rhythm in flies, with the absence of LKRSDH exacerbating the disorder in the circadian rhythm of these flies.

**Fig. 1: *LKRSDH* down regulation or overexpression leads to circadian rhythm defects.**

To investigate whether LKRSDH regulates the circadian rhythm of aging flies by affecting their activity ability, we conducted an analysis of the activity of both mutant and control groups. We observed a significant decrease in activity counts (number of activities per 30 min) in both the w¹¹¹⁸ control and LKRSDH^MB01942 homozygous allele during aging (Supplementary Fig. 1l). While the 3-day-old LKRSDH^MB01942 homozygous allele exhibited significantly reduced activity counts compared to the w¹¹¹⁸ control, this difference was not observed in older flies (Supplementary Fig. 1l). We also examined the locomotor response using the rapid iterative negative geotaxis (RING) assay and found no difference between the 3-day-old LKRSDH^MB01942 homozygous allele and the 3-day-old w¹¹¹⁸ control (Supplementary Fig. 1m). Consistently, the locomotor response did not show any significant changes in the mutant after aging (Supplementary Fig. 1m), indicating that the LKRSDH mutation did not affect the locomotor response.

Interestingly, the circadian rhythm under the 12hrs/12hrs light dark (LD) conditions was significantly affected in LKRSDH^MB01942 homozygous allele in both young and aged flies, with a significant change in power value specifically observed in aged flies (Supplementary Fig. 1n, o).

To confirm the regulatory effect of LKRSDH on circadian rhythm during aging, we performed rescue experiments using transgenic flies generated through the CRISPR/Cas9 transcriptional activation method to conditionally express LKRSDH³⁰. LKRSDH^TOE.GS01508, referred to as LKRSDH OE, specifically activates the LKRSDH gene expression in the presence of Gal4³⁰. As anticipated, induction of LKRSDH expression by LKRSDH-Gal4 in 40-day-old flies led to a rescue of both the percentage of rhythmicity and power value of the LKRSDH^MB01942 mutant (Fig. 1c, d, LKRSDH^MB01942, UAS-dCas9/LKRSDH-Gal4, LKRSDH OE in 40d). Genotypes of LKRSDH^MB01942, UAS-dCas9/+ and LKRSDH-Gal4, LKRSDH OE were used as controls for LKRSDH^MB01942, UAS-dCas9/LKRSDH-Gal4, LKRSDH OE. In 3-day-old Drosophila, the percentage of rhythmicity and power value of LKRSDH^MB01942, UAS-dCas9/LKRSDH-Gal4, LKRSDH OE was not significantly changed compared to LKRSDH^MB01942, UAS-dCas9/+. However, in 40-day-old Drosophila, there was a significantly increased in both the percentage of rhythmicity and power value of LKRSDH^MB01942, UAS-dCas9/LKRSDH-Gal4, LKRSDH OE when compared to LKRSDH^MB01942, UAS-dCas9/+ and LKRSDH-Gal4, LKRSDH OE (Fig. 1c, d). In conclusion, supplemented LKRSDH-Gal4-driven LKRSDH can rescue the phenotypes caused by LKRSDH^MB01942 in 40-day-old flies.

The expression of LKRSDH in both neurons and glial cells is necessary for maintaining circadian rhythm in aged flies

To determine the expression pattern of LKRSDH in the adult fly brain, we performed florescence in situ hybridization (FISH) experiments. The results indicated that the LKRSDH had a ubiquitous expression pattern in the fly brain, some of which was represented by LKRSDH-Gal4 (Fig. 1e–m, t). The specificity of the FISH signals was verified by the decrease of signals in LKRSDH RNAi driven by LKRSDH-Gal4 (Fig. 1n–p, u). The blank control experiment yielded no detectable signals (Fig. 1q–s, v). The data indicated that the LKRSDH-Gal4 expressed in a portion of the LKRSDH expressing cells. It is common for Gal4 lines of this nature, created through the insertion of a Gal4-expressing cassette into a specific gene’s genomic region, only captures a fraction of the gene’s overall expression in many cases. This phenomenon has been previously confirmed in published studies as well (Fig. 2f–h in ref. ³¹).

**Fig. 2: The expression of LKRSDH in both neurons and glial cells is necessary for maintaining the circadian rhythm in aged flies.**

We also performed immunofluorescence staining experiments to verify the identity of GFP-expressing cells of the LKRSDH^MB01942 allele, which is an insertional mutation carrying a GFP fluorescent marker³². We used anti-Repo and anti-Elav antibodies to label glial cells and neurons, respectively, in the brains of Drosophila^33,34. We found that LKRSDH was expressed in a portion of glial cells (Fig. 2a–o, rectangular box and arrow) and neurons (Fig. 2a–o, round box and arrow). Consistently, the expression of GFP driven by LKRSDH-Gal4 also demonstrated widespread LKRSDH expression in both neurons and glial cells of the fly brain (Supplementary Fig. 2a–j).

To determine whether LKRSDH expressed by neurons or glial cells is required for circadian rhythm regulation during aging, we used LKRSDH RNAi to knock down LKRSDH in glial cells and neurons. The efficiencies of the RNAis were verified through RT-PCR analysis (Supplementary Fig. 2k–m). The downregulation of LKRSDH in glial cells or neurons did not affect the circadian rhythm of young flies (Fig. 2p). Nevertheless, with the progression of aging, the percentage of rhythmicity of flies with LKRSDH downregulated in both neurons and glial cells was significantly decreased compared to the controls, which was consistent with the phenotype exhibited by the LKRSDH^MB01942 allele (Fig. 2p, Fig. 1a). Calculation of the power value of the rhythmic flies in these genotype groups did not show any significant changes (Supplementary Fig. 2n). These results indicated that LKRSDH expression in both neurons and glial cells was required for Drosophila to maintain rhythm during aging.

Overexpression of LKRSDH in neurons and glial cells using nsyb-Gal4 and repo-Gal4, respectively, led to additional functions. In both young and aging flies, overexpression of LKRSDH using nsyb-Gal4 resulted in a further decrease in the power value and percentage of rhythmicity (Fig. 2q and Supplementary Fig. 2o), while overexpression using repo-Gal4 led to a further decrease in the power value (Fig. 2q and Supplementary Fig. 2o). In contrast, overexpression of LKRSDH using LKRSDH-Gal4 showed no significant difference in the percentage of rhythmicity compared to LKRSDH-Gal4 controls in young flies. As flies aged, the percentage of rhythmicity in flies overexpressing LKRSDH using LKRSDH-Gal4 was found to be significantly lower compared to the LKRSDH-Gal4 control group (Fig. 1c, d).

In the rescue experiment, the expression of LKRSDH in both neurons and glial cells of the LKRSDH^MB01942 allele led to notably low level of rhythmicity in aged flies, particularly when utilizing nsyb-Gal4 (Fig. 2r and Supplementary Fig. 2p). However, young flies were not significantly affected by expressing LKRSDH using nsyb-Gal4 and repo-Gal4 (Fig. 2r and Supplementary Fig. 2p). These findings deviate from the results observed with the LKRSDH-Gal4 expression of LKRSDH in the LKRSDH^MB01942 allele, which exhibited a more effective rescue of the mutant phenotype (Fig. 1c, d). These results align with the observation that LKRSDH is expressed in both neurons and glial cells (Fig. 2). Consequently, ectopic overexpression of LKRSDH in either all neurons or glial cells alone cannot effectively rescue the phenotype caused by the LKRSDH^MB01942 allele.

In conclusion, our findings highlight the indispensable role of LKRSDH expression in both neurons and glial cells for the maintenance of circadian rhythm in aged flies. As evidenced by the expression patterns, LKRSDH is consistently present in both neurons and glial cells. Consistently, overexpression of LKRSDH in either all neurons or glial cells alone results in additional functions but fails to rescue the phenotype caused by the LKRSDH^MB01942 allele.

Genome-wide RNA-seq revealed the downstream genes of LKRSDH

To investigate the downstream target genes of LKRSDH involved in regulating the circadian rhythm during aging, we performed genome-wide RNA-seq analysis in both w¹¹¹⁸ control and LKRSDH^MB01942 mutant at the ages of 3 days and 40 days at the time point of ZT8. Our analysis revealed that the majority of differentially expressed genes (DEGs) were upregulated in both young and aged LKRSDH^MB01942 flies (Fig. 3a and Supplementary Data 1). In the w¹¹¹⁸ control group, there were more downregulated DEGs after aging compared to upregulated DEGs (Fig. 3a and Supplementary Data 1). Conversely, in the LKRSDH^MB01942 mutant group, the number of downregulated DEGs after aging exhibited only a marginal decrease compared to the number of upregulated DEGs (Fig. 3a and Supplementary Data 1). These findings provide further support for the role of LKRSDH as a transcriptional regulator in ecdysone signaling²⁸.

**Fig. 3: *Dilps* are downstream target genes of *LKRSDH* shown by RNA-seq.**

Further analysis of overlapping DEGs revealed features in the DEG profiles associated with both the LKRSDH mutation and aging process. There were fewer DEGs in the young flies than in the aged flies of LKRSDH^MB01942 allele (Fig. 3a), indicating a heightened sensitivity of aging flies to LKRSDH mutation. Comparative analysis showed that 656 DEGs in the aged LKRSDH^MB01942 mutant overlapped with those in the aged w¹¹¹⁸ control, comprising more than half of the total DEGs (Fig. 3a). Notably, only 42 DEGs caused by LKRSDH^MB01942 allele in young flies were shared with those caused by aging in w¹¹¹⁸ control (Fig. 3a). These findings highlight the distinct DEG profiles resulting from aging and LKRSDH mutation.

Nevertheless, despite the disparities in the profile of DEGs, KEGG enrichment analysis revealed remarkably consistent pathway enrichment results. This analysis indicated that the LKRSDH allele in flies led to alterations in genes associated with the longevity regulation pathway, both in young and old flies (Fig. 3b and Supplementary Data 2). Consistently, aging in the w¹¹¹⁸ control also led to gene changes in the longevity regulation pathway (Fig. 3b and Supplementary Data 2). However, the LKRSDH mutation did not lead to significant enrichment of this group of genes, suggesting a potential relationship between LKRSDH and aging (Fig. 3b and Supplementary Data 2).

Gene Ontology (GO) analysis of these DEGs revealed common enrichment in pathways associated with the aging process for both the LKRSDH^MB01942 mutant and w¹¹¹⁸ control (Supplementary Fig. 3a and Supplementary Data 2). These common pathways included ion transmembrane transport, ATP metabolism, cellular respiration, etc. (Supplementary Fig. 3a and Supplementary Data 2). The aged w¹¹¹⁸ control exhibited specific enrichment in pathways related to the regulation of membrane potential, cytoplasmic translation, etc. (Supplementary Fig. 3a and Supplementary Data 2). The aged LKRSDH^MB01942 mutant showed specific enrichment in pathways such as carbohydrate derivative catabolic process, chemical homeostasis, neuropeptide signaling pathway, etc. (Supplementary Fig. 3a and Supplementary Data 2).

Among the three gene profiles enriched in the longevity regulating pathway, we identified dilp3 and dilp5 on the list (Supplementary Data 2). We then examined the expression levels of all the dilp family members and found that the dilp2, dilp3 and dilp5 exhibited changes in their expression levels under these conditions (Fig. 3c, d and Supplementary Fig. 3b). Consequently, we conducted detailed studies on the relationship between LKRSDH and these dilp genes.

We also examined the oscillation patterns of these dilps and the glucose levels across the 24 hours. The results indicated that while there were significant changes in the expression levels, the oscillating patterns of the expression remained unaltered across different conditions (Supplementary Fig. 3c–e). Moreover, the glucose level was significantly elevated in LKRSDH allele compared to the w¹¹¹⁸ control in young and old flies (Supplementary Fig. 3f). Additionally, within the same genotype, the glucose level was significantly higher in young flies compare to aged flies (Supplementary Fig. 3f). These findings suggest that the LKRSDH allele exerts a considerable influence on glucose metabolism in Drosophila.

Dilp2, dilp3 and dilp5 contribute to the circadian rhythm and longevity phenotypes caused by LKRSDH mutation

As indicated above, dilp2, dilp3 and dilp5 were in the list of differentially expressed genes (Fig. 3c, d). Previous evidence has shown that the insulin pathway in Drosophila is involved in regulating lifespan^35,36 and circadian rhythm³⁷. Therefore, we aimed to investigate whether dilps contributed to these phenotypes caused by LKRSDH mutation. Through RNA-seq and qRT‒PCR, we found that both dilp3 and dilp5 were upregulated in the LKRSDH^MB01942 allele (Fig. 4a). dilp2 was upregulated in the LKRSDH^MB01942 allele in aged flies, but not in young flies (Fig. 4a). In comparison to the control group, the overexpression of dilp3 in aged flies decreased the percentage of rhythmicity and the power value, which were consistent with the phenotypes of the LKRSDH^MB01942 allele (Fig. 4b and Supplementary Fig. 4a). Compared with the control group, knockdown of LKRSDH by dilp5-Gal4 resulted in a significant decrease in the percentage of rhythmicity and the power value of the aged flies (Fig. 4b and Supplementary Fig. 4a). More importantly, although dilp1-5^KO/+ did not cause a significant defect in the percentage of rhythmicity, presumably because of heterozygosity, dilp1-5^KO rescued the defect in the percentage of rhythmicity caused by the LKRSDH^MB01942 allele (Fig. 4c and Supplementary Fig. 4b). In conclusion, these results reveal that dilps contribute to the circadian phenotypes caused by LKRSDH mutation.

**Fig. 4: Overexpression or knockout of *dilps* resulted in circadian rhythm defects.**

To distinguish the specific roles of dilp2, dilp3 and dilp5, we compared the circadian rhythm phenotypes and the rescue effects of each single mutant. The results indicated that the dilp2, dilp3 and dilp5 single mutants exhibited varying degree of defects in the percentage of rhythmicity in both young and old flies (Fig. 4d). The dilp3 /dilp5 double mutant showed a significant higher percentage of arrhythmicity compared to each single mutant (Fig. 4d). Furthermore, all the dilp2, dilp3, dilp5 single mutants and dilp3 /dilp5 double mutant were able to rescue the phenotype caused by the LKRSDH^MB01942 allele in aged flies (Fig. 4d). The rescue effects of dilp2 were better compared to dilp3 and dilp5 single mutants, and were comparable to dilp3 /dilp5 double mutant (Fig. 4d). However, the power value of these genotypes did not show significant changes (Supplementary Fig. 4c). In conclusion, these results collectively reveal that dilp2, dilp3 and dilp5 contribute to the circadian phenotypes caused by the LKRSDH mutation.

Subsequent experiments provided further evidence regarding the genetic relationship mentioned above and its impact on the longevity phenotype. Detection of the longevity phenotype of the LKRSDH allele indicated that it shortened the lifespan (Supplementary Fig. 4d). The expression of LKRSDH rescued this shortened lifespan phenotype (Supplementary Fig. 4e). More importantly, dilp1-5 knockout also rescued the longevity phenotype caused by the LKRSDH allele (Supplementary Fig. 4f). Overexpressing dilp3 caused a decreased lifespan (Supplementary Fig. 4g). These results suggest that dilp3 and dilp5 are downstream of LKRSDH and play a crucial role in regulating both circadian rhythm and longevity.

LKRSDH regulates dilp2, dilp3 and dilp5 through H3R17me2 and H3K27me3

In addition to its enzymatic activity, LKRSDH also functions as a transcriptional corepressor in ecdysone signaling. It inhibits the methylation of histone H3 on R17 by CARMER (ART4 in Drosophila), resulting in the downregulation of gene expression through chromatin remodeling^28,38,39. Building upon this understanding, we conducted experiments to investigate the potential involvement of art4 in this regulatory process. Overexpression of art4 by LKRSDH-Gal4 resulted in defects in the percentage of rhythmicity in both young and old flies (Fig. 5a). The power value was also affected in young flies overexpressing art4 (Fig. 5b). More importantly, the knockdown of art4 by LKRSDH-Gal4 led to significant changes in the percentage of rhythmicity of old flies compared to other Gal4s (Fig. 5a). Consistently, knocking down art4 by dilp5-Gal4 resulted in a suppression of the phenotype caused by LKRSDH allele in aged flies (Fig. 5c). The power value showed more decrease in young flies, while, the down regulation of the power value by LKRSDH allele in aged flies was not suppressed by the art4 RNAi in aged flies (Fig. 5d compared with Fig. 1b). These results indicate that the art4 activity is up regulated in LKRSDH allele. Consistently, the dilp expression levels could be affected by up or down regulation of art4 (Fig. 5e, f). Therefore, we hypothesize that LKRSDH regulates dilp2, dilp3 and dilp5 through art4. To investigate this hypothesis, we conducted an examination of various histone modifications, including H3R17me2, among others.

Fig. 5: *LKRSDH* regulates circadian rhythm through *art4.*

Subsequently, we investigated the impact of the LKRSDH mutation on the global level of H3R17me2 and found that its levels were elevated in young LKRSDH^MB01942 mutant flies (Fig. 6a, c). However, H3R17me2 was significantly decreased in aged w¹¹¹⁸ and LKRSDH^MB01942 mutant flies (Fig. 6a, c). Subsequent results demonstrated that the decline in H3R17me2 levels observed in aged flies was attributed to a decrease in the expression level of art4 mRNA (Fig. 4a). We speculated that the decrease in H3R17me2 contributes to the downregulation of most genes during aging, including dilps (Fig. 4a). Furthermore, in the young LKRSDH^MB01942 flies, the decrease in art4 transcription (Fig. 4a) may be a result of feedback regulation in response to LKRSDH depletion and the more efficient methylation of histone H3R17 by art4.

**Fig. 6: *LKRSDH* regulates *dilps* through H3R17me2 and H3K27me3.**

To further investigate whether LKRSDH regulates dilps through histone modifications, we examined the co-expression of LKRSDH and dilps in the same set of cells. The immunostaining results showed that dilp5-Gal4-driven Redstinger was expressed in the same set of cells as LKRSDH, labeled by the GFP trap in LKRSDH^MB01942 (Fig. 6d–i). Furthermore, we analyzed H3R17me2 in gene body and promoter regions of dilps (Fig. 6j–m). We found that in the young LKRSDH^MB01942 mutant, H3R17me2 was significantly increased at the dilps promoter region, which correlated with elevated dilps expression (Fig. 4a). Surprisingly, we also found a significant increase in H3R17me2 levels at the dilps gene body and promoter in the aged w¹¹¹⁸ control (Fig. 6k–m). Moreover, H3R17me2 was significantly downregulated in old flies carrying the LKRSDH^MB01942 mutation (Fig. 6k–m). Neither of these findings fully explained the changes in dilps expression during aging (Fig. 4a). Hence, we hypothesized that other histone modifications could be involved in dilps regulation. Previous results have shown that CARM1 plays a role in modulating the antagonism between EZH2 and BAF155 to facilitate the H3K27me3 deposition by EZ in cancer cells⁴⁰. In the modENCODE database, we identified binding sites of H3K27me3 in the dilps gene body and promoter regions (Supplementary Fig. 5a). Therefore, we conducted tests for H3K27me3.

The detection of H3K27me3 at the global level revealed its dependency on LKRSDH. H3K27me3 increased significantly during the aging process of w¹¹¹⁸ control (Fig. 6b, c). LKRSDH mutation led to a significant increase in H3K27me3 in young flies (Fig. 6b, c). In old flies, there was no significant change in the global level of H3K27me3 in the LKRSDH^MB01942 mutant (Fig. 6b, c). In the LKRSDH^MB01942 mutant, the aging process resulted in significant increase in H3K27me3 (Fig. 6b, c).

We also examined H3K27me3 on the dilps gene body and promoter. We found that the LKRSDH^MB01942 allele did not affect H3K27me3 in young flies. However, as the flies aged, the H3K27me3 modification at the dilps gene body and promoter regions decreased compared with the w¹¹¹⁸ control (Fig. 6j–m). Moreover, H3K27me3 was significantly increased in the dilps gene body and promoter region in the aged w¹¹¹⁸ control (Fig. 6j–m). These results shed light on the mechanism underlying changes in dilps expression levels. Specifically, in young flies, LKRSDH^MB01942 allele caused an increase in H3R17me2 at the dilps locus, without affecting H3K27me3. As a result, this promoted the expression of dilps. As the flies aged, the LKRSDH^MB01942 allele induced a significant decrease in both H3R17me2 (activating mark) and H3K27me3 (repressive mark) at the dilps locus compared to the aged w¹¹¹⁸ control, resulting in increased dilps expression (Supplementary Fig. 5b–d). Consistently, in aged flies, although H3R17me2 of the dilps locus in the LKRSDH^MB01942 mutant decreased compared with the w¹¹¹⁸ control, it appeared that H3K27me3 at the dilps locus decreased relatively more, potentially contributing to the increased expression of dilps (Supplementary Fig. 5b–d). Interestingly, overexpressing LKRSDH in neurons or glia resulted in down regulation of H3R17me2 (Supplementary Fig. 5e–g). Although there were no notable changes in H3K27me3 observed in young flies, a significant increase was detected in aged flies (Supplementary Fig. 5h–j).

Interestingly, the changes in the levels of H3R17me2 and H3K27me3 at specific gene loci were not always consistent with the global levels. In aged flies, when mRNA of art4 was greatly downregulated, the downregulation of LKRSDH did not result in global changes in H3R17me2 and H3K27me3 levels (Figs. 4a, 6c). However, at the dilp3 and dilp5 gene loci, the downregulation of LKRSDH led to decreased H3R17me2 and H3K27me3 levels in aged flies (Fig. 6k–m), suggesting the existence of specific regulatory mechanisms for the two histone modifications at particular gene loci. Subsequently, we asked to what extent H3R17me2 associated with H3K27me3 and the effect of aging and LKRSDH on this association.

The alterations of H3R17me2 genome widely and at the gene locus supported the regulatory relationship between LKRSDH and art4. The downregulation of art4 resulted in a decrease in H3R17me2 levels (Supplementary Fig. 6a–d). While, the upregulation of art4 resulted in an increase in H3R17me2 levels (Supplementary Fig. 6e–h). Consistently, the downregulation of art4 further decreased H3R17me3 in the presence of the LKRSDH^MB01942 allele (Supplementary Fig. 6i–k), indicating the art4 was downstream of LKRSDH. However, the alterations of H3K27me3 genome widely and at the gene locus indicated the mechanisms in addition to art4 may be involved in mediating the effects of LKRSDH on H3K27me3. Downregulation of art4 in neurons resulted in an increase in H3K27me3 (Supplementary Fig. 7a–d). While, the upregulation of art4 resulted in a decrease in the H3K27me3 (Supplementary Fig. 7e–g). If the art4 were the only factor mediating the effects of LKRSDH on H3K27me3, LKRSDH mutant which results in the upregulation of art4 activity should have led to a decrease in H3K27me3. However, global H3K27me3 were either upregulated or not significantly changed by the LKRSDH allele, which arguing the presence of other mechanisms (refer to the Discussion session). This data also suggested that the H3K27me3 levels changes caused by LKRSDH mutation on the dilp gene sites in aged flies were dependent on art4. The downregulation of art4 led to further up regulation of H3K27me3 in the presence of the LKRSDH^MB01942 allele (Supplementary Fig. 7h–k). Interestingly, the downregulation of art4 resulted in the decrease of LKRSDH, suggesting the existence of a feedback mechanism at transcriptional level (Supplementary Fig. 8).

H3R17me2 is positively correlated with H3K27me3, and both are widely repressed by the LKRSDH genome

To clarify the relationship between H3R17me2 and H3K27me3, we conducted chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments on H3R17me2 and H3K27me3 in 3-day-old w¹¹¹⁸ control, 3-day-old LKRSDH^MB01942 mutant, 40-day-old w¹¹¹⁸ control and 40-day-old LKRSDH^MB01942 mutant flies at ZT8. Through these experiments, we obtained maps representing the distribution of H3R17me2 and H3K27me3 along chromosomes. We found that H3R17me2 exhibits more extensive occupancy across the genome (Fig. 7a, b). Consistently, H3R17me2 and H3K27me3 predominantly bind to the promoter region (Supplementary Fig. 9a). Further analysis indicated that the majority of binding sites for H3R17me2 and H3K27me3 are located within a 1 kb radius around transcriptional start sites (TSSs) (Supplementary Fig. 9b). In addition, the peak count frequency signal profiles of H3R17me2 and H3K27me3 showed increased average enrichment scores in the regions upstream and downstream of the TSS (Supplementary Fig. 9c). LKRSDH^MB01942 mutation or aging altered the distribution and occupancy of H3R17me2 and H3K27me3 (Fig. 7a–d).

**Fig. 7: Analysis of ChIP-seq data revealed that H3R17me2 is positively correlated with H3K27me3.**

The data obtained from ChIP-seq and RNA-seq analyses at the dilp2, dilp3 and dilp5 gene loci confirmed the findings presented in Fig. 4. In 3-day-old flies, LKRSDH^MB01942 allele promoted the enrichment of H3R17me2 at the gene bodies and promoters of dilp3 and dilp5, thereby resulting in increased transcription of dilp3 and dilp5 mRNA (Figs. 4a, 6l, m, 7e–g). Aging promoted the enrichment of H3R17me2 and H3K27me3 at the gene bodies and promoters of dilp2, dilp3 and dilp5, leading to a decline in dilp2, dilp3 and dilp5 mRNA transcription (Figs. 4a, 6k–m, 7e–g). This decrease may be attributed to higher levels of H3K27me3 binding. In 40-day-old flies, LKRSDH^MB01942 allele led to decreased H3R17me2 and H3K27me3 levels at the gene bodies and promoters of dilp2, dilp3 and dilp5, which collectively resulted in elevated dilp2, dilp3 and dilp5 mRNA transcription (Figs. 4a, 6k–m, 7e–g). While, the levels of H3R17me2 and H3K27me3 at the dilp1 and dilp4 locus were not significantly affected under the same conditions (Supplementary Fig. 9d, e).

To investigate the relationship between H3R17me2 and H3K27me3 occupancy across the genome, we conducted a correlation analysis based on the peak data generated by H3R17me2 and H3K27me3⁴¹. The results showed a weak positive correlation at a genome-wide scale (Fig. 7h–k). Surprisingly, the correlation between H3R17me2 and H3K27me3 appeared to increase with aging, suggesting a potential concordance in their partial functions during the aging process (Fig. 7h–k). We also examined the levels of correlation between H3K27me3 and other types of histone modifications using data published previously (see Methods for details). The level of correlation between H3R17me2 and H3K27me3 was comparable to the correlation between H3K36me3 and H3K27me3 (Supplementary Fig. 9g–v). As expected, the level of correlation between H3K9me3 and H3K27me3 was relatively high, while the levels of correlation between H3K4me3 or H3K27ac and H3K27me3 were very low (Supplementary Fig. 9g–v).

Correlation analysis was conducted to examine the changes in H3R17me2 or H3K27me3 occupancy peaks during aging, comparing them with those caused by LKRSDH. The analysis revealed the involvement of LKRSDH in regulating the aging process. The weak correlation (R = 0.45) provides support for the role of LKRSDH in regulating H3R17me2 during the aging process (Fig. 7l). Similarly, the weak correlation (R = 0.34) also elucidates the important role of LKRSDH in regulating H3K27 me3 during aging (Fig. 7m). Consistently, the correlation analysis of the DEGs indicated a weak correlation in the gene alterations resulting from LKRSDH^MB01942 allele or aging (Fig. 7n), further confirming the involvement of LKRSDH in the regulation of aging.

Cluster analysis was performed on the data obtained from ChIP-seq and RNA-seq to identify genes with similar levels of modifications and expression under different conditions. It was observed that when genes displayed low levels of H3K27me3, their expression levels were correlated with H3R17me2 levels. For example, Cluster 1 included genes involved in ncRNA metabolic process, Cluster 5 consisted mainly of genes related to mRNA metabolic process, and cluster 6 was primarily associated with cytoplasmic translation. These clusters demonstrated a correlation between low-to-high expression levels and high-to-low levels of H3R17me2 (Supplementary Fig. 9f). However, when genes exhibited high H3K27me3 levels, the expression of this gene class was not favored, regardless of the H3R17me2 levels. This was observed in Clusters 2, 3, and 4 (Supplementary Fig. 9f). Cluster 2 consisted mainly of genes involved in mitotic cell cycle process, Cluster 3 mainly encompassed genes associated with sensory perception of chemical stimulus and Cluster 4 included genes related to positive regulation of transcription by RNA polymerase II. Moreover, based on the expression level and binding of H3K27me3 and H3R17me2, it was found that H3K27me3 played a dominant role in gene expression regulation (Supplementary Fig. 9f), where the gene expression level depended on H3K27me3. Collectively, these results highlight the relevance of genome-wide H3R17me2 and H3K27me3 levels and their relationship with gene regulation.

Cluster analysis of data obtained from ChIP-seq in different samples revealed the biological features of genes with similar trends of changes in modifications under different conditions (Fig. 7o). Cluster 1 to cluster 5 represented five types of trends of changes (Fig. 7o). For example, in cluster 1, genes enriched in chemotaxis and locomotion had relatively low levels of H3R17me2. The H3K27me3 was downregulated by LKRSDH mutation in 3-day-old flies, while the opposite was observed in 40-day-old flies. The trend of changes in H3R17me2/H3K27me3 co-modified genes was opposite to that of genes with only H3K27me3 modifications. In cluster 2, which included genes enriched in body fluid regulators, chemical stimulus detector etc., H3R17me2 and H3K27me3 were down regulated after LKRSDH mutation in 3-day-old flies. H3K27me3 was also down regulated after LKRSDH mutation in 40-day-old flies, while the H3R17me2 was upregulated. H3R17me2/H3K27me3 co-modified genes were up regulated after LKRSDH mutation in both 3-day-old and 40-day-old flies. These data indicated that the changes in H3R17me2 and H3K27me3 in LKRSDH mutation and during aging were site specific.

In conclusion, we found that in fly heads, gene expression and H3R17me2 and H3K27me3 levels changed during the aging process. Genetic evidence also demonstrated that dilp3 and dilp5 were major mediators of LKRSDH mutant phenotypes during aging. More importantly, our investigation unveiled a correlation between gene expression levels, H3R17me2 levels and H3K27me3 levels in the alterations induced by LKRSDH mutation and by aging, highlighting the role of LKRSDH in mediating the aging process, which was proven by mutant phenotypes. These conclusions are summarized in a diagram (Fig. 8). Based on our data, we propose that the age dependent phenotype of LKRSDH mutant is attributed to the exceptionally high levels of dilps, including dilp2, dilp3, dilp5, which are presented in aged LKRSDH mutant. Data presented in this study collectively establish a mechanism that indicates LKRSDH regulates the expression of downstream genes by affecting art4 activity dependent H3R17me2 and H3K27me3.

**Fig. 8: Model for LKRSDH function during aging process.**

Discussion

In this study, we conduct a genetic screen to identify epigenetic factors that regulate the circadian rhythm of aged flies. We discover that LKRSDH-dependent histone modifications of insulin-like peptide genes play a role in age-related circadian rhythm changes. This study reports the regulation of H3R17me2 in both aging and circadian rhythm. Through RNA-seq analysis, we identify the differentially expressed genes profile of LKRSDH mutant in young and aged flies, thereby identifying LKRSDH downstream targets. More importantly, by examining the H3K17me2 and H3K27me3 profiles of the LKRSDH mutant and control groups in both young and aged flies in the whole genome, we unveil the correlation between H3R17me2 and H3K27me3 during the aging process. The global features of H3R17me2 and H3K27me3 in the LKRSDH mutant and control groups highlight the role of LKRSDH in the aging process. Therefore, our study provides valuable insights into the relationship between LKRSDH and H3R17me2/ H3K27me3 histone modifications during the aging process.

These findings provide valuable resources for future studies on aging and the nervous system. While aging is a physiological process that affects various tissues in the body, each tissue exhibits common and unique features during aging^42,43. Although our genome-wide assays are performed in the Drosophila head, we observe an impact on lifespan in multiple mutants and double mutants (Supplementary Fig. 4d–g). Indeed, it remains an open question whether H3R17me2 and LKRSDH are involved in the aging process of other tissues.

Our results are consistent with previous studies investigating the impact of insulin on aging. Data in this study reveal that LKRSDH mutation resulted in increased dilps and reduced lifespan. Previous research has demonstrated that decreased insulin pathway activity results in a longer lifespan^44,45,46,47. A study in pancreatic cells has indicated that H3R17me2 is involved in the regulation of insulin⁴⁸. Therefore, this study reveals the upstream regulation of dilps, which may have conservation and influence in vertebrates.

In terms of the mechanism underlying LKRSDH’s regulation of H3K27me3, our findings strongly imply that this regulation is contingent upon the involvement of art4. Evidence supporting this includes the observation that the presence or absence of art4 changes the trend of H3K27me3 at the dilps locus in the background of the LKRSDH mutant (Supplementary Fig. 7), suggesting art4 as downstream of LKRSDH. In addition, it was found that the regulation of JH by LKRSDH was independent of its enzymatic activity²⁸. However, as mentioned in the results section of our study, it is important to acknowledge that other mechanisms may also contribute to this mechanism. For instance, UTX, an alpha-ketoglutarate-dependent H3K27me3 demethylase enzyme, may be influenced by LKRSDH, thus affecting H3K27me3 levels. Further investigation could involve manipulating the enzyme-related domain to explore the involvement of LKRSDH in modulating UTX.

The neurons responsible for LKRSDH regulation on circadian rhythm may include neurons other than dilp expressing neurons. Our data indicates that the LKRSDH-Gal4 co-localized with tim-Gal4 expressing neurons (Supplementary Fig. 10a–f). In addition, knocking down of LKRSDH by tim-Gal4 driven LKRSDH RNAi recapitulates the LKRSDH mutant phenotype (Supplementary Fig. 10g, h). These results collectively indicate that the clock neurons are also responsible for LKRSDH function.

This study suggests features of changes of histone modifications during aging process. Our study reveals alterations in LKRSDH expression and genome-wide H3K27me3 and H3K17Rme2 levels in the Drosophila head during the aging process. LKRSDH expression is found to decrease during aging. H3K27me3 levels, in accordance with previous reports^49,50, increases with aging. H3R17me2 levels decrease during aging. The LKRSDH mutation leads to heightened levels of H3R17me2 and H3K27me3, as well as a shortened lifespan. These findings indicate that merely reverting the H3R17me2 level alone is insufficient for inhibiting aging. Second, analysis also reveals a strong correlation between features resulting from the mutation of LKRSDH and aging process (Fig. 7l–n). The LKRSDH mutation leads to an increase in H3K27me3, consistent with changes during aging, and it also cause an increase in H3R17me2, which is inconsistent with aging-related changes. This result suggests that the effects of H3K27me3 on gene expression changes during aging may be more significant than those of H3R17me2. Third, our results indicate that changes in H3R17me2 and H3K27me3 are correlated with aging. Previous studies have shown that histone modifications, such as H4K16 acetylation, H3K4 trimethylation, and H3K9 or H3K27 trimethylation, serve as hallmarks of aging. These modifications may result in changes in gene transcription, disruption of cellular balance, and a decline in metabolic function associated with aging. These findings suggest that H3R17me2 may have similar effects on the aging process as other histone modifications and may also serve as a hallmark of aging. Our study further reveals that H3R17me2 specifically correlates with aging. The regulation of aging-related H3R17me2 changes is influenced by LKRSDH. Lifespan is found to be affected by LKRSDH mutation. Previous reports have indicated that art4, which is responsible for H3R17me2, is linked to the aging process. These pieces of evidence suggest that the mechanism underlying changes in H3R17me2 and H3K27me3 during aging is linked to their writers or modifiers, which may be influenced by alterations in metabolic or signaling pathways outside of the nucleus that occur during the aging process.

The level of H3R17me2 detected in this study is significantly lower than that of H3K27me3. This may be caused by two possible reasons. First, the antibodies used to detect H3R17me2 and H3K27me3 may have different affinities. Second, H3R17me2 may be more specifically distributed within cells compared to H3K27me3. However, further evidence is required to verify these possibilities.

Analysis also reveals a correlation between H3R17me2 and H3K27me3 (Fig. 7h–k). This correlation could be enhanced by LKRSDH, as LKRSDH mutation promoted both modifications (Fig. 6c). On the other hand, art4 may inhibit this correlation since it promotes H3R17me2 modification while inhibiting H3K27me3 modification (Supplementary Fig. 7a). This could explain why the correlation between H3R17me2 and H3K27me3 was downregulated in the LKRSDH mutant (compare Fig. 7i, k with 7h, j). Moreover, in aged flies where the expression of art4 was significantly downregulated (Fig. 4a), we observe an enhancement in the correlation between H3R17me2 and H3K27me3 (compare Fig. 7j, k with 7h, i). It is possible that art4 plays a more predominant role compared to LKRSDH, as the expression of LKRSDH is also downregulated during aging (Fig. 4a). Other mechanisms may also contribute to this crosstalk between H3R17me2 and H3K27me3, making it an intriguing area for further investigation.

This study identifies specific genomic loci enriched with H3R17me2 and H3K27me3 during aging. A recent study has suggested that chromatin accessibility increases with aging¹⁸, and the loss of H3K9me3, a constitutive heterochromatin mark, also occurs during aging^18,51. It is possible that there is a shift in histone modification from H3K9me3, which is a constitutive heterochromatin mark, to H3K27me3, which is a facultative heterochromatin modification during aging. It would be fascinating to examine the correlation between these H3K9me3 genomic loci and H3R17me2/ H3K27me3 in the same tissue during aging.

Methods

Fly stocks

w¹¹¹⁸ (NO.5905), LKRSDH^MB01942 (NO.23382), LKRSDH-Gal4 (NO.92724), LKRSDH^OE (NO.79712), UAS-LKRSDH^RNAi3 (NO.35470), nsyb-Gal4 (NO.51941), UAS-RedStinger (NO.8547), UAS-mCD8: GFP (NO.5137) and dilp1-5^KO (NO.30890) were obtained from the Bloomington stock center. Repo-Gal4 was a gift from Dr. Yi Rao’s laboratory (Peking University, China). UAS-dilp3 and dilp5-Gal4 were gifts from Dr. Li Yan’s laboratory. UAS-LKRSDH^RNAi1 (NO.THO2254) and UAS-dCas9-VPR (NO.THN0701) were from the TsingHua Fly Center. UAS-LKRSDH^RNAi2 (NO.7144R-1) was from National Institute of Genetics (NIG). UAS-art4^RNAi (NO.V15645) was from the Vienna Drosophila Resource Center (VDRC). dilp2¹ (NO.30881), dilp3¹ (NO.30882) and dilp5¹ (NO.30884) were gifts from Dr. Liwei Zhang’s laboratory (China Agricultural University, China). UAS-art4 was generated by germline transformation. Fly stocks were maintained on standard food in an incubator at 25 °C and 60% humidity with a 12 h light/12 h dark cycle. All mutants and transgenes were backcrossed by a w¹¹¹⁸ for seven generations to clean up the genetic background. Flies for aging were collected and transferred to fresh food every 3 days. All experiments in this study were conducted using male flies.

Drosophila Activity Monitor-based method for circadian rhythm measurement

Circadian rhythm of individual male flies was measured using the Drosophila Activity Monitoring (DAM) System (Trikinetics). Male flies (aged 3 and 40 days old) 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 h light/12 h dark cycle for 3 days and then released to constant darkness for at least six days to measure their rhythmicity.

Chi-squared periodogram analysis determined whether a fly was significantly rhythmic (α = 0.05)⁵². Data analysis is done on a Macintosh computer running the FaasX (Fly activity analysis suite) software (obtained from the website https://trikinetics.com). Individual flies with power ( ≥ 10) and “width” value of 1.5 or more (representing the peak number within 30 min increments above the 95% confidence line of the periodogram) were considered rhythmic⁵³. Activity data was performed using MATLAB (MathWorks, Natick, MA) for each fly were binned every 30 min for the circadian rhythm analyses.

Cell culture and transient transfection

S2 cells were obtained from ATCC (ATCC, CRL-1963). S2 cells were grown in Serum-Free Insect Cell Culture Medium (HyClone, SH30278.02) at 25 °C. Transient transfection was performed using X-tremeGENE HP DNA Transfection Reagent (Roche, 06366236001), according to the manufacturer’s protocol.

Plasmid constructions

To obtain the LKRSDH, PCR amplification was performed using the cDNA from fly heads and specific primers, namely NotI-LKRSDH and HindIII-LKRSDH. The resulting LKRSDH fragment was then inserted into the digested plasmid pAC-4v5, which had been cut with NotI and HindIII. As a result of this molecular manipulation, the final construct pAC-LKRSDH−4v5 was generated. All the primers used are listed in Supplementary Table 1.

Drosophila Lifespan assays

Mated males were collected and maintained at a density of 20 flies per vial. The flies were kept at a temperature of 25 °C with 60% humidity and a 12 hr light/12 hr dark cycle. Every other day, the flies were transferred to new vials and their survival rate was scored. Statistical analysis and survival curve plotting were performed using Prism 8.0 (Graphpad). Each experiment was conducted with three biological replicates, and a total of 300 flies were used for lifespan assays.

Rapid Iterative Negative Geotaxis (RING) Assay

The RING assay was conducted following a similar method as described by ref. ⁵⁴, with minor modifications⁵⁵. Ten flies were collected under brief CO2 anesthesia for 1–2 min and allowed to recover overnight at a temperature of 25 °C with 65% relative humidity before the assay. To assess RING, the flies were transferred into a tube with a graduated line positioned 5 cm from the bottom. The tube was gently tapped vertically to cause the flies to ascend the wall. The number of flies crossing the graduated line within 10 s was recorded and the percentage was calculated. Three consecutive trials were conducted with a 60-second interval between each trial. It is important to note that, to ensure fairness, the genotypes of the flies were concealed during the experiment and uninformed individuals carried out the RING assay following the experimental protocol. Each experiment was performed with six biological replicates.

RNA fluorescence in situ hybridization (FISH)

FISH labeling followed the protocol of Long et al. ⁵⁶. with the following modifications. After acetic acid treatment and 0.5% PBT washing, the brains were incubated with pre-hybridization buffer (RIBOBIO, C10910) at 37 °C for 2 h. Then the brains were incubated with FISH probes (Labeled with Cy3 and synthesized by RIBOBIO biological company, dilute to 1:25 with hybridization buffer) in 37 °C for 48 h according to the manufacturer’s instructions. After the series of wash steps described in the FISH protocol, the tissues were mounted with DPX and imaged using confocal microscopy (Leica SP8) with LAX software. GFP with a 488-nm laser and 498–543-nm detection, Cy3 with a 561-nm laser and 569–595 nm detection. The laser intensity is uniformly 10% and FISH intensity is measured using LAS X.

Immunofluorescence experiments of Drosophila brain

The flies, which were three days old, were first anesthetized with CO₂. Subsequently, their brains were dissected in PBST (1 × PBS with 0.03% Triton X-100; coolaber, CT11451) and fixed with 4% paraformaldehyde at room temperature (RT) for 50 min. After fixation, the brains were washed in PBST three times at RT for 15 min each time. Following this, the brains were blocked in 10% Normal Goat Serum (NGS, diluted with 1 × PBS with 2% Triton) overnight at 4 °C. The brains were then incubated with primary antibodies for 36 h on a rotator at 4 °C. Subsequently, the brains were washed again three times for 15 min in PBST at RT before being incubated with secondary antibodies for 12 h on a rotator at 4 °C. Once again, the brains were washed three times at RT with PBST and mount on a slide using an anti-fading Mounting medium (Solarbio, S2110). Finally, the brains were imaged using confocal microscopy (Leica SP8) with LAX software.

The primary antibodies used in this experiment were anti-Elav rat (DSHB, 7E8A10, 1:200) and anti-Repo mouse (Developmental Studies Hybridoma Bank, 8D12; 1:200). The secondary antibodies employed were Alexa Fluor^TM 568 (Thermo Fisher Scientific, A11004; 1:200) and Alexa Fluor^TM 647 (Thermo Fisher Scientific, A21247; 1:200). Both primary and secondary antibodies were diluted in dilution buffer consisting of 1.25% PBST and 1% NGS.

Western Blots

Fly heads were collected at ZT8 and used for Western Blot analysis. Total protein was extracted from approximately 30 fly heads using RIPA buffer (150 mM NaCl, 1.0% NP-40, 0.5% sodium deoxycholate, 0.1% SDS, 50 mM Tris-HCl, pH 8.0). A protease inhibitor cocktail (Roche, 04693132001) was added to the buffer according to the manufacturer’s instructions. The protein concentration was determined using a BCA protein assay kit (Solarbio, PC0020). 20 µg of each sample was separated on 15% SDS–polyacrylamide mini-gels, and transferred onto 0.22 µm PVDF membranes (Millipore, ISEQ85R). The corresponding primary antibodies were diluted 1:1000 and incubated with the membranes overnight at 4 ˚C, while the HRP-labeled secondary antibodies were diluted 1:1000 and incubated for 4 h at room temperature. The signals were detected using ECL (ABclonal, RM00021P) and visualized using the Amersham ImageQuant 800 (GE Healthcare, Sweden). After detecting the signal of histone modification, we used stripping buffer (0.1 L: 20 mL SDS 10%; 12.5 mL Tris HCl, pH 6.8, 0.5 M; 67.5 mL distilled water and add 0.8 mL ß-mercaptoethanol under the fume hood) to remove the primary and secondary antibodies from the western blot membrane. The purified membrane was subsequently incubated with anti-Histone H3 and secondary antibody, resulting in the detection of the H3 signal. To generate the anti-LKRSDH signals, the PVDF membrane was incubated with LKRSDH antibody. The primary and secondary antibodies were then removed using a stripping buffer, after which the membrane was incubated with v5 antibody to generate specific anti-v5 signals. The primary antibodies used in this study were anti-Histone H3 (asymmetric di methyl R17) (Abcam, ab8284; 1:500), anti-Histone H3 (tri methyl K27) (Abcam, ab6002; 1:1000), anti-Histone H3 (EASYBIO, BE3015; 1:1000), anti-LKRSDH antibody (Abconal, A24544; 1:50), anti-β-tubulin (Abconal, AC008; 1:1000) and anti-v5 antibody (Abconal, AE017; 1:1000). The secondary antibodies used in this study were HRP Goat Anti-Rabbit IgG (H + L) (ABconal, AS014; 1:1000) and HRP Goat Anti-Mouse IgG (H + L) (ABconal, AS003; 1:1000). The signal intensity was quantified using ImageJ software (NIH). The relative normalization of histone modifications was performed with Histone H3 as controls. Three biological replicates were conducted for each experiment.

Quantitative RT–PCR (qRT-PCR)

Total RNA was extracted from approximately 30 fly heads using TRIzol Reagent (vazyme, R401-01). The cDNAs were synthesized by reverse transcription using HiScript III All-in-one RT SuperMix (Vazyme, R333), with 1 μg total RNA used as a template for each sample. The cDNA preparation was then subjected to real time quantitative PCR (Applied Biosystem, Step One Real-Time PCR system) according to the protocol of ChamQ SYBR qPCR Master Mix (High ROX Premixed) (Vazyme, Q341). Data analyses were performed using Prism 8.0 (Graphpad). All the primers used are listed in Supplementary Table 1.

Chromatin immunoprecipitation (ChIP) assays followed by quantitative PCR

Male fly heads of 3-day-old w¹¹¹⁸, 3-day-old LKRSDH^MB01942, 40-day-old w¹¹¹⁸ and 40-day-old LKRSDH^MB01942 were collected at ZT8 for ChIP-qPCR analysis. Briefly, 25 fly heads were gently triturated in 450 µl 1 × PBS, and then 6.02 µl 37% formaldehyde was added for cross-linking. The mixture was incubated at RT for 10 min. After de-crosslinking with 1 × glutamate and washing the pellet three times with 1 × PBS, 250 µl SDS lysis buffer (containing protease inhibitor cocktail) was added and homogenized. The solution was then centrifuged, and the supernatant was collected. Chromatin was sonicated on ice for 2.5 min (settings 10 s on, 30 s off, high power), resulting in sheared chromatin with an average length of 0.1–0.5 kb. The lysate was immunoprecipitated overnight at 4 °C with 40 µl Protein A/G Magnetic beads (Thermo Fisher Scientific, 88803) and 2 µg of antibody. Unbound chromatin was discarded, and the beads were washed with Low Salt Wash buffer, High Salt Wash buffer, LiCl Wash buffer and TE Wash buffer. The beads were then resuspended in freshly prepared ChIP elution buffer. DNA was eluted by adding 5 M NaCl and incubating for 4 h in a 65 °C water bath. The obtained DNA was purified and used for qPCR. For this experiment, anti-IgG, anti-Histone H3 (asymmetric di methyl R17) (abcam, ab8284) and anti-Histone H3 (tri methyl K27) (abcam, ab6002) were used. All ChIP analyses were independently repeated three times as biological replicates. Data analyses were performed using Prism 8.0 (Graphpad). All the primers used are listed in Supplementary Table 1.

RNA-seq, ChIP seq and data analysis

Fly heads of 3-day-old w¹¹¹⁸, 3-day-old LKRSDH^MB01942, 40-day-old w¹¹¹⁸ and 40-day-old LKRSDH^MB01942 were collected at ZT8 for RNA-seq analysis. The Illumina library preparation and sequencing steps were conducted at the sequencing platform of Biomics Company in Beijing, China (http://www.biomics.com.cn/). Raw sequencing data underwent processing and quality control using Trim-galore (https://github.com/FelixKrueger/TrimGalore) and FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The RNA-seq reads were aligned to the Drosophila melanogaster (dm6) reference genome (https://hgdownload.soe.ucsc.edu/goldenPath/dm6/bigZips/) using HISAT2⁵⁷. HTSeq-count⁵⁸ was used to count RNA-seq reads, and differentially expressed genes (DEGs) were identified using the R package DESeq2⁵⁹ with FDR value < 0.05 and | log₂ (fold change) | > 0.5 as criteria. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were conducted using the R package Cluster Profiler⁶⁰ and org.Dm.eg.db dictionary. Three biological replicates were performed for the RNA-seq experiment.

For ChIP-seq analysis, 25 fly heads of 3-day-old w¹¹¹⁸, 3-day-old LKRSDH^MB01942, 40-day-old w¹¹¹⁸, and 40-day-old LKRSDH^MB01942 were collected at ZT8. In this experiment, anti-Histone H3 (asymmetric di methyl R17) (abcam, ab8284) and anti-Histone H3 (tri methyl K27) (abcam, ab6002) antibodies were used. The Illumina library preparation and sequencing were performed at the Novogene’s (https://cn.novogene.com/) sequencing platform. Adaptor sequence trimming, mapping to the Drosophila melanogaster (dm6) reference genome using Bowtie 2⁶¹ and PCR duplicate removal using Picard Tools (http://broadinstitute.github.io/picard/) were carried out. MACS2⁶² was used to call broad peaks, and the R package ChIPseeker⁶³ was employed for peak annotation and analysis. The deepTools bamCompare function⁶⁴, multiBigWigSummary function⁶⁴ and R package pheatmap were used to generate ChIP-seq signal heatmaps. The RNA-seq signal heatmaps represented the log₂-ratio of fold change over the gene body. deepTools multiBigWigSummary function⁶⁴ was used to compute average RPKM-normalized read count in peaks, and the Pearson correlation between different ChIP-seq targets was calculated by ggplot2 in Rstudio. H3K27me3/H3R17me2 co-modified genes were those genes with both H3K27me3 and H3R17me2 covered genes. To identify co-modified genes, we derived the H3K27me3 and H3R17me2 covered matrix using the same cut-off during the identification of dynamic histone modification domains (greater than the max H3K27me3 and H3R17me2 signal level of non-marked domains)⁶⁵. We used k-means clustering to cluster different co-modified genes, with k setting to 5 for H3K27me3/H3R17me2 co-modified genes analysis. Heatmap and cluster analysis in Fig. 7o and Supplementary Fig. 9f were generated by R package Complex Heatmap and ClusterGVis, respectively^66,67. Two biological replicates were conducted for the ChIP-seq experiment.

For Fig. 7h–n, the Pearson correlation coefficient R serves as a measure of the degree of correlation between variables, with values ranging from −1 to 1. A positive value indicates a positive correlation, while a negative value indicates a negative correlation. R values between 0 and 0.25 or between 0 and −0.25 are commonly interpreted as indicating the absence of correlation. R values between 0.25 and 0.50 or between −0.25 and −0.50 suggest a poor correlation between variables. R values ranging from 0.50 to 0.75 or −0.50 to −0.75 indicate a moderate to good correlation, while R values between 0.75 and 1 or between −0.75 and −1 signify a very good to excellent correlation between the variables⁴¹.

Glucose measurement

Six male flies were collected at ZT8 and homogenized in 250 μL of PBS with 0.2% Triton-X. The homogenates were then incubated at 70 °C for 5 min to inactivate endogenous enzymes. After centrifugation at 13,500 × g for 15 min at 4 °C, 150 μL of supernatant was collected for subsequent measurement. The PGO Enzyme Preparation Solution (Sigma, P7119) was prepared following the manufacturer’s instructions. The PGO Enzyme Reaction Solution was prepared by adding water, glucose standard solution (Sigma, G6918), and the glucose-containing sample according to the manufacturer’s instructions. All tubes were then incubated at 37 °C for 30 min, and the absorbance (A) of the standards and tests was measured at 450 nm using the Blank as the reference. The glucose concentration of the sample was determined using the following formula:

Sample Glucose Concentration (mg/mL) =

$$\frac{{{{{{\rm{Absorbance}}}}}}\,\left({{{{{\rm{Test}}}}}}\right)\times {{{{{\rm{Dilution}}}}}}\,{{{{{\rm{of}}}}}}\,{{{{{\rm{sample}}}}}}\times 1{{{{{\rm{mg}}}}}}/{{{{{\rm{mL}}}}}}}{{{{{{\rm{Absorbance}}}}}}({{{{{\rm{Standard}}}}}})}$$

Statistical analysis

Statistics analysis of rhythmic datasets was performed in Graphpad Prism v.8.0 using Fisher’s exact test or an unpaired two-tailed Student’s t-test. The Log-rank (Mantel-ox) test was used for survival rate analysis. For comparisons of two groups unpaired two-tailed Student’s t-test was used. The error bars in the related types of figures represent the standard error of mean (s.e.m.). The precise test method used for the different figures is also specified in the corresponding figure legends. P-value < 0.05 was considered significant. All the P-values are listed in Supplementary Data 3.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The RNA-seq data generated in this study have been deposited in the National Center for Biotechnology Information (NCBI) database under accession code PRJNA965887. The ChIP-seq data generated in this study have been deposited in the NCBI’s Gene Expression Omnibus (GEO) database under accession code GSE235532. Published gene expression datasets used in this study can be find from GEO database under accession code GSE153901, GSE37032, GSE59769, GSE94922^68,69,70,71. All other data needed to reproduce the results presented here are contained within the manuscript, figures, supplementary information and Zenodo database and are accessible to the public through the accession code: 10785489. Source data are provided with this paper.

Code availability

All the custom computer codes and programs required to generate the data of this work are publicly available in the Zenodo repository and accessible through the [https://zenodo.org/records/10785489]⁷².

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant number 32122017 and 32070492) to J.D.

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Department of Entomology and MOA Key Lab of Pest Monitoring and Green Management, College of Plant Protection, China Agricultural University, Beijing, 100193, China
Pengfei Lv, Xingzhuo Yang & Juan Du

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Pengfei Lv
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P.L. designed and performed the experiments, analyzed the data and wrote the manuscript. X.Y. did bioinformatic analysis. J.D. designed experiments, analyzed data and wrote and edited the manuscript.

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Lv, P., Yang, X. & Du, J. LKRSDH-dependent histone modifications of insulin-like peptide sites contribute to age-related circadian rhythm changes. Nat Commun 15, 3336 (2024). https://doi.org/10.1038/s41467-024-47740-4

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Received: 05 June 2023
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Published: 18 April 2024
DOI: https://doi.org/10.1038/s41467-024-47740-4

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