Next Article in Journal
High Glucose Increases DNA Damage and Elevates the Expression of Multiple DDR Genes
Previous Article in Journal
Single Cell Transcriptome Analysis of Peripheral Blood Mononuclear Cells in Freshly Isolated versus Stored Blood Samples
Previous Article in Special Issue
Integrated Profiles of Transcriptome and mRNA m6A Modification Reveal the Intestinal Cytotoxicity of Aflatoxin B1 on HCT116 Cells
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 14
Issue 1
10.3390/genes14010143
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Modification and Expression of mRNA m6A in the Lateral Habenular of Rats after Long-Term Exposure to Blue Light during the Sleep Period

by
Yinhan Li
1,2,3,
Jinjin Ren
4,5,
Zhaoting Zhang
6,
Yali Weng
6,
Jian Zhang
6,
Xinhui Zou
6,
Siying Wu
1,2,3 and
Hong Hu
1,3,7,*
1
Fujian Key Laboratory of Environmental Factors and Cancer, School of Public Health, Fujian Medical University, Fuzhou 350108, China
2
Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou 350108, China
3
Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou 350108, China
4
Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350108, China
5
Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350108, China
6
School of Public Health, Fujian Medical University, Fuzhou 350108, China
7
Department of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou 350108, China
*
Author to whom correspondence should be addressed.
Genes 2023, 14(1), 143; https://doi.org/10.3390/genes14010143
Submission received: 15 October 2022 / Revised: 26 December 2022 / Accepted: 30 December 2022 / Published: 4 January 2023
(This article belongs to the Special Issue RNA Epigenetics: RNA Modification and Epitranscriptome Analysis)
Browse Figures
Review Reports Versions Notes

Abstract

:
Artificial lighting, especially blue light, is becoming a public-health risk. Excessive exposure to blue light at night has been reported to be associated with brain diseases. However, the mechanisms underlying neuropathy induced by blue light remain unclear. An early anatomical tracing study described the projection of the retina to the lateral habenula (LHb), whereas more mechanistic reports are available on multiple brain functions and neuropsychiatric disorders in the LHb, which are rarely seen in epigenetic studies, particularly N6-methyladenosine (m6A). The purpose of our study was to first expose Sprague-Dawley rats to blue light (6.11 ± 0.05 mW/cm2, the same irradiance as 200 lx of white light in the control group) for 4 h, and simultaneously provide white light to the control group for the same time to enter a sleep period. The experiment was conducted over 12 weeks. RNA m6A modifications and different mRNA transcriptome profiles were observed in the LHb. We refer to this experimental group as BLS. High-throughput MeRIP-seq and mRNA-seq were performed, and we used bioinformatics to analyze the data. There were 188 genes in the LHb that overlapped between differentially m6A-modified mRNA and differentially expressed mRNA. The Kyoto Encyclopedia of Genes and Genomes and gene ontology analysis were used to enrich neuroactive ligand–receptor interaction, long-term depression, the cyclic guanosine monophosphate-dependent protein kinase G (cGMP-PKG) signaling pathway, and circadian entrainment. The m6A methylation level of the target genes in the BLS group was disordered. In conclusion, this study suggests that the mRNA expression and their m6A of the LHb were abnormal after blue light exposure during the sleep period, and the methylation levels of target genes related to synaptic plasticity were disturbed. This study offers a theoretical basis for the scientific use of light.

1. Introduction

Humans and other creatures have adapted to a consistent and predictable 24 h solar cycle [1]. However, with the wide application of artificial light sources with high blue light abundance, such as light-emitting diodes (LED), mobile phones, and computers, light-at-night (LAN) has become increasingly common. LAN may cause dysregulation of physiological functions such as biological rhythm, sleep and arousal, cognition, and mood [2,3].
It has previously been reported that long-term and high-intensity blue light can induce genotoxic stress in cells [4]. In addition, exposure to low illumination (less than 1000 lx) blue light for 4 h stimulates the growth speed of the eyes of chicks and affects the ocular rhythm, which indicates that such exposure may be deleterious to emmetropization in children [5]. Previous studies have shown that excessive exposure to LAN can increase the risk of depression [1,2,6]. The intrinsically photosensitive retinal ganglion cells (ipRGCs) in the retina are involved in the regulation of serum melatonin levels, sleep, and biological circadian rhythms [7,8]. The ipRGCs are most sensitive to blue light [9,10]. A recent study showed that blue light at night can induce depression in mice via the circadian rhythm-controlled subcortical intrinsically photosensitive retinal ganglion cell-habenula dorsal-nucleus accumbens (ipRGC-dpHb-NAc) pathway [11]. However, the mechanisms by which blue light affects the central nervous system are still poorly understood.
According to the coordinates of the Montreal Neurological Institute (MNI), the LHb is located in the posterior parietal thalamus (PPtha) [12] and mainly carries glutamate neurons that pass the GABAergic relay in the nodular tegmental nucleus, which inhibits the reward system of the brain by connecting with the interneurons in the ventral tegmental area (VTA) and the dorsal raphe nucleus (DRN) [13]. It is reported to be involved in various brain functions and neuropsychiatric disorders, such as drug abuse, reward aversion, pain, sleep, and other pathophysiological changes in mental diseases, especially severe depression [14]. Recently, the LHb, the brain’s “anti-reward system”, was shown to play an important role in various molecular and electrophysiological characteristics in depression. It was found that the new antidepressant ketamine inhibits the downstream monoaminergic reward center by blocking the N-methyl-D-aspartate receptor (NMDAR) dependent burst activity of the LHb [15]. Kir4.1 in LHb astrocytes is up-regulated in depressed mice [16], and regulation of CB1R in LHb astrocytes may help regulate neuronal and synaptic activity as a way to regulate mood and improve depressive symptoms [17]. However, little is known about the epigenetic mechanisms of the LHb.
Recent studies showed a close relationship between epigenetics and the molecular mechanisms of depression. At present, epigenetic modifications related to depression include DNA methylation, post-translational histone modifications, and microRNAs (miRNAs) or long non-coding RNAs (lncRNAs) [18]. In addition, some studies showed that methylation of the fifth carbon atom of cytosine in the heterocyclic aromatic ring produces 5-methylcytosine (5mC) [19]. In addition, epigenetic modifications of RNA also include N1 methyladenosine (m1A) [20], N6-methyladenosine (m6A) [21], and 7-methylguanine nucleoside (m7G) [22], which affect various protein functions that regulate disease development [23].
Desrosiers et al. (1975) first proposed a new RNA epigenetic modification, N6-methyladenosine (m6A); m6A-related regulatory proteins include methyltransferases (“writers”), demethylases (“erasers”), and methylated reading proteins (“readers”) [24]. The modification of m6A is co-catalyzed by methyltransferases composed of METTL3, METTL14, and WTAP, and then preferentially combined with YTHDF and IGFBP to participate in the translation and degradation of downstream RNA [25]. The m6A modification of RNA has also proved to be reversible because it is bi-directionally regulated by m6A methyltransferase and demethylase (including ALKBH5 and FTO) [26]. m6A-related proteins are expressed in almost all cells. In the nervous system, m6A is involved in neurogenesis, brain capacity, learning and memory, memory formation, and consolidation, and is related to the development of Parkinson’s disease, Alzheimer’s disease, multiple sclerosis, depression, epilepsy, brain tumors, and other diseases [23]. Research shows that 23 m6A modifying genes were genotyped in healthy individuals and patients with major depressive disorder (MDD), and found rs12936694 within the ALKBH5 region to be significantly associated with MDD [27]. Additional studies found that the RNA demethylase FTO, one of the genes associated with depression, was significantly downregulated in the serum of depressed patients and the hippocampus of mice with depression-like behaviors [28].
In this study, we hypothesized that the genes in the LHb of rats underwent m6A methylation modification after 12 weeks of blue light exposure during their sleep period. We further analyzed the data using high-throughput m6A MeRIP-seq and mRNA-seq to determine the potential epigenetic molecular mechanisms.

2. Materials and Methods

2.1. Animals

Male Sprague-Dawley (SD) rats weighing 74.3 ± 0.78 g (3 weeks) were obtained from the Experimental Animal Center of Fujian Medical University. Two rats in each cage were placed in transparent (polycarbonate) cages with controlled temperatures (23 ± 1 °C), relative humidity 50–70%, and controlled noise (less than 60 dB (A)). The light was set to a 10 h light/14 h dark cycle. The rats were free to obtain standardized granular food and tap water.

2.2. Blue Light Treatment

An LED light source was used in the experiment, and the light spectrum was measured (Figure 1A,B). After five days of adaptation, 36 male SD rats were randomly divided into control and BLS groups according to body weight. (n = 18/group). According to the requirements for the environment and housing facilities of experimental animals (GB 149,252,010, China), the cage containing the control subjects was raised from 10:00 to 20:00 under a 10 h light/14 h dark cycle (200 L × white light). BLS was supplemented with blue light for 4 h from 6:00 to 10:00 before it was given white light, and the irradiance of blue light was the same as that of the control group to exclude the influence of different light energies. The illumination intensity and irradiance were detected using a spectral irradiance colorimeter (Everfine, Hangzhou, China). The lighting intensity during the dark period was 0 Lx. At the 12th week of the experiment, the rats were anesthetized with isoflurane, and we collected serum and LHb (Figure 1C). All treatments were performed under mild care, and mice suffering was minimized. The Institutional Animal Care and Use Committee of Fujian Medical University approved all experiments in the present study.

2.3. RNA Preparation

Total RNA was extracted by adding TRIzol reagent (Invitrogen, Waltham, MA, USA; cat. no. 15,596,026) to the LHb. RNA quality was determined by testing A260/A280 using a NanodropTM OneC spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA). RNA integrity was confirmed using 1.5% agarose gel electrophoresis. Qualified RNA was quantitated with Qubit3.0 and the QubitTM RNA Broad Range Assay kit (Life Technologies, Thermo Fisher Scientific Inc., Waltham, MA, USA; Q10210).

2.4. High-Throughput m6A

MeRIP experiments, high-throughput sequencing, and data analysis were conducted by SeqHealth Technology Co., Ltd. (Wuhan, China). First, we used 50 μg of total RNA-enriched polyadenylate RNA through VAHTS mRNA Capture Beads (VAHTS, Nanjing, China; cat. no. N401-01/02). Then, 20 mM ZnCl2 was added to the mRNA and incubated at 95 °C for 5–10 min until the RNA fragments were mainly distributed in 100–200 nt. Subsequently, 10% of the RNA fragments were stored as “inputs”, and the rest were used for m6A immunoprecipitation (IP). A specific anti-m6A antibody (Synaptic Systems, Göttingen, Germany; 202,203) was used for m6A IP. TRIzol reagent (cat. no. 15,596,026) to prepare RNA samples for input and IP. Finally, according to the manufacturer’s instructions, the KC-DigitalTM Stranded mRNA Library Prep Kit for Illumina® (Wuhan SeqHealth Co., Ltd., Wuhan, China; cat. no. DR08502) was used to construct the chain mRNA. This kit eliminates duplication bias in the PCR and sequencing steps by labeling pre-amplified cDNA molecules with a unique molecular identifier (UMI) of eight random bases. Library products corresponding to 200–500 base points were enriched, quantified, and sequenced on a DNBSEQ-T7 sequencer (MGI Tech Co., Ltd., Shenzen, China) using the PE150 model.

2.5. Sequencing Data Analysis

Raw data were processed using Trim Galore (Cambridge, UK). StringTie (Baltimore, MD, USA) [29] was used to analyze the RNA expression levels, and DESeq was used to calculate the differential expression [30]. Exomepeak2 (Suzhou, China) [31,32] was used for m6A peak calling and differential methylation detection, and a Poisson generalized linear model was used to estimate methylation levels and detect differential methylation regions. ExomePeak2 estimates the size factor of the sequencing depth in the non-methylated background area. The consensus m6A motif sequences were identified using STREME [33]. The STREME algorithm integrated the position weight matrix Markov model to report a useful estimate of the statistical significance of each discovered motif. The annotation of KEGG and GO was completed using KOBAS (Beijing, China) and DAVID (Frederick, MD, USA) [34,35], respectively. DirectRMDB (Suzhou, China) [36] was used to analyze the post-transcriptional RNA modifications. Geo2vec (Suzhou, China) [37] was used to analyze them6A prediction models based on geographic information. The m6A methylation sites were obtained from m6A-TSHub (Suzhou, China) [38]. The pitranscriptome analysis was performed by MetaTX (Suzhou, China) [39]. The m6A regulator substrate identified using CLIP technology can be downloaded from the POSTAR3 [40] and ENCORI [41] databases (Guangzhou, China).

2.6. Western Blotting Assay

Western blotting was performed as previously described [42]. Proteins were extracted using RIPA Lysis Buffer and denatured with 5X protein loading buffer. SDS-PAGE was performed using 12% running gels, and the resolved proteins were transferred onto PVDF membranes. The PVDF membranes were blocked with non-fat milk for 1 h and incubated with FTO (1:1000), ALKBH5 (1:1000), METTL3 (1:1000), and YTHDC2 (1:1000) antibodies at 4 °C overnight. Next, the secondary antibodies covered the membranes for 1 h at 37 °C. The grayscale of the protein bands was analyzed using ImageJ (NationalInstitutes of Health, USA) software.

2.7. Enzyme-Linked Immunosorbent Assays for Serum Determinations

Serum melatonin levels were determined according to the manufacturer’s instructions (Elabscience, Wuhan, China). Briefly, 50 mL of standard or sample was added to each well, and then 50 mL of biotinylated detection antibody was immediately added to each well. Next, the plate was incubated for 45 min at 37 °C and washed three times. Then, 100 mL of HRP conjugate was added to each well, and the plate was incubated for 30 min at 37 °C and washed five times. Then, 90 mL of substrate reagent was added, and the plate was incubated for 15 min at 37 °C. Finally, 50 mL of Stop Solution was added, and the absorbance at 450 nm was immediately determined. Subsequently, the results were analyzed.

2.8. Molecular Docking

To explore whether melatonin can interact with m6A-modified differential expression regulators, SYBYL-X 2.0 software (Tripos, St. Louis, MO, USA) was used for molecular docking. SYBYL-X 2.0 was used to prepare protein structures to remove water molecules and heteroatoms, add hydrogen atoms, and repair side chains [43]. The 2D structure of melatonin was downloaded from PubChem. The total score, which is a comprehensive evaluation of hydrophobic complementarity, polar complementarity, solvation terms, and entropic terms, was considered a stable interaction when the value was higher than 5 [44].

2.9. Statistical Analysis

Results are presented as mean ± standard deviation (SD). Unpaired Student’s t-tests were conducted using SPSS 25. A p-value of <0.05 was assumed to be statistically significant.

3. Results

3.1. Transcriptome-Wide Detection of m6A Modification in LHb

To explore the mechanism of blue light exposure in SD rats during the sleep period in the lateral habenular nucleus, MeRIP-seq and RNA-seq analyses were performed. In BLS, the R package exomePeak identified 10,561 m6A peaks containing 21,039 gene transcripts. Similarly, 10,708 m6A peaks were found in the control group, representing 24,458 transcripts of genes. In addition, 10,140 peaks were found at the intersection of the BLS and control groups, corresponding to 15,342 genes. After crossing the normal m6A peak in the rat brain, 232 genes in the BLS and control groups did not undergo m6A methylation changes (Figure 2A,B). Most genes had 1–3 m6A methylation peaks, whereas in the BLS and control groups, relatively few genes had four or more m6A methylation peaks (Figure 2C). We used Streme to determine the presence of an m6A consensus sequence for RRACH (where R represents a purine, A is m6A, and H is a non-guanine base) reported for the detection of m6A (Figure 2D).

3.2. Distribution of m6A Modification in Transcriptome

The distribution of m6A methylation in the whole transcriptome of the BLS and control groups was analyzed. The results showed that m6A modification was enriched in the 5′untranslated region (5’UTR), starting codon, coding sequence (CDS), ending codon, and 3′UTR. The m6A peak density increased rapidly between the 5 ′UTR and the starting codon and was relatively flat in the CDS region. The highest-density region existed near the stop codon. In the 3′UTR region, the density of the m6A peak decreased rapidly, and the number of m6A peaks was also very high (Figure 3).

3.3. Differentially Methylated Genes and Differentially Expressed Genes

We set the statistical standard of differential methylation and differential expression genes as p ≤ 0.05. A total of 4171 differentially expressed mRNA modified by m6A were identified through m6A sequence data. RNA sequence analysis revealed that 585 mRNA were differentially expressed between the BLS and control groups. In addition, 188 genes were observed in the overlap of differentially m6A-modified mRNA and differentially expressed mRNA (Figure 4A), 41 of which were upregulated and 64 were downregulated (Figure 4B).

3.4. KEGG and GO Annotation of the Overlap of Differentially m6A-Modified mRNA and Differentially Expressed mRNA

Using KEGG pathway and GO enrichment analysis on the DAVID web server, 188 overlapping genes of differentially m6A-modified mRNA and differentially expressed mRNA were related to important pathways and biological functions. KEGG pathway analysis showed that these genes were mainly involved in neuroactive ligand–receptor interactions, long-term depression, the cGMP-PKG signaling pathway, and circadian entrainment (Figure 5). GO enrichment analysis can be divided into three functional categories: molecular function (MF), cellular component (CC), and biological process (BP). MF terms included calcium ion binding and phosphatidylserine binding. The CC term included the regulation of excitatory synapses, integral components of the postsynaptic density membranes, and GABAergic synapses. BP terms included regulation of presynaptic assembly, neuron projection morphogenesis, and nervous system development (Figure 6).

3.5. Neuroactive Ligand Receptor Interaction, Circadian Rhythm Entrainment, cGMP-PKG Signal Pathway and Regulation of Potential Regulators

According to the RNA sequence data of LHb, there were significant differences in mRNA expression levels of genes related to neuroactive ligand–receptor interaction (DRD2, NTS, CCK, LEPR, GRIN2A), circadian entrainment (CACNA1C, PLCB4, GUCY1A1, CACNA1I), and the cGMP-PKG signaling pathway (PDE2A, ADRA1B, ATP2B1, ADRA2C). CLIP technology was used to search the POSTAR3 and ENCORI databases, and IGF2BP1, a reader, was found to participate in the regulation of most of these genes (Table 1, Figure 7A–C).

3.6. Potential RNA m6A Regulators of Different Methylation Genes

To identify potential regulators of RNA m6A methylation, we analyzed the mRNA expression levels of 19 RNA m6A methylation writers, readers, and erasers. As shown in Table 2, there was no difference in the p-values. We performed western blot analysis on METTL3, FTO, ALKBH5, and YTHDC2 and found that METTL3 (p = 0.148639; Figure 8A) was not significantly different from the control group, but FTO (p = 0.017; Figure 8B) and ALKBH5 (p = 0.025; Figure 8D) were up-regulated (Figure 8B), and YTHDC2 (p = 0.049; Figure 8D) was downregulated.

3.7. Molecular Interactions of Melatonin with Differently Expressed Regulators of RNA m6A Modification

Theoretically, the melatonin content in the BLS was significantly lower than that in the control group, with a statistically significant difference (p = 0.001124; Figure 9A). We evaluated the molecular interactions of melatonin with differentially expressed regulators of m6A modification, including METTL5, FTO, ALKBH5, YTHDF2, YTHDF3, IGF2BP2, and FMR1. The results are shown in Table 3 and Figure 9B–H. Melatonin bound to METTL5 via the formation of one hydrogen bond at Met104 and 12 hydrophobic contacts with Ile102, Amn0, Asp103, Lys2, Thr5, Met116, Leu98, Leu9, Leu23, Val105, Pro114, and Gly112 (Figure 9B); FTO via the formation of five hydrogen bonds at Leu435, Va1421, Leu496, Ile492, and Leu439, and five hydrophobic contacts with Arg431, Gln499, Leu500, Leu439, and Ile492 (Figure 9C); ALKBH5 via the formation of one hydrogen bond at Arg1102 and one hydrophobic contact with Trp1007 (Figure 9D); YTHDF3 via the formation of one hydrogen bond at Lys414 (Figure 9E); YTHDF2 via the formation of two hydrogen bonds at Arg447 and Ala444 and one hydrophobic contact with Ser448 (Figure 9F); IGF2BP2 via the formation of three hydrogen bonds at Ile55, Ile52, and Tyr73, and one hydrophobic contact with Ala51 (Figure 9G); and FMR1 via the formation of two hydrogen bonds at Leu54 and Ser49, and seven hydrophobic contacts with Glu50, Asp48, Ser52, Ile55, Pro46, His21, and Lys19 (Figure 9H).

4. Discussion

Epidemiology and animal experiments have shown that overexposure to visible light in the high-energy blue light band leads to circadian rhythm disorder. This leads to sleep disorders and induces various negative emotions, resulting in various physiological and functional diseases [45,46]. When melanopsin, which is most sensitive to blue light in ipRGCs, receives a light signal from the retina, it sends the light information to the suprachiasmatic nucleus (SCN) and other structures in the brain, including the LHb. Some studies have confirmed that LHb neurons respond to retinal light using multi-electrode probes that simultaneously record the multilevel activity of the LHb [47]. However, the molecular mechanisms of the LHb after blue light exposure remain largely unknown.
Epigenetic studies have demonstrated a common genetic basis for mental diseases. In this study, rats that entered the sleep period were exposed to 4 h of blue light for 12 weeks, and then the LHb was extracted. Based on the high-throughput m6A MeRIP and mRNA sequences, our research results first identified the potential epigenetic changes in m6A modification and mRNA transcription profiles in the LHb exposed to blue light.
In this study, GO and KEGG pathway enrichment analyses were performed to explore the biological functional changes caused by mRNA m6A modification. A total of 188 genes overlapped with the differentially m6A-modified mRNAs and differentially expressed mRNAs. The top 20 KEGG terms included cGMP-PKG signaling pathway, calcium signaling pathway, long-term depression, neuroactive ligand receptor interaction, and circadian rhythm. In GO terms, calcium ion binding and metal ion binding were enriched in MF. Excitatory synapses, a component of the postsynaptic density membrane, are abundant in the CC, as are GABAergic synapses, regulation of presynaptic assembly in the BP, and projection neuron morphogenesis, which is enriched in nervous system development. Previous studies have shown that the occurrence of depression is closely related to changes in synaptic plasticity. Most antidepressants modulate synaptic transmission by affecting the levels of monoamine neurotransmitters secreted at the synapse and by using the neurotransmitter glutamate, suggesting that antidepressants can modulate synaptic plasticity [48]. In addition, the recently discovered antidepressant properties of ketamine work through sustained potentiation of excitatory synapses [49]. More studies have found that a single exposure to a stressor facilitates the induction of LTP in the LHb, suggesting that animal models of depression or post-traumatic stress disorder have altered synaptic plasticity in the LHb [50].
Through the analysis of m6A MeRIP sequence data, the distribution of m6A modification peaks in the control and BLS groups showed that most genes had 1–3 peaks and parts. When the gene was divided into the 5′ UTR, start codon, CDS, stop codon, and 3′ UTR, most m6A modification peaks were located in the CDS and 3’ UTR regions, and the highest value was near the stop codon. The 3′ UTR was shown to regulate different protein characteristics, including the formation of protein complexes or post-translational modifications [51]. Therefore, the increase in the number of m6A modification sites at the termination codon and 3′-UTR region may be related to mRNA stability and translation.
The biological effects of RNA m6A modification depend on the recognition and binding of m6A binding proteins [52]. Using the computational prediction method, we found no difference in the mRNA of m6A related methylation regulatory proteins in the LHb. However, we measured the protein content of METTL3, FTO, ALKBH5, and YTHDC2 and found that the results were different from the mRNA expression. Methylated regulatory proteins may change during translation because tRNAs also interact with ribosomes to play a central role in translation; thus, the abundance, availability, and codon usage of tRNAs in mRNAs have been reported to strongly affect the rate and efficiency of translation [53]. Studies have shown that IGF2BP1 is located in the axon, dendritic spine, and neuronal cell bodies, enabling mRNA 3′-UTR binding activity [54]. It is also involved in RNA localization [54] and dendritic dendronization [55,56]. Our results showed that IGF2BP1 is involved in the m6A modification of relevant differentially expressed genes in this pathway. For example, GUCY1A1 (guanylate cyclase 1 (1) is a soluble guanylate-circulating enzyme. It was found that GUCY1A1 can regulate the release of glutamate and GABA in the somatosensory cortex of mice through nitric oxide/cGMP signals [57]. In our sequencing results, we found that after BLS exposure, the methylation level of GUCY1A1 mRNA m6A in the LHb was low, and the stability and translation level of GUCY1A1 mRNA changed after the recognition of GUCY1A1 mRNA by IGF2BP1, which may affect the release of glutamate and GABA, thus affecting synaptic function.
Since blue light is the most effective light to inhibit melatonin [58], we measured the content of melatonin in the serum of rats in the two groups and found that melatonin in the BLS group was significantly lower than that in the control group. We combined melatonin with m6A regulatory factors, among which METTL3, FTO, ALKBH5, and YTHDCF2 had total scores higher than 5. The lower melatonin levels in the BLS might be caused by METTL3, FTO, ALKBH5, and YTHDF2.

5. Conclusions

In conclusion, our data confirmed that the level of m6A methylation in the LHb changed when exposed to blue light. The sequencing results showed that blue light altered the expression of genes related to synaptic function in the lateral habenular nucleus. Our results showed that m6A modification might play a functional role in depression caused by synaptic dysfunction following blue light exposure (Figure 10; figdraw ID: SPTUPb1c0c). These findings provide a better understanding of the epigenetic mechanisms of the effects of blue light in the neural system.

Author Contributions

Conceptualization, H.H.; methodology, J.R. and Y.L.; software, J.R., J.Z., X.Z. and Y.L.; data curation, Y.L., J.R., J.Z. and X.Z.; formal analysis, Y.L., J.R., Y.W., J.Z., Z.Z. and X.Z.; investigation, Y.L. and J.R.; visualization, J.R. and X.Z.; writing—original draft, Y.L.; writing—review and editing, H.H. and S.W.; supervision, H.H. and S.W.; funding acquisition, H.H. and S.W. All authors have read and agreed to the published version of the manuscript.

Funding

Natural Science Foundation of Fujian Province (2021J01734) and Fujian Medical University High-level Talent Research Startup Funding Project (XRCZX2020036).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All relevant data are provided in the manuscript. Please contact [email protected] for any raw data files and further analysis.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

LHblateral habenula
cGMP-PKGcyclic guanosine monophosphate-protein kinase
BLSBlue light during the sleep period
NMDARN-methyl-D-aspartate receptor
GABAγ-2Aminobutiric acid
LEDlight emitting diode
LANlight-at-night
ipRGCsintrinsically photosensitive retinal ganglion cells
3′UTR    3′untranslated regions
5′UTR    5′untranslated regions
CDScoding sequence (CDS)
GOGene Ontology
LEGGKyoto Encyclopedia of Genes and Genomes
m6AN6-methyladenosine
MeRIP-seqmethylated RNA immunoprecipitation sequencing

References

  1. Bedrosian, T.A.; Nelson, R.J. Influence of the modern light environment on mood. Mol. Psychiatry 2013, 18, 751–757. [Google Scholar] [CrossRef] [PubMed]
  2. LeGates, T.A.; Fernandez, D.C.; Hattar, S. Light as a central modulator of circadian rhythms, sleep and affect. Nat. Rev. Neurosci. 2014, 15, 443–454. [Google Scholar] [CrossRef] [PubMed]
  3. Vandewalle, G.; Maquet, P.; Dijk, D.J. Light as a modulator of cognitive brain function. Trends Cogn. Sci. 2009, 13, 429–438. [Google Scholar] [CrossRef] [PubMed]
  4. Walsh, K.D.; Burkhart, E.M.; Nagai, A.; Aizawa, Y.; Kato, T.A. Cytotoxicity and genotoxicity of blue LED light and protective effects of AA2G in mammalian cells and associated DNA repair deficient cell lines. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 2021, 872, 503416. [Google Scholar] [CrossRef] [PubMed]
  5. Nickla, D.L.; Rucker, F.; Taylor, C.P.; Sarfare, S.; Chen, W.; Elin-Calcador, J.; Wang, X. Effects of morning and evening exposures to blue light of varying illuminance on ocular growth rates and ocular rhythms in chicks. Exp. Eye Res. 2022, 217, 108963. [Google Scholar] [CrossRef]
  6. Zielinska-Dabkowska, K.M. Make lighting healthier. Nature 2018, 553, 274–276. [Google Scholar] [CrossRef] [Green Version]
  7. Brainard, G.C.; Hanifin, J.P. Photons, clocks, and consciousness. J. Biol. Rhythm 2005, 20, 314–325. [Google Scholar] [CrossRef]
  8. Prayag, A.S.; Najjar, R.P.; Gronfier, C. Melatonin suppression is exquisitely sensitive to light and primarily driven by melanopsin in humans. J. Pineal Res. 2019, 66, e12562. [Google Scholar] [CrossRef]
  9. Jenkins, A.; Muñoz, M.; Tarttelin, E.E.; Bellingham, J.; Foster, R.G.; Hankins, M.W. VA opsin, melanopsin, and an inherent light response within retinal interneurons. Curr. Biol. 2003, 13, 1269–1278. [Google Scholar] [CrossRef]
  10. Dacey, D.M.; Liao, H.W.; Peterson, B.B.; Robinson, F.R.; Smith, V.C.; Pokorny, J.; Yau, K.W.; Gamlin, P.D. Melanopsin-expressing ganglion cells in primate retina signal colour and irradiance and project to the LGN. Nature 2005, 433, 749–754. [Google Scholar] [CrossRef]
  11. An, K.; Zhao, H.; Miao, Y.; Xu, Q.; Li, Y.F.; Ma, Y.Q.; Shi, Y.M.; Shen, J.W.; Meng, J.J.; Yao, Y.G.; et al. A circadian rhythm-gated subcortical pathway for nighttime-light-induced depressive-like behaviors in mice. Nat. Neurosci. 2020, 23, 869–880. [Google Scholar] [CrossRef]
  12. Zhu, Y.; Qi, S.; Zhang, B.; He, D.; Teng, Y.; Hu, J.; Wei, X. Connectome-Based Biomarkers Predict Subclinical Depression and Identify Abnormal Brain Connections with the Lateral Habenula and Thalamus. Front. Psychiatry 2019, 10, 371. [Google Scholar] [CrossRef]
  13. Jhou, T.C.; Geisler, S.; Marinelli, M.; Degarmo, B.A.; Zahm, D.S. The mesopontine rostromedial tegmental nucleus: A structure targeted by the lateral habenula that projects to the ventral tegmental area of Tsai and substantia nigra compacta. J. Comp. Neurol. 2009, 513, 566–596. [Google Scholar] [CrossRef] [Green Version]
  14. Yang, Y.; Cui, Y.; Sang, K.; Dong, Y.; Ni, Z.; Ma, S.; Hu, H. Ketamine blocks bursting in the lateral habenula to rapidly relieve depression. Nature 2018, 554, 317–322. [Google Scholar] [CrossRef]
  15. Barker, D.J.; Miranda-Barrientos, J.; Zhang, S.; Root, D.H.; Wang, H.L.; Liu, B.; Calipari, E.S.; Morales, M. Lateral Preoptic Control of the Lateral Habenula through Convergent Glutamate and GABA Transmission. Cell Rep. 2017, 21, 1757–1769. [Google Scholar] [CrossRef] [Green Version]
  16. Cui, Y.; Yang, Y.; Ni, Z.; Dong, Y.; Cai, G.; Foncelle, A.; Ma, S.; Sang, K.; Tang, S.; Li, Y.; et al. Astroglial Kir4.1 in the lateral habenula drives neuronal bursts in depression. Nature 2018, 554, 323–327. [Google Scholar] [CrossRef] [Green Version]
  17. Arjmand, S.; Landau, A.M.; Varastehmoradi, B.; Andreatini, R.; Joca, S.; Wegener, G. The intersection of astrocytes and the endocannabinoid system in the lateral habenula: On the fast-track to novel rapid-acting antidepressants. Mol. Psychiatry 2022, 27, 3138–3149. [Google Scholar] [CrossRef]
  18. Czarny, P.; Białek, K.; Ziółkowska, S.; Strycharz, J.; Barszczewska, G.; Sliwinski, T. The Importance of Epigenetics in Diagnostics and Treatment of Major Depressive Disorder. J. Pers. Med. 2021, 11, 167. [Google Scholar] [CrossRef]
  19. Ma, J.; Song, B.; Wei, Z.; Huang, D.; Zhang, Y.; Su, J.; de Magalhães, J.P.; Rigden, D.J.; Meng, J.; Chen, K. m5C-Atlas: A comprehensive database for decoding and annotating the 5-methylcytosine (m5C) epitranscriptome. Nucleic Acids Res. 2022, 50, D196–D203. [Google Scholar] [CrossRef]
  20. Dominissini, D.; Nachtergaele, S.; Moshitch-Moshkovitz, S.; Peer, E.; Kol, N.; Ben-Haim, M.S.; Dai, Q.; Di Segni, A.; Salmon-Divon, M.; Clark, W.C.; et al. The dynamic N1-methyladenosine methylome in eukaryotic messenger RNA. Nature 2016, 530, 441–446. [Google Scholar] [CrossRef]
  21. Shu, L.; Huang, X.; Cheng, X.; Li, X. Emerging Roles of N6-Methyladenosine Modification in Neurodevelopment and Neurodegeneration. Cells 2021, 10, 2694. [Google Scholar] [CrossRef] [PubMed]
  22. Song, B.; Tang, Y.; Chen, K.; Wei, Z.; Rong, R.; Lu, Z.; Su, J.; de Magalhães, J.P.; Rigden, D.J.; Meng, J. m7GHub: Deciphering the location, regulation and pathogenesis of internal mRNA N7-methylguanosine (m7G) sites in human. Bioinformatics 2020, 36, 3528–3536. [Google Scholar] [CrossRef] [PubMed]
  23. Zhang, N.; Ding, C.; Zuo, Y.; Peng, Y.; Zuo, L. N6-methyladenosine and Neurological Diseases. Mol. Neurobiol. 2022, 59, 1925–1937. [Google Scholar] [CrossRef] [PubMed]
  24. Yang, Y.; Hsu, P.J.; Chen, Y.S.; Yang, Y.G. Dynamic transcriptomic m(6)A decoration: Writers, erasers, readers and functions in RNA metabolism. Cell Res. 2018, 28, 616–624. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Lei, C.; Wang, Q. The Progression of N6-methyladenosine Study and Its Role in Neuropsychiatric Disorders. Int. J. Mol. Sci. 2022, 23, 5922. [Google Scholar] [CrossRef]
  26. Mathoux, J.; Henshall, D.C.; Brennan, G.P. Regulatory Mechanisms of the RNA Modification m(6)A and Significance in Brain Function in Health and Disease. Front. Cell. Neurosci. 2021, 15, 671932. [Google Scholar] [CrossRef]
  27. Du, T.; Rao, S.; Wu, L.; Ye, N.; Liu, Z.; Hu, H.; Xiu, J.; Shen, Y.; Xu, Q. An association study of the m6A genes with major depressive disorder in Chinese Han population. J. Affect. Disord. 2015, 183, 279–286. [Google Scholar] [CrossRef]
  28. Shen, J.; Yang, L.; Wei, W. Role of Fto on CaMKII/CREB signaling pathway of hippocampus in depressive-like behaviors induced by chronic restraint stress mice. Behav. Brain Res. 2021, 406, 113227. [Google Scholar] [CrossRef]
  29. Pertea, M.; Kim, D.; Pertea, G.M.; Leek, J.T.; Salzberg, S.L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 2016, 11, 1650–1667. [Google Scholar] [CrossRef]
  30. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef]
  31. Tang, Y.; Chen, K.; Song, B.; Ma, J.; Wu, X.; Xu, Q.; Wei, Z.; Su, J.; Liu, G.; Rong, R.; et al. m6A-Atlas: A comprehensive knowledgebase for unraveling the N6-methyladenosine (m6A) epitranscriptome. Nucleic Acids Res. 2020, 49, D134–D143. [Google Scholar] [CrossRef]
  32. Meng, J.; Cui, X.; Rao, M.K.; Chen, Y.; Huang, Y. Exome-based analysis for RNA epigenome sequencing data. Bioinformatics 2013, 29, 1565–1567. [Google Scholar] [CrossRef] [Green Version]
  33. Bailey, T.L. STREME: Accurate and versatile sequence motif discovery. Bioinformatics 2021, 37, 2834–2840. [Google Scholar] [CrossRef]
  34. Jiao, X.; Sherman, B.T.; Huang, D.W.; Stephens, R.; Baseler, M.W.; Lane, H.C.; Lempicki, R.A. DAVID-WS: A stateful web service to facilitate gene/protein list analysis. Bioinformatics 2012, 28, 1805–1806. [Google Scholar] [CrossRef] [Green Version]
  35. Bu, D.; Luo, H.; Huo, P.; Wang, Z.; Zhang, S.; He, Z.; Wu, Y.; Zhao, L.; Liu, J.; Guo, J.; et al. KOBAS-i: Intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis. Nucleic Acids Res. 2021, 49, W317–W325. [Google Scholar] [CrossRef]
  36. Zhang, Y.; Jiang, J.; Ma, J.; Wei, Z.; Wang, Y.; Song, B.; Meng, J.; Jia, G.; de Magalhães, J.P.; Rigden, D.J.; et al. DirectRMDB: A database of post-transcriptional RNA modifications unveiled from direct RNA sequencing technology. Nucleic Acids Res. 2022, 51, D106–D116. [Google Scholar] [CrossRef]
  37. Huang, D.; Chen, K.; Song, B.; Wei, Z.; Su, J.; Coenen, F.; de Magalhães, J.P.; Rigden, D.J.; Meng, J. Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation. Nucleic Acids Res. 2022, 50, 10290–10310. [Google Scholar] [CrossRef]
  38. Song, B.; Huang, D.; Zhang, Y.; Wei, Z.; Su, J.; Pedro de Magalhães, J.; Rigden, D.J.; Meng, J.; Chen, K. m6A-TSHub: Unveiling the Context-specific m(6)A Methylation and m6A-affecting Mutations in 23 Human Tissues. Genom. Proteom. Bioinform. 2022; in press. [Google Scholar] [CrossRef]
  39. Wang, Y.; Chen, K.; Wei, Z.; Coenen, F.; Su, J.; Meng, J. MetaTX: Deciphering the distribution of mRNA-related features in the presence of isoform ambiguity, with applications in epitranscriptome analysis. Bioinformatics 2021, 37, 1285–1291. [Google Scholar] [CrossRef]
  40. Zhao, W.; Zhang, S.; Zhu, Y.; Xi, X.; Bao, P.; Ma, Z.; Kapral, T.H.; Chen, S.; Zagrovic, B.; Yang, Y.T.; et al. POSTAR3: An updated platform for exploring post-transcriptional regulation coordinated by RNA-binding proteins. Nucleic Acids Res. 2022, 50, D287–D294. [Google Scholar] [CrossRef]
  41. Li, J.H.; Liu, S.; Zhou, H.; Qu, L.H.; Yang, J.H. starBase v2.0: Decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 2014, 42, D92–D97. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Guo, Z.; Zhang, Z.; Wang, Q.; Zhang, J.; Wang, L.; Zhang, Q.; Li, H.; Wu, S. Manganese chloride induces histone acetylation changes in neuronal cells: Its role in manganese-induced damage. Neurotoxicology 2018, 65, 255–263. [Google Scholar] [CrossRef] [PubMed]
  43. Gao, H.; Zhang, L.; Zhu, A.; Liu, X.; Wang, T.; Wan, M.; Yang, X.; Zhang, Y.; Zhang, Y. Metabolic Profiling of Nuciferine In Vivo and In Vitro. J. Agric. Food Chem. 2020, 68, 14135–14147. [Google Scholar] [CrossRef] [PubMed]
  44. Laskowski, R.A.; Swindells, M.B. LigPlot+: Multiple ligand-protein interaction diagrams for drug discovery. J. Chem. Inf. Model. 2011, 51, 2778–2786. [Google Scholar] [CrossRef] [PubMed]
  45. Daut, R.A.; Hartsock, M.J.; Tomczik, A.C.; Watkins, L.R.; Spencer, R.L.; Maier, S.F.; Fonken, L.K. Circadian misalignment has differential effects on affective behavior following exposure to controllable or uncontrollable stress. Behav. Brain Res. 2019, 359, 440–445. [Google Scholar] [CrossRef]
  46. Kecklund, G.; Axelsson, J. Health consequences of shift work and insufficient sleep. Br. Med. J. 2016, 355, i5210. [Google Scholar] [CrossRef]
  47. Baño-Otálora, B.; Piggins, H.D. Contributions of the lateral habenula to circadian timekeeping. Pharmacol. Biochem. Behav. 2017, 162, 46–54. [Google Scholar] [CrossRef]
  48. Popoli, M.; Gennarelli, M.; Racagni, G. Modulation of synaptic plasticity by stress and antidepressants. Bipolar Disord. 2002, 4, 166–182. [Google Scholar] [CrossRef]
  49. Krzystyniak, A.; Baczynska, E.; Magnowska, M.; Antoniuk, S.; Roszkowska, M.; Zareba-Koziol, M.; Das, N.; Basu, S.; Pikula, M.; Wlodarczyk, J. Prophylactic Ketamine Treatment Promotes Resilience to Chronic Stress and Accelerates Recovery: Correlation with Changes in Synaptic Plasticity in the CA3 Subregion of the Hippocampus. Int. J. Mol. Sci. 2019, 20, 1726. [Google Scholar] [CrossRef] [Green Version]
  50. Park, H.; Rhee, J.; Park, K.; Han, J.S.; Malinow, R.; Chung, C. Exposure to Stressors Facilitates Long-Term Synaptic Potentiation in the Lateral Habenula. J. Neurosci. 2017, 37, 6021–6030. [Google Scholar] [CrossRef]
  51. Mayr, C. What Are 3′ UTRs Doing? Cold Spring Harb. Perspect. Biol. 2019, 11, a034728. [Google Scholar] [CrossRef] [Green Version]
  52. Zhao, Y.; Shi, Y.; Shen, H.; Xie, W. m(6)A-binding proteins: The emerging crucial performers in epigenetics. J. Hematol. Oncol. 2020, 13, 35. [Google Scholar] [CrossRef] [Green Version]
  53. Hoernes, T.P.; Hüttenhofer, A.; Erlacher, M.D. mRNA modifications: Dynamic regulators of gene expression? RNA Biol. 2016, 13, 760–765. [Google Scholar] [CrossRef] [Green Version]
  54. Eom, T.; Antar, L.N.; Singer, R.H.; Bassell, G.J. Localization of a beta-actin messenger ribonucleoprotein complex with zipcode-binding protein modulates the density of dendritic filopodia and filopodial synapses. J. Neurosci. 2003, 23, 10433–10444. [Google Scholar] [CrossRef] [Green Version]
  55. Tiruchinapalli, D.M.; Oleynikov, Y.; Kelic, S.; Shenoy, S.M.; Hartley, A.; Stanton, P.K.; Singer, R.H.; Bassell, G.J. Activity-dependent trafficking and dynamic localization of zipcode binding protein 1 and beta-actin mRNA in dendrites and spines of hippocampal neurons. J. Neurosci. 2003, 23, 3251–3261. [Google Scholar] [CrossRef] [Green Version]
  56. Lüscher, C.; Malenka, R.C. NMDA receptor-dependent long-term potentiation and long-term depression (LTP/LTD). Cold Spring Harb. Perspect. Biol. 2012, 4, a005710. [Google Scholar] [CrossRef] [Green Version]
  57. Wang, Q.; Mergia, E.; Koesling, D.; Mittmann, T. Nitric oxide/cGMP signaling via guanylyl cyclase isoform 1 modulates glutamate and GABA release in somatosensory cortex of mice. Neuroscience 2017, 360, 180–189. [Google Scholar] [CrossRef]
  58. McIntyre, I.M.; Norman, T.R.; Burrows, G.D.; Armstrong, S.M. Human melatonin suppression by light is intensity dependent. J. Pineal Res. 1989, 6, 149–156. [Google Scholar] [CrossRef]
Genes 14 00143 g001 550
Figure 1. (A) White light spectrum. (B) Blue light spectrum. (C) Experimental design for control or BLS paradigms.
Figure 1. (A) White light spectrum. (B) Blue light spectrum. (C) Experimental design for control or BLS paradigms.
Genes 14 00143 g001
Genes 14 00143 g002 550
Figure 2. The m6A modification patterns in BLS and control groups. (A) The Venn diagram shows the overlap of two groups of m6A genes. (B) Venn diagram shows the overlap of m6A peak in the brain of two groups and normal rats. (C) The distribution of m6A methylation peaks in each gene. (D) The motifs for m6A peak regions based on STREME.
Figure 2. The m6A modification patterns in BLS and control groups. (A) The Venn diagram shows the overlap of two groups of m6A genes. (B) Venn diagram shows the overlap of m6A peak in the brain of two groups and normal rats. (C) The distribution of m6A methylation peaks in each gene. (D) The motifs for m6A peak regions based on STREME.
Genes 14 00143 g002
Genes 14 00143 g003 550
Figure 3. Density of m6A methylation peaks in mRNA transcripts.
Figure 3. Density of m6A methylation peaks in mRNA transcripts.
Genes 14 00143 g003
Genes 14 00143 g004 550
Figure 4. (A) Differences in m6A-modified mRNA and mRNA expression between BLS and control groups. (B) Volcanic map of mRNA differentially expressed between the two groups.
Figure 4. (A) Differences in m6A-modified mRNA and mRNA expression between BLS and control groups. (B) Volcanic map of mRNA differentially expressed between the two groups.
Genes 14 00143 g004
Genes 14 00143 g005 550
Figure 5. The top 20 KEGG analysis enriched pathways of the 188 overlapped genes of differentially m6A-modified mRNA and differentially expressed mRNA.
Figure 5. The top 20 KEGG analysis enriched pathways of the 188 overlapped genes of differentially m6A-modified mRNA and differentially expressed mRNA.
Genes 14 00143 g005
Genes 14 00143 g006 550
Figure 6. GO functional annotation of 188 overlapped genes of differentially m6A-modified mRNA and differentially expressed mRNA. (A) GO molecular function. (B) GO cellular component. (C) GO biological process.
Figure 6. GO functional annotation of 188 overlapped genes of differentially m6A-modified mRNA and differentially expressed mRNA. (A) GO molecular function. (B) GO cellular component. (C) GO biological process.
Genes 14 00143 g006
Genes 14 00143 g007 550
Figure 7. (A–C) The effects of IGF2BP1 on the differentially expressed genes of neuroactive ligand–receptor interaction, circadian entrainment, and cGMP-PKG signal pathway.
Figure 7. (A–C) The effects of IGF2BP1 on the differentially expressed genes of neuroactive ligand–receptor interaction, circadian entrainment, and cGMP-PKG signal pathway.
Genes 14 00143 g007
Genes 14 00143 g008 550
Figure 8. The protein expression level of m6A regulatory factor in LHb. (A–D) the protein levels of METTL3, FTO, ALKBH5, and YTHDC2 (* p < 0.05).
Figure 8. The protein expression level of m6A regulatory factor in LHb. (A–D) the protein levels of METTL3, FTO, ALKBH5, and YTHDC2 (* p < 0.05).
Genes 14 00143 g008
Genes 14 00143 g009 550
Figure 9. The secretion of melatonin and the molecular interaction between different expression regulators modified with m6A and carnitine. A two-dimensional interaction model is proposed. (A) Melatonin contents, (B) METTL3, (C) FTO, (D) ALKBH5, (E) YTHDF2, (F) YTHDF3, (G) IGF2BP2, and (H) FMR1. (** p < 0.01).
Figure 9. The secretion of melatonin and the molecular interaction between different expression regulators modified with m6A and carnitine. A two-dimensional interaction model is proposed. (A) Melatonin contents, (B) METTL3, (C) FTO, (D) ALKBH5, (E) YTHDF2, (F) YTHDF3, (G) IGF2BP2, and (H) FMR1. (** p < 0.01).
Genes 14 00143 g009
Genes 14 00143 g010 550
Figure 10. Blue light is delivered to the LHb via the SCN pacemaker after it is accepted by melanopsin in the retina, where m6A methylation of genes related to synaptic function is abnormal. Therefore, blue light exposure may disrupt the translation process of related genes as well as mRNA stability through m6A modification processes.
Figure 10. Blue light is delivered to the LHb via the SCN pacemaker after it is accepted by melanopsin in the retina, where m6A methylation of genes related to synaptic function is abnormal. Therefore, blue light exposure may disrupt the translation process of related genes as well as mRNA stability through m6A modification processes.
Genes 14 00143 g010
Table
Table 1. The level of protein interaction and mRNA interaction between IGF2BP1 and some related differential genes in the neuroactive ligand–receptor interaction, circadian entrainment, and cGMP-PKG signal pathway.
Table 1. The level of protein interaction and mRNA interaction between IGF2BP1 and some related differential genes in the neuroactive ligand–receptor interaction, circadian entrainment, and cGMP-PKG signal pathway.
Genelog2FoldChangep ValueIGF2BP1
POSTAR3ENCORI
Neuroactive ligand-receptor interactionDRD212.4107173480.00071873900
NTS2.2894837760.00081438700
CCK−3.8919769010.00273212100
LEPR−1.5449002370.01807553111
GRIN2A−1.3836160130.04889680300
Circadian entrainmentCACNA1C−1.3259972650.00264126701
PLCB4−1.7694499480.00398613411
GUCY1A1−.3592427380.03295375611
CACNA1I0.0776546980.8579227301
cGMP-PKG signaling pathwayPDE2A1.636344440.00048364401
ADRA1B−2.7892757770.00087873300
ATP2B1−1.7530446110.00674013211
ADRA2C1.7175129150.01749416811
1 means that it interacts with IGF2BP1, and 0 means not.
Table
Table 2. mRNA expression level of m6A regulatory factor in LHb.
Table 2. mRNA expression level of m6A regulatory factor in LHb.
GeneRegulationBase Meanlog2FoldChangep Value
ALKBH5eraser341.9384302−0.1091706570.771338043
CBLL1writer346.95514460.1165919860.640932024
FMR1reader1847.917149−0.0715759280.756365327
FTOeraser1766.6613010.1568345150.728325104
HNRNPA2B1reader23816.122420.044146990.831082269
HNRNPCreader5475.182130.1881886850.223239932
IGF2BP1reader1.1684479893.5747333530.364559468
IGF2BP2reader53.117260130.1475124640.760550998
IGF2BP3reader8.3762923150.6136189840.540055918
METTL14writer1787.9171220.1169465770.522853135
METTL3writer2373.613619−0.0980015270.667618896
METTL5writer1275.304982−0.1799984130.419116968
VIRMAwriter1113.272801−0.2266310230.341256528
WTAPwriter1863.7469670.1069617080.735133477
YTHDC1reader7452.265261−0.0776334360.679592938
YTHDF1reader1043.115245−0.0876325910.633796561
YTHDF2reader1082.1376790.1538620120.481990904
YTHDF3reader732.48641410.0641474250.865441311
ZC3H13writer13329.50949−0.2843218260.402424357
Table
Table 3. Molecular interactions between melatonin and m6A modified different expression regulators.
Table 3. Molecular interactions between melatonin and m6A modified different expression regulators.
ProteinRegulationTotal ScoreH-Bond NumberResidues Involved in H-Bond FormationHydrophobic Contacts NumberResidues Involved in Hydrophobic Contacts
METTL5writer101Met10412Ile102, Amn0, Asp103, Lys2, Thr5, Met116, Leu98, Leu9, Leu23, Val105, Pro114, Gly112
FTOeraser9.135Leu435, Va1421, Leu496, Ile492, Leu4395Arg431, Gln499, Leu500, Leu439, Ile492
ALKBH5eraser4.391Arg11021Trp1007
YTHDF3reader8.1521Lys4140
YTHDF2reader6.4012Arg447, Ala4441Ser448
IGF2BP2reader4.8913Ile55, Ile52, Tyr731Ala51
FMR1reader3.192Leu54, Ser497Glu50, Asp48, Ser52, Ile55, Pro46, His21, Lys19
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Li, Y.; Ren, J.; Zhang, Z.; Weng, Y.; Zhang, J.; Zou, X.; Wu, S.; Hu, H. Modification and Expression of mRNA m6A in the Lateral Habenular of Rats after Long-Term Exposure to Blue Light during the Sleep Period. Genes 2023, 14, 143. https://doi.org/10.3390/genes14010143

AMA Style

Li Y, Ren J, Zhang Z, Weng Y, Zhang J, Zou X, Wu S, Hu H. Modification and Expression of mRNA m6A in the Lateral Habenular of Rats after Long-Term Exposure to Blue Light during the Sleep Period. Genes. 2023; 14(1):143. https://doi.org/10.3390/genes14010143

Chicago/Turabian Style

Li, Yinhan, Jinjin Ren, Zhaoting Zhang, Yali Weng, Jian Zhang, Xinhui Zou, Siying Wu, and Hong Hu. 2023. "Modification and Expression of mRNA m6A in the Lateral Habenular of Rats after Long-Term Exposure to Blue Light during the Sleep Period" Genes 14, no. 1: 143. https://doi.org/10.3390/genes14010143

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop