ORIGINAL RESEARCH article

Front. Mol. Biosci., 28 July 2021

Sec. RNA Networks and Biology

Volume 8 - 2021 | https://doi.org/10.3389/fmolb.2021.670160

Lipopolysaccharide Alters the m6A Epitranscriptomic Tagging of RNAs in Cardiac Tissue

  • 1. Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

  • 2. Department of Physiology and Pathophysiology, Peking University School of Basic Medical Sciences, Beijing, China

  • 3. Peking University Fifth School of Clinical Medicine (Beijing Hospital), Beijing, China

Abstract

N6-methyladenosine (m6A) modification plays important roles in the pathology of a variety of diseases. However, the roles of m6A modification in sepsis-induced myocardial dysfunction are not well defined. Rats were divided into control and lipopolysaccharide (LPS)-induced sepsis group. Global m6A levels of left ventricle tissue were measured by LC-MS/MS, and transcriptome-wide m6A modifications were profiled using epitranscriptomic microarrays (mRNAs and lncRNAs). Bioinformatics analysis was conducted to understand the functional implications of m6A modifications during sepsis. Methylated lncRNAs and mRNAs were measured by m6A single-base site qPCR. The global m6A levels in left ventricle tissue were significantly decreased in the LPS group. While 27 transcripts (23 mRNAs and four lncRNAs) were hypermethylated, 46 transcripts (39 mRNAs and 7 lncRNAs) were hypomethylated in the LPS group. The mRNA expression of writers and readers was significantly decreased in the LPS group. The m6A modification of Clec1b, Stk38l and Tnfrsf26 was associated with platelet activation and apoptotic pathways. Moreover, the decrease in m6A modification of lncRNA XR_346,771 may be related to cation import in cardiac tissue. Our data provide novel information regarding changes to m6A modifications in cardiac tissue during sepsis, and m6A modifications might be promising therapeutic targets.

Introduction

Sepsis is a life-threatening condition involving organ dysfunction that is caused by a dysregulated host response to infection (Cohen et al., 2015), It has been reported that approximately 50% of patients with septic shock are diagnosed with septic cardiomyopathy, which causes myocardial dysfunction, and these patients tend to have poor prognoses (Zaky et al., 2014). Myocardial dysfunction is an important factor that contributes to the high mortality rate of sepsis. Therefore, it is necessary to elucidate the pathogenesis of myocardial dysfunction in order to develop new treatment strategies to reduce the mortality rate of sepsis.

To date, a series of reversible posttranscriptional modifications located in RNAs have gained increasing attention. More than 140 chemical modifications have been discovered in RNAs. The modifications range from simple methylation or isomerisation, such as N6-methyladenosine (m6A), 5-methylcytosine (m5C), N1-methyladenosine (m1A), pseudouridine (Ψ), 5-methyluridine (m5U),1-methylguanosine, and 7-methylguanosine (m1G and m7G, respectively) and inosine (I), to complex multistep chemical modifications, such as N6-threonylcarbamoyladenosine and 5-methoxycarbonyl-methyl-2-thiouridine (mcm5s2U) (Pan, 2018). Among them, the m6A is one of the most abundant and influential modifications in eukaryotes. M6A modification is involved in regulating a variety of posttranscriptional events, including pre-miRNA processing and RNA stability, translation and alternative splicing (Chokkalla et al., 2019). M6A modifications occur via the m6A methyltransferases called “writers”; they are removed by the demethylases called “erasers” and are recognized by m6A-binding proteins called “readers”. The orchestrated interplay among writers, erasers, and readers drives the dynamics and outcomes of the m6A modification of RNAs. An increasing number of studies have reported that m6A modification plays important roles in biological and pathophysiological processes, such as tumorigenesis, embryonic stem cell differentiation, and viral infection (Kennedy et al., 2016). In mouse GC-1 SPG cells, meclofenamic acid inhibits spermatogonial proliferation by affecting CDKs expression through a m6A-dependent mRNA degradation pathway (Huang et al., 2019). In liver cancer, overexpression of METTL3 leads to the degradation of suppressor of cytokine signalling two and promotes tumor growth (Lin et al., 2019).

M6A modification is also associated with the pathophysiological processes of cardiac differentiation and a variety of cardiovascular diseases. Methyltransferase-like 3 (METTL3) is a major factor involved in abnormal m6A modification. Silencing METTL3 enhances the autophagic flux and represses apoptosis in hypoxia/reoxygenation-treated cardiomyocytes (Song et al., 2019). Overexpression METTL3 increased m6A levels in mRNAs isolated from transverse aortic constriction (TAC) mice hearts. TAC inducd pathological hypertrophic cellular growth was attenuated in hearts of METTL3-overexpressing mice, as evidenced by the crosssectional area of myocytes (Kmietczyk et al., 2019). In addition, aging caused METTL3/METTL14 down-regulation in aorta and atria in male animals while the differentiation-induced increased level of METTL16 (Arcidiacono et al., 2020). Prabhu M et al. found that downregulation of fat mass and obesity associated protein (FTO) expression in failing mammalian hearts and hypoxic cardiomyocytes increased the m6A modification of RNAs and reduced the contractile function of cardiomyocytes (Mathiyalagan et al., 2019). FTO also played a vital role in cardiac contractile function during homeostasis and remodeling. FTO overexpression attenuated the ischemia-induced elevation in m6A modification and significantly improved cardiac function of post-myocardial infraction (Mathiyalagan et al., 2019). Berulava et al. reported that differently expressed genes of m6A methylation are involved in heart failure development. Bioinformatics analysis has revealed that the differentially m6A are mainly involved in metabolism and cardiac signaling (Berulava et al., 2020). However, the regulatory role of the m6A modification in cardiac function is still unclear.

The contraction of the ventricle transports blood to the capillaries of the body or lungs, while the contraction of the atria send blood into the ventricle. In addition, the right ventricle mainly supplies blood to the lungs, and the blood in the right ventricle is venous blood. The left ventricle mainly supplies blood to the organs and tissues of the body, and the blood in the left ventricle is arterial blood. Therefore, the function of the left ventricle is very important to maintain organ perfusion. If the left ventricular function declines, it will leads to organ perfusion insufficiency and, eventually, organ failure. In this direction, we focused on the mechanism underlying left ventricle injury in sepsis to find any potential clinical significance for improving organ perfusion. The overall goal of this study was to explore a new layer of epigenetic alterations through the genome-wide screening of the altered m6A-tagged transcript profiles in lipopolysaccharide (LPS)-induced myocardial dysfunction. We successfully mapped m6A transcripts in left ventricle tissue in LPS-induced sepsis. Some potential roles of the m6A modification in the physiological and pathological mechanisms of sepsis were revealed.

Materials and Methods

Animals

Eight-week-old male Wistar rats weighing 250–350 g were used in this study. All the rats used in our study were obtained from Charles River Laboratories (Beijing, China). Water and standard laboratory food were freely available to the animals. The Experimental Animal Welfare Ethics Branch and the Biomedical Ethics Committee of Peking University approved our study protocol (LA 2020343). All the procedures for handling the animals were in accordance with the Guide for the Care and Use of Laboratory Animals (NIH Publication No. 85–23, revised 1996).

After acclimating for 1 week, 10 Wistar rats were randomly divided into two groups. The rats in the LPS group (n = 5) were intraperitoneally injected with LPS (10 mg/kg, 5 mg LPS dissolved in 1 ml 0.9% saline). The rats in the control group (n = 5) were intraperitoneally injected with 0.9% saline (2 ml/kg). 24 hours after LPS injection, mean blood pressure was measured by the tail-cuff method with a noninvasive blood pressure measurement system. The left ventricle tissue samples were immediately transferred to liquid nitrogen and stored at −80°C for preservation.

RNA Extraction and Quality Control

Total RNA was isolated from the left ventricle tissues of the LPS (n = 5) and control (n = 5) groups using TRIzol Reagent (Invitrogen, United States). The quantity and purity of the total RNA samples were measured by a NanoDrop ND-1000 (ThermoFisher, United States).

Quantification of Global m6A Levels

Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based mRNA modification detection was performed according to the Aksomics standard protocol. Briefly, 5 μg of total RNA from the heart tissues of the LPS (n = 5) and control (n = 5) groups was used to isolate mRNA using the NEBNext Poly(A) mRNA Magnetic Isolation Module (NEB, E7490, United States). The purified mRNA was quantified using the Qubit RNA HS Assay kit (ThermoFisher, United States) and digested to single dephosphorylated nucleosides by an enzyme mixture. Pretreated nucleosides solution was deproteinized using Satorius 10,000-Da MWCO spin filter. LC-MS/MS analysis was performed on an Agilent 6460 QQQ mass spectrometer with an Agilent 1260 HPLC system using Multi reaction monitoring (MRM) detection mode (Agilent, United States. The nucleosides were quantified by using retention time and the nucleoside to base ion mass transitions of 268-136 (A) and 282-150 (m6A). Quantification was performed in comparison with the standard curve obtained from pure nucleoside standards running with the same batch of samples. The m6A level was calculated as the ratio of m6A to A based on the calibrated concentrations.

Quantitative Real-Time PCR

After extraction, the total RNA was reverse-transcribed into cDNA using SuperScriptTM III Reverse Transcriptase (Invitrogen) according to the manufacturer’s instructions. Amplification and detection were performed using 2× SYBR Green PCR Master Mix (Arraysta, United States) on a ViiA 7 Real-Time PCR System (Applied Biosystems). The sequences of the gene-specific primers are listed in Supplementary Table S1. GAPDH was used as the control housekeeping gene.

Western Blot Analysis

Protein extraction of left ventricle tissue and the protein concentration was determined by Bradford method (M&C Gene Technology Ltd.). The protein samples were then subjected to SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to polyvinylidene difluoride membrane. The membranes were probed with antibodies of interest. The antibodies used were as follows: METTL3, METTL14, Wilms tumor 1-associated protein (WTAP), and YTH N6-methyladenosine RNA binding protein 1 (YTHDF1), YTHDF3 (all1:1,000; Cell Signal Technology), and β-actin (1:4,000; Abcam).

M6A mRNA&lncRNA Epitranscriptomic Microarray

Sample preparation and microarray hybridization were performed based on Arraystar’s standard protocols (Arraystar). Briefly, total RNA from the heart tissues of the LPS (n = 5) and control (n = 5) groups was immunoprecipitated with an anti-m6A antibody (Synaptic Systems, 202003). The modified RNAs immunoprecipitated by the magnetic beads were labelled as “IP”, and the unmodified RNAs in the supernatant were labelled as “Sup”. The “IP” and “Sup” RNAs were labeled with Cy5 and Cy3, respectively, as cRNAs in separate reactions using the Arraystar Super RNA Labeling Kit (ArrayStar). The cRNAs were combined and hybridized onto an Arraystar Rat mRNA&lncRNA Epitranscriptomic Microarray (4 × 44K, Arraystar) that contained 27,770 mRNA and 10,582 lncRNA degenerate probes. The hybridized arrays were scanned in two-color channels by an Agilent Scanner G2505C.

Microarray Data Analysis

Agilent Feature Extraction software (version 11.0.1.1, United States) was used to analyze the acquired array images. Each probe signal was evaluated and flagged as present, absent or marginal in at least 5 out of 10 samples. The raw intensities of the IP (immunoprecipitated, Cy5-labeled) and Sup (supernatant, Cy3-labeled) samples were normalized by the average of log2-scaled spike-in RNA intensities. The differentially m6A-methylated RNAs between the LPS and control groups were identified by filtering with thresholds of fold change >1.5 and statistical significance (p < 0.05).

Bioinformatics Analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using some package in the R environment for statistical computing and graphics. The differentially m6A-methylated mRNAs, as well as the downstream mRNAs predicted by the competing endogenous RNA (ceRNA) network, were classified into different GO terms and enriched in certain biological pathways. Protein-protein interaction (PPI) analysis was performed using the STRING database (https://string-db.org). The SRAMP database (http://www.cuilab.cn/sramp) was used to predict the number of m6A sites on the differentially m6A-methylated transcripts (Zhou et al., 2016).

M6A Single-Base Site qPCR

Before amplification and detection, the LPS (n = 5) and control (n = 5) group samples were treated with an Escherichia coli toxin and RNA endoribonuclease, MazF. MazF is reported to be sensitive to m6A modification within the ACA motif (Imanishi et al., 2017). The MazF-digested mRNA samples and the nondigested samples were subjected to reverse transcription using SuperScriptTM III Reverse Transcriptase (Invitrogen) for qPCR as described above. The targeted lncRNAs and mRNAs were predicted by SRAMP to identify the ACA motif and m6A position. The primer sequences specific for the methylated lncRNAs and mRNAs are listed in Supplementary Table S2. The relative expression levels were calculated using the 2−△△Ct method, and the tested genes were calibrated with MazF as follows:

Competing Endogenous RNA Network Construction

The targeted lncRNAs were verified by m6A single-base site qPCR to construct the ceRNA network. Only three steps were used for ceRNA network construction. First, the potential target microRNAs of lncRNAs were predicted with Aksomics’s homemade miRNA target prediction software based on TargetScan and miRanda (Enright et al., 2003; Pasquinelli, 2012). Second, the target genes of the miRNAs involved in the lncRNA-miRNA interaction network were predicted with the database described above. Finally, the lncRNA-microRNA-mRNA interaction networks were constructed by Cytoscape v2.8.3. The targeted mRNAs were also analyzed by GO and KEGG to completely understand the ceRNA effects.

Statistical Analysis

The statistical significance of the GO and KEGG analysis of mRNAs enrichment were calculated by Fisher’s exact test p < 0.05 and -log10(p) transformed as the enrichment score as well as mRNAs predicted by the ceRNA network for GO and KEGG analysis. The significance of the differences in the expression and methylation levels between the LPS and control groups was evaluated with an unpaired two-sided t-test for LC-MS/MS, qRT-PCR, microarray analysis and m6A single-base site qPCR, and the recommended p-value threshold was less than 0.05. “*” indicates a significant difference compared with control group (p < 0.05).

Results

The Global Levels of m6A Modification Were Decreased

In about 24 h after giving intraperitoneal injection of LPS, we measured the MAP of rats. As previous paper, the rats with a MAP declined to 25–30% or below were chosen as the sepsis model (Nie et al., 2020). Furthermore, signs of shock such as lassitude, tachycardia and a sharp drop in body temperature were observed in all sepsis rats. In the control group, there was no significant change in MAP in rats injected with saline. Previous study showed that there is a positive relationship between MAP and heart function (Yang et al., 2018; Zhang et al., 2019). Therefore, we isolated heart tissue and chose the left ventricle for identification of m6A modification levels (Figure 1A). There was no measurement of cardiac function or sepsis physiology in these experiments.

FIGURE 1

To determine the difference in the m6A modification levels between the LPS and control groups, we used LC-MS/MS to measure these levels. We found that the global m6A modification level in the rat left ventricle tissues from the LPS group was significantly decreased compared with control group (Figure 1B).

The Expression of m6A Writers and Readers Was Downregulated

The m6A methylation modification is dynamically modulated by RNA methyltransferases, RNA-binding proteins and demethylases. We analyzed the mRNAs level of RNA methyltransferases (writers: METTL3, METTL14, and WTAP), RNA-binding proteins (readers: YTHDF 1 and YTHDF 3) and a demethylase (eraser: FTO). It was found that the expression of the METTL3/METTL14/WTAP/YTHDF 1, and YTHDF 3 in the LPS group was significantly downregulated compared with control group. However, the expression of FTO has no significant difference (Figure 1C). In addition, we measured the protein expression of METTL3/14, WTAP, and YTHDF1/3. We found that protein expressions of these proteins significantly downregulated in LPS group (Supplementary Figure S1). The expressions of METTL3/14, WTAP, and YTHDF1/3 were decreased by 42.2, 64, 47.6, 49.6 and 56.6%, respectively. The results of protein expression and mRNA expression are consistent.

M6A Modification Profiles of lncRNAs and mRNAs

Probes specific for 27,770 mRNAs and 10,582 lncRNAs were used to analyze the samples from the LPS and control groups using an m6A mRNA and lncRNA Arraystar epitranscriptomic microarray. The results showed that the m6A modification levels of 62 mRNAs and 11 lncRNAs were significantly altered in the LPS group compared with control group (fold change >1.5, p-value < 0.05) (Figures 2A,B). The altered lncRNAs and mRNAs were aligned for cluster analysis (Figures 2C,D). Based on their fold changes, we selected 11 lncRNAs with significantly altered m6A modification levels and the top 10 up- and downregulated mRNAs to present the results (Tables 1, 2).

FIGURE 2

TABLE 1

Transcript_IDTypeGene symbolRNA lengthLocusRegulationFold changep-value
XR_346771lncRNALOC1025527861764chr5:148464894–148467759:−Hyper1.8826516320.007280323
XR_338486lncRNALOC102547952997chr10:14824098–14825718:+Hyper1.7115753960.004096892
XR_595034lncRNALOC1036935431931chr15:32741179–32746755:−Hyper1.6846974840.000352,285
ENSRNOT00000080394lncRNAAABR07059875.4185chr4:39636928–39637206:−Hyper1.5166566290.012635768
XR_349185lncRNALOC1025485421841chr9:93297368–93301959:−Hypo0.5026937430.000096980
XR_590580lncRNALOC103691283540chr12:22434074–22434701:−Hypo0.5344794820.004268491
XR_594228lncRNALOC103690476702chr8:130823601–130827254:+Hypo0.5558945870.00206,689
XR_589378lncRNALOC103690602719chr11:79090944–79093774:+Hypo0.6231676910.00376,447
ENSRNOT00000092334lncRNAComp991chr16:20803585–20805482:−Hypo0.6421648610.003406754
ENSRNOT00000086846lncRNAAABR07026021.1524chr16:59517000–59524544:+Hypo0.6541357080.012402534
XR_596592lncRNALOC1025484025498chr17:5486419–5494314:−Hypo0.6607125590.015797815

The detailed information of the hyper-methylated and hypo-methylated lncRNAs.

TABLE 2

Transcript_IDTypeGeneSymbolRNA lengthLocusRegulationFold changep-value
ENSRNOT00000005066protein_codingCfap522237chr10:54470835–54512169:−Hyper2.2991002940.000003881
ENSRNOT00000000010protein_codingTmco5b1361chr3:104749051–104765436:+Hyper1.82630710.001794981
ENSRNOT00000030007protein_codingPtk2b3848chr15:42827310–42947656:−Hyper1.7380450270.019677042
ENSRNOT00000006443protein_codingRtn44665chr14:114126966–114174458:+Hyper1.6995590990.010322126
ENSRNOT00000008760protein_codingCd441437chr3:92697833–92749121:−Hyper1.6833129140.003639311
ENSRNOT00000045153protein_codingLOC1036899831223chrX:158623240–158655198:Hyper1.682142180.021749985
ENSRNOT00000082537protein_codingClec1b958chr4:163162211–163170466:+Hyper1.6514271120.017707274
NM_053484protein_codingGas76796chr10:54086843–54240798:+Hyper1.6236293550.000157904
ENSRNOT00000024137protein_codingDrd41416chr1:214278296–214281483:+Hyper1.6031018860.019629213
ENSRNOT00000088345protein_codingAABR07052508.1258chr3:58380143–58386375:+Hyper1.5991835020.011739780
NM_001083336protein_codingStk38l2406chr4:181027212–181087530:+Hypo0.4619365570.000120507
ENSRNOT00000076624protein_codingSlpil3663chr3:160774855–160777092:−Hypo0.4761972760.000093915
ENSRNOT00000015918protein_codingTnfrsf214350chr9:20546159–20621051:Hypo0.4872178030.000042853
ENSRNOT00000092513protein_codingRps15a528chr1:187759865–187766670:Hypo0.5136738690.000863271
ENSRNOT00000011141protein_codingGprc5d1402chr4:168872897–168884886:−Hypo0.5243978230.000189455
NM_001109118protein_codingElovl23731chr17:21382461–21422410:+Hypo0.5255639580.000016059
ENSRNOT00000013732protein_codingIl61045chr4:3043231–3047807:+Hypo0.5321284190.002095281
ENSRNOT00000077994protein_codingSsr11196chr17:27496353–27510918:+Hypo0.5596237580.010488981
ENSRNOT00000083955protein_codingFkbp11717chr7:140398253–140401686:−Hypo0.5698555080.000004472
NM_053372protein_codingSlpi667chr3:160799979–160802228:−Hypo0.5904195220.004477119

The detailed information of the top ten hyper-methylated and top ten hypo-methylated mRNAs.

The data that support the findings of this study are openly available in the GenBank databases under accession number GSE159309.

GO and KEGG Analyses of Differentially Methylated mRNAs

GO analysis of the mRNAs with increased and decreased m6A modification levels was performed. Among the mRNAs with decreased m6A modification levels, the “cytokine-mediated signaling pathway” in BP, “immune response” in BP and “cytokine activity” in MF were revealed to have the maximum enrichment scores for GO terms (Figures 3A,B; Tables 3, 4).

FIGURE 3

TABLE 3

GO.IDTermOntologyCountp-valueFdrEnrichment score
GO:0000902Cell morphogenesisBiological process77.0344E-050.3244735514.152772946
GO:0032989Cellular component morphogenesisBiological process70.0001284770.3244735513.891174337
GO:0022604Regulation of cell morphogenesisBiological process50.0001664880.3244735513.778618248
GO:0006928Movement of cell or subcellular componentBiological process80.0003930.3244735513.405607496
GO:2000463Positive regulation of excitatory postsynaptic potentialBiological process20.0005250540.3244735513.279795677
GO:0120,039Plasma membrane bounded cell projection morphogenesisBiological process50.000588020.3244735513.230607569
GO:0048858Cell projection morphogenesisBiological process50.0005999810.3244735513.22186279
GO:0031175Neuron projection developmentBiological process60.0006271380.3244735513.202636833
GO:0019221Cytokine-mediated signaling pathwayBiological process40.0006576480.3244735513.18200643
GO:0045785Positive regulation of cell adhesionBiological process40.000676980.3244735513.169424277
GO:0120,025Plasma membrane bounded cell projectionCellular component80.0008402050.2321676143.075614779
GO:0042995Cell projectionCellular component80.0009725470.2321676143.012089301
GO:0043197Dendritic spineCellular component30.0010886610.2321676142.963107415
GO:0044309Neuron spineCellular component30.0011200310.2321676142.950770139
GO:0044456Synapse partCellular component50.0013799250.2321676142.860144529
GO:0098794PostsynapseCellular component40.0016896470.2321676142.772204087
GO:0014069Postsynaptic densityCellular component30.0027401570.2321676142.562224576
GO:0099572Postsynaptic specializationCellular component30.0027960980.2321676142.55344754
GO:0032279Asymmetric synapseCellular component30.0028813340.2321676142.540406329
GO:0045202SynapseCellular component50.0029603840.2321676142.528652018
GO:0051219Phosphoprotein bindingMolecular function20.00543196112.265043371
GO:0017124SH3 domain bindingMolecular function20.00666386712.176273674
GO:0030594Neurotransmitter receptor activityMolecular function20.00738455912.131675429
GO:0048406Nerve growth factor bindingMolecular function10.01117635511.951699809
GO:0001591Dopamine neurotransmitter receptor activity, coupled via Gi/GoMolecular function10.01228745911.910537933
GO:0015250Water channel activityMolecular function10.01450611811.838448788
GO:0004952Dopamine neurotransmitter receptor activityMolecular function10.01561367711.806494817
GO:0005372Water transmembrane transporter activityMolecular function10.01561367711.806494817
GO:0043121Neurotrophin bindingMolecular function10.01561367711.806494817
GO:0035240Dopamine bindingMolecular function10.0200321311.698272881

Gene Ontology analysis of the top ten hyper-methylated mRNAs.

TABLE 4

GO.IDTermOntologyCountp-valueFdrEnrichment score
GO:0019221Cytokine-mediated signaling pathwayBiological process63.15545E-050.167235864.500939235
GO:0045071Negative regulation of viral genome replicationBiological process38.13812E-050.167235864.089475756
GO:0051707Response to other organismBiological process80.0001103040.167235863.957409854
GO:0043207Response to external biotic stimulusBiological process80.0001135420.167235863.944843243
GO:0009607Response to biotic stimulusBiological process80.0001464220.167235863.834392194
GO:0006955Immune responseBiological process90.0001479750.167235863.829813087
GO:1903901Negative regulation of viral life cycleBiological process30.0002722860.2637675443.564974349
GO:0045069Regulation of viral genome replicationBiological process30.0003508150.2973598863.454921375
GO:0048525Negative regulation of viral processBiological process30.0005168580.3894240833.286628429
GO:0019079Viral genome replicationBiological process30.0006159080.391154533.210484097
GO:0000782Telomere cap complexCellular component10.01942985611.711530414
GO:0000783Nuclear telomere cap complexCellular component10.01942985611.711530414
GO:0005883NeurofilamentCellular component10.02117794211.674116247
GO:0005576Extracellular regionCellular component80.02162035911.665137091
GO:0034709MethylosomeCellular component10.02292300611.63972844
GO:0005687U4 snRNPCellular component10.02466505211.607917962
GO:0034719SMN-Sm protein complexCellular component10.03160316911.500269361
GO:0071004U2-type prespliceosomeCellular component10.03160316911.500269361
GO:0005615Extracellular spaceCellular component60.03559375911.448626141
GO:0098802Plasma membrane receptor complexCellular component20.03950618711.40333488
GO:0005125Cytokine activityMolecular function40.0001233430.15541233.908885157
GO:0005126Cytokine receptor bindingMolecular function40.0008049670.5071289133.094222177
GO:0008009Chemokine activityMolecular function20.0013524910.5680463382.868865526
GO:0098772Molecular function regulatorMolecular function80.0020306620.6396585972.692362313
GO:0042379Chemokine receptor bindingMolecular function20.0031830340.7337016182.497158668
GO:0048018Receptor ligand activityMolecular function40.0034938170.7337016182.456699818
GO:0030545Receptor regulator activityMolecular function40.0045054660.8109839682.346260236
GO:0004791Thioredoxin-disulfide reductase activityMolecular function10.01613909511.792120819
GO:0070990snRNP bindingMolecular function10.01613909511.792120819
GO:0004312Fatty acid synthase activityMolecular function10.01759382211.754639806

Gene Ontology analysis of the top ten hypo-methylated mRNAs.

KEGG analysis of the mRNAs with increased and decreased m6A methylation modification levels were also conducted. The results showed that the mRNAs with increased m6A modification levels were enriched in only one pathway, namely, the “PPAR signaling pathway” (Figure 3C). The mRNAs with decreased m6A modification levels were enriched in 27 pathways, and the following main pathways reached higher enrichment scores: “IL-17 signaling pathway”, “TNF signaling pathway” and “Cytokine-cytokine receptor interaction” (Figure 3D).

qPCR Results of a Single-Base m6A Site

By using the SRAMP database, we predicted the mRNAs and lncRNAs with differential m6A modification in the order from high to low fold change of m6A modification. As a result, we identified four lncRNAs and six mRNAs with m6A ACA sites and with high confidence scores of qPCR validation of single-base m6A sites. The Detail information of lncRNAs and mRNAs detected by m6A single-base site qPCR was shown in Table 5. The results showed that among the lncRNAs, m6A modification levels were significantly increased in XR_346771. Among the mRNAs, m6A modification levels were significantly increased in C-type lectin domain family 1 member B (Clec1b) and tumor necrosis factor receptor superfamily member 26 (Tnfrsf26) and significantly decreased in serine/threonine kinase 38 like (STK38L) (Figure 4).

TABLE 5

GeneSymbolTranscript_IDPositionSequence contextScoreDecision
LOC103693543XR_5950341359GAGAG AUCUA CUGAA0.674m6A site (very high confidence)
CAUCU GGACA ACCUC
ACAUC AUACC AUCUC
LOC102552786XR_3467711712GGUCC UCCAA UGAAU0.707m6A site (very high confidence)
UCAAA GGACA AUAGG
CGACC ACCAG UGAAU
LOC102547952XR_3384861546CUGCA ACAUG GGAAG0.676m6A site (very high confidence)
AAGAG GGACA UAAGA
GAGGA CCCCA CCCCC
LOC102548402XR_5965923965AUCUG ACGGC AGGAU0.677m6A site (very high confidence)
UUGGA GGACA CCUCU
GAAAG GGCCC CAGAA
Clec1bENSRNOT00000082537831ACCCA GCTTC CTGTA CAGAG AGACA TTACT TAATA TGTGA GAGAA0.731m6A site (very high confidence)
Tnfrsf26ENSRNOT00000066943416CAGGA ATGCA ATGCC ACAAT GGACA CTGTG TGTGA CTCCA AGCAA0.634m6A site (high confidence)
Ptk2bENSRNOT000000300073278GTGCT ACTTG GGCTA CATCT GGACA GAAAG GACTC TGGGC ACAGA0.732m6A site (very high confidence)
Tnfrsf21ENSRNOT00000015918928CTGGG GTGTG AGGAA GAAAG GGACA GAGAA TGAAG ATGTG CGGTG0.747m6A site (very high confidence)
Stk38lNM_0010833362090ACCGG CTGCA AGGAA CTTAA GGACA CTGACTCCGA CATTA GAATT0.640m6A site (high confidence)
Ankrd54ENSRNOT000000144131173CCCTC TGGCT GTTAG GGAAG GGACA GGAAC CCCAG AACAG AGGAA0.643m6A site (high confidence)

Detail information of lncRNAs and mRNAs with hyper- and hypo-methylation detected by m6A single-base site qPCR.

FIGURE 4

PPI Analysis of Targeted Genes

By employing the STRING database, we performed PPI analysis of Clec1b, Stk38l, and Tnfrsf26 (Figures 5A–C). KEGG analysis revealed that the Clec1b interaction-related proteins were mainly enriched in “platelet activation” and other pathways. The Stk38l interaction-related proteins were mainly enriched in the “hippo signaling pathway”, and the Tnfrsf26 interaction-related proteins were mainly enriched in “apoptosis” (Table 6).

FIGURE 5

TABLE 6

Target geneTerm IDTerm descriptionFDRMatching proteins in the network
Clec1brno04650Natural killer cell mediated cytotoxicity0.000002500Lcp2, Ptpn11, Ptpn6, Syk
rno04611Platelet activation0.000360,000Gp6, Lcp2, Syk
rno04662B Cell receptor signaling pathway0.004900000Ptpn6, Syk
rno04664Fc epsilon RI signaling pathway0.004900000Lcp2, Syk
rno04660T Cell receptor signaling pathway0.006500000Lcp2, Ptpn6
rno04380Osteoclast differentiation0.007300000Lcp2, Syk
rno04072Phospholipase D signaling pathway0.009700000Ptpn11, Syk
rno04630Jak-STAT signaling pathway0.009700000Ptpn11, Ptpn6
rno05205Proteoglycans in cancer0.012500000Ptpn11, Ptpn6
Stk38lrno04392Hippo signaling pathway - multiple species0.000260000Mob1a, Mob1b
rno04390Hippo signaling pathway0.003500000Mob1a, Mob1b
Tnfrsf26rno04210Apoptosis0.023800000Fadd, Tnfsf10
rno04217Necroptosis0.023800000Fadd, Tnfsf10

Pathways of the matching proteins in the PPI network.

ceRNA Analysis of lncRNA XR_346771

A ceRNA network of the lncRNA-microRNA-mRNA interaction network was constructed for XR_346,771 (Figure 6A). GO and KEGG analyses were performed on the predicted mRNAs (Figures 6B,C). The “inorganic cation import across plasma membrane”, “transmembrane transport”, and “import across plasma membrane” in BP were significantly enriched for GO terms (Figure 6B). FAM155B and PICALM are downstream proteins related to transmembrane transport that are regulated by XR_346771, and the corresponding sponged miRNAs are shown in Table 7.

FIGURE 6

TABLE 7

LncRNATargeted miRNATargeted mRNA
XR_346771miR-146a-3p, miR-148a-3pPICALM
miR-148b-3p, miR-152-3p, miR-361-3p, miR-384-5p, miR-500-3p, miR-501-3p
miR-122-5p, miR-146a-3p, miR-188-3p, miR-298-5p, miR-326-5p, miR-329-5p, miR-361-3p, miR-500-3p, miR-501-3p, miR-542-3p, miR-674-5p, miR-92a-1-5pFAM155B

LncRNA XR_346771targeted miRNAs and mRNAs.

Discussion

Our study suggested that the m6A modification level was decreased in septic heart tissues. Our genome-wide screening of m6A-tagged transcript profiles indicated that the m6A modification levels in 39 mRNAs and 7 lncRNAs were significantly decreased and that the m6A modification levels in 23 mRNAs and four lncRNAs were significantly increased.

Pathway analyses of mRNAs with altered m6A modification were conducted in our study. The results showed that the mRNAs with decreased m6A modification levels were enriched in many pathways related to immune and inflammatory responses. In recent years, the roles of m6A modification in autoimmune and inflammatory diseases have attracted substantial attention (Rubio et al., 2018). METTL3 is a key enzyme of m6A methylation modification and is involved in immune and inflammatory regulation. Feng et al. found that METTL3 inhibits inflammation by affecting the alternative splicing of MyD88 (Feng et al., 2018). METTL3 expression is significantly upregulated in blood samples from patients with rheumatoid arthritis. In addition, METTL3 attenuates LPS-induced macrophage-mediated inflammation through the NF-κB pathway (Wang et al., 2019a). The m6A “reader” YT521-B homology domain family 2 (YTHDF2) inhibits HCC cells and tumor vasculature by degrading the mRNAs of IL11 and serpin family E member 2 (Wang et al., 2019b). These findings indicate the important role of m6A modification in inflammatory and immune disorders. Sepsis is mainly caused by the host’s unbalanced response to infection. In sepsis, microbial infections or necrotic tissues release a large amount of harmful substances, leading to the activation of the systemic immune response and the excessive activation of immune cells. The excessive release of cytokines is destructive. There is currently no effective treatment for sepsis because in the past few decades, attempts to use anti-inflammatory treatments to limit the tissue damage caused by excessive inflammation have failed (Zeni et al., 1997). Our study suggests that alterations in m6A modification in sepsis are closely related to inflammatory and immune responses. Since we study cardiac tissue, we speculate that m6A modification plays an important role in sepsis-induced cardiac function dysfunction.

We selected four lncRNAs and six mRNAs for the qPCR validation of single-base m6A sites in our study. The results showed that among the lncRNAs, the m6A modification levels were significantly increased in XR_346771. Among the mRNAs, the m6A modification levels were significantly increased in Clec1b and Tnfrsf26 and significantly decreased in Stk38l. These results are consistent with those of high-throughput sequencing; therefore, we attempted to conduct an in-depth analysis of this lncRNA and the three mRNAs.

First, we performed PPI analysis of Clec1b, STK38L, and Tnfrsf26 to investigate the downstream possible regulatory genes and biological functions of these proteins. The Clec1b-interacting proteins are enriched in the pathway of “platelet activation”. C-type lectin-like receptor 2 (CLEC-2) is a protein encoded by the Clec1b gene. Platelets play a critical role in innate and adaptive immunity (Semple et al., 2011). Platelets can alleviate LPS-induced septic shock by regulating the ability of macrophages to engulf and kill bacteria (Xiang et al., 2013). In two mouse models of sepsis (intraperitoneal LPS injection and cecal ligation and puncture), platelet CLEC-2 reduced the severity of sepsis by controlling the migration of monocytes/macrophages to the infection site, the expression of inflammatory mediators, and damage to organs (Rayes et al., 2017). In our sepsis model, we found that the m6A modification level of Clec1b mRNA was significantly increased. Considering that the degree and pattern of m6A modification may affect the transport, splicing, storage, translation, stability, and decay of mRNAs, we speculated that the elevated level of Clec1b m6A modification affects the functions of Clec1b and associate with activation of platelets. The interacting proteins of Clec1b were glycoprotein VI (Gp6), lymphocyte cytosolic protein 2 (Lcp2), and spleen tyrosine kinase (Syk). These proteins might be used as target proteins for in-depth research.

Our study suggests that the m6A modification level of Tnfrsf26 mRNA is increased and of Stk38l mRNA is decreased. According to PPI network analysis, Stk38l-interacting proteins are enriched in the “Hippo signaling pathway” pathway. Hippo signaling pathway may promote cell differentiation and death and inhibit cell proliferation (Chang et al., 2019). Tnfrsf26-interacting proteins are mainly enriched in the pathway of apoptosis. Sepsis-induced cardiac dysfunction has been reported by many studies to be closely related to apoptosis. When LPS stimulates H9C2 cells (rat embryonic cardiomyoblasts), the overexpression of miR-146a suppresses apoptosis and helps attenuate myocardial depression (An et al., 2018). Chao et al. found that the overexpression of Tid1-S can enhance ER-α to activate p-PI3K/p-Akt, attenuating LPS-induced apoptosis in H9C2 cells (Chao et al., 2019). In recent years, as the understanding of m6A modification has gradually deepened, studies have found that m6A modification plays an important role in the process of apoptosis. In the human glioma cell line U251, lower level of m6A modification can reduce apoptosis and promote cell proliferation (Li et al., 2019). Moreover, METTL3 can regulate the expression of several important proteins that regulate the survival, apoptosis, invasion, and proliferation of lung cancer cells (Lin et al., 2016). We hypothesized that the m6A modification of Tnfrsf26 and Stk38l might be related to cardiomyocyte apoptosis.

LncRNAs are a class of noncoding RNAs that are more than 200 nucleotides in length. In recent years, m6A-modified lncRNAs have received extensive attention. M6A modification may control gene expression by regulating the translation efficiency and stability of lncRNAs (Coker et al., 2019). For example, the m6A-modified lncRNA MALAT1 is implicated in ischemia reperfusion injury-induced inflammation (Yang et al., 2020). LncRNAs contain miRNA-responsive elements, which function as ceRNAs, interact with miRNAs and indirectly regulate mRNAs. In the present study, we established and analyzed a potential ceRNA network of XR_346771 through bioinformatics analysis. We performed GO and KEGG analyses of the predicted mRNAs to further explore the function of XR_346771.

GO analysis showed that “inorganic cation import across plasma membrane”, “inorganic ion import across plasma membrane”, “transmembrane transport”, and “import across plasma membrane” were significantly enriched. Cardiac contraction requires Ca2+, so abnormal Ca2+ homeostasis may play a key role in the pathogenesis of common cardiovascular diseases. Studies have reported that m6A modification promotes neurite extension and neuronal differentiation by acting as a Ca2+ channel (Mukobata et al., 2002). In 6-OHDA-induced PC12 cells and the cerebral striatum of rats with Parkinson’s disease, decreased m6A modification induces the expression of N-methyl-d-aspartate receptor 1 and increases oxidative stress and Ca2+ influx, leading to apoptosis of dopaminergic neurons (Chen et al., 2019). These studies indicate that m6A modification is involved in Ca2+ transport. We speculated that the m6A modification of XR_346771 might associate with abnormal Ca2+ homeostasis in cardiac tissues.

It is interesting that even though the methyltransferase complex was downregulated upon sepsis induced myocardial dysfunction; there were still 27 transcripts that were significantly hypermethylated. Chokkalla et al. found that, compared with sham group, global m6A increased significantly at 12 and 24 h of reperfusion in transient middle cerebral artery occlusion in C57BL/6J mice. The FTO decreased significantly after stroke compared with sham. While 139 transcripts (122 mRNAs and 17 lncRNAs) were hypermethylated, 8 transcripts (5 mRNAs and 3 lncRNAs) were hypomethylated in the ischemic brain at 12 h reperfusion (Chokkalla et al., 2019). Wang et al. found that METTL3 was downregulated after traumatic brain injury in mice. In total, 922 m6A peaks were differentially expressed as determined by m6A-RIP-seq, with 370 upregulated and 552 downregulated methylated mRNA (Wang et al., 2019b). These studies are consistent with our research. Previous research indicated that the methylation modification of m6A has been proved to be reversible, as it modulated by RNA methyltransferases, RNA-binding proteins and demethylases. The m6A methyltransferase, also known as “Writers”, including METTL3/14, WTAP, KIAA1429 and RNA binding motifs protein 15/15B (RBM15/15B), E3 ubiquitin-protein ligase Hakai (HAKAI) and zinc finger CCCH-type containing 13 (ZC3H13). The RNA-binding proteins, also known as “readers”, including: YTHDF1/2/3, YTH domain-containing reader proteins 1/2 (YTHDC1/2), insulin-like growth factor 2 mRNA-binding proteins 1/2/3 (IGF2BP1/2/3), epithelium-specific ETS (ESE) transcription factors (ELF3), heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1). The m6A demethylase, also known as “eraser”, including: FTO, α-ketoglutarate-dependent dioxygenase alkB homologue 5 (ALKHB5). In our research we tested mRNA level of some enzymes, but not all enzymes. Therefore, there may be other enzymes involved in m6A modification upon sepsis induced myocardial dysfunction. The m6A modification status of specific RNA was catalyzed by different enzymes. METTL3 positively regulates expression of MYD88, a critical upstream regulator of NF-κB signaling, by facilitating m6A methylation modification to MYD88-RNA in mesenchymal stem cells (Yu et al., 2020). However, reduced expression of FTO increased Nanog mRNA m6A methylation under TNF-α stimulation, decreased Nanog mRNA and protein expression, and significantly inhibited mesenchymal stem cells capacity for differentiation to sweat gland cells (Wang et al., 2020). In our research, these transcripts that have undergone m6A modification might have been modified by different enzymes.

In conclusion, our experiments revealed that the level of m6A modification is significantly decreased in septic cardiac tissue. Through the genome-wide profiling of m6A-tagged mRNAs and lncRNAs and subsequent bioinformatics analysis, we revealed some potential functions of transcripts with altered m6A modification.

Statements

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/genbank/, GSE159309.

Ethics statement

The animal study was reviewed and approved by The Experimental Animal Welfare Ethics Branch and the Biomedical Ethics Committee of Peking University (LA 2020343).

Author contributions

S-YZ and Y-CH conceived and planned the experiments. Y-CH, H-ZX, and BL performed the experiment and acquired the data. R-LX, H-PZ, and J-YL analyzed and interpreted the data. Y-CH drafted the manuscript. S-YZ revised the manuscript. All authors provided critical feedback and help in shaping the research, analysis, and manuscript. All authors have read and approved the final submitted manuscript.

Funding

This work was supported by the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (No. 2016-I2M-1-011). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.670160/full#supplementary-material

Supplementary Figure 1

Expressions of m6A-related enzymes were detected by western blot analysis. n = 3-4; *P < 0.05. Ctrl, control; LPS, lipopolysaccharide.

Supplementary Table 1

Sequence of primers used for qRT-PCR analysis of mRNA levels.

Supplementary Table 2

Sequence of primers used for m6A single base site qPCR analysis of relative mRNA methylation levels.

Abbreviations

BP, biological processes; CC, cellular components; ceRNA, Competing endogenous RNA; ELF3, epithelium-specific ETS (ESE) transcription factors; FTO, Fat mass and obesity-associated protein; GO, Gene Ontology; HAKAI, E3 ubiquitin-protein ligase Hakai; HCC, epatocellular carcinoma; hnRNPA2B1, heterogeneous nuclear ribonucleoprotein A2B1; IGF2BP1/2/3, insulin-like growth factor 2 mRNA-binding proteins 1/2/3; KEGG, Kyoto Encyclopedia of Genes and Genomes; LC-MS/MS, Liquid chromatography-tandem mass spectrometry; lncRNAs, long non-coding RNAs; LPS, lipopolysaccharide; m6A, N6-methyladenosine; METTL14, methyltransferase-like 14; METTL3, methyltransferase like 3; MF, molecular functions; PPI, Protein-protein interaction; RBM15/15B, RNA binding motifs protein 15/15B; WTAP, Wilms tumor 1-associated protein; YTHDC1/2, YTH domain-containing reader proteins 1/2; YTHDF 2, YTH domain family 2; ZC3H13, zinc finger CCCH-type containing 13.

References

  • 1

    AnR.FengJ.XiC.XuJ.SunL. (2018). miR-146a Attenuates Sepsis-Induced Myocardial Dysfunction by Suppressing IRAK1 and TRAF6 via Targeting ErbB4 Expression. Oxid. Med. Cell Longev.2018, 19. 10.1155/2018/7163057

  • 2

    ArcidiaconoO. A.KrejčíJ.BártováE. (2020). The Distinct Function and Localization of METTL3/METTL14 and METTL16 Enzymes in Cardiomyocytes. Int. J. Mol. Sci.21, 8139. 10.3390/ijms21218139

  • 3

    BerulavaT.BuchholzE.ElerdashviliV.PenaT.IslamM. R.LbikD.et al (2020). Changes in m6A RNA Methylation Contribute to Heart Failure Progression by Modulating Translation. Eur. J. Heart Fail.22, 5466. 10.1002/ejhf.1672

  • 4

    ChangY.FuX. R.CuiM.LiW. M.ZhangL.LiX.et al (2019). Activated Hippo Signal Pathway Inhibits Cell Proliferation and Promotes Apoptosis in NK/T Cell Lymphoma Cells. Cancer Med.8, 38923904. 10.1002/cam4.2174

  • 5

    ChaoC. N.LoJ. F.KhanF. B.DayC. H.LaiC. H.ChenC. H.et al (2019). Tid1‐S Attenuates LPS‐induced Cardiac Hypertrophy and Apoptosis through ER‐a Mediated Modulation of p‐PI3K/p‐Akt Signaling cascade. J. Cel Biochem.120, 1670316710. 10.1002/jcb.28928

  • 6

    ChenX.YuC.GuoM.ZhengX.AliS.HuangH.et al (2019). Down-Regulation of m6A mRNA Methylation Is Involved in Dopaminergic Neuronal Death. ACS Chem. Neurosci.10, 23552363. 10.1021/acschemneuro.8b00657

  • 7

    ChokkallaA. K.MehtaS. L.KimT.ChelluboinaB.KimJ.VemugantiR. (2019). Transient Focal Ischemia Significantly Alters the M 6 A Epitranscriptomic Tagging of RNAs in the Brain. Stroke50, 29122921. 10.1161/strokeaha.119.026433

  • 8

    CohenJ.VincentJ.-L.AdhikariN. K. J.MachadoF. R.AngusD. C.CalandraT.et al (2015). Sepsis: a Roadmap for Future Research. Lancet Infect. Dis.15, 581614. 10.1016/s1473-3099(15)70112-x

  • 9

    CokerH.WeiG.BrockdorffN. (2019). m6A Modification of Non-coding RNA and the Control of Mammalian Gene Expression. Biochim. Biophys. Acta Gene Regul. Mech.1862, 310318. 10.1016/j.bbagrm.2018.12.002

  • 10

    EnrightA. J.JohnB.GaulU.TuschlT.SanderC.MarksD. S. (2003). MicroRNA Targets in Drosophila. Genome Biol.5, R1. 10.1186/gb-2003-5-1-r1

  • 11

    FengZ.LiQ.MengR.YiB.XuQ. (2018). METTL 3 Regulates Alternative Splicing of MyD88 upon the Lipopolysaccharide‐induced Inflammatory Response in Human Dental Pulp Cells. J. Cel Mol. Med.22, 25582568. 10.1111/jcmm.13491

  • 12

    HuangT.GuoJ.LvY.ZhengY.FengT.GaoQ.et al (2019). Meclofenamic Acid Represses Spermatogonial Proliferation through Modulating m6A RNA Modification. J. Anim. Sci. Biotechnol.10, 63. 10.1186/s40104-019-0361-6

  • 13

    ImanishiM.TsujiS.SudaA.FutakiS. (2017). Detection ofN6-Methyladenosine Based on the Methyl-Sensitivity of MazF RNA Endonuclease. Chem. Commun.53, 1293012933. 10.1039/c7cc07699a

  • 14

    KennedyE. M.BogerdH. P.KornepatiA. V. R.KangD.GhoshalD.MarshallJ. B.et al (2016). Posttranscriptional M 6 A Editing of HIV-1 mRNAs Enhances Viral Gene Expression. Cell Host Microbe19, 675685. 10.1016/j.chom.2016.04.002

  • 15

    KmietczykV.RiechertE.KalinskiL.BoileauE.MalovrhE.MaloneB.et al (2019). m6A-mRNA Methylation Regulates Cardiac Gene Expression and Cellular Growth. Life Sci. Alliance2, e201800233. 10.26508/lsa.201800233

  • 16

    LiF.ZhangC.ZhangG. (2019). m6A RNA Methylation Controls Proliferation of Human Glioma Cells by Influencing Cell Apoptosis. Cytogenet. Genome Res.159, 119125. 10.1159/000499062

  • 17

    LinS.ChoeJ.DuP.TribouletR.GregoryR. I. (2016). The M 6 A Methyltransferase METTL3 Promotes Translation in Human Cancer Cells. Mol. Cel62, 335345. 10.1016/j.molcel.2016.03.021

  • 18

    LinX.ChaiG.WuY.LiJ.ChenF.LiuJ.et al (2019). RNA m6A Methylation Regulates the Epithelial Mesenchymal Transition of Cancer Cells and Translation of Snail. Nat. Commun.10, 2065. 10.1038/s41467-019-09865-9

  • 19

    MathiyalaganP.AdamiakM.MayourianJ.SassiY.LiangY.AgarwalN.et al (2019). FTO-dependent N 6 -Methyladenosine Regulates Cardiac Function during Remodeling and Repair. Circulation139, 518532. 10.1161/circulationaha.118.033794

  • 20

    MukobataS.HibinoT.SugiyamaA.UranoY.InatomiA.KanaiY.et al (2002). M6a Acts as a Nerve Growth Factor-Gated Ca2+ Channel in Neuronal Differentiation. Biochem. Biophys. Res. Commun.297, 722728. 10.1016/s0006-291x(02)02284-2

  • 21

    NieM. W.HanY. C.ShenZ. J.XieH. Z. (2020). Identification of circRNA and mRNA Expression Profiles and Functional Networks of Vascular Tissue in Lipopolysaccharide‐induced Sepsis. J. Cel Mol. Med.24, 79157927. 10.1111/jcmm.15424

  • 22

    PanT. (2018). Modifications and Functional Genomics of Human Transfer RNA. Cell Res28, 395404. 10.1038/s41422-018-0013-y

  • 23

    PasquinelliA. E. (2012). MicroRNAs and Their Targets: Recognition, Regulation and an Emerging Reciprocal Relationship. Nat. Rev. Genet.13, 271282. 10.1038/nrg3162

  • 24

    RayesJ.LaxS.WichaiyoS.WatsonS. K.DiY.LombardS.et al (2017). The Podoplanin-CLEC-2 axis Inhibits Inflammation in Sepsis. Nat. Commun.8, 2239. 10.1038/s41467-017-02402-6

  • 25

    RubioR. M.DepledgeD. P.BiancoC.ThompsonL.MohrI. (2018). RNA m6A Modification Enzymes Shape Innate Responses to DNA by Regulating Interferon β. Genes Dev.32, 14721484. 10.1101/gad.319475.118

  • 26

    SempleJ. W.ItalianoJ. E.Jr.FreedmanJ. (2011). Platelets and the Immune Continuum. Nat. Rev. Immunol.11, 264274. 10.1038/nri2956

  • 27

    SongH.FengX.ZhangH.LuoY.HuangJ.LinM.et al (2019). METTL3 and ALKBH5 Oppositely Regulate m6A Modification of TFEB mRNA, Which Dictates the Fate of Hypoxia/reoxygenation-Treated Cardiomyocytes. Autophagy15, 14191437. 10.1080/15548627.2019.1586246

  • 28

    WangJ.YanS.LuH.WangS.XuD. (2019a). METTL3 Attenuates LPS-Induced Inflammatory Response in Macrophages via NF-κB Signaling Pathway. Mediators Inflamm.2019, 18. 10.1155/2019/3120391

  • 29

    WangY.MaoJ.WangX.LinY.HouG.ZhuJ.et al (2019b). Genome-wide Screening of Altered m6A-Tagged Transcript Profiles in the hippocampus after Traumatic Brain Injury in Mice. Epigenomics11, 805819. 10.2217/epi-2019-0002

  • 30

    WangY.WangR.YaoB.HuT.LiZ.LiuY.et al (2020). TNF-α Suppresses Sweat Gland Differentiation of MSCs by Reducing FTO-Mediated m6A-Demethylation of Nanog mRNA. Sci. China Life Sci.63, 8091. 10.1007/s11427-019-9826-7

  • 31

    XiangB.ZhangG.GuoL.LiX.-A.MorrisA. J.DaughertyA.et al (2013). Platelets Protect from Septic Shock by Inhibiting Macrophage-dependent Inflammation via the Cyclooxygenase 1 Signalling Pathway. Nat. Commun.4, 2657. 10.1038/ncomms3657

  • 32

    YangC.FanZ.YangJ. (2020). m6A Modification of LncRNA MALAT1: A Novel Therapeutic Target for Myocardial Ischemia-Reperfusion Injury. Int. J. Cardiol.306, 162. 10.1016/j.ijcard.2019.11.140

  • 33

    YangN.ShiX.-L.ZhangB.-L.RongJ.ZhangT.-N.XuW.et al (2018). The Trend of β3-Adrenergic Receptor in the Development of Septic Myocardial Depression: A Lipopolysaccharide-Induced Rat Septic Shock Model. Cardiology139, 234244. 10.1159/000487126

  • 34

    YuJ.ShenL.LiuY.MingH.ZhuX.ChuM.et al (2020). The m6A Methyltransferase METTL3 Cooperates with Demethylase ALKBH5 to Regulate Osteogenic Differentiation through NF-κB Signaling. Mol. Cel Biochem.463, 203210. 10.1007/s11010-019-03641-5

  • 35

    ZakyA.DeemS.BendjelidK.TreggiariM. M. (2014). Characterization of Cardiac Dysfunction in Sepsis. Shock41, 1224. 10.1097/shk.0000000000000065

  • 36

    ZeniF.FreemanB.NatansonC. (1997). Anti-inflammatory Therapies to Treat Sepsis and Septic Shock. Crit. Care Med.25, 10951100. 10.1097/00003246-199707000-00001

  • 37

    ZhangT.-N.YangN.GoodwinJ. E.MahrerK.LiD.XiaJ.et al (2019). Characterization of Circular RNA and microRNA Profiles in Septic Myocardial Depression: a Lipopolysaccharide-Induced Rat Septic Shock Model. Inflammation42, 19902002. 10.1007/s10753-019-01060-8

  • 38

    ZhouY.ZengP.LiY.-H.ZhangZ.CuiQ. (2016). SRAMP: Prediction of Mammalian N6-Methyladenosine (m6A) Sites Based on Sequence-Derived Features. Nucleic Acids Res.44, e91. 10.1093/nar/gkw104

Summary

Keywords

N6-methyladenosine, sepsis, myocardial dysfunction, epitranscriptomics, lncRNA

Citation

Han Y-C, Xie H-Z, Lu B, Xiang R-L, Zhang H-P, Li J-Y and Zhang S-Y (2021) Lipopolysaccharide Alters the m6A Epitranscriptomic Tagging of RNAs in Cardiac Tissue. Front. Mol. Biosci. 8:670160. doi: 10.3389/fmolb.2021.670160

Received

20 February 2021

Accepted

14 July 2021

Published

28 July 2021

Volume

8 - 2021

Edited by

Teng Ma, Capital Medical University, China

Reviewed by

Sunny Sharma, The State University of New Jersey, United States

Eva Bartova, Academy of Sciences of the Czech Republic, Czechia

Updates

Copyright

*Correspondence: Shu-Yang Zhang,

This article was submitted to Protein and RNA Networks, a section of the journal Frontiers in Molecular Biosciences

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Outline

Figures

Cite article

Copy to clipboard


Export citation file


Share article

Article metrics