Li et al. Journal of Neuroinflammation(2024) 21:197
https://doi.org/10.1186/s12974-024-03194-5

Journal of Neuroinflammation

Open Access

RESEARCH

FOSL1-mediated LINC01566 negatively
regulates CD4+ T-cell activation in myasthenia
gravisLifang Li1, Danyang Li1, Jingnan Jin1, Fanfan Xu1, Ni He1, Yingjie Ren1, Xiaokun Wang1, Liting Tian1, Biying Chen1,
Xiaoju Li1, Zihong Chen1, Lanxin Zhang1, Lukuan Qiao1, Lihua Wang1* and Jianjian Wang1*
Abstract
Background Myasthenia gravis (MG) is an autoimmune disease characterized by pathogenic antibodies thattarget structures of the neuromuscular junction. The evidence suggests that the regulation of long noncoding RNAs
(lncRNAs) that is mediated by transcription factors (TFs) plays a key role in the pathophysiology of MG. Nevertheless,the detailed molecular mechanisms of lncRNAs in MG remain largely undetermined.
Methods Using microarray analysis, we analyzed the lncRNA levels in MG. By bioinformatics analysis, LINC01566 was
found to potentially play an important role in MG. First, qRT‒PCR was performed to verify the LINC1566 expressions
in MG patients. Then, fluorescence in situ hybridization was conducted to determine the localization of LINC01566
in CD4 + T cells. Finally, the impact of LINC01566 knockdown or overexpression on CD4 + T-cell function was also
analyzed using flow cytometry and CCK-8 assay. A dual-luciferase reporter assay was used to validate the binding of
the TF FOSL1 to the LINC01566 promoter.
Results Based on the lncRNA microarray and differential expression analyses, we identified 563 differentiallyexpressed (DE) lncRNAs, 450 DE mRNAs and 19 DE TFs in MG. We then constructed a lncRNA-TF-mRNA network.
Through network analysis, we found that LINC01566 may play a crucial role in MG by regulating T-cell-related
pathways. Further experiments indicated that LINC01566 is expressed at low levels in MG patients. Functionally,
LINC01566 is primarily distributed in the nucleus and can facilitate CD4 + T-cell apoptosis and inhibit cell proliferation.
Mechanistically, we hypothesized that LINC01566 may negatively regulate the expressions of DUSP3, CCR2, FADD,SIRPB1, LGALS3 and SIRPB1, which are involved in the T-cell activation pathway, to further influence the cellular
proliferation and apoptosis in MG. Moreover, we found that the effect of LINC01566 on CD4 + T cells in MG was
mediated by the TF FOSL1, and in vitro experiments indicated that FOSL1 can bind to the promoter region of
LINC01566.

*Correspondence:
Lihua Wangwanglh211@163.com
Jianjian Wangwangjian_427@163.com
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified thelicensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to thematerial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation
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Li et al. Journal of Neuroinflammation(2024) 21:197

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Conclusions In summary, our research revealed the protective roles of LINC01566 in clinical samples and cellularexperiments, illustrating the potential roles and mechanism by which FOSL1/LINC01566 negatively regulates
CD4 + T-cell activation in MG.
Keywords lncRNA microarray chip, LINC01566, Bioinformatics analysis, CD4 + T cell, Myasthenia gravis

Introduction
Myasthenia gravis (MG) is an autoimmune disease inwhich antibodies bind to functional molecules in thepostsynaptic membrane at neuromuscular junctions[1]. Autoantibodies against the acetylcholine receptor(AChR), muscle-specific kinase (MuSK), and lipoprotein-related protein 4 (LRP4) have been identified as
pathogenic factors in MG [2]. Antibodies induce skeletal
muscle weakness, which typically increases with exercise and repetitive muscle use (fatigue) and varies overthe course of a day and from day to day [1]. However, theorigin of the autoimmune dysfunction that leads to theonset of MG remains unclear, and the molecular mechanisms regulating its progression are highly complex.
It has been widely accepted that the MG autoantibody response is T-cell-dependent. Specifically, CD4 + T
cells are overactivated in MG patients [3, 4] and stimulate B cells to produce autoantibodies via a complex network involving interactions among cells and cytokines.
CD4 + T cells can be subdivided into Th1, Th2, Th17, andTreg cells [5]. Abnormal CD4 + T-cell selection and activation have been intensively investigated in MG [6, 7].
Analysis of the T-cell subset composition in the blood
revealed an increased proportion of mature CD4 + T
cells in MG patients compared to healthy controls [8,9]. MG patients have more Th1 and Th17 cells and theirassociated cytokines, namely, IL-1, IL-6, IL-17, interferon-gamma (IFN-γ), and tumour necrosis factor alpha(TNF-α) [10–12]. Furthermore, studies have confirmedthat there are defects in or dysfunction of Foxp3(+)
CD25(+) CD4(+) Treg cells in MG patients comparedto those in healthy subjects [13, 14]. Therefore, abnormal CD4 + T-cell activation and cytokine production areimportant in the pathogenesis of MG. In recent years,
long noncoding RNAs (lncRNAs) have attracted increasing attention in the field of autoimmune disease [15–17]
and thus have provided novel insight into the pathogenesis, diagnosis, and treatment of MG.
LncRNAs are defined as transcripts of more than 200
nucleotides that are not translated into proteins [18]and have also been found to play important roles in different biological functions, including the regulation of
immune responses [19]. LncRNAs have been proposed
to carry out diverse functions, including transcriptional
regulation when cis- or trans-acting, the organization of
nuclear domains, and the regulation of proteins or RNA
molecules [20]. There have been many studies on themolecular mechanism underlying the effects of lncRNAs

and T cells on disease status [21–23]. It has also beenreported that the lncRNA GAS5 can regulate the Th17/
Treg balance by targeting miR-23a expression in MG[24]. The influence of the lncRNA IFNG-AS1 on theexpression level of CD40L and CD4+ T-cell activation in
MG patients partly depends on its effect on HLA-DRB1
expression [25]. However, few lncRNAs have been identified in MG, and the correlations between these lncRNAs
and CD4 + T-cell activation have not been actively
investigated. Therefore, it is necessary to systematically
explore the regulatory mechanisms of CD4 + T cells
involving lncRNAs in MG.
In our research, we designed an integrative analysis based on lncRNA and mRNA microarray sequencing in MG (Fig. 1). We first showed that LINC01566 isexpressed at low levels in peripheral blood mononuclear
cells (PBMCs) obtained from MG patients and that its
expression is mediated by the transcription factor (TF)
FOSL1. Intriguingly, the expression level of LINC01566
was positively correlated with the neutrophil count andneutrophil-lymphocyte ratio (NLR) but negatively correlated with the lymphocyte and monocyte counts in
MG patients. Functionally, LINC01566 is primarily distributed in the nucleus and can facilitate CD4 + T-cell
apoptosis and inhibit CD4 + T-cell proliferation. Mechanistically, we hypothesized that LINC01566 may negatively regulate the expression of the T-cell activation
pathway molecules DUSP3, CCR2, FADD, SIRPB1,LGALS3 and P2RX7 to further influence cellular proliferation and apoptosis in MG. Our study revealed thepotential roles of LINC01566 and provided new insight
into the regulatory mechanism of lncRNAs in MG.

Materials and methods
Microarray analysis

Peripheral blood mononuclear cells (PBMCs) were isolated from whole blood using lymphocyte isolation fluid(TBD, Tianjin, China) according to the manufacturer’s
instructions. Total RNA was isolated from PBMCs with
TRIzol® Reagent (Invitrogen, Carlsbad, CA, USA) andquantified using a NanoDrop ND-1000 (Thermo Fisher
Scientific) system. RNA integrity was assessed by standard denaturing agarose gel electrophoresis. Each RNA
sample was amplified and transcribed into fluorescentcDNA along the entire length of the transcripts without
3’ bias utilizing a random priming method. The labeled
cDNAs were hybridized onto the Human LncRNA Array,version 5.0 (8 × 60 K, Arraystar). After hybridization

Li et al. Journal of Neuroinflammation(2024) 21:197

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Fig. 1 Flowchart of this study

and washing, the arrays were scanned using an Agilent
G2505C scanner and Agilent Feature Extraction software, version 11.1, to analyze the acquired array images.
Quantile normalization and subsequent data processing
were performed using Agilent GeneSpring GX software,version 12.1. The lncRNA array data have been depositedin the Gene Expression Omnibus database under accession GSE263220.
Differential expression analysis

We identified the significantly DE lncRNAs and DE
mRNAs through volcano plot filtering and calculateda P value using a student’s t test. The thresholds for the
up- and downregulated genes were a fold change of 2.0

or greater and a P value of 0.05 or less. Heatmaps representing the differentially regulated genes were generatedusing the “pheatmap” package in R.
Functional enrichment analysis

To screen the potential functions of the differentiallyexpressed genes (DEGs), gene ontology (GO) functional
enrichment analysis was performed with the Databasefor Annotation Visualization and Integrated Discovery(DAVID) [26], an online bioinformatics tool, to interpretthe gene ontology biological processes (GO_BP) of those
DEGs. The R package “ggplot” was used for visualizationof the functional clustering of gene sets or gene clusters.

Li et al. Journal of Neuroinflammation(2024) 21:197

A p value < 0.05 and FDR < 0.05 were set as the cutoff
criteria.
Clinical samples

Seventy MG patients and fifty healthy controls (HCs)were recruited at the Neurology Department of the Second Affiliated Hospital of Harbin Medical University
between September 2021 and February 2023. The firstcohort, which included MG patients (n = 8) and age- andsex-matched HCs (n = 8), was used for the microarray
analysis. The second cohort, which included MG patients(n = 62) and HCs (n = 42), was used for qRT‒PCR analysis. The P-value for sex and age between the two group
showed no statistical significance (p > 0.05) in second
cohort. MG was diagnosed based on a combination offluctuating muscle weakness with a positive neostigmine
test, abnormal single-fiber EMG test, or positivity forserum antibodies [27]. The autoantibody results included
AChR and MUSK antibodies. All eight MG patients inthe first cohort were positive for AChR antibodies. The
clinical samples in the second cohort consisted of 59
that were AChR antibody-positive, one that was MuSK
antibody-positive, and two seronegative MG patients. 9
MG patients were diagnosed with or were suspected tohave thymoma among seventy MG patients. None of thepatients had undergone hormone or immunosuppressanttreatment during the previous 6 months. Fifty healthy
subjects with no autoimmune disease were enrolled ascontrols. All patients and HCs signed informed consent
forms. This study was approved by the Ethics Committee of the Second Affiliated Hospital of Harbin Medical
University.
Coexpression analysis

To explore the regulatory mode between lncRNAs andtheir host mRNAs, we calculated their Pearson correlation coefficients (PCCs) and classified their relationships
into three groups: positively correlated (PCC > 0.9 andP value ≤ 0.01), negatively correlated (PCC < − 0.9 and P
value ≤ 0.01) and noncorrelated based on the PCC.
Identification of lncRNA-gene and TF-target interactionsFirst, the interactions of lncRNA-mRNA pairs wereacquired from the above coexpression analysis results.
The promoter sequences (-2000 bp of the transcription
start site) of the DE lncRNAs and coexpressed mRNAs
were collected from the UCSC Table Browser [28]. Furthermore, using the JASPAR database tools [29], we
computed the transcription factor binding site (TFBS)
enrichments in the collected promoter sequences toobtain the target genes of the DE TFs in MG. The significant sites with relative profile scores > 85% within the
promoter of genes were retained.

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Construction of the TF-lncRNA-gene network

A regulatory network was built by integrating all thepairs obtained from the previous analysis for MG. Network visualization was performed with Cytoscape software version 3.9.1. In the network, nodes denote genes,
TFs, and lncRNAs, and edges denote their interactions.
After constructing the network, network analysis was
performed in Cytoscape.
RNA isolation and qRT‒PCR analysis

A quantitative reverse transcription polymerase chain
reaction (qRT‒PCR) was performed to evaluate the validity of the key lncRNAs. Total RNA was extracted using
TRIzol reagent (Sigma Life Science, Darmstadt, Germany) according to the manufacturer’s instructions. The
RNA purity and concentration of each sample were evaluated and quantified using spectrometry. The purified
RNA was reverse-transcribed to generate cDNA usinga Transcriptor First Strand cDNA Synthesis Kit (Roche,
Basel, Switzerland). qRT‒PCR was performed using FastStar Universal SYBR Green Master Mix (Roche, Basel,
Switzerland). The 2−ΔΔCT method was used to determine
the relative gene expressions, which were normalizedagainst the level of GAPDH.
Fluorescence in situ hybridization (FISH)

FISH assays were carried out using a Fluorescent in Situ
Hybridization Kit (RiboBio Biotechnology, Guangzhou,
China). The suspended cells that were fixed on slides
were washed with phosphate-buffered saline (PBS) and
fixed with 4% formaldehyde at room temperature for 10
min. Then, the cells were permeabilized with PBS containing 0.5% Triton X-100 on ice for 5 min. Hybridization
was performed by incubating the cells with a Cy3-labeledprobe at 37 ℃ overnight. After the probes were removed,
the cells were stained with 4’,6-diamidino-2-phenylindole(DAPI) at room temperature for 5 min. Images wereacquired with a confocal microscope (LSM980, Zeiss) toanalyze the localization of LINC01566.
Cell culture and transfection

Jurkat cells (a CD4 + T-cell line) and 293T cells werepurchased from the BeNa Culture Collection (Henan,
China). The Jurkat cells were cultured in RPMI 1640
(Gibco, Grand Island, NY, USA) medium and were
supplemented with 10% fetal bovine serum (Excell Bio,
Suzhou, China) and 1% penicillin/streptomycin (Beyotime Biotechnology, Nanjing, China). The 293T cells were
cultured in DMEM (Gibco, NY, Grand Island, USA) andwere supplemented with 10% fetal bovine serum (Excell
Bio, Suzhou, China) and 1% penicillin/streptomycin(Beyotime Biotechnology, Nanjing, China). All cell lines
were cultured in a 5% CO2 humidified incubator at 37 °C.

Li et al. Journal of Neuroinflammation(2024) 21:197

PLenti-CMV-LINC00566 was constructed by Gene
Pharma (Shanghai, China). LINC00566 antisense oligonucleotide (ASO_LINC01566) was purchased from RiboBio (Guangdong, China). Jurkat cells in the exponential
growth phase were centrifuged (1000 r, 5 min), inoculated in a 6-well plate at a density of 1 × 105 cells/well,and then incubated for subsequent transfections. The
pLenti-CMV-LINC00566 plasmid, ASO_LINC00566, orcorresponding negative controls were used for cell transfection in accordance with the different purposes of therespective experiments. All transfection processes wereconducted using LipofectamineTM 3000 transfection
reagent (Invitrogen, Carlsbad, CA, USA) according to therelevant protocols. The transfected cells were incubatedfor 48 h for subsequent analysis. In addition, the transfection efficiency was evaluated by extracting total RNA forqRT‒PCR analysis.
Dual-luciferase reporter assay

FOSL1 binding site prediction to the LINC01566 promoter was performed using the JASPAR database. The
2.0 kb LINC01566 promoter fragment (WT) and mutant(MUT) sections containing binding sites for LINC01566
were amplified, and the resulting products were subsequently cloned and inserted into the corresponding vectors. Functional assays between FOSL1 and the
LINC01566 promoter were performed via transienttransfections of 293T and Jurkat cells. The transfections
included the pGL4.10-LINC01566 WT/vec-FOSL1/pRLTK group, pGL4.10-LINC01566 WT/vec/pRL-TK group,
pGL4.10-LINC01566 MUT/vec-FOSL1/pRL-TK group,
pGL4.10-LINC01566 WT/vec/pRL-TK group, pGL4.10/
vec-FOSL1/pRL-TK group, and pGL4.10/vec/pRL-TK
group. After 48 h of incubation, the luciferase activities
of firefly and Renilla were detected in harvested 293T and
Jurkat cells using a dual-luciferase reporter assay system(Promega, Madison, WI, USA) according to the manufacturer’s instructions. All experiments were repeatedthree times independently.
Flow cytometry

Cell apoptosis was determined with an FITC Annexin
V Apoptosis Detection Kit I (Becton-Dickinson, FL, NJ,
USA) according to the manufacturer’s instructions. The
cells were washed twice with cold 1× PBS and resuspended in binding buffer at a concentration of 1 × 106
cells/ml. Five microliters of annexin V-FITC and 5 µL of
propidium iodide (PI) were added to a total of 100 µL ofcell suspension. The mixtures were incubated for 15 min
at RT in the dark and then subjected to flow cytometry
analysis (Becton Dickinson FACSCalibur, Franklin, NJ,
USA).

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Cell proliferation

Cell proliferation was assessed using a Cell Counting
Kit-8 (CCK-8) assay (Seven, Beijing, China). After transfection, Jurkat cells at 24 h were seeded into 96-wellplates at a density of 3000 cells/well in 100 µl of complete
medium and cultured in a humidified atmosphere containing 5% CO2 at 37 ℃. Then, 10 µl of CCK8 reagent
was added to each well at 24, 48, 72, and 96 h after transfection, and the cells were further cultured for 2 h at
37 °C. Subsequently, the optical density (OD) was measured using a microplate reader (BioTek, Vermont, New
England, USA).
Western blotting analysis

The cells were collected and lysed in radioimmunoprecipitation assay lysis buffer (P0013B, Beyotime) supplemented with 1% protease and phosphatase inhibitors(Roche, Basel, Switzerland). The total protein concentrations were measured using a Bicinchoninic Acid Protein
Assay Kit (Beyotime Biotechnology, Nanjing, China).
Sample proteins were separated by SDS‒polyacrylamide
gel electrophoresis and transferred onto polyvinylidene
difluoride membranes (Roche, Basel, Switzerland). Then,the membranes were blocked in protein-free rapid blocking buffer (PS108, Epizyme Biotech) for 0.5 h at room
temperature before being incubated with various primary antibodies overnight at 4 °C. The following primary
antibodies were used: rabbit anti-Bcl-2 (A0208, 1:1000,
ABclonal); anti-BAX (5032S, 1:1000 CST); anti-DUSP3(ab125077, 1:100000, Abcam); anti-P2RX7 (ab307718,
1:100000, Abcam); anti-FADD (A5819, 1:1000, ABclonal);
anti-LGALS3 (A22442, 1:500, ABclonal); anti-CCR2(A2385, 1:1000, ABclonal); anti-SIRPB1 (DF4507, 1:800,
Affinity); anti-beta-actin (AF7018, 1:10000, Affinity); andanti-beta-tubulin (T0023, 1:20000, Affinity). The membranes were then incubated with the corresponding secondary antibodies (Immunoway, 1:10000, China) for one
hour on the following day. After the membranes were
washed with TBST, the protein expressions were visualized with enhanced chemiluminescence reagent, andImageJ software was used for density measurements andquantification.
Statistical analysis

Statistical analysis and plotting of the statistical graphs
were performed with GraphPad Prism 9.0 software. For
qRT-PCR and western blot results, statistical analysis was
performed on the 2−ΔΔCT values and protein studied/βactin data comparing the negative control group andexperimental group. Student’s t test was used for comparisons of the differences between two groups. Correlation
analysis was performed using Pearson correlation. All thedata are presented as the mean ± standard deviation (SD).

Li et al. Journal of Neuroinflammation(2024) 21:197

When the P values were < 0.05, the differences were considered to be statistically significant.

Results
LncRNA and mRNA expression profiles in MG

To determine the expression patterns of the lncRNAs
associated with MG, we performed lncRNA and mRNA
microarray analysis on PBMCs that were isolated from
MG patients and healthy controls (HCs). First, the samples were separated into two groups through hierarchicalclustering based on similar expression patterns, and thisdifference could distinguish MG patients from healthy
individuals (Fig. 2A, B). The results also showed that theexpression patterns of the lncRNAs were mostly downregulated in MGs compared to HCs. We observed thatthe expressions of 83 lncRNAs were significantly upregulated and that the expressions of 444 lncRNAs were
downregulated in the MG group compared to the HCsgroup (Fig. 2C). Moreover, 469 DE mRNAs were identified, of which 189 mRNAs were highly expressed and 280
mRNAs were expressed at low levels (Fig. 2D).
The functional GO term enrichment analysis revealed
that the upregulated DEGs were significantly enrichedin the regulation of the Fc receptor signaling pathway,
T-cell proliferation, positive regulation of T-cell activation and regulation of the extrinsic apoptotic signaling
pathway (Fig. 2E). The downregulated DEGs were significantly enriched in the insulin-like growth factor pathway,
the L-lysine process to acetyl-CoA, neuronal cell to specific neuron, skeletal system morphogenesis and bindingof sperm to the zona pellucida (Fig. 2F). These findings
indicate that the upregulated DEGs were enriched in the
immune system process, inflammatory response, andinnate immune response in MG patients. These findings
were consistent with the pathogenesis of MG. Therefore,
we used the upregulated genes for follow-up analysis.
Construction of the lncRNA-TF-mRNA regulatory network
associated with MG and identification of the key lncRNAs

TFs were screened from the JASPAR database, and 19
differentially expressed TFs were identified among the469 DE mRNAs (Fig. 3A). A heatmap of the expressionsof 19 DE TFs, including 19 upregulated TFs and 8 downregulated TFs, is shown in Fig. 3B. The TFs were CDX2,
DRGX, E2F8, ESR1, FEZF2, FOSL1, HES5, HOXA9,
HOXB8, HOXC8, LEF1, LHX1, NEUROD2, NR1I3,
NR3C1, PLAGL2, SOX12, SOX9 and TFEC. First, we
obtained lncRNA‒mRNA pairs via coexpression analysis. That the functional analysis of upregulated mRNAs
revealed that these genes are significantly enriched inT-cell-related pathways. Therefore, lncRNA‒mRNA
coexpression analysis was performed to explore the relationships among DE lncRNAs and upregulated mRNAs
in MG. The results revealed 20,616 lncRNA‒mRNA

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pairs consisting of 388 DE lncRNAs and 164 DE mRNAs.
Functional enrichment analysis revealed that the 164 DE
mRNAs coexpressed with DE lncRNAs were enriched inT-cell activation, regulation of T-cell activation and T-cellproliferation (Fig. 3C).
Next, by combining the above coexpression analysis
results, we predicted the possible targets of 19 DE TFs.
We obtained 2942 TF-lncRNA pairs and 2310 TF-mRNApairs and constructed a lncRNA-TF-mRNA network toillustrate the potential functions of the lncRNAs in MG.
This network consisted of 568 nodes and 25,569 edges(Fig. 3D). To characterize the topological features of the
network, we calculated the degree of the lncRNA-TFmRNA network. We primarily analyzed the degree distribution of the lncRNAs and ranked the top 10 lncRNAs
according to degree (Fig. 3E). LINC01566 had the highest degree. In terms of LINC01566, the target genes weresignificantly enriched in several terms that are closely
related to MG-related biological processes (Fig. 4C).
Furthermore, we found that the expression level of
LINC01566 was downregulated in the MG microarray
data.
LINC01566 was expressed at low levels in MG patients andwas associated with clinical indices in MG patients

In this study, we focused on the lncRNA LINC01566,
which is encoded by a gene at chromosome position
16p11.1 in the genome. qRT‒PCR confirmed the significant decrease in LINC01566 expressions in the PBMCs
of MG patients compared with those in the PBMCs of
HCs (p < 0.0001, Fig. 4A). The decreased expression of
LINC01566 in PBMCs was found in these subgroups
including AChR Ab + MG patients (85.8%)、seronegative MG patients (100%) and thymoma-associatedMG patients (89.8%). However, the expression levelof LINC01566 was higher in 100% (1/1) of the MuSK
Ab + MG patient than in normal healthy controls. We
performed receiver operating characteristic (ROC) curve
analysis to evaluate the diagnostic performance of the
LINC01566 signature in the validation set, the results
demonstrated that the lncRNA precisely distinguishedpatients with MG from healthy people (AUC = 0.873,
Fig. 4B). To further explore the biological function of
LINC01566, we obtained lncRNA coexpressed genes
from the previous lncRNA-TF-gene network to perform
GO functional analysis, and the results revealed significant enrichment of genes related to neutrophil activation involved in the immune response, leukocyte cell‒cell
adhesion, T-cell activation, monocyte chemotaxis andregulation of the leukocyte apoptotic process (Fig. 4C).
In addition, we also investigated the association
between the LINC01566 expressions and routine clinical blood data from patients. There was a correlation
between higher expression levels of LINC01566 andLi et al. Journal of Neuroinflammation(2024) 21:197

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Fig. 2 Differential expression and functional analysis of lncRNAs and mRNAs in MG microarray data. A, C. Heatmap plot and volcano plot of the differentially expressed lncRNAs between MG patients and controls (fold change > 2, P value < 0.05). B, D. Heatmap plot and volcano plot of the differentiallyexpressed mRNAs between MG patients and controls. E. The top 20 most enriched GO_BP terms for the upregulated mRNAs. F. The top 20 most enriched
GO_BP terms for downregulated mRNAs

Li et al. Journal of Neuroinflammation(2024) 21:197

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Fig. 3 Construction of the lncRNA-TF-mRNA network in MG. (A) Venn diagram showing DE TFs. (B) Heatmap of the expressions of 17 DE TFs. (C) The
top 10 most enriched GO terms for the upregulated mRNAs that were coexpressed with DE lncRNAs. (D) The lncRNA-TF-mRNA network. The red ellipses
represent TFs, the orange triangles represent mRNAs, and the blue diamond represent lncRNAs. Lines represent their regulatory interactions. (E) The top
10 lncRNAs according to the degree ranking in the networkLi et al. Journal of Neuroinflammation(2024) 21:197

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Fig. 4 Validation experiment and analysis of LINC01566 in clinical samples (A) The expressions of LINC01566 were detected in MG patients and control
subjects by qRT‒PCR. (B) Receiver operating characteristic (ROC) curve analysis showed that the LINC01566 expression levels have diagnostic value in
MG patients. (C) The top 20 most enriched GO_BP terms for coexpressed mRNAs of LINC01566. (D) Molecules related to T-cell proliferation and activation
terms among the coexpressed genes of LINC01566. E, F. Relationships between the expression levels of LINC01566 and the neutrophil-to-lymphocyte
ratios and neutrophil counts in MG patients. G, H. Relationships between the expression levels of LINC01566 and the lymphocyte and monocyte counts
in MG patientsLi et al. Journal of Neuroinflammation(2024) 21:197

higher NLR (R = 0.427, p < 0.01; Fig. 4E), and the neutrophil counts were also positively correlated with theexpression levels of LINC01566 (R = 0.472, p < 0.01;
Fig. 4F). Moreover, the LINC01566 expressions were
negatively correlated with the lymphocyte and monocyte
counts (R = -0.415, p < 0.01; R = -0.511, p < 0.001; Fig. 4G,
H). These data demonstrate that LINC01566 may be an
important biological signature in MG.
Localization of LINC01566 in CD4 + T cells and LINC01566
inhibition of CD4 + T-cell proliferation

Our results showed that LINC01566 was down-regulated
in MG. However, the detailed molecular mechanism that
contributes to these roles has not yet been investigated.
That functional analysis of LINC01566 coexpressed genes
revealed that LINC01566 was significantly enriched inT-cell-related terms. To clarify the molecular mechanism of LINC01566 in T lymphocytes, we first evaluated the subcellular localization of LINC01566 in Jurkat
cells, which are a common CD4 + T-cell line. FISH was
performed to determine the localization of LINC01566
in Jurkat cells, and the results indicated that LINC01566
predominantly localized to the nucleus (Fig. 5A).
To further confirm the functional connection between
LINC01566 and T-cell activation in MG, we next analyzed the effect of LINC01566 knockdown (KD) or overexpression (OE) on CD4 + T-cell function using flow
cytometry and western blotting analysis. Jurkat cells
were transfected with ASO negative control (ASO_NC)
or ASO_LINC01566, and the qRT‒PCR results revealed
that LINC01566 KD decreased LINC01566 expressions. In addition, Jurkat cells were transfected with
pLenti-CMV-control or pLenti-CMV-LINC01566. The
qRT‒PCR results indicated that LINC01566 OE significantly increased LINC01566 expressions (Fig. 5B).
We found that the apoptosis rate of the LINC01566 OE
group increased, while that of the LINC01566 KD groupdecreased compared to that of the negative control group(Fig. 5C、D). Similarly, compared to those in the negative control group, the levels of the proapoptotic molecules BAX in the LINC01566 OE group were significantly
increased, and the level of the antiapoptotic protein Bcl-2
was significantly decreased. The expressions of apoptotic
proteins in the LINC01566 KD group showed the opposite trend, which further confirmed the above results(Fig. 5E、F). Moreover, the CCK-8 assay showed that the
proliferation of Jurkat cells was notably repressed in the
LINC01566 OE group compared with that in the negative control group; in contrast, cell proliferation was promoted in the LINC01566 KD group (Fig. 5G). The results
showed that LINC01566 could inhibit CD4 + T-cell proliferation and promote apoptosis.

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LINC00566 influences CD4+ T-cell activation andproliferation by regulating six molecules

Next, we investigated the molecular mechanism by which
LINC01566 regulates CD4 + T-cell function. According
to previous functional analysis results of the coexpressed
LINC01566 genes, we identified T-cell activation-related
molecules, such as DUSP3, FADD, CCR2, LGALS3,
P2RX7, and SIRPB1 (Fig. 4D). The microarray analysis
results showed that all six molecules were upregulated.
Given that LINC01566 is located in the nucleus, we speculated that LINC01566 may regulate T-cell activationrelated proteins through the nuclear pathway. To verify
whether LINC01566 regulates these six molecules, Jurkat cells were transfected with pLenti-CMV-LINC01566,
pLenti-CMV-vector, ASO_LINC01566 or ASO_NC. The
results showed that the mRNA levels of DUSP3, CCR2,
FADD, LGALS3, P2RX7 and SIRPB1 in the pLenti-CMVLINC01566 group were lower than those in the pLentiCMV-vector control group, while the mRNA levels of
DUSP3, CCR2, FADD, LGALS3, P2RX7 and SIRPB1 in
the ASO_LINC01566 group were greater than those in
the ASO_NC group (Fig. 6A). Western blot analysis further confirmed that LINC01566 downregulated the protein levels of DUSP3, CCR2, FADD, LGALS3, P2RX7 andSIRPB1 (Fig. 5B, C). Our data showed that LINC01566
regulated the expressions of six key molecules related
to T-cell activation and proliferation, which further
explored the molecular mechanism of LINC01566 in
MG.
The transcription factor FOSL1 regulates LINC01566
expression

Our results showed that LINC01566 plays a crucial rolein MG. Consequently, we aimed to identify the regulatorsof LINC01566. TFs are fundamental players in regulating
gene expressions. To further determine whether the TF
regulates LINC01566 expressions, we extracted potential
TFs that might interact with LINC01566 promoters fromthe previous lncRNA-TF-mRNA network and identified
16 candidate TFs (Fig. 7A). Among those TFs, FOSL1
was significantly downregulated in MG (Fig. 7B). The
qRT‒PCR results also demonstrated that FOSL1 overexpression significantly contributed to LINC01566 upregulation (Fig. 7C). Localization analysis of FOSL1 was
performed in CD4 + T-cell lines, and FOSL1 was found tobe located in the nucleus, consistent with its regulatoryrole as a transcription factor in MG (Fig. 7D).
To confirm that LINC01566 is a transcriptional target
of FOSL1, we cloned a 2.0-kb region of the LINC01566
promoter into the pGL4.10 vector and measured theluciferase activity after transfection into 293T cells andJurkat cells. A significantly increased luciferase activity was obtained using a plasmid containing a 2.0-kb
section of the LINC01566 promoter in 293T cells thatLi et al. Journal of Neuroinflammation(2024) 21:197

Page 11 of 17

Fig. 5 LINC01566 inhibited CD4 + T-cell proliferation and promoted CD4 + T-cell apoptosis (A) Subcellular localization of LINC01566 in Jurkat cells. (B) The
efficiency of LINC01566 knockdown and overexpression was measured by qRT‒PCR. C, D. Flow cytometric analysis was performed using cells stainedwith Annexin-V-FITC/PI after transfecting negative controls, pLenti-CMV-LINC01566, or ASO_LINC01566 into Jurkat cells. E, F. Western blot analysis andrelative protein levels of Blc-2 and Bax in the pLenti-CMV-LINC01566 group, pLenti-CMV-vector group, ASO_LINC01566 group and ASO_NC group. The
data are presented as the mean ± SD (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). G. The proliferation of Jurkat cells was analyzed using CCK-8 assays
by transfecting negative controls, pLenti-CMV-LINC01566, or ASO_LINC01566 into Jurkat cells

Li et al. Journal of Neuroinflammation(2024) 21:197

Page 12 of 17

Fig. 6 LINC01566 regulated six key molecules related to T-cell activation. A. The mRNA expression levels of CCR2, FADD, DUSP3, P2RX7, LGALS3 andSIRPB1 were measured by qRT‒PCR after transfecting negative controls, pLenti-CMV-LINC01566, or ASO_LINC01566, into Jurkat cells. The data are presented as the mean ± SD (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). B, C. Western blot analysis and relative protein levels of CCR2, FADD, DUSP3,
P2RX7, LGALS3 and SIRPB1 in the pLenti-CMV-LINC01566, pLenti-CMV-vector, ASO_LINC01566 and ASO_NC groups. The data are presented as themean ± SD (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001)

Li et al. Journal of Neuroinflammation(2024) 21:197

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Fig. 7 The transcription factor FOSL1 regulates LINC01566 expression. (A) The potential TFs targeting LINC01566. (B) The expression level of FOSL1 in MG
clinical samples was measured by qRT‒PCR. (C) The expression levels of LINC01566 and FOSL1 were detected after transfection with pLenti-CMV-FOSL1
or pLenti-CMV-vector. The data are presented as the mean ± SD (*p < 0.05). (D) FISH of LINC01566 (red stain) and IF of FOSL1 (green stain) in Jurkat cells.
F. HEK-293T cells and Jurkat cells were cotransfected with luciferase reporters containing sections containing the WT or MUT LINC01566 promoter andvec-FOSL1 to test the effect of FOSL1 overexpression on LINC01566 promoter activity. G. Target binding sequences of FOSL1 in the LINC01566 promoter
regionLi et al. Journal of Neuroinflammation(2024) 21:197

were cotransfected with FOSL1 overexpression plasmids(Fig. 7E). Furthermore, to identify specific binding sites
of FOSL1 in the promoter of LINC01566, we predicted
potential binding sites using the JASPAR database tools
and selected the sites with the highest scores for subsequent experimental verification. Through dual-luciferase
reporter assays using mutated LINC01566 reporter constructs, we identified the promoter region at -300~-288
nt as the likely specific binding site of FOSL1 (Fig. 7E, G).
Similarly, we demonstrated these results in Jurkat cells(Fig. 7F). Our results revealed that LINC01566 downregulation is indeed mediated by FOSL1 in MG.

Discussion
MG is a T-cell-dependent antibody-mediated disease.
Abnormal lncRNA expression can influence the emergence, development, and prognosis of various immune
diseases by controlling CD4 + T-cell differentiation [30].
In this study, we aimed to understand lncRNA transcriptional changes and elucidate the underlying mechanismsof the effects of key lncRNAs on CD4 + T-cell activationin MG. We revealed that a subset of aberrant lncRNAs
can be used to distinguish MG patients from healthy individuals. Subsequently, we constructed lncRNA–TF–gene
networks and determined that LINC01566 might play a
critical role in MG. The potential regulatory mechanismof LINC01566 in MG was also further investigated.
Jin et al. reported that LINC01566 may be involvedin the pathogenesis of periodontitis [31]. To date, therole and mechanism of LINC01566 in MG have notbeen reported. We discovered that LINC01566 levels
are considerably lower in the peripheral blood of MG
patients than in that of healthy controls, which suggested that LINC01566 might play an important protective role in MG. Precisely annotating the functions oflncRNAs remains complex. Here, the biological functionof LINC01566 was significantly enriched in neutrophil
activation involved in the immune response, leukocyte
cell‒cell adhesion, T-cell activation, monocyte chemotaxis and regulation of the leukocyte apoptotic process.
In our study, we found that LINC01566 was positively
correlated with neutrophil counts and the NLR. The NLR
is a reliable indicator of systemic inflammatory status anddisease activity [32]. Previous research has shown that
the NLR is significantly increased in patients with severe
MG [32, 33]. Accumulating evidence has indicated that
inflammation could be a key factor in MG [34]. There is
genetic evidence from genome-wide association studies
that the neutrophil respiratory burst and ROS production are important risk factors for autoimmune disease[35]. Studies have also shown that lncRNAs can promote
neutrophil inflammation or neutrophil infiltration [36,37]. Therefore, we suspected that LINC01566 may influence the number or function of neutrophils to mediate

Page 14 of 17

the inflammatory effects of MG. In addition, LINC01566
expression was correlated with lymphocyte and monocyte counts, which illustrated that LINC01566 may berelated to the presence and activity of MG.
MG is considered a largely T-cell-dependent antibodymediated disease, and the activation and proliferation of
T cells play significant roles in the pathogenesis of MG.
Functional analysis revealed that there was enrichment in
the genes coexpressed with LINC01566 in T-cell-related
pathways. Hence, an experiment was designed to validate
whether LINC01566 affects the apoptosis and proliferation of CD4 + T cells. Jurkat cells were used for functionalverification. Functionally, we discovered that LINC01566
facilitates T-cell proliferation and represses apoptosis.
The results showed that LINC01566 is involved in MG
pathogenesis by inhibiting CD4 + T-cell proliferation andpromoting apoptosis. LncRNAs are themselves functional units, making their subcellular localization critical to their function. Different subcellular localizations
may lead to lncRNAs exerting their functions via various
mechanisms. FISH experiments showed that LINC01566
was predominantly localized to the nuclei of Jurkat cells.
This finding suggested that LINC01566 could be an
essential regulator in the nuclei of CD4 + T cells.
However, the underlying molecular mechanism bywhich LINC01566 regulates T-cell function in MG
remains unclear. Furthermore, we hypothesized that
LINC01566 might regulate T-cell activation-related
proteins to affect T-cell function. We identified T-cell
activation-related molecules, namely, DUSP3, FADD,
CCR2, LGALS3, P2RX7, and SIRPB1, from LINC01566
coexpression analysis. Kim et al. reported increased antiAChR antibody titres in patients with the CCR2 G/Ggenotype, which suggests that the CCR2 gene plays a rolein the pathophysiology of MG [38]. FADD constitutes
a mechanism to maintain TCR-induced programmed
necrotic signaling during the early phases of T-cell clonal
expansion [39]. Research by Aruna et al. strongly indicated that the upregulation of apoptosis via the Fas-FasL
pathway is one of the mechanisms by which dual APL
reverses EAMG manifestations in C57BL/6 mice [40],while DUSP3, P2RX7, LGALS3 and SIRPB1 have notbeen reported to be involved in MG pathogenesis. However, the specific correlations between LINC01566 andthese T-cell activation-related molecules have yet to be
identified. In our present study, qRT‒PCR and western
blot analysis demonstrated that LINC01566 regulates six
T-cell activation-related proteins. DUSP3 is known tonegatively regulate the ERK and JNK pathways [41, 42],and previous studies have confirmed that the proteins
p38 and ERK1/2 are overexpressed in MG thymic epithelial cells (TECs) [43]. Therefore, it is reasonable to suspect that altering the expression of DUSP3 may regulate
CD4 + T-cell activation via the p38/ERK pathway in MG.

Li et al. Journal of Neuroinflammation(2024) 21:197

In addition, we hypothesized that LINC01566 influences
CD4 + T-cell apoptosis by regulating the expression of
FADD, which is a crucial molecule in the Fas-FasL pathway [44]. These findings indicated that low LINC01566
expression is key in the pathogenesis of MG. In addition, we investigated the mechanism underlying the low
expression of LINC01566 and discovered that FOSL1
regulates LINC01566 expression in MG. Our results
provide a new understanding of the role of LINC01566
in CD4 + T-cell activation and a new direction for future
research.
Our study has several limitations. Although we foundthat the expression level of LINC01566 decreased in the
PBMCs of MG patients, the expression of LINC01566
in a specific type of immune cell in MG patients was
not further explored. In addition, we studied the ability of LINC01566 to inhibit CD4 + T-cell proliferation,
but we did not explore the role of LINC01566-mediatedinflammation in MG. Further research will be needed to
explore the specific roles of LINC01566 in immune cells
and inflammation and its potential mechanisms in MG.

Conclusion
In summary, we showed that nuclear LINC01566 isdownregulated in MG and is correlated with neutrophil
counts, lymphocyte counts, monocyte counts and NLR in
MG patients. We described its regulatory function in the
proliferation and apoptosis of CD4 + T cells through theregulation of six T-cell activation-related molecules. Our
study suggested that low expressions of LINC01566 in
MG might contribute to the T lymphocyte proliferation
and activation that characterize the immune abnormalities in MG. Furthermore, we found that the transcription
factor FOSL1 regulates LINC01566 expression in MG.
Our findings describe a potential mechanism underlying the protective effect of LINC01566 on MG and revealthat LINC01566 may be a valuable biomarker and therapeutic target in MG.
Abbreviations
MG	Myasthenia gravis
lncRNAs	Long noncoding RNAs
TFs	Transcription factors
qRT-PCR	Quantitative reverse transcription polymerase chain reaction
FISH	Fluorescence in situ hybridization
DEGs	Differentially expressed genes
NLR	Neutrophil-to-lymphocyte ratio
GO_ BP	Gene ontology biological process
AChR	Acetylcholine receptor
MuSK	Muscle-specific kinase
LRP4	Lipoprotein-related protein 4
PBMCs	Peripheral blood mononuclear cells
HCs	Healthy controls
ASO	Antisense oligonucleotide
CCK-8	Cell Counting Kit-8

Page 15 of 17

Supplementary Information

The online version contains supplementary material available at https://doi.
org/10.1186/s12974-024-03194-5.
Supplementary Material 1
Supplementary Material 2
Supplementary Material 3
Supplementary Material 4
Supplementary Material 5
Supplementary Material 6
Supplementary Material 7
Supplementary Material 8
Supplementary Material 9
Supplementary Material 10
Supplementary Material 11
Supplementary Material 12
Supplementary Material 13
Supplementary Material 14
Supplementary Material 15
Supplementary Material 16
Supplementary Material 17
Supplementary Material 18
Supplementary Material 19
Supplementary Material 20
Supplementary Material 21
Supplementary Material 22
Acknowledgements
Not applicable.
Author contributions
JW and LW conceived and supervised the project. LL and DL performed thebioinformatics analysis. XW, NH, LT and BC collected the clinical samples. LL,
JJ, FX and YR conducted the experiments. LL, ZC, LZ, XL and LQ performed thestatistical analyses. LL wrote the paper. All the authors read and approved thefinal manuscript.
Funding
This work was supported by the National Natural Science Foundation of China[grant numbers 82071407, 82171396]; Heilongjiang Provincial Natural Science
Foundation [grant number YQ2021H012].
Data availability
Datasets generated using microarray analysis are available at NCBI Gene
Expression Omnibus Accession GSE263220.

Declarations
Ethics approval and consent to participate
The studies involving human participants were approved by the Ethics
Committee of the Second Affiliated Hospital of Harbin Medical University(Ky2020-104) and performed in accordance with the latest version of theHelsinki Declaration. The patients/participants provided their written informed
consent to participate in this study.

Li et al. Journal of Neuroinflammation(2024) 21:197

Competing interests
The authors declare no competing interests.
Author details
1
Department of Neurology, The Second Affiliated Hospital of Harbin
Medical University, Harbin 150081, China
Received: 18 April 2024 / Accepted: 31 July 2024

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