dTrmt10A impacts Hsp70 chaperone m6A levels and the stress response in the Drosophila brain

Perlegos, Alexandra E.; Quan, Xiuming; Donnelly, Kirby M.; Shen, Hui; Shields, Emily J.; Elashal, Heidi; Fange Liu, Kathy; Bonini, Nancy M.

doi:10.1038/s41598-023-50272-4

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Published: 28 December 2023

dTrmt10A impacts Hsp70 chaperone m⁶A levels and the stress response in the Drosophila brain

Alexandra E. Perlegos^1,2,
Xiuming Quan²,
Kirby M. Donnelly²,
Hui Shen³,
Emily J. Shields^4,5,
Heidi Elashal^6,7,
Kathy Fange Liu^6,7 &
…
Nancy M. Bonini^1,2

Scientific Reports volume 13, Article number: 22999 (2023) Cite this article

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Abstract

Chronic cellular stress has a profound impact on the brain, leading to degeneration and accelerated aging. Recent work has revealed the vital role of RNA modifications, and the proteins responsible for regulating them, in the stress response. In our study, we defined the role of CG14618/dTrmt10A, the Drosophila counterpart of human TRMT10A a N¹-methylguanosine methyltransferase, on m⁶A regulation and heat stress resilience in the Drosophila brain. By m⁶A-IP RNA sequencing on Drosophila head tissue, we demonstrated that manipulating dTrmt10A levels indirectly regulates m⁶A levels on polyA + RNA. dTrmt10A exerted its influence on m⁶A levels on transcripts enriched for neuronal signaling and heat stress pathways, similar to the m⁶A methyltransferase Mettl3. Intriguingly, its impact primarily targeted 3' UTR m⁶A, setting it apart from the majority of Drosophila m⁶A-modified transcripts which display 5' UTR enrichment. Upregulation of dTrmt10A led to increased resilience to acute heat stress, decreased m⁶A modification on heat shock chaperones, and coincided with decreased decay of chaperone transcripts and increased translation of chaperone proteins. Overall, these findings establish a potential mechanism by which dTrmt10A regulates the acute brain stress response through m⁶A modification.

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Introduction

RNA modifications are emerging as a critical layer of gene expression that impact various biological functions in the brain, such as learning and memory, neurogenesis, and developmental transitions^{1,2,3,4,5,6,7}. Mutations in methylation complex proteins and dysregulation in RNA modification can lead to severe developmental defects, compromised cellular stress responses, and tumorigenesis^8,9,10. TRMT10A is an S-adenosylmethionine-dependent methyltransferase that installs the RNA modification N¹-methylguanosine (m¹G) on tRNAs at the ninth position; it is the only known methyltransferase that installs m¹G on human tRNA^11,12. tRNAs are among the most heavily modified classes of RNA and these modifications are critical for the stability and translation functionality of tRNAs^11,12. Mutations of TRMT10A in humans have been linked to primary microcephaly, mild intellectual disability, young onset diabetes, and apoptosis in pancreatic cells^{13,14,15,16,17}. However, the mechanisms by which genetic mutations in TRMT10A cause these phenotypes are not well understood.

N⁶-methyladenosine (m⁶A) is the most abundant internal modification of eukaryotic mRNA^1,18,19. It is deposited co-transcriptionally by a methyltransferase, METTL3, with the help of additional structural proteins. m⁶A levels are dynamically regulated by writer, reader, and eraser proteins^8,19. The m⁶A modification of mRNA provides a critical layer of regulation in neuronal responses to cellular stress and disease^20,21,22. Interaction of m⁶A transcripts with reader proteins can dictate downstream processing, including splicing, decay, and translation^23,24,25,26. TRMT10A has been shown to interact with the m⁶A RNA demethylase FTO and promote its demethylase activity in HEK293T cells²⁷. Loss of TRMT10A increases m⁶A methylation on FTO RNA targets in cultured cells²⁷. m⁶A levels are enriched in brain tissue of human, mice, and Drosophila, and mark transcripts involved in synaptic growth, axon guidance and signaling^22,28,29,30. m⁶A reader proteins, or YTH domain-containing proteins, are known for their ability to promote translation and decay of m⁶A-modified RNAs^7,31,32,33. One of their characterized functions is the ability to regulate cortical neurogenesis through precise decay of neuronal m⁶A transcripts during developmental transitions in mouse and human cells^30,33.

During cellular stress, the role of m⁶A modifications becomes amplified with increased methylation dynamically regulating RNAs for rapid degradation and/or selective translation^{20,28,34,35,36,37}. During UV DNA damage stress, m⁶A methylation functions in recruitment of DNA damage machinery³⁸, and during heat stress transcripts become marked for decay and translation in in vitro human cell culture models^39,40,41. Neurons are critically vulnerable to prolonged stress and degenerative disease. Heat shock protein chaperones (HSPs) are known to protect neuronal cells against protein misfolding, accumulation and aggregation, but HSPs are dysregulated during aging and disease⁴².

Drosophila is a powerful genetic model system that has pioneered numerous fundamental discoveries in brain development and disease. Homologous m⁶A methyltransferase complex components are enriched in the Drosophila central nervous system (CNS), making the fly an ideal system for understanding m⁶A modification in brain functions¹⁹. In Drosophila, m⁶A is implicated in female specific alternative splicing of the master sex determination regulatory gene Sex-lethal (Sxl)^23,43,44. Additionally, the Drosophila cytoplasmic m⁶A reader protein Ythdf regulates axonal overgrowth through interactions with Fmr1, the fly homolog of Fragile X mental retardation protein, and together they inhibit translation of m⁶A modified targets of Fmr1⁴⁵. Furthermore, there is no identified homologous m⁶A demethylase in Drosophila.

Here, we investigate the fly homolog of TRMT10A, CG14618 (which we now refer to as dTrmt10A), to uncover its regulation of m⁶A levels and the stress response in the Drosophila brain. The role of TRMT10A/dTrmt10A and its effects on m⁶A levels have not been studied in vivo. Our m⁶A-IP sequencing results indicate that dTrmt10A impacts m⁶A on signaling transcripts, and alters m⁶A on key heat stress chaperone transcripts^3,46. We find that upregulation of dTrmt10A acts similarly to loss of m⁶A methyltransferase Mettl3 in the Drosophila brain, and highlights m⁶A peaks in the 3’UTR of transcripts. These findings indicate that dTrmt10A modulates m⁶A in Drosophila and plays a critical role in regulating the stress response of the brain.

Results

m⁶A is enriched in the brain and dTrmt10A knockdown increases m⁶A levels on Hsp70 transcripts.

The Drosophila gene CG14618 is the predicted homolog of the human TRMT10A methyltransferase. Using Clustal Omega, human TRMT10A and Drosophila CG14618 share 36.8% amino acid sequence identity, with 100% conservation in the m¹G catalytic domain "YVIGGLVDHNH" (Fig. 1A). We refer to CG14618 as Drosophila Trmt10A (dTrmt10A) from here on. Knockdown of dTrmt10A in vivo produced viable offspring. Using liquid chromatography-tandem mass spectrometry (LC–MS/MS), we confirmed significant m¹G level reduction in dTrmt10A RNAi brains (small RNA fraction < 200 nucleotides), compared to control brains (Fig. 1B). dTrmt10A mRNA levels in head tissue were significantly reduced by RT-qPCR and RNA-seq analysis (Fig. 1C,D and Supplementary Data 1).

Given that TRMT10A interacts with FTO in mammalian cells²⁷, we conducted m⁶A-IP sequencing analysis of polyA + RNA from Drosophila heads expressing dTrmt10A RNAi. IP sequencing confirmed that m⁶A is enriched in the 5'UTR of transcripts in Drosophila heads, consistent with our and others previous work^3,46. Enrichment of m⁶A in the 5'UTR is unique to Drosophila, as mammalian m⁶A is primarily located in the 3'UTR²⁸. Global metagene analysis highlighted that dTrmt10A knockdown increased m⁶A levels in the 5'UTR of transcripts, and unexpectedly also showed increased m⁶A in the 3'UTR of a subset of transcripts (Fig. 2A).

To determine which transcripts showed significant differential methylation between control and dTrmt10A knockdown conditions, we utilized RADAR analysis with a beta-threshold of 0.5. Using these parameters, only transcripts from a single gene showed a significant increase in m⁶A. These transcripts encode the heat shock chaperone protein Hsp70 (Fig. 2B). We were particularly intrigued by this finding as there are normally low levels of m⁶A in the 5'UTR on Hsp70 transcripts at baseline, and m⁶A signal is lost upon acute heat stress in the Drosophila brain⁴⁶. Previously, we found that knockdown of m⁶A methyltransferase Mettl3 showed increased RNA and protein levels of Hsp70 at baseline along with increased heat stress resilience⁴⁶. We asked if an opposite effect occurred with knockdown of dTrmt10A. In dTrmt10A RNAi brains, RNA and protein levels of Hsp70 were reduced (Fig. 2C,D). Additionally, dTrmt10A RNAi animals exhibited increased stress sensitivity (Fig. 2E). These data indicate that dTrmt10A RNAi exerts contrasting effects on Hsp70 RNA, protein, and m⁶A levels compared to Mettl3 RNAi.

To visualize global changes in m⁶A, regardless of peak calling threshold, we generated a scatterplot of m⁶A-IP reads divided by input read coverage in the 5'UTR for Mettl3-dependent m⁶A transcripts. Upon dTrmt10A knockdown, we observed a global shift towards increased levels of m⁶A in the 5'UTR of transcripts (Fig. 2F), similar to the global metagene analysis (Fig. 2A). We observed a slight increase of 5’UTR m⁶A on other chaperones such as Heat shock chaperone 40 (DnaJ-1) (Fig. 2G), but no changes comparable to the increase on Hsp70 5’UTR. These findings highlight the regulation of Hsp70 transcripts by the m⁶A machinery, and indicate it as key target of dTrmt10A.

To analyze the impact of m⁶A on RNA levels with dTrmt10A manipulation, we employed an ex-vivo brain RNA decay assay. Reduced m⁶A by Mettl3 RNAi lead to higher steady state levels and decreased rate of decay of m⁶A transcripts⁴⁶. We examined the relative levels of RNA remaining in dTrmt10A RNAi brain samples after 4 h, in the presence of actinomycin D. dTrmt10A RNAi brains (with increased m⁶A) had increased decay of Hsp70 RNA transcript levels compared to control brains, and no significant difference of RpL32, which is a control non-m⁶A modified transcript (Fig. 2H). These data suggest that dTrmt10A influences the stability of Hsp70 transcripts and overall protein and RNA levels, likely by modulating their m⁶A levels.

Upregulation of dTrmt10A decreases m⁶A levels and enhances the stress response of the brain.

Studies investigating the role of TRMT10A in cultured cells have focused on mutations or gene downregulation^17,27. To determine whether upregulation of dTrmt10A could also regulate m⁶A levels, we utilized a FLAG-HA C-terminal tagged ORF clone of dTrmt10A to generate animals that upregulate dTrmt10A (Supplementary Fig. 1A). Upregulation of dTrmt10A showed no global effect on m¹G levels, likely due to the saturation of m¹G levels on tRNAs (Supplementary Fig. 1B). m⁶A-IP sequencing analysis conducted on Drosophila heads with dTrmt10A upregulation revealed a striking reduction in m⁶A levels (Fig. 3A). A closer look at heat stress chaperones revealed 5’UTR m⁶A normally present at baseline on Hsp70 transcripts was lost upon dTrmt10A upregulation, conferring the opposite effect from dTrmt10A RNAi (Fig. 3B). Additionally, Hsp70 RNA and protein levels were elevated in UAS-dTrmt10A brains (Fig. 3C,D), and animals were more resilient to heat stress (Fig. 3E). Loss of Mettl3 also leads to elevated Hsp70 transcript and protein levels. To provide support that the change in Hsp70 with dTrmt10A upregulation was due to effects on m⁶A, vs other possibilities, we simultaneously reduced Mettl3 and upregulated dTrmt10A in neurons. Co-expression of Mettl3 RNAi and UAS-dTrmt10A in the brain showed a similar increase in Hsp70 protein levels compared to expression of either transgene alone (Fig. 3F). These data suggest that dTrmt10A is acting through the m⁶A/Mettl3 pathway to modulate Hsp70 levels.

Similar to Mettl3 RNAi brains, dTrmt10A upregulation significantly reduced 5’UTR m⁶A on DnaJ-1(Hsp40 chaperone), and UAS-dTrmt10A brains had increased protein levels of DnaJ-1 (Fig. 3G,H). RNA decay analysis indicated upregulation of dTrmt10A led to higher stability of DnaJ-1 RNA while there was no significant difference observed in the RNA levels of the unmodified RpL32 transcript (Fig. 3I). Overall, dTrmt10A upregulation showed similar effects on heat shock chaperone levels and heat shock resilience as observed with Mettl3 RNAi⁴⁶. Elevated levels of stress chaperones at baseline in the brain potentially precondition the brain to acute heat stress. Taken together, these data highlight that upregulation of dTrmt10A reduces m⁶A levels and confers heat stress resilience.

Because dTrmt10A is a tRNA methyltransferase, we performed additional experiments to confirm that its role to modulate Hsp70 and the stress response is due to an impact on m⁶A, and not its role as a tRNA methyltransferase. We generated animals that expressed dTrmt10A with a known mutation in the conserved m¹G catalytic domain of the methyltransferase activity that abolishes tRNA methyltransferase activity in vitro¹⁴ (G206R; see Fig. 1A). We assessed the impact of this mutation using sensitive readouts of dTrmt10A function: the levels of Hsp70 and animal stress sensitivity. Baseline levels of Hsp70 were increased in the brains of the animals expressing the catalytic mutant (UAS-dTrmt10a-cat), and these animals had increased stress resilience comparable to wild type (Fig. 3J,K). These data indicate that the phenotypic effects of dTrmt10A on Hsp70 and stress resilience are not dependent on the m¹G catalytic activity. We also examined three additional tRNA methyltransferases for effects on Hsp70 levels. RNAi of these additional tRNA methyltransferases (CG9386 RNAi, Mt2 RNAi, Trm7-32 RNAi) had no effect on basal Hsp70 levels in the brain (Fig. 3L). Together these data indicate that the effects of dTrmt10A on Hsp70 levels and stress resilience are independent of its catalytic activity as a tRNA methyltransferase, and are not a general property of compromised tRNA methyltransferase activity.

Upregulation of dTrmt10A mimics loss of methyltransferase Mettl3, and globally decreases m⁶A on neurogenesis and signaling transcripts.

A total of 675 transcripts showed differential m⁶A methylation upon dTrmt10A upregulation, and only a few of these transcripts were HS chaperones (Fig. 4A). To investigate dTrmt10A further, we compared the m⁶A sites from dTrmt10A upregulation m⁶A-IP to the defined Mettl3-dependent methyltransferase m⁶A sites and transcripts⁴⁶. 62% of the transcripts from genes with Mettl3-dependent m⁶A were also regulated by dTrmt10A (Fig. 4A). We focused on these transcripts, as m⁶A is Mettl3-dependent. Closer analysis of these methylated transcripts revealed 41% of dTrmt10A-dependent m⁶A sites were in the 5'UTR, while 48% were in the 3'UTR (Fig. 4B), with a small number (81 genes) showing both 5'UTR and 3'UTR-dependent m⁶A changes (Fig. 4C). Modulation of m⁶A on the 3’UTR of transcripts was surprising due to the enrichment of m⁶A on the 5’UTR in Drosophila^3,46,47,48. This finding suggests that dTrmt10A may have a specialized role to regulate 3'UTR m⁶A. When we examined all Mettl3-dependent m⁶A transcripts with peaks in either the 5’UTR (Fig. 4D) or 3’UTR (Fig. 4E) regions; there was a similar shift towards decreased m⁶A-IP/Input enrichment upon dTrmt10A upregulation for both regions of transcripts. Reactome and KEGG^49,50 pathway analyses showed that these transcripts are enriched for dynamic signaling pathways and axon guidance pathways (Fig. 4F and Supplementary Data 3). Similar GO terms were enriched regardless of 5’UTR or 3’UTR modification site.

We examined select targets with available antibodies (Nrg, Myc, and fl(2)d) to determine whether dTrmt10A-dependent m⁶A modification of transcripts impacted protein levels. Upregulation of dTrmt10A showed decreased m⁶A on these transcripts, and we observed increased protein levels in Drosophila brain tissue (Fig. 4G,H). These findings support previous work in mammalian cells that observe changes in Myc levels due to m⁶A-TRMT10A interaction²⁷. We extended these data to global protein synthesis using a puromycin assay to assess nascent protein synthesis. These data showed that upregulation of dTrmt10A was associated with increased puromycin incorporation, thus increased global nascent protein translation in the brain (Supplementary Fig. 1D). We observed the opposite response in puromycin levels upon dTrmt10A RNAi (Supplementary Fig. 1D). The catalytic mutant animals still showed increased global protein synthesis in the brain by puromycin assay (Supplementary Fig. 1E), confirming that puromycin changes are not due to m¹G activity of dTrmt10A. The additional tRNA methyltransferases, which did not impact Hsp70 protein levels, also did not affect global puromycin incorporation (Supplementary Fig. 1F). Therefore, dTrmt10A modulation of m⁶A transcripts likely leads to the influence on overall protein levels in the brain.

Many chromatin regulatory RNAs are m⁶A modified, and m⁶A methyltransferase complex components are known to bind chromatin and regulate transcription elements and histone modifications^51,52. We therefore considered whether dTrmt10A may also be associated with chromatin. dTrmt10A was overexpressed in the Drosophila salivary glands and dTrmt10A protein localization was examined on the large polytene chromosomes by immunostaining to the HA tag. dTrmt10A showed robust localization to the nucleolus as anticipated for its role in tRNA modification; however, dTrmt10A also showed binding to band locations on the polytene chromosomes (Supplementary Fig. 2A). Chromatin immunoprecipitation followed by western immunoblot validated dTrmt10A association with chromatin (Supplementary Fig. 2B) and chromatin binding was not dependent on RNA (Supplementary Fig. 2C). These data indicate that dTrmt10A is positioned for an additional layer to m⁶A-modified gene regulation on chromatin, similar to Mettl3^51,53.

With stress, dTrmt10A primarily impacts 3’UTR m⁶A sites

Based on the heat stress resilience of upregulated dTrmt10A, we looked more closely at the regulation of m⁶A during heat stress with overexpression of dTrmt10A. We performed acute heat shock (HS) experiments then analyzed m⁶A-IP from Drosophila head polyA + RNA. These data showed that dTrmt10A upregulation reduced overall m⁶A levels in both basal and HS conditions (Fig. 5A). Interestingly, dTrmt10A upregulation led to modification of mostly 3'UTR m⁶A sites during heat stress, with 73% of dTrmt10A-dependent sites located in this region of transcripts upon HS (Fig. 5B).

Upon heat stress and dTrmt10A upregulation, there was a shift towards increased expression of m⁶A marked transcripts (Fig. 5C, blue vs black). Furthermore, the majority of these differentially expressed transcripts exhibited dTrmt10A-dependent m⁶A modifications on their 3' UTR (Fig. 5D). Notably, there was no trend in the levels of non-m⁶A modified transcripts, with equal numbers of transcripts being up and downregulated between control and dTrmt10A upregulation (Fig. 5C). Among the m⁶A transcripts, we identified critical genes involved in signaling processes and neurogenesis, such as pAbp, SK, para, Sh, bru3 and even the nuclear m⁶A reader protein Ythdc1 (Fig. 5E and Supplementary Fig. 3A). These findings highlight the essential role of dTrmt10A in m⁶A modification during stress, particularly in modifying transcripts that are integral to neuronal signaling processes.

Compared to other tissues, many genes in the Drosophila nervous system have substantially extended 3’UTRs and alternative polyadenylation^54,55. We found that 25% of Drosophila CNS transcripts with extended 3’UTRs also have m⁶A modification in their 3’UTR (Supplementary Fig. 3B). Furthermore, this subset of m⁶A transcripts are enriched for cognition, learning, and memory pathways, indicating this subclass of 3’UTR m⁶A modified transcripts are alternatively polyadenylated and involved in learning and memory. We also examined the overall length of 3’UTRs of m⁶A modified transcripts and found that the average length of their 3’UTR was longer compared to all 3’UTR weighted lengths from wildtype Drosophila CNS⁵⁵ (Supplementary Fig. 3C).

Proteins that interact with dTrmt10A

In mammalian cells, TRMT10A regulates m⁶A by interacting with the m⁶A demethylase FTO. Although there are no known homologs in Drosophila to the demethylases FTO or ALKBH5, we considered that a fly demethylase, should it exist^24,27,56, may interact with dTrmt10A. To define interacting proteins, we expressed UAS-dTrmt10A-HA and immunoprecipitated dTrmt10A from Drosophila head lysates using the HA tag. Mass spectrometry was used to evaluate enriched protein interactions of dTrmt10A-HA compared to IgG control (Supplementary Data 4). This analysis identified 6 high confidence interacting proteins (Pp1-87B, CG18680, CG9953, hrm, Dhx15), with no apparent homologs to FTO or ALKBH5. When we analyzed all interactions (low, medium, and high), there was enrichment for proteins involved in RNA metabolism and deadenylation, including DCP1, Atx2, twin, and Rga, as well as proteins involved in translation regulation, such as eIF3a (Fig. 6A). These proteins may interact with and regulate downstream m⁶A-modified RNA processing through interaction with dTrmt10A. We also investigated whether dTrmt10A binds or alters the levels of known m⁶A regulators such as Mettl3 or m⁶A reader proteins Ythdc1 or Fmr1, but found no interaction or altered protein levels (Supplementary Fig. 4), indicating that dTrmt10A does not interact with these known m⁶A binding proteins to mediate its effect on m⁶A.

Both mammalian and Drosophila m⁶A-modified transcripts are enriched for similar biological pathways^22,30,57, highlighting the importance of m⁶A in signaling and stress. Although a candidate demethylase is not identified in Drosophila, we used FTO CLIP targets from mammalian cells²⁷ to define the homologous Drosophila genes. We compared these FTO CLIP targets to dTrmt10A-dependent m⁶A transcripts and found that ~ 23% of dTrmt10A-dependent m⁶A transcripts overlap with FTO targets (Fig. 6B). This overlap is similar to the extent of overlap of transcript targets between TRMT10A and FTO in mammalian cells (35%²⁷). Notably, 3’UTR m⁶A transcripts showed a greater overlap with FTO targets (Fig. 6B). These data suggest that the interaction of dTrmt10A toward 3’UTR m⁶A RNA binding proteins and/or a putative demethylase may fine-tune aspects of RNA processing.

Discussion

This study offers valuable insights into the in vivo role of dTrmt10A on m⁶A modification within the Drosophila brain, and its impact on stress resilience and gene regulation. Of note, dTrmt10A modulates m⁶A peaks both on the 5’UTR of transcripts (where most m⁶A peaks are in Drosophila transcripts), but also on the 3’UTR of Drosophila genes. Upregulation of dTrmt10A caused a global decrease in m⁶A levels on transcripts and increased the animal’s resilience to heat stress, with increases in overall protein and RNA levels of key players like Hsp70 (see Figs. 3 and 4). These findings highlight that Drosophila transcripts are modulated by m⁶A in the 3’UTR in addition to 5’UTR, confirming that m⁶A modification plays a crucial role in dampening the biological response of the brain to acute stress, and that dTrmt10A acts as an additional regulator of m⁶A modification of transcripts in the brain. Moreover, modulation of dTrmt10A showed the ability to modulate m⁶A peak levels in transcripts, despite not being a canonical component of the methyltransferase complex (Fig. 7). dTrmt10A with a mutation in the m¹G catalytic domain of the tRNA methyltransferase functioned similarly in our assays as the wild-type protein, and additional Drosophila tRNA methyltransferases failed to show these phenotypes, confirming that the function of dTrmt10A as a tRNA methyltransferase is not influencing Hsp70 protein levels and protein translation. There are many known mutations in human TRMT10A that impact brain development^13,14,15, and these data now indicate that the function of TRMT10A/dTrmt10A on m⁶A levels may also play a role in these diseases.

m⁶A modification in brain homeostasis

Many biological functions in the brain are regulated by RNA modification, including axon growth, embryonic stem cell fate, learning and memory, and neurogenesis^30,58. Furthermore, mutations in RNA modification proteins have been linked to compromised cellular stress responses, tumorigenesis, and severe developmental defects^{9,10,20,33,38}. Our findings contribute to the current knowledge of RNA modifications regulating the neuronal response to cellular stress, and the regulation of m⁶A overall in the brain. dTrmt10A regulated m⁶A levels on signaling pathway transcripts, such as MAPK and WNT signaling pathways that are critical for normal neuronal homeostasis. These signaling pathway transcripts were also regulated by Mettl3, the m⁶A methyltransferase. We validated m⁶A changes using Mettl3 knockdown and cross compared to other Drosophila studies; future non-antibody detection techniques would be important to apply.

Although m⁶A methylation primarily occurs in the 5’UTR of transcripts in Drosophila, dTrmt10A upregulation surprisingly skewed towards selective 3’UTR m⁶A regulation. Thus far, little is understood regarding why m⁶A is highly enriched in the 5’UTR of Drosophila transcripts vs 3’UTR as in mammals; however, dTrmt10A has uncovered that m⁶A regulation occurs in the 3’UTR of transcripts in Drosophila. Many critical signaling and neurogenesis transcripts such as Sh, bru3, para, and pAbp contain dTrmt10A-dependent m⁶A in the 3’UTR. Additionally, these transcripts were upregulated in brains with increased dTrmt10A expression, indicating they become more stabilized by loss of m⁶A modification. Transcripts of Myc and Nrg with 3’UTR m⁶A showed increased protein levels with dTrmt10A upregulation. These findings suggest that both 3’UTR and 5’UTR m⁶A serve functionally to keep transcript and protein levels low in the Drosophila brain. Furthermore, m⁶A marked transcripts are enriched in neural and dynamic signaling processes, indicating that one mechanism by which the brain regulates precise dynamics of neural signaling pathways may be through marking key transcripts with m⁶A for tighter control. Dysfunction in m⁶A is therefore predicted to have a larger impact on those transcripts critical for neural function. Additionally, we find that many 3'UTR m⁶A transcripts also have extended 3’UTR lengths in the Drosophila nervous system. A detailed analysis of dTrmt10A effect on alternative polyadenylation may be of interest, particularly within the context of the Drosophila nervous system, given the enrichment of the transcripts in learning, memory, and cognition.

We speculated that dTrmt10A may interact with a demethylase, similar to the mammalian enzyme. By mass spectrometry analysis of dTrmt10A IPed from head lysates and defining the interacting proteins, an obvious candidate was not revealed. The absence of a demethylase homologous to FTO in the fly suggests alternative mechanisms may be at play, such as interactions with RNA-binding proteins involved in decay. Additionally, dTrmt10A could impact proteins within the methyltransferase complex, which themselves have a more direct role on m⁶A modification; for example, dTrmt10A modulated fl(2)d levels. The mass spectrometry analysis showed dTrmt10A associates with key RNA degradation components including DCP1, Twin, and Rga. Thus, through interaction with dTrmt10A, these proteins may confer specificity and degradation impact of dTrmt10A on m⁶A targets. Furthermore, disease-associated protein Atx2, mutated in spinocerebellar ataxia 2, was found to co-IP with dTrmt10A. Atx2 is involved in RNA metabolism including RNA stability, RNA localization to stress granules and P-bodies, and interacts with other disease-associated RNA binding proteins such as TDP-43⁵⁹. We also compared dTrmt10A-m⁶A modified transcripts to human FTO CLIP RNA targets. There was a significant overlap, and the degree of overlap was comparable to that of mammalian cells²⁷. The overlapped targets are enriched for signaling pathways, similar to GO term analysis of all m⁶A targets⁴⁶. Considering that Drosophila m⁶A is predominantly deposited in the 5’UTR, we were surprised to find more m⁶A transcripts modified in the 3’UTR that overlapped with FTO targets, indicating specificity in potential demethylase/degradation targeting.

Cellular stress and m⁶A modifications

To reduce cellular burden during stress, global protein synthesis is repressed and non-essential RNAs are decayed or stored in stalled mRNA-protein granules. During this process, vital RNAs are translated for stress survival. In the recovery phase, stress mediated RNAs are in turn decayed and RNAs necessary for normal cell maintenance are once again transcribed and translated³⁶. The process of stress response activation and resolution is a fine-tuned dance of RNA decay and translation, such that prolonged stress or failure to activate stress machinery is detrimental. RNA modifications have emerged as critical regulators of dynamic signaling in normal cell maintenance and also during stress recovery^20,31,38. Particularly neurons, which are postmitotic and cannot remove harmful accumulations through cell division, show specialized mechanisms to combat cellular stress.

In mammalian cells m⁶A modification has been shown to increase upon heat stress in the 5’UTR of Hsp70 to facilitate cap-independent translation²⁶. Other studies in vitro indicate that Hsp70 becomes co-transcriptionally marked by m⁶A, which leads to its degradation³⁹. Furthermore during stress resolution, the lncRNA Heat, a transcriptional activator of HSF1, becomes heavily methylated by m⁶A, and Ythdc1 degrades the lncRNA to prevent stress gene activation⁴⁰. In the Drosophila brain, Hsp70 is m⁶A modified in the 5’UTR at baseline, and m⁶A is lost upon acute heat stress⁴⁶. We hypothesize this limits Hsp70 translation at baseline and that the loss of m⁶A facilitates accumulation of the transcript for translation during stress. Thus, loss of m⁶A in the 5’UTR of Hsp70 leads to elevated levels of Hsp70 protein, which may allow for a state of preconditioning of the brain to future stress. With dTrmt10A knockdown, we were surprised to see a dramatic increase in 5’UTR m⁶A on Hsp70—especially given that no other transcripts showed such a dramatic increase in m⁶A. These data indicate that stress resilience may be a particularly important target of TRMT10A/dTrmt10A gene mutation.

In summary, using m⁶A-IP sequencing, we find that dTrmt10A impacts 5’UTR m⁶A methylation on key stress chaperones, but dTrmt10A also highlights that 3’UTR m⁶A occurs and is regulated in Drosophila. These studies provide a framework for future insight into how m⁶A regulation is achieved at specific sites, like the 5’UTR vs 3’UTR. These data reveal that additional m⁶A regulators like dTrmt10A outside of the core complex components may profoundly modulate region specific methylation and/or demethylation. m⁶A regulation in the brain likely serves to fine tune signaling pathways for the continued maintenance of axons and dynamic synaptic connections. This comes at the cost of dampening the acute stress response, which compromises the overall ability of the brain to withstand insults. Given that mutations in TRMT10A are associated with developmental brain disease, some mutations that compromise TRMT10A function may also include impacts on m⁶A and stress resistance.

Materials and methods

Drosophila stocks and maintenance

A full list of Drosophila stocks used in this study are described in Supplementary Data 1. RNAi and UAS lines were generated by the Harvard Transgenic RNAi Project (TRiP) and the Vienna Drosophila Resource Center (VRDC)^60,61, and stocks were obtained from the Bloomington Drosophila Stock Center, Indiana, USA. Crosses were performed at 25 °C and grown on standard cornmeal molasses agar. Driver lines used: daGAL4, elavGal4, nSybGal4 as indicated per experiment. For all experiments male flies were used, for consistency in the experiments, and to avoid issues in food consistency due to egg laying of females.

Brain dissections

Brain dissections were conducted as previously described⁶². Briefly, flies were anesthetized using CO₂ and decapitated using forceps. The head was placed posterior side down and the proboscis was then removed using Dumont #5 fine forceps (Fine Science Tools, 11254-20). The brain was then gently popped out through the proboscis cavity, cleaned in PBS, and transferred to an RNAse free microfuge tube and PBS was aspirated. Brains were then ground in Laemmli Buffer (5 μL per brain, 10–20 brains for each sample) for Western immunoblotting, or in Trizol for RNA analysis.

Western immunoblot analysis

Brain or head samples were homogenized in sample buffer of 1 × Laemmli sample Buffer (Bio-rad, 1610737), 50ul b-mercaptoethanol (Sigma, m6250), 1 × protease inhibitor (Roche, 11836170001), and 1 mM PMSF (Sigma, P7626). 5ul of sample buffer is added per brain, 7.5 ul added per head, and 40ul added per whole fly. Samples are boiled at 98 °C for 3 min, and then centrifuged at 1500 rpm for 3 min at room temp. Sample was loaded onto 15 well 1.0 mm 4%–12% Bis-Tris NuPAGE gels (Thermo Fisher, WG1401) with pre-stained protein ladder (Thermo Scientific, 22619). 1 brain, 1 head, or 8% of whole fly tissue is loaded on each lane per experiment. Gel electrophoresis was performed using Xcell Surelock Mini-Cell Electrophoresis System at 140 V, and transferred overnight onto a nitrocellulose membrane 0.45 μM (Bio-rad, 1,620,115), using a Bio-rad mini transblot cell at 90A for 16 h. Membranes were stained in Ponceau S (Sigma, P7170-1L), washed in DI water, and imaged with Amersham Imager 600. Ponceau S was washed off in 3 × 5 min in Tris-buffered saline with 0.1% Tween20 (TBST). Membrane was blocked in 5% non-fat dry milk (LabScientific, M08410) in TBST for 1 h, and incubated with primary antibodies with blocking buffer overnight at 4 °C. Following 3 × 5 min washed in TBST, membranes were incubated with HRP-conjugated secondary antibodies at 1:5,000 for 1 h at room temp in blocking solution. Membranes were washed 3 × 5 min in TBST and the signal was developed using ECL prime (Cytivia, #RPN2232) and detected using an Amersham Imager 600. Primary antibodies used: anti-tubulin (1:5000, DHSB, #AA4.3, Lot.5/31/18-44ug/ml), anti-Hsp70 (1:5000, Sigma, #7FB-SAB5200204-100uG, Lot.141002), anti-Mettl3 (1:5000, Proteintech, #15073-1-AP, Lot.Ag7110), anti-HSP40 (1:5000, Enzo Life Sciences, #ADI-SPA-400-D, Lot.04062141), anti-fl(2)d (1:10, #DSHB-9G2, Lot.10/18/18-42ug/ml), anti-dFmr1 (1:500, #DSHB-5A11), anti-Nrg (1:600, #DSHB-BP104), anti-Myc (1:50, #DSHB-P4C4-B10), anti-puromycin (1:1000, Kerafast, #EQ0001, Lot.200517). Rabbit anti-Ythdc1 (1:5000) was created by Vivitide against 18 residues of Ythdc1 (157–173 “CRTKIPSNANDSAGHKSD”)⁴⁶. Secondary Antibodies used: Goat anti-mouse (1:5000, Jackson lmmunoResearch, #115-035-146, Lot.153978), Goat anti-Rabbit (1:5000, Jackson lmmunoResearch, #111-035-144, Lot.138306), Goat anti-rat (1:5000, Thermo Fisher Scientific, #A10549, Lot.2273679).

RNA extraction

Tissue was homogenized in 200ul of Trizol (Thermo Scientific, #15596026) in RNase free 1.5 ml microfuge tubes (Thermo Scientific, #AM12400). 800ul of Trizol (Thermo Scientific, #15596026) was added to the tube and 200ul of chloroform (Fisher Scientific, #AC423555000) and was vigorously shaken for 20 s at room temp. Samples were left for 5 min at RT to form upper aqueous phase, and centrifuges at 4 °C for 15 min at 12,000×g. The Upper aqueous phase was transferred to a fresh RNase free tube. RNA samples were then processed using the Zymo RNA clean & concentrator -5 kit (Zymo, #R1013), using their RNA clean-up from aqueous phase after Trizol /chloroform extraction protocol plus on column DNase I treatment. RNA amount was measured using a nanodrop and integrity was validated through on an Agilent 2100 Bioanalyzer using an RNA nano chip.

Real time PCR

400 ng RNA was used per cDNA reaction using the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Thermo Scientific, #4368814). cDNA was then used for qPCR reactions set up with SYBR Green Fast Reagents, using a 384-well plates on the Applied Biosystems ViiA7 machine. Primers used are in Supplementary Data 1. Mean fold change was determined using the ∆∆Ct method. Each experiment used technical triplicates as well as three biological replicates, Graphpad prism 8/9 software was used for statistics.

Stress sensitivity assay

Fly crosses were carried out at 25 °C. Adult flies were collected and aged to 6d post eclosion. Flies were anesthetized and transferred to clear plastic 13 ml vials, and cotton was placed at the 4 ml mark on the vials to concentrate the flies near the bottom. Each vial contained 20 flies. Flies were allowed to recover for 30 min, and then transferred to a water bath for mild non-lethal heat stress (30 min at 38.5 °C) or a longer heat shock (1–1.5 h at 38.5 °C) for a severe stressor to measure stress sensitivity. The flies were then transferred to normal food and allowed to recover overnight at 25 °C. After recovery, the percent of flies alive versus dead was recorded per vial.

LC–MS/MS analysis of m⁶A levels

PolyA + RNA was extracted from brains, heads, and whole fly RNA using the NEBNext Poly(A) mRNA Magnetic Isolation Module (NEB, #E7490L). LC–MS/MS was conducted as previously described²⁷. All quantifications were performed by using the standard curve obtained from pure nucleoside standards running with the same group of samples. Then, the percentage ratio of m⁶A to A or m¹G to G was used to compare the different modification levels.

m⁶A-IP sequencing

Total RNA was extracted from 200 Drosophila heads per replicate using Trizol/ chloroform extraction. PolyA + mRNA was obtained using NEBNext Poly(A) mRNA Magnetic Isolation Module. PolyA + RNA was fragmented using the NEBNext Magnesium Fragmentation Module (NEB, #E6150S) for 4 min at 95 °C for a 250 ng sample of polyA + RNA, and RNA was repurified using the Zymo RNA clean & concentrator -5 kit (Zymo, #R1013). 10% of the fragmented polyA + RNA was saved as an input control for sequencing. M⁶A -immunoprecipitation was done using the EpiMark N6-Methyladenosine Enrichment kit protocol with some minor alterations described. 30ul of protein G magnetic beads (NEB, #S1430) were washed and resuspended in IP buffer (150 mM NaCl, 10 mM Tris–HCL, 0.1% NP-40). 4ul of Synaptic Systems antibody (Synaptic Systems, #202003, Lot.2-119) was conjugated to protein G-magnetic beads (NEB, #E1611A, Lot.10015190) for 2 h at 4 °C. Beads/ antibody were washed twice in IP buffer. ~ 1 μg polyA + RNA was incubated with beads/antibody in IP buffer supplemented with 0.1% SUPERase-In RNase Inhibitor (Thermo Scientific; #AM2696) for 2 h at 4 °C. After incubation, RNA/beads/antibody are washed twice in IP buffer, twice in low salt IP buffer (50 mM NaCl, 10 mM Tris–HCL, 0.1% NP-40), and twice in high salt IP buffer (500 mM NaCl, 10 mM Tris–HCL, 0.1% NP-40). RNA is eluted from beads with 25 μl of RLT buffer twice and elution was pooled and concentrated using Zymo RNA clean and concentrator -5 kit (Zymo, #R1013). Libraries were made using SMARTer Stranded Total RNA-Seq Kit V2 without rRNA depletion (Takarabio, #634411) for IPed and input RNA, and sequenced using illumina HiSeq X series with 40M paired end reads (2 × 150 bp). Library preparation and sequencing was done by Admera Health. Three biological replicates per genotype and condition were done with NEB m⁶A antibody, and two biological replicates were done with Synaptic Systems m⁶A antibody.

m⁶A enrichment analysis

Regions of m⁶A enrichment were found for each condition using MetPeak (v.1.1)⁶³ with default parameters, using the input and m⁶A pulldown bam files as input, and with the FlyBase FB2019_05 annotation provided. Peak locations (5’ UTR, CDS, or 3′ UTR) were defined from the regions indicated by MetPeak as having significant m⁶A enrichment. If a peak was not contained in one region (i.e., if the peak is partly in the CDS and partly in the 3’ UTR), it was assigned to the region where more of the peak resided.

Differential m⁶A peak analysis

Regions of differential methylation between two conditions (frequently called “dTrmt10A-dependent” or “m⁶A genes”) were found using RADAR (v.0.2.4)⁶⁴ with input and m⁶A pulldown bam files as input, as well as the FlyBase FB2019_05 annotation. All replicates were used for differential peak calling. The minimum cutoff for bin filtering was 15, the cutoff was set as 0.05, and the Beta_cutoff was set as 0.5. Any region with an adjusted p-value < 0.05 was retained, and regions with a fold-change < − 1 from the control (KK Control) to the upregulation (UAS-dTrmt10A) at basal or heat shock (30 min) conditions were kept as dTrmt10A-dependent peaks. All other genes expressed in the brain that did not have dTrmt10A-dependent m⁶A were considered as “all other genes.” Full RADAR differential peaks with no cut-off are listed in Supplementary Data 2.

m⁶A metaplots and genome browser visualization

Heatmaps and metagene plots showing the location of m⁶A enrichment on a specific set of genes were constructed with using pheatmaps (v.1.0.12) and meRIPtools (v.0.2.1). Specifically, the exons of all transcripts in each gene were collapsed using the GenomicRanges (v.1.44.0) function reduce⁶⁵. Genes with a 5′ UTR or 3′ UTR shorter than 30 base pairs, a CDS shorter than 100 bp, or lacking a 5′/3′ UTR (i.e., lncRNAs) were not considered in this analysis. For each gene, the 5’ UTR and 3′ UTR were tiled in 30 evenly spaced bins, and the CDS was tiled in 100 evenly spaced bins. The number of input and m⁶A reads overlapping each bin was calculated and this number was divided by the bin width and library size and a normalization factor of one million to produce a normalized reads per million in each bin. For genome browser snapshots, separated input and m⁶A-IP tracks vere visualized. Tracks were made by first converting bam files to bigWig files using deepTools (v.3.5.1)⁶⁶ bamCoverage using CPM normalization, then deepTools bigwigCompare with operation log2.

GO and pathway analysis

GO analysis for genes with dTrmt10A-dependent m⁶A was conducted using FlyMine (v.53)⁶⁷. The test correction was set to Holm-Bonferroni with a max p-value of 0.05. KEGG^49,50 pathway analysis was done using the “enrichKEGG” function from ClusterProfiler (v.4.0.5) package in R⁶⁸. A list of all genes with detectable expression was used as background for both GO and pathway analysis.

RNA-seq analysis

Raw paired-end fastqs were processed with TrimGalore (v.0.6.6) (https://github.com/FelixKrueger/TrimGalore) with default settings to remove Illumina adapters and mapped using STAR 2.7.3a⁶⁹ to the Drosophila melanogaster genome annotation dm6. Unmapped and improperly paired reads were filtered out of aligned bam files. Reads per gene in the FlyBase release 2019_05 were computed using an R script using GenomicRanges (v.1.44.0)⁷⁰ function summarizeOverlaps that counts the number of reads overlapping with the exons of each gene in the default “union” mode. Differential expression analysis was performed using DESeq2 (v.1.32.0)⁷¹, with count files produced by summarizeOverlaps as input. PCA plots were made using the plotPCA function in DESeq2, with variance stabilized counts as the input. MA plots were constructed from the adjusted p-values and baseMean values output from DESeq2, and volcano plots were constructed from adjusted p-values and fold changes reported by DESeq2. Normalized counts produced by DESeq2 were used to show expression levels. Differentially expressed genes were considered to be any gene with a p-adjusted values of < 0.05.

Actinomycin assay

Brains from KK Control, UAS-dTrmt10A, and Trmt10A RNAi adults were dissected and incubated in 250ul of Schneider's Drosophila Medium that was pre-warmed to 25 °C in RNase free 1.5 ml microfuge tubes. 12–15 brains were dissected for each timepoint. 169.5ug/ml actinomycin D (Sigma-Aldrich, A1410) was added to each sample. Samples were incubated at 25 °C shaking at 300 rpm for 4 h, and mixed by pipetting every 1 h. After the incubation time samples were collected, spun at room temp, washed in cold RNAse free PBS, and processed in Trizol. Brain total RNA was collected using trizol/chloroform and Zymo RNA clean and concentrator kit-5 (R1015). RNA was then used for RT-qPCR. 400 ng of RNA is used per cDNA reaction. cDNA is also quantified by Qubit ssDNA Assay (Invitrogen, Q10212) before use in RT-qPCR experiments.

Puromycin assay

Food was made by mixing 600 µM puromycin (Sigma-Aldrich, P8833-100MG) with 2% agar + 5% sucrose. Adult flies were placed onto puromycin food for 24 h. Brains were dissected from flies after 24 h of feeding and samples were processed in for western blot analysis. Protein loading was determined by Ponceau S stain of membrane.

Quantification and statistical analysis

Statistical tests used were performed on GraphPad Prism (v.9), and are indicated in the figure legend. p-values of < 0.05 were considered significant. Unpaired two-tailed t-tests were used when comparing 2 groups; One-way ANOVA was used when comparing multiple groups followed by Tukey’s post-test when each group was compared against every other group, Sidak’s post-test when pre-defined groups were compared to each other, or Dunnett’s test when comparing to a defined control sample. Two-way ANOVA was used when there were 2 factors in the analysis. One-sided Hypergeometric test was used to compare Venn diagram overlaps.

Salivary gland immunohistochemistry

Third instar larva were dissected in PBST (0.05% Tween 20) and salivary glands were fixed in 2% paraformaldehyde (Electron Microscopy Sciences, #15713) in 45% acetic acid (Alfa Aesar, #36289) for 1 min at room temperature. Fixed salivary glands were loaded in 45% acetic acid on the slide glass and covered with siliconizing reagent (Sigmacote, Sigma-Aldrich, #SL2) coated coverslips to squash polytene chromosomes. Slides were snap frozen with liquid nitrogen and coverslips removed. Samples were blocked with 3% BSA (Sigma, #A7906) solution in PBST for 1 h at room temperature. Primary antibody against HA (Santa cruz, sc-805) and goat anti-rabbit IgG Alexa Fluor488 (Invitrogen, #A11008) were diluted with blocking solution in 1:50 and 1:200 ratio, respectively. VECTASHIELD mounting medium with DAPI (Vector Laboratories, #H-1500) was added at the last step to stain nucleic acids and mount samples. Imaging was conducted on a Leica STELLARIS 5 confocal microscope.

ChIP-WB and RNase treatment

Fly brains (30 brains/sample) were dissected in cold PBS buffer and fixed in 1% formaldehyde (Fisher Scientific, #BP531) for 10 min at room temperature, and washed 3 times with PBS buffer. Fixed brain samples were homogenized in lysis buffer 1(50 mM HEPES pH7.5, 140 mM NaCl, 1 mM EDTA, 10% Glycerol, 0.5% NP-40, 0.25% Triton X-100), and treated with lysis buffer 2 (10 mM Tris pH8, 200 mM NaCl, 1 mM EDTA, 0.5 mM EGTA), then chromatin samples were collected after sonication in lysis buffer 3 (10 mM Tris pH 8, 100 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 0.1% Sodium Deoxycholate, 0.5% N-lauroylsarcosine). 10% of chromatin sample was used for input samples. Anti-HA (Santa Cruz, #sc-7392X, 2ug) and normal mouse IgG (Santa cruz, #sc-2025, 2ug) antibodies were added to Dynabeads Protein A (Invitrogen, #10002D) and incubated for 6 h at 4 °C. Then applied beads to chromatin samples for overnight incubation at 4 °C. Beads were washed and then denatured with LDS sample buffer (Invitrogen, #NP0007) at 95 °C for 5 min. Beads and supernatant were separated on a magnet stands, and the supernatant was used to perform western blot. For RNase treatment, RNase A/T1 mix (Thermo Scientific, #EN0551) was added to lysis buffer 2, and incubated with brain pellets for 2 h at 4 °C.

Immunoprecipitation with dTrmt10A

To immunoprecipitate dTrmt10A from adult fly heads the Pierce Co-Immunoprecipitation kit (Thermo Scientific, #26149) was utilized according to manufacturer’s instructions. 15ug either anti-HA antibody (mouse, Santa Cruz, #sc-7392X) or mouse IgG (mouse, Santa Cruz, #sc-2025) was immobilized to agarose resin according to kit instructions. Heads of adult male flies expressing UAS-dTrmt10A-FLAG-HA with daGal4 were isolated and homogenized for 1 min on ice in 200 uL of IP Lysis/wash buffer (Thermo Scientific) that was supplemented with cOmplete Mini EDTA-free protease inhibitor (Roche, #11836170001). 20 uL of sample was removed for input. Remaining sample lysate was pre-cleared with agarose resin (1 h at 4 °C with gentle rocking). Precleared lysate was incubated with antibody-coupled resin overnight (~ 18 h) at 4 °C with gentle rocking. Agarose resin was then washed three times with IP Lysis/wash buffer supplemented with protease inhibitor (10 min each with rotation at 1000×g, 4 °C). IP samples were eluted in either 50 uL for immunoblotting or 30 uL for mass spec analysis. Samples for immunoblotting were prepared in 4 × LDS Sample Buffer (Invitrogen, #NP0007), heat denatured at 90 °C for 6 min and electrophoresed on a 4–12% Bis–Tris gel and transferred to nitrocellulose. dTrmt10A signal was detected using anti-HA-peroxidase antibody (1:500 rat, Roche, #12013819001).

Mass spectrometry analysis of dTrmt10A protein

dTrmt10A was immunoprecipitated from adult fly heads (80 per replicate, n = 3 biological replicates per antibody) as described above using anti-HA antibody or mouse IgG as control. Samples were run on NuPAGE™ 10%, Bis–Tris gel for 0.5 cm, and stained using Imperial™ Protein Stain (ThermoFisher, #24615). Mass spectrometry analysis was done by Wistar Proteomics and Metabolomics Facility. The entire stained gel regions were excised, reduced with TCEP, alkylated with iodoacetamide, and digested with trypsin, and tryptic digests were analyzed using a standard 1.5-h LC gradient on the Thermo Q Exactive Plus mass spectrometer. MS data were searched with full tryptic specificity against the UniProt Drosophila melanogaster proteome database (7/21/2022) plus the sequence provided (dTrmt10A-HA), and a common contaminant database using MaxQuant 1.6.3.3. Fold change was calculated based on intensity of HA signal to IgG signal, and intensity is the sum of the peptide MS peak intensities for the protein. To calculate the fold change, protein intensity values were processed by: Log2 transformation of the intensity values and replacing missing values (proteins not identified) with the minimum value of the dataset, for comparison between two sample groups, Log2 ratio, Fold change and Student's t-test p-value were calculated using the Log2 imputed intensity, the more stringent q-value (t-test p-value adjusted to account for multiple testing using Benjamini–Hochberg FDR) was also calculated. "High" confident identification of proteins with significant change ("Confident" column) refers to proteins satisfying the 4 criteria: minimum absolute fold change of 2, and q-value < 0.1, and identified by a minimum of 2 razor + unique peptides in HA-IP group, and detected in at least 2 of the HA-IP replicates. "Medium" confident protein identifications refer to proteins satisfying the 3 criteria: Identified by a minimum of 2 razor + unique peptides in HA-IP group, and detected in at least 2 of the HA-IP replicates, and not detected in any of the IgG-IP replicates.

Data availability

The Raw sequencing data generated in this study have been deposited in the Gene Expression Omnibus GSE229430. Any additional inquiries can be directed to the corresponding author.

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Acknowledgements

We thank the Drosophila Genomics Resource Center (NIH grant 2P40OD010949), the Bloomington Drosophila Stock Center (NIH P40OD018537) and the Transgenic RNAi Project (TRiP) at Harvard Medical School (NIH/NIGMS R01-GM084947). We thank the Proteomics and Metabolomics Facility at The Wistar Institute. This work was supported by T32-GM007229 and F31-AG063470 (to AEP), R35-GM133721 (to KFL) and R35-NS097275 (to NMB).

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Authors and Affiliations

Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
Alexandra E. Perlegos & Nancy M. Bonini
Department of Biology, University of Pennsylvania, Philadelphia, PA, 19104, USA
Alexandra E. Perlegos, Xiuming Quan, Kirby M. Donnelly & Nancy M. Bonini
School of Life Science and Technology, China Pharmaceutical University, Nanjing, 210009, Jiangsu, China
Hui Shen
Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Emily J. Shields
Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Emily J. Shields
Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
Heidi Elashal & Kathy Fange Liu
Graduate Group in Biochemistry and Molecular Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
Heidi Elashal & Kathy Fange Liu

Authors

Alexandra E. Perlegos
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Xiuming Quan
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Kirby M. Donnelly
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Hui Shen
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Emily J. Shields
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Heidi Elashal
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Kathy Fange Liu
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Nancy M. Bonini
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Contributions

A.E.P. conceived, designed, and performed experiments, statistical analysis, bioinformatic analysis, and analyzed data. X.Q. performed chromatin experiments. K.M.D performed immunoprecipitations and assisted in experiments. E.J.S. designed bioinformatic pipeline. H.S. and H.E. performed mass spec experiments. K.F.L. supervised research and analyzed data. N.M.B. conceived, designed experiments, analyzed data, and supervised the research. K.F.L and H.S. gave input and edited the manuscript. A.E.P and N.M.B. wrote the manuscript.

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Correspondence to Nancy M. Bonini.

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Dataset S1.

Dataset S2.

Dataset S3.

Dataset S4.

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Perlegos, A.E., Quan, X., Donnelly, K.M. et al. dTrmt10A impacts Hsp70 chaperone m⁶A levels and the stress response in the Drosophila brain. Sci Rep 13, 22999 (2023). https://doi.org/10.1038/s41598-023-50272-4

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Received: 27 June 2023
Accepted: 18 December 2023
Published: 28 December 2023
DOI: https://doi.org/10.1038/s41598-023-50272-4

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