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\begin{document}

\title{Increased expression of RNA binding protein GNL3 has potential influence
on osteoarthritis by up-regulating IL24 and PTN}



\author[1]{Zhen Zhu}%
\author[1]{Jun Xie}%
\author[1]{Yawen Bian}%
\author[1]{Upasana Manandhar}%
\author[1]{Xiaomin Yao}%
\author[1]{Bo Zhang}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{June 08, 2024}


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\selectlanguage{english}
\begin{abstract}
Osteoarthritis (OA) is a degenerative joint disorder which affects about
80\% of the population above 65 years revealing radiographic evidence of
OA. Recently, more and more researches have been conducted on the
molecular mechanism of OA to find target treatments. RNA binding
proteins (RBPs) play a key role in genome regulation. Nucleolar
GTP-binding Protein 3 (GNL3) is abundantly expressed in bone marrow
mesenchymal stem cells and correlates with chondrocytes differentiation.
Here we report the transcriptome study of GNL3, which regulates
transcription in osteoarthritis (OA). In this study, RNA sequencing
(RNA-seq) was used to analyze the global transcription level in HeLa
cells. The results showed that the expression of IL24 and PTN was
down-regulated when GNL3 was knocked down. Likewise, in the lesions of
osteoarthritis, the expression level of GNL3, IL24 and PTN gene were
significantly up-regulated compared with the control group. IL24
contributes to the progression of osteoarthritis by inducing bone cells
apoptosis at the joint, and PTN contributes to the progression of
osteoarthritis by promoting angiogenesis. This study aimed to assess the
association between GNL3 and both of these two downstream genes as a
potential biomarker for investigating the development of OA.%
\end{abstract}%



\sloppy


\textbf{1 Introduction}

Osteoarthritis (OA) is a degenerative joint disorder characterized by
progressive destruction of articular cartilage, synovial hyperplasia and
subchondral osteosclerosis {[}1{]}. It is one of the main causes of
disability in middle-aged and elderly people. With the increase in the
aging population and its high medical costs, osteoarthritis and
disability caused by it have become an urgent public health issue
{[}2{]}. The prevalence of diagnosed symptomatic knee osteoarthritis was
4.2\%\textasciitilde{}15.5\%, which increased with age. About 80\% of
the population above 65 years reveals radiographic evidence of KOA
{[}3{]}. Among the 291 disabilities listed by global burden of disease
(GBD), hip and knee OA ranked 11th globally in 2010 and 38th in
disability affects life year (DALY) {[}4{]}. Although osteoarthritis is
considered a disease of the elderly, disabling osteoarthritis can still
have a significant impact on work. It is reported that 44.4\% of people
who are unemployed due to illness are related to OA in Canada, and the
influence is expected to increase over time {[}5{]}. In addition to
aging, many other factors play a role in the pathogenesis of OA. Both
metabolic syndrome and dietary habit play a role in the susceptibility
and development of OA {[}6{]}. Recently, more and more researches have
been conducted on the molecular mechanism of OA to find target
treatments for OA. More than 25 miRNAs involved in the occurrence of
chondrogenesis and OA have been revealed. In particular, it is regulated
by more than one miRNA in chondrocyte formation, chondrocyte
differentiation, chondrocyte proliferation, chondrocyte hypertrophy,
endochondral ossification and proteolytic enzyme regulation {[}7{]}.
After knocking down RNA binding-protein zinc finger protein 36 type-like
1 (ZFP36L1) gene, the mRNA expression of two heat shock protein 70
(HSP70) family members increased, the two genes inhibited apoptosis of
cartilage, so RNA-binding protein ZFP36L1 plays a role in the
pathogenesis of osteoarthritis by modulating members of the HSP70
family, inhibiting chondrocytes apoptosis {[}8{]}. However, the specific
mechanism of RNA-binding protein in the pathogenesis of osteoarthritis
still needs to be clarified. So, the role of RNA-binding protein GNL3
was studied in this research in association with the pathogenesis of OA,
which can provide new target treatments for OA.

RNA-binding proteins play a key role in post-transcriptional regulation
and can affect different stages of RNA metabolism, and process of cell
proliferation and apoptosis {[}9-11{]}. GNL3 ( Nucleolar GTP-binding
Protein 3, Nucleostemin), also known as nucleostem factor, is an
RNA-binding protein which plays an important role in cell proliferation,
cell cycle regulation, differentiation inhibition, ribosome
biosynthesis, stem cell characteristic maintenance, genome stability and
telomere integrity {[}12-14{]}. It is abundantly expressed in bone
marrow mesenchymal stem cells, and correlates with chondrocytes
differentiation. GNL3 levels were found to be significantly increased in
synovial tissue and fluid of osteoarthritis patients in the clinical
experiment {[}15{]}. Furthermore, the relationship between GNL3 gene and
osteoarthritis in Han Chinese population has also been verified
{[}16{]}. Further study found that cis-acting regulatory polymorphisms
acting on GNL3 contributed to the OA association signal at chromosome
3p21, and the osteoarthritis susceptibility locus had been mapped to
3p21 chromosome {[}17{]}. These results proved that GNL3 is closely
related to the pathogenesis of osteoarthritis. Therefore, it was
speculated that GNL3 is involved in the pathogenesis of osteoarthritis
as an RNA-binding protein. So, in this study we explored the role of
RNA-binding protein GNL3 in the pathogenesis of osteoarthritis, which
can provide a new target for the treatment of osteoarthritis.

In this study, unbiased transcriptome analysis has been utilized to
investigate how GNL3 regulates gene transcription in Hela cells. We
successfully knocked down the GNL3 gene by shRNA Hela. High-throughput
RNA sequencing (RNA-seq) were performed for the experimental group and
the control group to obtain the gene expression profiles regulated by
GNL3. The results showed evident changes in the transcription profile of
specific genes after knocking down the GNL3 gene, some of which were
correlated with the incidence of osteoarthritis, including IL24 gene and
PTN gene.

IL24 inhibits cell proliferation and angiogenesis, and induces apoptosis
in tumor cells {[}18{]}. It is determined that IL24 plays an important
role in the classical apoptotic pathway p38 {[}19{]}, and participates
in inducing apoptosis in a variety of diseases. It is reported that IL24
reduces the production of dickkopf-1 by fibroblast-like synovial cells
in the pathogenesis of spinal arthritis, and also induces osteogenic
mineralization, playing a role in aggravating the progression of
osteoarthritis {[}20{]}. PTN is a secretory growth factor that
contributes to the skeletal development of human embryos {[}21{]}. Bone
epiphysis in the state of ischemia and hypoxia leads to extensive bone
cell and tissue death {[}22{]}, while it is suggested that PTN directly
participates in angiogenesis and promotes bone repair {[}23, 24{]}.
Angiogenesis is essential in the pathogenesis of OA, facilitating the
invasion of inflammatory cells and increase in local pain receptors that
contribute to structural damage and pain. So, PTN can aggravate the
development of osteoarthritis by promoting angiogenesis {[}25{]}. In
addition, the PTN promotes original bone cell chemotaxis, which can
promote the total colony formation of osteoblast, alkaline phosphatase
positive colony formation and specificity of alkaline phosphatase
activity {[}26{]}.

Based on previous studies, it was speculated that IL24 mainly aggravates
the progression of osteoarthritis by inducing bone cells apoptosis at
the joint and PTN may contribute to the progression of osteoarthritis by
promoting angiogenesis {[}19, 24{]}. While, IL24 and PTN contribute to
the progression of osteoarthritis directly, GNL3 promotes the
development of osteoarthritis by acting on both of these two downstream
genes.

\textbf{2 Materials and methods}

ShRNA clones were downloaded from sigma website or designed by Online
software.

\textbf{2.1 Silence sequence} : GTCCTGGTCTTGAACAAGATT

Sense and antisense strands were annealed to become shRNA. Vector
pGFP-B-RS was dissolved by HindIII and BamHI at 37 for
2h\textasciitilde{}3h,and enzyme-digested vector was also run on 1.0\%
agarose gel and refined by Qiagen column kit. A linearized vector DNA
digested by HindIII and BamHI and drop-in shRNA were ligase by T4 DNA
Ligase (NEB). Plasmids were brought into Escherichia coli by chemical
transformation. Cells were put onto LB plates containing kanamycin. Then
plates were incubated overnight at 37. Colonies were screened by colony
PCR (30 cycles) with universal primers (located on the backbone vector).
The interference sequence of shRNA was checked by Sanger sequencing.
CDNA synthesis was done by standard procedures and RT-PCR was performed
on the Bio-Rad S1000 with Bestar SYBR Green RT-PCR Master Mix (DBI
Bioscience, Shanghai, China). The depth of sequencing information is 10G
per specimen.

\textbf{2.2 Cell culture and transfections}

Human CC (Cervical Carcinoma) cell lines, HeLa (CCTCC@GDC0009) were
obtained from CCTCC (China Center for Type Culture Collection, Wuhan,
Hubei, China). HeLa cells were cultured at 37 with 5\%
CO\textsubscript{2} in Dulbecco's modified Eagle's medium (DMEM) with
10\% fetal bovine serum (FBS), 100 \selectlanguage{greek}µ\selectlanguage{english}g/mL streptomycin, and 100 U/mL
penicillin. Plasmid transfection of HeLa cells was operated using
Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) according to the
manufacturer's protocol. Transfected cells were harvested for RT-qPCR
analysis after 48h.

\textbf{2.3 MTT assay}

All cells were maintained in low serum, containing 2\% FBS (Invitrogen)
with starvation for 10 hours before the experiments were performed. An
aliquot of cells (1 *10\textsuperscript{3} cells/well) was seeded in a
96-well plate with 2\% FBS and serum-starved for 10 hours, transfected
with corresponding vector or siRNA and cultured in normal medium. At 24,
48, 72, and 96 hours after transfection, MTT (3-(4,
5-dimethylthiazol-2-yl)-2, 5-diphenyl-tetrazolium bromide) solution (5
mg/mL, 20 mL) was added to each well. After incubation for 4 hours, the
medium was removed and 100 mL dimethyl sulfoxide was added to each well.
The relative number of surviving cells was assessed by measuring the
optical density of cell lysates at 570 nm. All assays were performed 5
times.

\textbf{2.4 Assessment of gene expression}

GAPDH (glyceraldehyde-3-phosphate dehydrogenase) was utilized as a
control gene for assessing the effects of GNL3 knockdown. cDNA synthesis
was done by standard procedures and RT-qPCR was performed on the Bio-Rad
S1000 with Bestar SYBR Green RT-PCR Master Mix (DBI Bioscience,
Shanghai, China). The information of primers is presented in Table 1.
The compression of each transcript was then standardized to GAPDH mRNA
level using 2-\selectlanguage{greek}ΔΔ\selectlanguage{english}CT method {[}27{]}. Comparisons were operated with the
paired Student's t-test by using GraphPad Prism software (San Diego,
CA).

\textbf{2.5 RNA extraction and sequencing}

The HeLa cells were triturated into fine powder before RNA extracted
with TRIZOL (Ambion). The RNA was further refined with two
phenol-chloroform treatments and then preformed with RQ1 DNase (Promega,
Madison, WI, USA) to remove DNA. The quality and quantity of the refined
RNA were rejudged by measuring the absorbance at 260 nm/280 nm
(A260/A280) using Smartspec Plus (BioRad, USA). The integrity of RNA was
further determined by 1.5\% agarose gel electrophoresis.

For each sample, 1\selectlanguage{greek}μ\selectlanguage{english}g of the total RNA was utilized for RNA-seq library
preparation by VAHTS Stranded mRNA-seq Library Prep Kit (Vazyme).
Polyadenylated mRNAs were refined and fragmented, and then translated
into double strand cDNA. After the step of end repair and A tailing, the
DNAs were roped to VAHTS RNA Adapters (Vazyme). Refined ligation
products corresponding to 200-500 bps were digested with heat-labile
UDG, and before sequencing ,the single strand cDNA was amplified,
refined, quantified and stored at -80.

The libraries were prepared following the manufacturer's instructions
and applied to Illumina HiSeq X Ten system for 150 nt paired-end
sequencing for high-throughput sequencing.

\textbf{2.6 RNA-Seq Raw Data Clean and Alignment}

Raw reads covering more than 2-N bases were first cast off. Then
adaptors and low-quality bases were removed from raw sequencing reads
using FASTX-Toolkit (Version 0.0.13). The short reads less than 16nt
were also discarded. Then, clean reads were aligned to the GRch38 genome
by tophat2 {[}28{]} allowing 4 mismatches. Uniquely mapped reads were
utilized for gene reads number counting and FPKM calculation (fragments
per kilobase of transcript per million fragments mapped) {[}29{]}.

\textbf{2.7 Differentially Expressed Genes (DEG) analysis}

The R Bioconductor package edgeR {[}30{]} was used to screen out the
differentially expressed genes (DEGs). A false discovery rate
\textless{}0.05 and fold change\textgreater{}2 or \textless{} 0.5 were
set as the cut-off criteria for identifying DEGs.

\textbf{2.8 Reverse transcription q-PCR validation of DEGs}

In this work, to elucidate the validity of the RNA-seq data,
quantitative reverse-transcription polymerase chain reaction (q-RT-PCR)
was performed for some of the DEGs. The information of primers is
presented in Table 4. Total RNA remaining from RNA-seq library
preparation was utilized for q-RT-PCR. RNA was reverse transcribed into
cDNA utilizing M-MLV Reverse Transcriptase (Vazyme). Real-time PCR was
performed with the StepOne RealTime PCR System using the SYBR Green PCR
Reagents Kit (Yeasen). The PCR conditions compose of denaturing at 95@\selectlanguage{english}C
for 10 min, 40 cycles of denaturing at 95@\selectlanguage{english}C for 15 s, annealing and
extension at 60@\selectlanguage{english}C for 1 min. For each sample, PCR amplifications were
performed in triplicate. The RNA expression levels of all the genes were
standardized against that of GAPDH.

\textbf{2.9 Functional enrichment analysis}

To pick out functional categories of DEGs, Gene Ontology (GO) terms and
KEGG pathways were identified using KOBAS 2.0 server {[}31{]}.
Hypergeometric test and Benjamini-Hochberg FDR controlling procedure
were utilized to define the enrichment of each term.

\textbf{2.10 Clinical verification}

Femoral head samples were collected from 20 patients with OA and
set-matched biopsies in 20 patients who had joint replacement surgery
caused by femoral neck fracture at Wuhan Union Hospital, Huazhong
University of Science and Technology. This study was approved by the
Medical Ethics Committee of Huazhong University of Science and
Technology Tongji Medicine College under protocol number (2019) IEC
(S531). Written informed consent was obtained from each participant
prior to sample collection, and all experiments were performed in
accordance with relevant guidelines and regulations. The following
inclusion criteria were used: Osteoarthritis was diagnosed according to
the American rheumatology association's diagnostic criteria and WOMAC
scale. And the control group was selected patients with joint
replacement surgery caused by femoral neck fracture excluded joint
lesions caused by gout, rheumatoid arthritis, steroid-induced necrosis
and alcohol-induced necrosis. The informed consent form was in the
Supporting Information.

\textbf{Results}

\textbf{3.1 GNL3 promotes cell proliferation in HeLa cells}

In order to verify the effect of knockdown GNL3 on cell reproduction and
apoptosis, cytological experiments were carried out. After 48 hours of
transfection, the green fluorescent protein (GFP) fluorescence
expression after shRNA transfection revealed that the cell transfection
was successful (Figure 1A). The reproduction and apoptosis of cells in
the experimental and the control groups were detected by MTT assay and
apoptosis kit, respectively. The results showed that cell reproduction
was inhibited while cell apoptosis was enhanced in the GNL3-knockdown
group when compared with the control group (Figure 1B and C).

\textbf{RNA-seq analysis of GNL3-regulated genes in HeLa cell}

In this experiment, cDNA libraries and high-throughput sequencing of
samples have been successfully constructed. The library was constructed
using Illunima HiSeq X Ten sequencing platform and paired with end
sequencing, and high quality transcriptome data were obtained. In this
study, after knocking down GNL3, the GNL3 expression in the experimental
group decreased significantly (Figure 2A and B), which indicated that
the modeling was successful.

To ensure the reliability of the experimental data, quality control on
the sequencing results were conducted (Table 2). As shown in table 2,
raw reads per sample were generated and clean reads per sample were
retained by the removal of adaptors and contaminating sequences. Among
these, the data of Clean Per in these four groups were all more than
95\%, which means that the polluted possibility of this sequencing was
little.

The samples in this experiment were from HeLa cell line, Homo sapiens,
and the reference genome version was GRCh38. We selected software TopHat
2 {[}28{]}. After a certain tolerance rate was set, clean reads was
matched to the reference genome (Table 3). Total mapped indicates the
number and proportion of reads that can be located in the genome. In
transcriptome sequencing, if there is no contamination and the reference
genome is selected appropriately, the proportion of this part of data is
usually higher than 70\%. The data in the four groups of this experiment
were all above 90\% which indicated that the data in this experiment was
reliable. As for the data analysis on the downstream, only this batch of
reads was used to ensure the reliability of the experimental results.

In order to compare the differences in gene expression among different
samples, FPKM value was used to represent gene expression. FPKM
(expected fragments per kilobase of transcript per million fragments
mapped) represents the number of reads per million base lengths from a
gene. In this research, most genes were at an expression level of FPKM [?]
20 (Figure 2D). Then the differential expression of transcriptome
between samples were analyzed (DEG). First, inter-sample clustering
analysis on the correlation between samples was conducted (Figure 2C) to
determine the correctness of sampling.

Based on the detections above, samples were compared and differentially
expressed genes (DEG) were obtained. Software edgeR was selected to
analyze the differential expression between two or more samples. The
fold change (FC) and false discovery rate (FDR) were used to determine
whether a gene was differentially expressed. The evaluation criteria for
significant difference expression were FC [?] 2 or [?] 0.5,FDR \textless{}
0.05. Through the comparison between the experimental group and the
control group, it is indicated that GNL3 extensively regulated gene
transcription. Heatmap analysis of the expression patterns of partial
DEGs in RNA-seq samples showed a high consistency of the GNL3-mediated
transcription in both data sets (Figure 2E).

\textbf{Functional clustering of GNL3-regulated genes}

In order to reveal the biological role of these DEGs, a taxonomic
enrichment analysis of the differentially expressed genes on GO and KEGG
was performed (Figure 3A and B). In the Figure 3, the top ten
representative events enriched in the biological processes subset of GO
and pathways of KEGG were selected. As shown in Figure 3 after knockdown
of GNL3, the down-regulated gene functions are concentrated in the
aspects of signal transduction, apoptotic, transcription, cell
proliferation and primary immunodeficiency. Through enrichment analysis
on GO and KEGG, it was speculated that GNL3 plays an important role in a
series of biological activities such as inflammation and angiogenesis.

Interestingly, it was found that GNL3 knockdown significantly affected
the expression of many genes including IL24 gene and PTN gene (Figure 3C
and D). Q-RT-PCR was performed to verify the expression of IL24 and PTN
(Figure 3E and F). The results showed that the expression of IL24 and
PTN was down-regulated when GNL3 was knocked down.

\textbf{Expression level of GNL3, IL24 and PTN gene in the OA patients}

To further verify the results, femoral head specimens from 20 patients
with osteoarthritis and 20 control samples were collected (Figure 4A and
B). The results showed that the expression level of GNL3, IL24 and PTN
gene in specimens were significantly up-regulated compared with the
control group (Figure 4C and D). GNL3 is abundantly expressed in bone
marrow mesenchymal stem cells and correlates with chondrocytes
differentiation. IL24 induces apoptosis and PTN promotes angiogenesis
respectively in the pathogenesis of osteoarthritis. So, the expression
of IL24 and PTN genes were up-regulated with OA.

\textbf{4 Discussion}

Through previous studies, it is known that osteoarthritis is a
degenerative bone and joint disorder. Although aging and obesity are
closely related to the onset of OA {[}32, 33{]}, the specific mechanism
has not yet been clarified. This study investigated the role of
RNA-binding protein GNL3 in the pathogenesis of osteoarthritis. It is
found that GNL3 indirectly affected the pathogenesis of osteoarthritis
by affecting the transcription of downstream genes. So this study has
provided new therapeutic targets of osteoarthritis.

GNL3 (Nucleolar GTP-binding Protein 3), also known as nucleostemin,
plays an important role in cell proliferation, cell cycle regulation and
other aspects {[}12, 13{]}. Although previous studies showed that the
expression of GNL3 gene increased in OA rats and Chinese Han population
{[}16{]}, the specific molecular mechanism has not been clarified. We
considered multiple possibilities of the potential mechanism of GNL3 in
OA. There have been few previous studies on GNL3 as an RNA-binding
protein, and some studies have confirmed that GNL3 interacts with the
susceptibility gene loci of osteoarthritis {[}34{]}. Therefore, we
speculated that RNA-binding protein GNL3 affects the transcription and
expression of downstream genes thus affecting the incidence of
osteoarthritis.

In this experiment, high-throughput RNA sequencing (RNA-seq) was
performed on the experimental and control groups after GNL3 gene was
knocked down in Hela cells to obtain complete transcriptome information.
Unlike traditional hybridization methods, RNA-seq has several
advantages. First of all, RNA-seq is not limited to detecting
transcripts corresponding to existing genome sequences. For example,
454-based RNA-seq was used to sequence the transcriptome of Glanville
fritillary butterfly {[}35{]}. RNA-seq also revealed changes in the
sequence of transcriptional regions {[}36, 37{]}. RNA-seq has no
quantification upper limit, which is associated with the number of
sequences obtained. So it can detect expression levels over a large
dynamic range. A greater than 9,000-fold range was measured in a study
that analyzed 16 million mapped reads in Saccharomyces cerevisiae
{[}38{]}, and a range spanning five orders of magnitude was measured for
40 million mouse sequence reads {[}39{]}. RNA-seq is controlled at very
precise quantifying expression levels using quantitative PCR (q-PCR) and
spike-in RNA of known concentrations {[}38{]} making the results highly
reproducible for both technical and biological replicates {[}36, 38{]}.

It was observed that during GNL3 gene knock-down, a large number of
genes showed differential expression, indicating that GNL3 extensively
regulated gene expression. The functions of these genes were clustered
into GO and KEGG databases, and interestingly the results showed that
the functions of down-regulated genes were mainly in signal
transduction, apoptotic, transcription, cell proliferation and primary
immunodeficiency. The DEGs were comprised in pathways such as Jak-STAT
signality pathway, p53 pathway, p53-Akt pathway, transcription
misregulation in cancer, NF-kappa B signality pathway and cell adhesion
molecules. Notably the two down-regulated genes, IL24 gene and PTN gene,
have been closely associated with the development of osteoarthritis in
previous researches. It is obviously that PTN is upregulated in our
eight sets of experiments. To draw the heat-map, we only selected two
appropriate sets of results where PTN gene up-regulation was not
significant.

In this work, it is confirmed that RNA-binding protein GNL3 affected the
expression of downstream genes, including IL24 and PTN, by affecting RNA
transcription. IL24 and PTN play an important role in promoting the
development of osteoarthritis. To further prove the role of GNL3 in the
pathogenesis of osteoarthritis, the local tissues of OA patients and the
control groups were selected for q-RT-PCR. The results are shown as the
mean \selectlanguage{ngerman}± standard deviation of three genes. It indicates that there is a
significant difference in gene expression between the OA groups and the
control groups. The expression level of IL24 gene in OA patients was
nearly twice higher than control group insisting that it mainly
aggravates the inflammatory response at the joint site in
osteoarthritis. IL24 is an immune-regulating cytokine with a wide range
of tumor-specific inhibitory effects {[}40, 41{]}, and it induces
apoptosis through intracellular and extracellular signaling pathways
{[}42{]}. It has also been observed that IL24 induces metaphase
chromosome protein 1 (MCP1) production in synovial mononuclear cells,
which aggravates the inflammatory response and promotes the development
of spinal joint arthritis and rheumatoid arthritis {[}43{]}. Similarly,
as PTN is responsible for promoting angiogenesis, the expression level
of PTN gene in OA patients was nearly seen to be seven times higher than
that in control group. PTN is a secretory polypeptide that plays an
important role in cell growth, migration and angiogenesis {[}44, 45{]}.
In addition to directly promoting angiogenesis, PTN also influences the
behavior of other cells by promoting the proliferation and migration of
endothelial progenitor cells {[}46{]}, and promotes the formation of
angiogenic microenvironment {[}47{]}. Angiogenesis is one of the
earliest histopathological changes in chronic non-communicable arthritis
{[}48{]}. GNL3 was also expressed about seven times higher than that in
control group. Hence, from the results observed in HeLa cells and in the
specimens of OA patients, GNL3 developed osteoarthritis by acting on
both of these two downstream genes.

GNL3 affects the JAK-STAT pathway through its interaction with Signal
Transducer And Activator Of Transcription 3 (STAT3) {[}49{]}. It has
been shown that when STAT3 was activated, the expression level of
downstream gene IL24 increased {[}50{]}. Binding anaplastic lymphoma
kinase (ALK) which induces MAPK pathway activation is an important step
in the anti- apoptotic signaling of PTN and regulation of cell
proliferation {[}51{]}. STAT3 inhibitor can inhibit the function of ALK,
thereby reducing the expression of PTN gene {[}52{]}. Therefore, we
believe that after knocking down GNL3, expression levels of IL24 and PTN
genes in the JAK-STAT pathway are reduced through the interaction with
STAT3 gene.

\textbf{Acknowledgement}

We gratefully acknowledge funding for this work provided by National
Natural Science Foundation of China (81501888 and 81802722). This study
was partially supported by ABLife (ABL-7702069).

\textbf{Conflict of interest}

The authors declare no financial or commercial conflict of interest.

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\textbf{Table 1. q-RT-PCR primers for gene expression qualification.}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{Gene} & \textbf{Primer} & \textbf{Sequence
(5'-3')}\tabularnewline
\midrule
\endhead
\textbf{GAPDH} & \textbf{Forward} &
\textbf{CGGAGTCAACGGATTTGGTCGTAT}\tabularnewline
& \textbf{Reverse} & \textbf{AGCCTTCTCCATGGTGGTGAAGAC}\tabularnewline
\textbf{GNL3} & \textbf{Forward} &
\textbf{AGAGGTGTTGGATGCCAGAG}\tabularnewline
& \textbf{Reverse} & \textbf{TGAACACCACTGTTGGCAAT}\tabularnewline
\textbf{PTN} & \textbf{Forward} &
\textbf{GAGAATGGCAGTGGAGTG}\tabularnewline
& \textbf{Reverse} & \textbf{GCTTGGAGATGGTGACAG}\tabularnewline
\textbf{IL24} & \textbf{Forward} &
\textbf{TCTCATCGTGTCACAACTG}\tabularnewline
& \textbf{Reverse} & \textbf{ATCCAGGTCAGAAGAATGTC}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2. High quality effective sequences}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{SampleID} & \textbf{Raw} & \textbf{Clean} & \textbf{Clean Per} &
\textbf{Unique Tag}\tabularnewline
\midrule
\endhead
shGnl3 & 76707134 & 73102684 & 95.30\% &
28316498(38.74\%)\tabularnewline
shCtrl & 78078976 & 74920851 & 95.96\% &
30932962(41.29\%)\tabularnewline
\bottomrule
\end{longtable}

a) Sample ID: Sample number. b) Raw: Raw reads, the number of original
sequences from the original image data obtained by sequencing and
converted by base calling. c) Clean: Clean reads, raw reads were removed
from the adaptor sequence and low-quality bases, and the number of
effective sequences was used for subsequent analysis. d) Clean Per: the
proportion of Clean reads in raw reads. e) Unique tag: Unique tag,
number of non-repeat reads and proportion of clean reads.

\textbf{Table 3. Effective sequencing data were compared to the
reference genome}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[b]{0.30\columnwidth}\raggedright\strut
\textbf{Sample}\strut
\end{minipage} & \begin{minipage}[b]{0.30\columnwidth}\raggedright\strut
\textbf{shCtrl}\strut
\end{minipage} & \begin{minipage}[b]{0.30\columnwidth}\raggedright\strut
\textbf{shGnl3}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\textbf{Total reads}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
72284154\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
70622804\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
\textbf{Total mapped}\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
66382194 (91.84\%)\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
66780627 (94.56\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
\textbf{Total Uniquely mapped}\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
64232872 (96.76\%)\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
64926867 (97.22\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
\textbf{Total Multiple mapped}\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
2149322 (3.24\%)\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
1853760 (2.78\%)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

a) Total reads: the number of pairs of reads in clean reads, which are
used for subsequent analysis.b) Total mapped: the number and proportion
of reads that have been mapped to the genome. c) Total mapped reads: the
number of reads uniquely located in the reference genome and the
proportion of reads in Total mapped reads. d) Total multiple reads: the
number of reads with Multiple locations in the reference genome and the
proportion of reads in Total mapped reads. Reads with Multiple locations
are mainly rRNA and tRNA.

\textbf{Figure legends}

\textbf{Figure 1.} GNL3 promotes cell viability. (A) GFP fluorescence
expression after transfection for 48 hours. (B)MTT assay revealed that
GNL3 knockdown inhibited cell reproduction of HeLa cells. P-value in
Rep1 and Rep2 sets are respectively 0.000268 and 0.0107.*P \textless{}
0.10, ***P \textless{} 0.01 compared to the control groups. (C) Flow
cytometer revealed that GNL3 knockdown promoted cell apoptosis of HeLa
cells.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 2.} RNA-seq analysis of GNL3-regulated genes in HeLa
cells. (A) GNL3 expression quantified by RNA sequencing data. FPKM
values were calculated as that has been explained in Materials and
Methods. P-value is 0.00441. (B) The result of GNL3 q-RT-PCR analysis.
P-value is 4.604E-10. (C) Identification of GNL3 regulated genes.
Up-regulated genes are labeled in red, whereas down-regulated are
labeled in blue in the heat map. (D) Cumulative distribution of FPKM
values of all detected genes in the sample. The horizontal coordinate
represents the FPKM value, the vertical coordinate on the left
represents the number of genes, and the vertical coordinate on the right
represents the proportion of genes. For each point on the curve, its
vertical coordinate represents the number and proportion of genes whose
FPKM value is less than or equal to the horizontal coordinate of this
point. (E) Heat map shows the hierarchically clustered Pearson
correlation matrix resulted from comparing the transcript expression
values for control and GNL3 knockdown samples.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 3.} Functional clustering of GNL3-regulated genes. (A)
The top ten representative GO biological processes of GNL3
down-regulated genes. (B) The top ten representative KEGG pathways of
GNL3 down-regulated genes. (C) IGV-sashimi plot shows a repression of
IL24 expression after GNL3 knockdown. The exon sequences are denoted by
boxes and intron sequences by the horizontal line. (D) IGV-sashimi plot
shows a repression of PTN expression after GNL3 knockdown. (E) q-RT-PCR
of IL24 gene in HeLa cells. (F) q-RT-PCR of PTN gene in HeLa cells.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 4.} Expression level of GNL3, IL24 and PTN gene in the OA
patients (A) The radiograph of the pelvis of a patient with OA shows
narrowing of the hip joints. (B) The femoral head specimen removed from
the patient with osteoarthritis shows degeneration of articular
cartilage. (C) Western Blot in OA patients vs controls (D) q-RT-PCR of
GNL3, IL24 and PTN gene in 20 patients with osteoarthritis and 20
control samples. P-value in GNL3, IL24 and PTN sets are respectively
0.0010, 0.0230 and 0.0073.*P \textless{} 0.10, ***P \textless{} 0.01
compared to the control groups.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

