Abstract

   Chronic kidney disease (CKD) is a serious disease that endangers human
   health. It is reported that inhibiting renal cell apoptosis can delay
   the progress of CKD. Our previous study found that the mice with
   protein phosphatase Mg2+/Mn2+ dependent 1K (PPM1K) gene deletion had
   obvious symptoms of glomerular vascular and interstitial vascular
   dilatation, congestion and hemorrhage, glomerular hemorrhage and
   necrosis, interstitial fibrous tissue proliferation, decreased urinary
   creatinine clearance, and increased urinary protein level. In addition,
   studies have found that PPM1K is essential for cell survival, apoptosis
   and metabolism. However, no study has confirmed that PPM1K can inhibit
   renal cell apoptosis. In this study, PPM1K was overexpressed in human
   kidney-2 cells (HK-2), and the biological process of differentially
   expressed genes and its effect on apoptosis were comprehensively
   screened by RNA sequencing (RNA-seq). Through sequencing analysis, we
   found that there were 796 differentially expressed genes in human renal
   tubular epithelial cells transfected with PPM1K gene, of which 553 were
   down-regulated and 243 were up-regulated. Enrichment analysis found
   that differentially expressed genes may play an important role in amino
   acid metabolism and biosynthesis. In the GO analysis functional pathway
   list, we also found that multiple genes can be enriched in apoptosis
   related pathways, such as G0S2, GADD45A, TRIB3, VEGFA, NUPR1 and other
   up-regulated genes, and IL-6, MAGED1, CCL2, TP53INP1 and other
   down-regulated genes. Then we verified these differentially expressed
   genes by RT-PCR, and found that only the RT-PCR results of G0S2, VEGFA
   and NUPR1 were consistent with the transcriptome sequencing results. We
   believe that G0S2, VEGFA, NUPR1 and other genes may participate in the
   apoptosis process of HK-2 cells induced by PPM1K.In conclusion, these
   findings provide some data support for the study of HK-2 cell apoptosis
   mechanism, and also provide a scientific theoretical basis for further
   study of the effect of PPM1K on kidney disease.

   Keywords: PPM1K, human kidney-2 cell, RNA-seq, transcriptome,
   enrichment analysis, apoptosis

Introduction

   Chronic kidney disease (CKD) has the characteristics of high
   prevalence, low awareness, poor prognosis and high medical costs. It is
   another disease seriously endangering human health after cardiovascular
   and cerebrovascular diseases, diabetes and malignant tumors ([42]Hwang
   et al., 2012; [43]Ene-Iordache et al., 2016). Renal tubules are the
   main part of the kidney and are vulnerable to various injuries,
   including hypoxia, proteinuria, toxins, metabolic disorders and aging.
   Studies have found that renal tubular cell injury drives the
   development of CKD. When suffering from injury reaction, renal tubular
   epithelial cells change and play a role as inflammatory cells and
   fibrotic cells, thus producing various bioactive molecules to drive
   interstitial inflammation and fibrosis ([44]Liu et al., 2018).
   Therefore, it is necessary to explore the regulatory mechanism of renal
   tubular injury in the process of CDK, including tubular cell apoptosis
   and inflammatory transformation, in order to delay the progress of CKD.

   Apoptosis is one of the important defense mechanisms of cells. It is a
   programmed death reaction, which is used to clear damaged cells and
   maintain the homeostasis of the host. It is closely related to cell
   replication, proliferation, immunity and cancer. Cell dysfunction and
   apoptosis are also key factors in the progress of chronic kidney
   disease (CKD) ([45]Xiang et al., 2021). Excessive apoptosis may lead to
   disease. Lin Xie found that homocysteine promotes podocyte apoptosis by
   regulating epigenetic modifiers DNMT1 and EZH2, which is accompanied by
   glomerular damage, leading to the progress of CKD ([46]Xie et al.,
   2021). Nan Yu found that podocytes are an important part of renal
   glomerular filtration barrier, and podocyte dysfunction and apoptosis
   are important factors leading to the progress of chronic kidney disease
   (CKD) ([47]Yu et al., 2020). Izquierdo research found that abnormal
   blood lipids and lipid accumulation in the kidney may lead to changes
   in renal lipid metabolism, thus inducing endoplasmic reticulum (ER)
   stress and the production of reactive oxygen species (ROS), and
   ultimately leading to cytotoxicity, apoptosis and inflammation, which
   play an important role in advanced CKD ([48]Izquierdo-Lahuerta et al.,
   2016). In addition, studies have shown that inhibiting renal tubular
   cell apoptosis can alleviate the progress of CKD. Xiang Cheng Xie found
   that relaxin inhibits human tubular epithelial cell apoptosis induced
   by Campbell acid by activating pi3k/akt signaling pathway ([49]Xie et
   al., 2017); Kahori Nasu found that mint3 in epithelial cells up
   regulates NF- κ The anti apoptotic effect of B protects cells from
   apoptosis and thus inhibits fibrosis, which may be the therapeutic
   target of CKD ([50]Nasu et al., 2020); Sandra Kostic found that
   apoptosis inducing factor (AIF) is a key factor leading to
   mitochondrial apoptosis, and plays an important role in apoptosis, cell
   function and homeostasis, and proved that AIF is a potential
   therapeutic target and marker of CKD progression ([51]Kostic et al.,
   2020); Nan Yu confirmed that curcumin reduced podocyte injury and
   apoptosis by inhibiting endoplasmic reticulum stress, thereby
   alleviating the progress of CKD ([52]Yu et al., 2020); The above
   research shows that inhibiting renal cell apoptosis is of great
   significance to alleviate the progress of CKD.

   RNA sequencing (RNA-seq) technology is the second-generation sequencing
   technology with high sensitivity, high accuracy and low cost ([53]Wang
   et al., 2009; [54]Hrdlickova et al., 2017). At present, this technology
   is increasingly used in the analysis of diseases and drugs at the
   genetic level. The transcriptome sequencing data analysis using RNA-seq
   technology to study the expression of disease related genes mainly
   focuses on the analysis of differentially expressed genes, GO (Gene
   Ontology) function enrichment analysis and KEGG (Kyoto Encyclopedia of
   Genes and Genomes) pathway enrichment analysis. GO function analysis is
   to use the annotation results of the database to explain the functional
   parts of differentially expressed genes in the three categories of gene
   Ontology: molecular function, cellular component, and biological
   process ([55]Ashburner et al., 2000). KEGG is a comprehensive database
   for understanding cells, organisms and ecosystems, and a database
   resource for practical programs of genome sequencing and other
   high-throughput experimental technologies generated from molecular
   level information, especially large molecular datasets ([56]Kanehisa et
   al., 2004), which is divided into three categories: system information,
   genome information and chemical information. KEGG pathway is a database
   of system information and a collection of hand-painted metabolic
   pathways, including metabolism, genetic information processing,
   environmental information, cell process processing, biological system,
   human disease and drug development. At present, transcriptome
   sequencing technology is used in the field of kidney disease. Meeyoung
   Park et al. used transcriptome sequencing technology to analyze the
   steady-state gene expression pattern of human kidney tissue under
   ischemia and reperfusion conditions. Differential expression analysis
   showed that the metabolic pathway of cells was disordered during
   ischemia, and cell development, migration and immune response related
   pathways were out of tune during reperfusion. Cluster analysis showed
   that the metabolism, apoptosis and fibrosis related pathways mediated
   by ischemia were significantly dysregulated, while the cell growth,
   migration and immune response related pathways were highly dysregulated
   in post ischemia reperfusion. During ischemia, the expression of pro
   apoptotic genes and death receptors were down regulated, indicating
   that there was a protective mechanism against ischemic injury ([57]Park
   et al., 2020). Kelly et al. Conducted transcriptome sequencing on the
   rat model of diabetes nephropathy to deeply study the pathogenic
   mechanism of diabetes nephropathy with progressive CKD. The results
   showed that the pathological genes of the model led to kidney
   inflammation, promoted apoptosis and blocked cell cycle ([58]Kelly et
   al., 2013). This study can be used to develop new clinical treatment
   strategies.

   Our previous study found that mice with protein phosphatase Mg2+/Mn2+
   dependent 1K (PPM1K) gene deletion significantly developed glomerular
   and interstitial vascular dilatation, congestion and hemorrhage,
   glomerular hemorrhagic necrosis, interstitial fibrous tissue
   proliferation with inflammatory cell infiltration, significantly
   decreased urinary creatinine clearance, and increased urinary protein
   levels. We speculate that PPM1K may play an important protective role
   in the development of renal fibrosis, but its molecular mechanism is
   not clear. Kun Lian found that the cardiac function of diabetes mice
   overexpressing PPM1k was improved, and the myocardial infarction area
   and apoptosis were reduced ([59]Lian et al., 2020). Alfonso confirmed
   that PPM1K is crucial to cell survival, apoptosis and metabolism
   ([60]Oyarzabal et al., 2013). Therefore, we speculate that PPM1K can
   alleviate the progress of CKD by inhibiting renal cell apoptosis.
   However, no study has confirmed that PPM1K can inhibit renal cell
   apoptosis. In this study, PPM1K was overexpressed in human kidney-2
   cells (HK-2). As the research object, the biological process of
   differentially expressed genes and its effect on apoptosis were
   comprehensively screened by RNA sequencing (RNA-seq). It is hoped that
   this study will bring a new way for the treatment of CKD.

Materials and methods

Culture of human renal tubular epithelial cells

   HK-2 cells were purchased from Wuhan prosai Life Technology Co., Ltd
   (Wuhan, China) and inoculated into a 25 cm^2 culture flask after
   resuscitation. The culture medium was DMEM medium (Gibico, Grand
   Island, New York, United States) containing 10% Fetal Bovine Serum
   (Gibico, Sandringham Ave, Thornton, NSW 2322, Australia) by mass. The
   culture flask was placed in a cell incubator (Thermo science, United
   States) with a volume fraction of 5% carbon dioxide at 37°C. Subculture
   when the cell fusion rate is 80%–90% under the inverted microscope.

Construction of PPM1K plasmid

   Hunan Youbao biological company (Hunan, China) was entrusted to
   construct PPM1K plasmid. After gene sequencing experiments, the
   accuracy of the sequence was proved.

Plasmid transfection HK-2

   The third generation human HK-2 is selected, and the cell density
   reaches 70%–80%. Two hours before transfection, the cells were cultured
   in serum-free medium, and 2.5 μG plasmid, add 100 μL incubate with opti
   MEM for 5min, and take 5 μL Lipofectamine ™ 2000(Invitrogen, Carlsbad,
   CA, United States) join 100 μL opti MEM, incubate for 5min and mix the
   above reagents. Add the above mixture to the cell culture medium, and
   change the fresh medium after 4–6 h.

Western blot verifies the expression of PPM1K

   Discard the culture medium, digest the cells with 0.25% trypsin
   solution (Thermo science, United States), collect the digested cells to
   1.5 ml EP tube, centrifuge the supernatant and add 100 μ L Ripa (Thermo
   science, United States) cracking liquid, put it on ice to crack for
   30min. After lysis, centrifuge for 15min (12000r/min), and take the
   supernatant for protein quantification. Prepare the quantitative
   protein solution and cook it at 100°C for 10 min. Make 10% SDS-PAGE gel
   (Thermo science, United States) and add 30% for each lane μG protein.
   Turn on the electrophoresis apparatus (Bio-Rad, United States), the
   upper glue voltage is 80 V (30min), the lower glue voltage is 100 V
   (2H), and the film is turned for 2 h (100V). After the film transfer,
   5% skimmed milk was sealed for 2 h, and tbst was cleaned three times.
   Cut the PVDF membrane, add 3 ml of marker antibody (PPM1K) (1:1,000,
   66008-2-IG, Proteintech) antibody and 3 ml of internal reference GAPDH
   (1:5,000, ATPA00013Rb, AtaGenix) antibody on the membrane, shake it
   overnight at 4°C, take it out on the second day, wash tbst for three
   times, add secondary antibody (Goat anti rabbit IgG 1:5000, Abcam),
   incubate at room temperature for 2 h, and clean tbst for three times.
   After cleaning, usie the enhanced chemiluminescence (ECL) reagent
   (Bio-Rad, 170506, United States) to avoid light and develop color.
   After the strip is clear, scan it with gel imaging system (Bio-Rad,
   United States), and calculate the gray value of the strip to be
   measured with image lab software.

RNA extraction and sequencing

   Total RNAs were extracted from HK2 cells using TRIzol Reagent
   (Invitrogen, cat. NO 15596026)following the methods by Chomczynski
   ([61]Chomczynski and Sacchi, 1987). DNA digestion was carried out after
   RNA extraction by DNaseI. RNA quality was determined by examining
   A260/A280 with Nanodrop™ OneCspectrophotometer (Thermo Fisher
   Scientific Inc). RNA Integrity was confirmed by 1.5% agarose gel
   electrophoresis. Qualified RNAs were finally quantified by Qubit3.0
   with Qubit™ RNA Broad Range Assay kit (Life Technologies,
   [62]Q10210).2 μg total RNAs were used for stranded RNA sequencing
   library preparation using KCTM Stranded mRNA Library Prep Kit for
   Illumina (Catalog NO. DR08402, Wuhan Seqhealth Co., Ltd. China)
   following the manufacturer’s instruction. PCR products corresponding to
   200–500 bps were enriched, quantified and finally sequenced on Novaseq
   6000 sequencer (Illumina) with PE150 model.

RNA-seq raw data clean and alignment

   Raw reads containing more than 2-N bases were first discarded. Then
   adaptors and low-quality bases were trimmed from raw sequencing reads
   using FASTX-Toolkit (Version 0.0.13). The short reads less than 16 nt
   were also dropped. After that, clean reads were aligned to the GRCh38
   genome by HISAT2 allowing four mismatches ([63]Kim et al., 2015).
   Uniquely mapped reads were used for gene reads number counting and FPKM
   calculation (fragments per kilobase of transcript per million fragments
   mapped) ([64]Trapnell et al., 2010).

Differentially expressed genes (DEG) analysis

   DEseq2 will model the original reads and use the scale factor to
   explain the difference of Library depth ([65]Love et al., 2014). Then
   DEseq2 estimates the gene dispersion, and reduces these estimates to
   produce more accurate dispersion estimates, so as to model the reads
   count. Finally, the model of negative binomial distribution is fitted
   by DEseq2, and the hypothesis is tested by Wald test or likelihood
   ratio test. DEseq2 can be used to analyze the differential expression
   between two or more samples, and the analysis results can be used to
   determine whether a gene is differentially expressed by fold change
   (FC) and probability value (p value). The p value for correction <0.05
   and fold change>2 or <0.5 were set as the cut-off criteria for
   identifying DEGs.

Functional enrichment analysis

   To sort out functional categories of DEGs, GO terms and KEGG pathways
   were identified using KOBAS 2.0 server ([66]Chen et al., 2011). Hyper
   geometric test and Benjamini–Hochberg FDR controlling procedure were
   used to define the enrichment of each term.

RT-qPCR validation of DEGs

   In order to clarify the reliability of DEGs in overexpressing PPM1K in
   HK-2 cells, RT-qPCR was performed on some of the up-regulated and
   down-regulated DEGs. The RNA samples used for RT-qPCR are the same as
   those used for RNA sequences. The PCR conditions were denaturation at
   95°C for 10 min, then denaturation at 95°C for 15 s, annealing and
   extension at 60°C for 1 min, a total of 40 cycles. Then extend at 60°C
   for 1 min, and store the product at 4°C. All samples were amplified by
   PCR three times.

Statistical analysis

   The experimental data are expressed by mean ± SD, and statistical
   analysis is carried out by SPSS v22.0 software (IBM SPSS statistics for
   Windows). Using one-way ANOVA Tukey’s HSD test, p < 0.01, it is
   considered that there is a statistical difference.

Result

Morphological observation of HK-2 cells

   HK-2 cells grow adherently, with different sizes, irregular shapes,
   circular and spindle shapes. They proliferate in a whirlpool. The cells
   in the center of the whirlpool are dense, and one to two nucleoli can
   be seen in the cells. Only a small number of floating cells and cell
   fragments are left in the culture medium; After transfection with
   PPM1K, the cell morphology changed little, such as round, spindle,
   etc., the cell proliferation rate did not change, and only a small
   number of floating cells and cell fragments remained in the culture
   medium.

The overexpression of PPM1K was verified by western blot

   Western blot showed that the expression of PPM1K protein existed in
   HK-2 cells transfected with PPM1K gene ([67]Figure 1B).

FIGURE 1.

   [68]FIGURE 1
   [69]Open in a new tab

   PPM1K-OE affect gene expression profiles of HK2 cells (A) The histogram
   showed the RT-qPCR results of control and treatment samples. Error bars
   represent mean ± SEM. *** p-value < 0.001. (B) The result of western
   blot experiment showed that the PPM1K over expression was successful
   (C) Bar plot showing the expression pattern and statistical difference
   of DEGs for PPM1K. Error bars represent mean ± SEM. ***p-value < 0.001.
   (D) PCA base on FPKM value of all detected genes PPM1K overexpression.
   The ellipse for each group is the confidence ellipse (E) Volcano plot
   showing all differentially expressed genes (DEGs) between
   overexpression (OE) and Ctrl samples. (F) Hierarchical clustering heat
   map showing expression levels of all DEGs.

PCR experiment verifies the overexpression of PPM1K

   PCR results showed that PPM1K gene was successfully transfected into
   HK-2 cells, and the transfection efficiency was 78.2% (p < 0.01). The
   transfection efficiency is high, and PPM1K is stably expressed, which
   can be used in subsequent experiments ([70]Figure 1A).

Transcriptome sequencing quality evaluation

   Transcriptome sequencing results show that the percentage of quality
   value 20 (Q20) is 100%, the percentage of quality value 30 (q30) is
   more than 99%, and the proportion of GC content is 50%–50.5% ([71]Table
   1). The overall sequencing quality is good, and subsequent analysis can
   be carried out. According to the characteristics of different genomes,
   compare the effective sequencing data (clean reads) to the reference
   genome ([72]Table 2). Statistics of the distribution of reads with
   unique location on the genome in each region ([73]Table 3).

TABLE 1.

   Valid sequence table.
   SampleID Raw Clean Clean Per(%) Raw base(G) Clean base(G) Base Per(%)
   Q20 (%) Q30 (%) GC (%) DUP(%)
   NC_1 81569682 79951252 98.02 12.24 11.62 94.93 100 99.15 50.5 30.9
   NC_2 84783386 83243760 98.18 12.72 12.07 94.89 100 99.2 50 32.7
   NC_3 85184602 83679342 98.23 12.78 12.16 95.15 100 99.2 50.5 30
   OE_PPM1K _1 87842902 86357578 98.31 13.18 12.53 95.07 100 99.2 50.5
   29.5
   OE_PPM1K _2 92718702 91075144 98.23 13.91 13.26 95.33 100 99.15 50.5
   28.8
   OE_ PPM1K _3 83827288 82362784 98.25 12.57 11.97 95.23 100 99.2 50.5
   31.1
   [74]Open in a new tab

TABLE 2.

   Mapping of clean reads on the reference genome.
   Sample NC_1 NC_2 NC_3 OE_PPM1K_1 OE_PPM1K_2 OE_PPM1K_3
   Total reads 79951252 83243760 83679342 86357578 91075144 82362784
   Total mapped 75935773 (94.98%) 78651556 (94.48%) 79288743 (94.75%)
   81692131 (94.60%) 86169279 (94.61%) 77942656 (94.63%)
   Total Uniquely mapped 72136329 (95.00%) 74747149 (95.04%) 75255465
   (94.91%) 77474949 (94.84%) 81695047 (94.81%) 74129880 (95.11%)
   Total Multiple mapped 3799444 (5.00%) 3904407 (4.96%) 4033278 (5.09%)
   4217182 (5.16%) 4474232 (5.19%) 3812776 (4.89%)
   Total Pairs 39975626 41621880 41839671 43178789 45537572 41181392
   Total Uniquely Concordant Pairs 34795095 (87.04%) 36016307 (86.53%)
   36312355 (86.79%) 37343261 (86.49%) 39443495 (86.62%) 35760567 (86.84%)
   Splice reads 40587436 (56.26%) 39704102 (53.12%) 41792107 (55.53%)
   44111549 (56.94%) 47131803 (57.69%) 42526915 (57.37%)
   Nonsplice reads 31548893 (43.74%) 35043047 (46.88%) 33463358 (44.47%)
   33363400 (43.06%) 34563244 (42.31%) 31602965 (42.63%)
   [75]Open in a new tab

TABLE 3.

   Reads distribution across reference Genomic Regions.
   Sample 5′UTR 3′UTR CDS Nc_exon Introns Intergenic Antisense
   NC_1 4179928 (5.79%) 7730910 (10.72%) 55093867 (76.37%) 2015322 (2.79%)
   1997313 (2.77%) 484181 (0.67%) 634803 (0.88%)
   NC_2 4149816 (5.55%) 7941809 (10.62%) 54569187 (73.01%) 2235797 (2.99%)
   2977100 (3.98%) 1403033 (1.88%) 1470404 (1.97%)
   NC_3 4248847 (5.65%) 8330963 (11.07%) 57092862 (75.87%) 2192660 (2.91%)
   2112622 (2.81%) 554408 (0.74%) 723099 (0.96%)
   OE_PPM1K_1 4841226 (6.25%) 8410511 (10.86%) 59172838 (76.38%) 2206522
   (2.85%) 1879153 (2.43%) 416448 (0.54%) 548247 (0.71%)
   OE_PPM1K_2 4997566 (6.12%) 9063648 (11.09%) 62842610 (76.92%) 2135777
   (2.61%) 1680215 (2.06%) 411526 (0.50%) 563701 (0.69%)
   OE_PPM1K_3 4756867 (6.42%) 7972704 (10.76%) 56693912 (76.48%) 1993208
   (2.69%) 1801478 (2.43%) 398243 (0.54%) 513463 (0.69%)
   [76]Open in a new tab

The effect of overexpression of PPM1K gene on the transcriptome of HK-2 cells

   RNA-seq results showed that 31369 genes were co expressed in
   overexpression group (OE) and control group (NC) cells, and 796 genes
   were specifically expressed, of which 243 genes were up-regulated and
   553 genes were down regulated ([77]Figure 1E). The down regulated genes
   were significantly more than the down regulated genes (FC ≥ 2 or ≤0.5,
   FDR <0.05). The heat map shows that NC group and OE group are
   different, and the two biological replication are highly correlated
   ([78]Figure 1F).

   By extracting the up-regulated epitope genes after PPM1K
   overexpression, GO analysis showed that the up-regulated genes were
   significantly enriched in the cellular amino acid biosynthetic process,
   tRNA aminoacylation for protein translation, intrinsic apoptotic
   signaling pathway in response to endoplasmic reticulum stress,
   apoptotic process, response to virus, response to endoplasmic reticulum
   stress, response to toxic substance, calcium ion transport, nervous
   system development, cellular calcium ion homeostasis and other
   biological processes ([79]Figure 2A).

FIGURE 2.

   [80]FIGURE 2
   [81]Open in a new tab

   The effect of overexpression of PPM1K gene on the transcriptome of HK-2
   cells (A) Scatter plot exhibiting the most enriched GO biological
   process results of the up-regulated DEGs (B) Scatter plot exhibiting
   the most enriched GO biological process results of the down-regulated
   DEGs. (C) Scatter plot exhibiting the most enriched KEGG pathway
   results of the up-regulated DEGs. (D) Scatter plot exhibiting the most
   enriched KEGG pathway results of the down-regulated DEGs.

   GO analysis showed that the down-regulated genes were significantly
   enriched in extracellular matrix organization, positive regulation of
   endothelial cell proliferation, acute−phase response, angiogenesis,
   cell adhesion, transport across blood−brain barrier, Notch signaling
   pathway, negative regulation of endopeptidase activity, female
   pregnancy, epithelial cell differentiation and other biological
   processes ([82]Figure 2B).

   KEGG analysis showed that the up-regulated genes were significantly
   enriched in biological processes such as cysteine and methionine
   metabolism, biosynthesis of amino acids, glycine, serine and threonine
   metabolism, transcriptional misregulation in cancer, vitamin B6
   metabolism, metabolic pathways, VEGF signaling pathway, insulin
   resistance, glycosphingolipid biosynthesis − globo and isoglobo series,
   endocrine and other factor−regulated calcium reabsorption and so on
   ([83]Figure 2C).

   KEGG analysis showed that the down-regulated genes were significantly
   enriched in biological processes such as pantothenate and CoA
   biosynthesis, pathways in cancer, breast cancer, Rap1 signaling
   pathway, AGE−RAGE signaling pathway in diabetic complications,
   regulation of actin cytoskeleton, protein digestion and absorption,
   IL−17 signaling pathway, axon guidance, complement and coagulation
   cascades ([84]Figure 2D).

PPM1K regulated expression of apoptosis associated genes in HK2 cells

   In the list of functional pathways by GO analysis, we also found that
   many genes can be enriched in biological processes related to
   apoptosis, and the up-regulated genes are significantly enriched in
   biological processes such as apoptotic process, intrinsic apoptotic
   signaling pathway in response to endoplasmic reticulum stress, positive
   regulation of apoptotic process, negative regulation of apoptotic
   process, cell cycle, positive regulation of transcription,
   DNA−templated, negative regulation of transcription by RNA polymerase
   II, signal transduction and so on ([85]Figure 3A).

FIGURE 3.

   [86]FIGURE 3
   [87]Open in a new tab

   PPM1K regulated expression of apoptosis associated genes in HK2 cells
   (A) Scatter plot exhibiting the most enriched GO biological process
   results of the all up-regulated genes of apoptosis-related (top) (B)
   Scatter plot exhibiting the most enriched GO biological process results
   of the all down-regulated genes of apoptosis-related (bottom). (C)
   Hierarchical clustering heat map showing expression levels of all
   apoptosis pathway genes.

   The down-regulated genes are significantly enriched in biological
   processes such as apoptotic process, positive regulation of apoptotic
   process, negative regulation of apoptotic process, regulation of
   apoptotic process, positive regulation of endothelial cell
   proliferation, negative regulation of neuron apoptotic process,
   activation of cysteine−type endopeptidase activity involved in
   apoptotic process, positive regulation of gene expression, positive
   regulation of transcription DNA−templated, neuron differentiation
   ([88]Figure 3B).

RT-PCR verifies the reliability of sequencing

   In order to verify the expression of apoptosis gene in HK-2 cells
   overexpressing PPM1K, we used RT-PCR to verify. In the GO functional
   pathway list, we performed RT-PCR verification on the up-regulated
   genes such as G0S2, GADD45A, TRIB3, VEGFA and NUPR1, and RT-PCR
   verification on the down-regulated genes such as IL-6, MAGED1, CCL2 and
   TP53INP1. The results showed that the RT-PCR expression of G0S2,
   GADD45A, VEGFA and NUPR1 genes increased in HK-2 cells overexpressing
   PPM1K gene, which was consistent with the transcriptome sequencing
   results. The RT-PCR results of G0S2, VEGFA and NUPR1 were statistically
   significant; The RT-PCR expression of TRIB3 gene decreased, which was
   statistically significant, but was inconsistent with the transcriptome
   sequencing results. In HK-2 cells overexpressing PPM1K gene, the RT-PCR
   expression of IL-6, MAGED1, CCL2 and TP53INP1 genes increased, and the
   RT-PCR results of IL-6, CCL2 and TP53INP1 were statistically
   significant; The RT-PCR results of MAGED1 were not statistically
   significant, but the RT-PCR results of IL-6, MAGED1, CCL2 and TP53INP1
   were inconsistent with the transcriptome sequencing results ([89]Figure
   4).

FIGURE 4.

   [90]FIGURE 4
   [91]Open in a new tab

   Related expression level (FPKM) and RT-qPCR measurement of
   apoptotic-related DEGs. ****p-value < 0.0001. ***p-value < 0.001.
   **p-value < 0.01.

Discuss

   The single nucleotide polymorphism (SNP) of PPM1K leading to protein
   function defects is associated with many human diseases, such as
   cardiovascular disease, maple diabetes disease (MSUD), type 2 diabetes
   and nervous system diseases ([92]Rostaminasab et al., 2018). However,
   the research on the mechanism of the important role of PPM1K in kidney
   disease is still limited. In this study, we overexpressed PPM1K in HK-2
   cells, trying to analyze the biological process of differentially
   expressed genes and their effects on apoptosis through transcriptome.

   Apoptosis occurs through two pathways, endogenous and exogenous
   apoptosis, both of which induce caspase cascade ([93]Kroemer et al.,
   2007). Apoptosis is considered to be an important process of cell death
   ([94]Elmore, 2007). In the GO analysis functional pathway list, we also
   found that multiple genes can be enriched in apoptosis related
   pathways, such as G0S2, GADD45A, TRIB3, VEGFA, NUPR1 and other
   up-regulated genes, and IL-6, MAGED1, CCL2, TP53INP1 and other
   down-regulated genes. Then we verified these differentially expressed
   genes by RT-PCR, and found that only the RT-PCR results of G0S2, VEGFA
   and NUPR1 were consistent with the transcriptome sequencing results,
   while the RT-PCR results of TRIB3, IL-6, MAGED1, CCL2 and TP53INP1 were
   inconsistent with the transcriptome sequencing results. Therefore, we
   believe that G0S2, VEGFA, NUPR1 and other genes may participate in the
   apoptosis process of HK-2 cells induced by PPM1K.

   G0S2 is a small protein with a length of 103 residues, which is
   involved in a variety of cellular processes ([95]Russell and Forsdyke,
   1991). In lipolysis, G0S2 interacts specifically with triglyceride
   lipase to inhibit its activity, resulting in down-regulation of
   lipolysis. Studies have found that the function of G0S2 is to act
   directly with key proteins. In a similar way, G0S2 is also involved in
   the regulation of apoptosis, cell proliferation and oxidative
   phosphorylation ([96]Páez-Pérez et al., 2020). In mitochondria, G0S2
   interacts specifically with Bcl-2 to prevent its interaction with Bax.
   In this way, G0S2 inhibits the formation of anti apoptotic heterodimer
   complex bcl-2/bax, which is conducive to apoptosis ([97]Welch et al.,
   2009).

   Nucleoprotein-1 (NUPR1) was initially found to be expressed as a small
   protein in rats and as a new gene activated during the acute phase of
   induced pancreatitis and pancreatic development ([98]Mallo et al.,
   1997). NUPR1 regulates cell injury and death in different forms,
   depending on the cellular environment and the type of stress induction.
   NUPR1 is involved in D9 tetrahydrocannabinol (THC) - induced cancer
   cell death through downstream targeted death inducing telomere repeat
   binding factor 3 (TRB3) protein ([99]Martin et al., 2021). NUPR1
   knockout can regulate autophagy by interfering with FoxO3 and promoting
   BNIP3 transcription to control autophagy, which is a stress-dependent
   self-defense mechanism that helps cells eliminate toxic
   microenvironment ([100]Kong et al., 2010). It is reported that NUPR1
   silences PI3K/AKT mammalian targets through the rapamycin (mTOR)
   pathway, inhibits autophagy activity of multiple myeloma (mm) cells and
   induces autophagy mediated apoptosis ([101]Li et al., 2020).

   Vascular endothelial growth factor (VEGF) is an endothelial cell
   specific protein, which can regulate the activity of endothelial cells,
   promote the formation of new blood vessels, and chemotactic vascular
   endothelial cells and monocytes, and mediate angiogenesis ([102]Almawi
   and Saldanha., 2013). Vascular endothelial growth factor A (VEGFA) is a
   highly effective angiogenesis factor, which can increase vascular
   permeability and prevent apoptosis. Ting Liu found that metformin can
   protect kidney by activating Hif-2 α- VEGF signal pathway can reduce
   the proteinuria of hypertensive rats ([103]Liu et al., 2020). Some
   studies have found that VEGFA signal can maintain the activity of
   glomerular endothelial cells, but when the concentration of VEGFA is
   too high, it may lead to endothelial cell damage ([104]Brosius and
   Coward, 2014). In the disease of endothelial hyperplasia, excessive
   release of VEGFA leads to endothelial cell proliferation and swelling,
   but too little release of VEGFA will lead to endothelial damage and
   apoptosis, resulting in glomerulosclerosis ([105]Sivaskandarajah et
   al., 2012). Fan found that inhibiting VEGFA can inhibit cell
   proliferation, promote cell apoptosis, and thus inhibit cell migration
   and invasion ([106]Zhengyan et al., 2016). Therefore, in order to
   maintain the structure and function of the glomerulus, it is necessary
   to regulate VEGFA signal transduction and regulate the concentration of
   VEGFA. In our study, although we found that VEGFA may participate in
   the apoptosis process of HK-2 cells induced by PPM1K, we also
   speculated that HK-2 cells expressing PPM1K may activate VEGFA signals.
   In order to verify this assumption, we will conduct more in-depth
   research.

   Gadd45a gene is a member of the Gadd45 family, which is involved in the
   regulation of cell cycle, immunity and apoptosis ([107]Fang et al.,
   2018). Under the induction of hypertonic pressure, renal medulla cells
   secrete Gadd45 protein. Hypertonic stress beyond a certain threshold
   will strongly induce apoptosis ([108]Mak and Kultz, 2004). TRIB3 is a
   pseudokinase in mammals, which can control cell proliferation,
   apoptosis, migration and invasion ([109]Ohoka et al., 2005;
   [110]Salazar et al., 2009; [111]Quan et al., 2016). Huo up-regulated
   the expression of TRIB3 level in mice. The results showed that there
   was obvious ER stress reaction in the kidney, liver and lungs of mice,
   and apoptosis was found in the lungs and kidneys of mice ([112]Lingling
   et al., 2015). Although our sequencing results show that GADD45A and
   TRIB3 are involved in the process of cell apoptosis, the PCR results do
   not support it. Later, we will further verify it through protein
   detection and other experiments.

   IL-6 is a typical cytokine, which also has pro-inflammatory and
   anti-inflammatory effects ([113]Ju et al., 2016). IL-6 can activate
   JAK-STAT signal pathway through membrane-bound IL-6 receptor (classical
   pathway) or soluble IL-6 receptor (cross signal) ([114]Hebenstreit et
   al., 2005), IL-6 connected to the receptor can start cascade reaction
   through JAK activation, activated JAK kinase phosphorylation induces
   dimer formation through STAT3 phosphorylation ([115]Rose-John., 2012;
   [116]Brooks et al., 2014), and this signal pathway promotes cell
   proliferation, differentiation, migration and apoptosis ([117]Rawlings
   and Harrison, 2004). Some studies have found that IL-6 can play an anti
   apoptotic role by regulating the expression of apoptosis related
   proteins such as Bcl-2, Bax, etc ([118]Meng et al., 2006; [119]Isomoto,
   2009). MAGE-D1 is a member of the type II melanoma associated antigen
   (MAGE) family. MAGE-D1 plays an important role in cell cycle, cell
   differentiation and apoptosis ([120]Zhang et al., 2016). MAGE-D1 has
   long been considered as an apoptosis promoting gene. MAGE-D1 is
   involved in the apoptosis of gastric cancer cells, adrenal
   pheochromocytoma, Hela cells and other cancer cells ([121]Kendall et
   al., 2005; [122]Salehi et al., 2006; [123]Cheng et al., 2008). Recent
   studies have found that MAGE-D1 may play an anti apoptotic role. Kumar
   et al. proposed that MAGE-D1 can inhibit the anoikis of lung cancer and
   breast cancer cells, and promote the anoikis sensitivity ([124]Kumar et
   al., 2011). Yang et al. found that MAGE-D1 can promote the
   proliferation of esophageal cancer cells, so they suspected that
   MAGE-D1 can inhibit the apoptosis of esophageal cancer cells. However,
   they did not find the difference in apoptosis between the esophageal
   cancer cells with MAGE-D1 knocked out and the control group ([125]Yang
   et al., 2014), and other researchers also found that MAGE-D1 has no
   effect on apoptosis ([126]Wen et al., 2004; [127]Xue et al., 2005;
   [128]Du et al., 2009). We believe that the effect of MAGE-D1 on
   apoptosis is a complex process, which needs a lot of research to
   confirm. C-C Motif chemokine ligand 2 (CCL2) is a member of the G
   protein coupled receptor family ([129]Kuang et al., 1996; [130]Bose and
   Cho, 2013). CCL2 can express chemokine C-C motif receptor 2 (CCR2)
   chemotactic cells, making monocytes, T lymphocytes and natural killer
   cells migrate and infiltrate into the inflammatory region ([131]Semple
   et al., 2010). Fan found that angiopoietin-1 can reduce the expression
   of chemokine C-C motif ligand 2 (CCL2) in endothelial cells of fibrotic
   kidney, thereby inhibiting macrophage migration and reducing cell
   apoptosis. Nucleoprotein 1 (TP53INP1) induced by tumor protein 53 is a
   p53 inducible gene that encodes two protein subtypes and regulates p53
   biological activity ([132]Jiang et al., 2006). Overexpression of
   TP53INP1 induces cell cycle arrest in G1 phase and enhances p53
   mediated apoptosis ([133]Tomasini et al., 2003). Many studies have
   shown that TP53INP1 is the target gene of many miRNAs([134]Ma et al.,
   2010; [135]Jiang, 2011; [136]Cai et al., 2017; [137]Dan et al., 2018),
   including miR-221, miR-30a and mir-205 ([138]Chen et al., 2016;
   [139]Wang et al., 2016; [140]Xu et al., 2016). In addition, TP53INP1 is
   a regulator of autophagy, which may interact with autophagy related
   molecules, including light chain three and autophagy related protein
   eight family proteins, indicating that it can not only regulate, but
   also promote autophagy ([141]Seillier et al., 2012; [142]Saadi et al.,
   2015). In our study, we sequenced the transcriptome of HK-2 cells
   overexpressing PPM1K and found that down-regulated genes such as IL-6,
   MAGED1, CCL2 and TP53INP1 were enriched in apoptosis related pathways.
   However, we did not find any reliable basis in the subsequent PCR
   validation, and we will conduct more in-depth research later.

   To sum up, based on RNA seq and bioinformatics analysis, this study
   identified and screened differentially expressed genes overexpressing
   PPM1K in HK-2 cells. Through GO and KEGG analysis, combined with PCR
   validation, it was further screened that G0S2, VEGFA and NUPR1 might
   participate in the apoptosis process of HK-2 cells induced by PPM1K.
   This study provides some data support for the study of the mechanism of
   HK-2 cell apoptosis, and also provides a scientific theoretical basis
   for the further study of the impact of PPM1K on kidney disease.

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: [143]https://www.ncbi.nlm.nih.gov/geo;
   GSE212681.

Author contributions

   LZ and XS conceived and designed the study. LZ, YH, AA, ME and ZM
   collected the data. YH, AA and SK performed the research, analyzed
   data. LZ, XS and YH wrote the paper. WY and CL reviewed and modified
   the article. All authors discussed the results and contributed to the
   final article. All authors read and approved the final article.

Funding

   Work toward this research was supported by the National Natural Science
   Foundation of China (No. 81960145).

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.

References