Abstract In recent years, there has been a notable rise in the incidence of pregnancies complicated by gestational diabetes mellitus (GDM), characterized by glucose intolerance first identified during pregnancy. Analysis of placental tissue has revealed that placentas from women with GDM tend to be larger and heavier compared to control placentas, indicating potential changes in trophoblast proliferation, differentiation, and apoptosis. In this study, transcriptome sequencing was conducted on placentas obtained from both normal pregnancies and pregnancies with GDM to investigate the molecular mechanisms underlying this condition. The original sequencing data were subjected to sequencing analysis, resulting in the identification of 935 upregulated genes and 256 downregulated genes. The KEGG and GO analysis techniques on differential genes uncovered evidence suggesting that the phosphoinositide 3-kinase (PI3K)/Akt signaling pathway may contribute to the pathogenesis of GDM. Subsequent analysis indicated that the expression levels of matrix metalloproteinases (MMP) 11, MMP12, MMP14, and MMP15, which are regulated by the PI3K/Akt pathway, were upregulated in the placentas of patients with GDM when compared to those of individuals with normal placental function. Additionally, our investigation into alternative splicing patterns revealed an increase in exon skipping alternative splicing of CSF3R in the placenta of patients with GDM compared to that in the control group. The CSF3R–PI3K-MMP pathway is speculated to regulate the pathogenesis of GDM. Keywords: Gestational diabetes mellitus, Granulocyte colony-stimulating factor, PI3K/Akt signaling pathway, Trophoblast cells, Glucose homeostasis, Alternative splicing 1. Introduction The prevalence of gestational diabetes mellitus (GDM) is increasing owing to the worldwide obesity epidemic and advancing maternal age. While GDM may improve after childbirth, a growing body of research suggests that it is linked to unfavorable outcomes for both the mother and fetus, resulting in complications during pregnancy and delivery, as well as an increased risk of developing postpartum type 2 diabetes mellitus (T2DM). Despite its transient nature, GDM can have lasting effects beyond the perinatal period, influencing the health of both the mother and fetus in the short and long term [[35]1,[36]2]. Common pathological changes observed in GDM include increases in placental volume and weight [[37]3,[38]4]. GDM occurs when insulin resistance during pregnancy is not regulated. As the placenta grows, the likelihood of insulin resistance also increases [[39]5]. Previous research has suggested that the PI3K/Akt signaling pathway plays a pivotal role in cellular physiology by facilitating growth factor signaling during organismal growth and crucial cellular processes, such as glucose homeostasis, lipid metabolism, cell proliferation, and cell function [[40][6], [41][7], [42][8]]. The activation of PI3k/Akt signaling in beta cells can potentially enhance beta cell proliferation by influencing the cell cycle regulators p21, p27, CDK4, and CyclinD1 [[43]9]. Furthermore, the activation of the PI3K/Akt pathway has been observed in placentas affected by preeclampsia (PE), possibly exhibiting a crucial role in the regulation of cell proliferation [[44]10]. Accumulating evidence indicates that the PI3K/Akt signaling pathway is indispensable for maintaining normal metabolic processes owing to its unique attributes. However, dysregulation of the PI3K/Akt pathway, characterized by its overexpression or mutation, has been found to play a significant role in the development of various human diseases. These diseases include obesity, T2DM, and cancer [[45]11]. Furthermore, the PI3K/Akt signaling pathway is involved in insulin metabolism and signaling, and is closely related to placental growth and development. Abnormalities in the PI3K/Akt signaling pathway may lead to poor placental development and restricted fetal growth. These factors may also be associated with the occurrence of GDM [[46]12]. we found that the expression levels of MMP11, MMP12, MMP14, and MMP15, which are regulated by the PI3K/Akt pathway, were upregulated in the placentas of patients with GDM when compared to those of individuals with normal placental function.The deregulation of membrane type-matrix metalloproteinases (MT-MMP) expression is associated with GDM [[47]13]. The regulation of MMP expression through the PI3K/Akt pathway may play a role in the onset and progression of GDM by influencing placental function, islet function, and the inflammatory response [[48]14]. Over 90 % of transcribed human genes undergo alternative RNA splicing, thereby enhancing proteomic diversity through the generation of isoforms with variable functions from a single gene [[49]15]. Abnormal pre-mRNA splicing is now acknowledged as the fundamental etiology of numerous human diseases, such as cancer, Alzheimer's, frontotemporal dementia, spinal muscular atrophy (SMA), and retinitis pigmentosa [[50]16]. Moreover, three colony-stimulating factor 3 receptor (CSF3R) transcripts that arise from alternative RNA splicing have been identified in humans [[51]17]. In the context of CSF3R, three documented splice variants exhibited distinct functional characteristics. Therefore, these three CSF3R splice variants, which possess inherently diverse signaling properties, have been hypothesized to interact with one another within the same cellular environment. Furthermore, alterations in their expression levels relative to other isoforms may determine cellular responses to proliferation or differentiation [[52]18]. Our investigation revealed upregulation of CSF3R splice variants in the placenta of individuals with GDM. Previous studies have provided evidence that differential expression of various splice variants of CSF3R can influence cellular signaling pathways. Given the available literature on the expression of colony-stimulating factor 3 (CSF3) and its receptor CSF3R in placental tissues [[53]19,[54]20]and trophoblast [[55]21], this cytokine has been hypothesized to play a role in placental development.Bioinformatics methodologies offer a fresh perspective for understanding GDM and its related complications, further investigating the potential influence of CSF3 and CSF3R. 2. Experimental materials and methods 2.1. Placental samples collection and analysis Fresh consecutively delivered placentae were collected from women with normal pregnancies (n = 60) and women with pregnancies complicated by GDM (n = 60), all of whom had singleton term pregnancies (37–42weeks) and underwent caesarean delivery before the onset of labor at the Affiliated Hospital of Jining Medical University (Jining, China) between March 2020 and December 2022. The exclusion criteria were as follows: age <18 years old; a history of diabetes and other disorders affecting glucose metabolism; mental illness; heart, liver or kidney failure; intrahepatic cholestasis during pregnancy; central type placenta previa; hypertensive disorder complicating pregnancy; placental abruption; none of the participants were subjected to any dietary restrictions. Finally, 120 pregnant women were registered to our study. Initially, the mean placental weights were calculated. Subsequently, four fragments of 1 cm^3 were precisely excised from the central region of the placenta on the maternal side. Three of these fragments were promptly frozen at a temperature of −80 °C and intended for use within 30 min after collection, while the remaining fragment was designated for examination under light microscopy. This study was approved by the Ethics Committee of the Affiliated Hospital of Jining Medical University (Jining, China; ethical approval no. 2021B139). Each participant who was over 18 years of age provided written informed consent and signed an informed consent form. GDM was diagnosed according to International Association of Diabetes and Pregnancy Study Group criteria(IADPSG criteria) [[56]22]. In the approach, a 75-g 2-h OGTT is administered to the fasting woman. Those whose FPG was ≥5.1 mmol/L (92 mg/dL) and/or 1-h PG was ≥10.0 mmol/L (180 mg/dL) and/or 2-h PG was ≥8.5 mmol/L (153 mg/dL) were defined as GDM by IADPSG criteria.The characteristics of the patients are listed in [57]Table 1. Table 1. Comparison of the study group characteristics. Normal group (n = 60) GDM group (n = 60) p-value Age(Years) 29.40 ± 5.34 31.20 ± 5.12 0.055 Gravidity 2.69 ± 1.21 2.97 ± 1.18 0.187 Parity 1.07 ± 0.80 1.23 ± 0.81 0.278 Gestational age at delivery (weeks) 38.97 ± 0.81 38.77 ± 0.72 0.126 Body mass gain during pregnancy (kg) 14.43 ± 6.18 13.56 ± 5.93 0.419 BMI (Kg/) 28.15 ± 3.17 29.94 ± 3.39 0.005 SBP in late pregnancy (mmHg) 118.61 ± 9.21 120.38 ± 9.68 0.293 DBP in late pregnancy (mmHg) 73.87 ± 8.25 74.82 ± 8.63 0.524 HB (g/L) 119.07 ± 13.08 120.41 ± 12.36 0.555 PLT (s) 227.33 52.85 219.38 61.82 0.434 PT (s) 10.84 ± 1.35 11.19 ± 0.98 0.100 APTT (s) 27.67 ± 1.89 27.51 ± 2.71 0.717 FIB (s) 4.40 ± 0.78 4.62 ± 1.63 0.347 HDL (mmol/L) 1.90 ± 0.29 1.77 ± 0.27 0.356 LDL (mmol/L) 3.22 ± 0.69 3.31 ± 1.05 0.795 VLDL (mmol/L) 0.51 ± 0.27 0.93 ± 0.34 0.004 TG (mmol/L) 2.94 ± 1.61 3.37 ± 1.03 0.545 TC (mmol/L) 5.62 ± 0.76 6.02 ± 1.29 0.500 Urea (mmol/L) 2.82 ± 0.67 3.22 ± 0.71 0.03 CR (μmol/L) 44.87 ± 8.04 47.20 ± 7.43 0.118 UR (μmol/L) 272.73 ± 70.26 278.45 ± 105.25 0.735 OGTT fasting glucose 4.16 ± 0.32 5.07 ± 0.52 0.000 OGTT1-h glucose 7.13 ± 1.51 9.61 ± 1.65 0.000 OGTT2-h glucose 6.21 ± 1.13 7.65 ± 1.54 0.000 HbA1C 4.94 ± 0.28 5.32 ± 0.55 0.000 Birth Weight (g) 3303.51 ± 330.15 3618.69 ± 462.76 0.000 Placenta weight (g) 552.07 ± 88.38 631.67 ± 126.05 0.024 [58]Open in a new tab 2.2. HE staining Placental tissue samples were immersed in ice-cold PBS to remove blood contamination. A piece of placenta tissue 1 cm × 1 cm was fixed in 4 % paraformaldehyde, and tissue blocks were embedded in paraffin. Sections with a thickness of 4 μm were cut from the block and stained with HE. The resulting slides were examined and photographed under a light microscope (Olympus). 2.3. Experimental method of sequencing For the experimental sequencing method, three placentas from the normal control group and three placentas from the GDM group were selected for transcriptome analysis. Total RNA was extracted from these samples to compare the expression levels of differentially expressed genes (DEGs). Total RNA was extracted, and RNA quality was assessed. After the RNA samples were qualified, common transcriptome libraries were constructed, and Illumina NovaSeq 6000 was used for sequencing. 2.4. Bioinformation analysis After obtaining the sequencing data, basic data quality control was carried out; these high-quality sequences were then compared to the reference genome, and gene expression level and gene structure analyses were performed. The Illumina NovaSeq high-throughput sequencing platform was used to sequence cDNA libraries and produce numerous base sequences (reads). The reads or bases produced by the sequencing platform are referred to as raw data. High-quality clean reads were obtained from the original data by strict quality filtering for subsequent data analysis. The raw reads sequenced were not all effective, as they contained a small number of repeated low-quality reads with joints. These reads may affect subsequent comparison and analysis; therefore carefully filtering the sequenced raw reads is necessary for obtaining an effective and high-quality number of clean reads. Clean reads were compared to the silva database using bowtie 2 software to remove rRNA, and the remaining reads were used for subsequent analysis. FastQC was used to count the content of different bases in read pairs to determine whether the content of G and C bases and A and T bases was separated. Reads with rRNA removed were compared with the reference genome using the Hisat2 software. The ratio of comparison between the sequencing data and the reference genome, and the regional distribution of comparison between the sequencing data and the reference genome were calculated according to the gene location information specified in the genome annotation file (GTF format). According to the comparison results, the distribution of protein-coding region (CDS), Intron, Intergenic region, 3′ untranslated region (3′ UTR) and 5 'untranslated region (5′ UTR) of the genome was statistically analyzed, and the source of sequencing reads on the genome was detected. 2.5. Transcription factor prediction Differentially expressed transcription factors (TFs) are likely involved in the regulation of biological processes related to sample handling or stress. Predictive analysis of the TFs of DEGs is effective in exploring the differentially expressed TFs of different groups and their differential regulatory mechanisms. Predictive analysis of TFs can help identify key regulators that are driving the observed gene expression patterns and understand how these TFs are differentially regulated in different conditions or disease states.This approach can be particularly useful in identifying potential therapeutic targets or biomarkers for specific diseases or conditions. By understanding the regulatory networks involving TFs and DEGs, researchers can uncover novel pathways and mechanisms that may be dysregulated in disease states, providing new opportunities for targeted interventions. Overall, predictive analysis of TFs of DEGs can provide valuable insights into the molecular mechanisms underlying gene expression changes and help uncover potential therapeutic targets for various diseases and conditions. 2.6. Statistical analysis SPSS (version 25.0; IBM Corp.) was used for statistical analyses. The relative expression of the investigated factors was expressed as mean ± standard deviation. Differences between the two groups were analyzed using an independent sample t-test. Spearman's correlation analysis was also conducted. A value of P < 0.05 was considered statistically significant. 3. Results 3.1. Clinical characteristics The clinical characteristics compared between the GDM group and the control group are described in [59]Table 1. The group diagnosed with GDM exhibited significantly higher body mass index (BMI), urea levels, fasting plasma glucose, 1- and 2-h plasma glucose levels during the oral glucose tolerance test (OGTT), HbA1C levels, very low-density lipoprotein (VLDL) levels, birth weight, and placental weight (P < 0.05) compared with the control group. Placental weight was measured in both groups, as depicted in [60]Fig. 1a. The placental weight in the GDM group was heavier than that of the control group (P < 0.05), as illustrated in [61]Fig. 1b. Fig. 1. [62]Fig. 1 [63]Open in a new tab Photos of the placenta were compared between the control group and the GDM group (a).The placental weight of the GDM group was heavier than that of the control group (b).*p＜0.05. 3.2. Histological examination The morphological features of term placental tissues from both the GDM and control groups were thoroughly examined using light microscopy. [64]Fig. 2a presented the typical morphological characteristics of term placentas from the control group, which included the presence of syncytiotrophoblasts lining the villous trees with multigrade branching and clearly visible erythrocytes within multiple placental villi. No indications of calcification, fibrin deposition, or villous edema were observed. The term placentas from the GDM group displayed the expected morphological features, as illustrated in [65]Fig. 2b–f. Qualitative observations of the terminal villi in certain areas of GDM placenta slides revealed the presence of chorioangiopathy ([66]Fig. 2b), intervillous stenosis ([67]Fig. 2c), an apparent increase in the number of syncytial knots ([68]Fig. 2d), a reduction in free villi with uneven villi thickness and degeneration of villi interstitium, villous edema, edematous stroma ([69]Fig. 2e), and perivillous fibrin deposition ([70]Fig. 2f). Fig. 2. [71]Fig. 2 [72]Open in a new tab Pathological morphology of placentas from control group (a) and GDM group (b,c, d,e,f). Placental sections were HE-stained. (a)The normal branching of villous trees, (b) Chorioangiopathy,(c)Intervillous stenosis, (d)The increased syncytial knotting(arrow), (e) Free villi reduce with uneven villi thickness and degeneration of villi interstitium, villous edema (arrows),（f）Perivillous fibrin depositions in GDM group. Scar Bar = 100 μm. 3.3. Identification of DEGs The volcano plot in [73]Fig. 3a illustrates that a total of 1191 DEGs were identified, with 935 upregulated and 256 downregulated genes in the GDM group compared to the control group. To visually represent the expression patterns of these DEGs under various experimental conditions and identify novel functional genes, differential gene heat maps were generated ([74]Fig. 3b). Fig. 3. [75]Fig. 3 [76]Open in a new tab Transcriptome sequencing of placental tissues in the control group and the GDM group. (a) Volcano plot analysis of 935 upregulated DEGs and 256 downregulated DEGs. (b) Differential gene heat maps were performed for the DEGs.（c）The upregulation of MMP11, MMP12, MMP14, and MMP15 in the placenta of individuals with GDM compared to the control group. In our study, we found that the upregulation of MMP11, MMP12, MMP14, and MMP15 in the placenta of individuals with GDM compared to the control group, as depicted in the heat map [77]Fig. 3c. 3.4 Gene Ontology (GO) annotation analysis of DEGsGO enrichment analysis was conducted on the entire set of DEGs. The results, depicted in [78]Fig. 4a, demonstrate the top 20 upregulated biological processes (BPs). The DEGs primarily encompassed BPs associated with multicellular organismal processes, multicellular organism development, and developmental processes. This suggests a potential association between GDM and the formation of multicellular organisms. The initiation of GDM is intricately associated with the physiological mechanisms of multicellular organisms [[79]23], specifically encompassing hormone regulation, placental function, cellular differentiation, and proliferation. Metabolic processes in multicellular organisms are associated with GDM. GDM patients experience heightened blood glucose levels during pregnancy because of inadequate insulin secretion or heightened insulin resistance, thereby affecting the glucose metabolism process in pregnant women. This correlation may be attributed to the regulatory mechanisms governing metabolism in multicellular organisms, including insulin synthesis and secretion and glucose uptake and utilization [[80]24]. The top 20 upregulated cellular components (CCs) are depicted in [81]Fig. 4b, revealing that the majority of DEGs are associated with membrane-related components. These include the membrane, its intrinsic components, and its integral components. The top 20 upregulated molecular functions (MFs) are illustrated in [82]Fig. 4c, indicating that the predominant MFs of the DEGs are related to various functions involving signaling receptors and transmembrane transporters. These include signaling receptor binding, transmembrane transporter activity, and inorganic molecular entity transmembrane transporter activity. Fig. 4. [83]Fig. 4 [84]Open in a new tab Gene Ontology (GO) analyses of DEGs in the control group and the GDM group.(a)Top 20 GO-BP terms for the total DEGs.(b)Top 20 GO-CC terms for the total DEGs.(c) Top 20 GO-MF terms for the total DEGs. 3.4. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs KEGG pathway analysis indicated that the DEGs were associated with various signaling pathways, including the PI3K/Akt, MAPK, and cAMP signaling pathways, proteoglycans in cancer, cytokine-cytokine receptor interactions, and cell adhesion molecules (CAMs) ([85]Fig. 5a and. b). Fig. 5. [86]Fig. 5 [87]Open in a new tab Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs.(a) The top 20 pathways of significant enrichment were selected and displayed by bar chart,(b) The top 20 pathways of significant enrichment were selected and displayed by scatter plots. 3.5. TFs prediction In our study, we observed differential expression of MMP11, MMP12, MMP14, and MMP15 between the GDM and control groups. Additionally, our analysis of the KEGG pathway suggests that transcription factors within the signaling pathway may play a role in promoting the transcription of these MMPs. Specifically, the activation of the PI3K signaling pathway has been shown to promote the activation of transcription factors, such as CREB3L2, CREB5 [[88]25], Stat5B [[89]26], FOX04 [[90]27], and SREBF1, which are involved in the regulation of gene expression. To further investigate the potential regulatory mechanisms, we used the JASPAR database to analyze these five transcription factors and predict the transcription of MMP genes. These five transcription factors had binding sites in the MMP11, MMP12, MMP14, and MMP15 promoter regions, the results are shown in [91]Table 2. Table 2. Binding situation between transcription factors and the promoter regions. Transcription factor Target gene NO score start end Predicted sequences Sequence logo FOXO4 MMP1 1 6.59 231 237 GTAAAAA Image 1 2 6.21 1113 1119 GTAAACT 3 5.61 547 553 GTCCACA 4 4.91 29 35 ATAAAAA 5 4.91 1500 1506 ATAAAAA 6 4.71 1296 1302 GCCAACA 7 4.68 23 29 AAAAATA SREBF1 MMP1 1 12.74 1438 1447 ATCACACCAC Image 2 2 10.35 1625 1634 ATCACACCAC 3 7.61 572 581 CTCACCTCCT 4 7.15 138 147 ATCGCGTCAC 5 6.26 1263 1272 ATCACTTGAG FOXO4 MMP12 1 8.57 1788 1794 ATAAATA Image 1 2 7.47 322 328 GGAAACA 3 7.47 971 977 GGAAACA 4 7.15 1888 1894 AAAAACA SREBF1 MMP12 1 12.69 1085 1094 ATCACACCAT Image 2 2 11.9 1265 1274 ATCACCTCAG 3 8.52 627 636 ATCAGACCAG 4 6.42 27 36 ATCAAATCAC FOXO4 MMP15 1 5.20 1853 1859 GTAAACG Image 1 2 4.60 656 662 TTAAACA 3 3.94 1594 1600 ATATACA 4 3.54 1596 1602 ATACATA SREBF1 MMP15 1 10.29 1341 1350 CTCACCCCAA Image 2 2 8.63 202 211 ATCACCAGAC 3 7.72 1777 1786 GTCACCCCCG FOXO4 MMP3 1 11.04 740 746 ATAAACA Image 1 2 8.57 1303 1309 ATAAATA 3 6.59 1670 1676 GTAAAAA 4 6.36 354 360 GAAAATA SREBF1 MMP3 1 10.44 942 951 ATCACCACAT Image 2 2 8.33 453 462 GTCACGGCAC FOXO4 MMP14 1 10.64 73 79 GTCAACA Image 1 2 6.78 2059 2065 GCAAACA 3 6.21 1559 1565 GTAAACT 4 5.11 107 113 ACAAACA SREBF1 MMP14 1 11.95 216 225 GTCACACCAT Image 2 2 8.86 1708 1717 ATCAAGCCAC 3 7.52 1300 1309 TTCACCCAAC 4 6.80 740 749 CACACACCAT [92]Open in a new tab 3.6. Variable splicing analysis Variable splicing is a significant factor in gene regulation, protein functionality, and disease onset. In humans, three CSF3R transcripts, resulting from alternative RNA splicing, have been identified, each of which exhibits distinct functional characteristics [[93]17]. Previous studies have documented the activation of the PI3K signaling pathway through CSF3R alternative splicing [[94]28]. In our study, we analyzed alternative splicing in DEGs and observed a higher occurrence of alternative splicing in the CSF3R gene in the GDM group than in the control group, as depicted in [95]Fig. 6. Fig. 6. [96]Fig. 6 [97]Open in a new tab An analysis of alternative splicing in DEGs and a higher occurrence of alternative splicing in the CSF3R gene within the GDM group compared to the control group. 4. Discussion Placental development plays a crucial role in fetal health. Pregnancies complicated by GDM are characterized by maternal insulin resistance, low-grade inflammation, and endothelial cell dysfunction, which are considered to be key factors. However, the molecular mechanisms underlying the pathogenesis of GDM remain largely unknown. Many signaling pathways are involved in the pathophysiology of GDM, including the peroxisome proliferator-activated receptors (PPARs) signaling pathway [[98]29], HIF1α signaling pathway, mTOR and JAK/STAT signaling pathway, nuclear factor-κB (NF-κB) signaling pathway, nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway, and PI3K/mTOR and MAPK signaling pathway [[99]30]. In this study, a total of six placental tissue samples were selected for transcriptome sequencing. These findings revealed significant differences in the molecular expression profiles of placental tissues between the GDM and control groups. Furthermore, analysis of the DEGs using the KEGG pathway demonstrated that the DEGs were primarily concentrated in the PI3K/Akt signaling pathway. Notably, the PI3K/Akt pathway has been extensively validated as a crucial factor that facilitates placental development and fetal growth in both humans and rodents [[100]31]. The PI3K/Akt signaling pathway is involved in the regulation of trophoblast proliferation [[101]10], migration, and invasion [[102][32], [103][33], [104][34]]. The diabetic placenta is characterized by increased size and weight [[105]35,[106]36], which is consistent with our results. This may be caused by the increased active proliferation and decreased placental apoptosis of trophoblast cells among patients with diabetes [[107][37], [108][38], [109][39]]. Because activation of the PI3K/Akt signaling pathway further regulates cellular physiological functions, such as promoting cell proliferation and survival, hyperglycemia may possibly stimulate proliferation and reduce apoptosis of trophoblast cells through the PI3K/Akt signaling pathway, thereby increasing placental hypertrophy and weight. The PI3K/Akt pathway is activated in preeclamptic placentas, and the inhibition of the PI3K/Akt pathway may be a useful therapeutic treatment for preeclampsia [[110]10]. The PI3K/Akt signaling pathway could increase insulin sensitization and act as an anti-hyperglycemic [[111]40]; therefore, dysfunctional PI3K/Akt-mediated glucose transport and glycogen synthesis play important roles in the development of obesity and T2DM [[112]41]. The treatment of obesity and T2DM is possible by altering the PI3K/AKT pathway [[113][42], [114][43], [115][44], [116][45]]. Additionally, certain studies have found that gene mutations or abnormal PI3K expression may be related to the development of diabetes. For example, studies have found that certain genetic mutations in PI3K may lead to abnormalities in insulin signaling, which can lead to diabetes [[117][46], [118][47], [119][48]]. Other studies have found that overexpression of PI3K may lead to the overactivation of insulin signaling, which can lead to insulin resistance and diabetes [[120]49,[121]50]. Therefore, the PI3K/Akt signaling pathway is closely related to the pathogenesis of diabetes and may be one of the important molecular mechanisms of diabetes. However, the role of the PI3K/Akt signaling pathway in GDM has not yet been studied. In the present study, the PI3K/Akt pathway was significantly upregulated in the GDM group. Additionally, upregulation of MMP11, MMP12, MMP14, and MMP15, along with increased expression of CSF3R splice variants, was found in the placenta of individuals with GDM compared to the control group. Placental MT-MMPs are required for cytotrophoblast migration, invasion of the uterine wall, and remodeling of spiral arteries, which participate in the fusion of cytotrophoblasts to generate syncytiotrophoblasts, as well as in the formation of new blood vessels (angiogenesis). Temporal and spatial regulation of MT-MMP activity is crucial. Aberrant MT-MMP expression has been associated with pregnancy complications, including PE [[122]51], fetal growth restriction (FGR), and GDM [[123]13]. Previous investigations have demonstrated the upregulation of MT1-MMP (MMP14) protein levels in the placenta during the third trimester in patients with GDM, which aligns with our findings. CSF3R is a cell membrane receptor associated with the PI3K signaling pathway [[124]52]. Variable splicing of CSF3R refers to the generation of multiple mRNA transcripts via a splicing mechanism during gene transcription. Three annotated variants of CSF3R have been described, which are identical in their extracellular domains but differ in their downstream sequences [[125]53,[126]54]. These alternatively spliced forms of CSF3R may exhibit different functions and regulatory mechanisms. Accumulating evidence has shown that CSF3 plays an essential role in placental metabolism, trophoblast development, decidualization of endometrial stromal cells, and ovulation by binding to CSF3R to regulate multiple signaling pathways [[127][52], [128][53], [129][54], [130][55]]. CSF3 could promote trophoblast invasion and migration through activating the PI3K/Akt signaling pathway, thereby involving the normal pregnancy program [[131]56]. CSF3 upregulates metalloproteinase-2 and VEGF through activating PI3K/Akt signaling pathway in human trophoblast Swan 71 cells, stimulating placental blood vessel formation, which is essential for placental formation [[132]52]. CSF3 upregulates β1 integrin and increases migration of human trophoblast Swan 71 cells via PI3K and MAPK signaling pathway activation, The above evidence indicates that CSF3 should be considered an additional regulatory factor that contributes to successful embryo implantation and placental development via the PI3K/Akt signaling pathway [[133]57]. Specifically, when the CSF3R receptor is activated by its ligand (CSF3), CSF3R interacts with relevant proteins in the PI3K signaling pathway through its intracellular structure, thereby activating the PI3K signaling pathway [[134]58]. We speculate that the alternative splicing of CSF3 and CSF3R results in the formation of three CSF3R splicing variants. These variants activate the PI3K/Akt signaling pathway in cells, leading to the activation of specific transcription factors. These transcription factors then bind to the promoter regions of MMPs, regulating their expression. This dysregulation of MMP expression ultimately leads to glucose metabolism disorders and insulin resistance in gestational diabetes mellitus (GDM). However, additional investigations are necessary to understand the importance of the interaction between CSF3 and CSF3R in trophoblasts. Further studies are required to fully understand the specific mechanisms by which MMPs contribute to GDM. 5. Conclusion The CSF3R–PI3K-MMP pathway is speculated to regulate the pathogenesis of GDM. Our study found for the first time that the PI3K pathway is upregulated in GDM, which may be related to the regulation of the signal axis of CSF3R–PI3K-MMP. This study provides a foundation for an in-depth understanding of the mechanisms of GDM and for the development of drugs related to the treatment of GDM. Funding statement This research was supported by the Natural Joint Project of Shandong Province (grant no.ZR2021LZY001); the Research Fund for Lin He Academician New Medicine (grant no. JYHL2021FMS06) and the Jining Research and Development.Program(grant no.2021YXNS114 and 2023YXNS188) Ethics statement This study was approved by the human research Ethics Committee of the Affiliated Hospital of Jining Medical University (Jining, China; ethical approval no. 2021B139). All patients provided informed consent to participate in the study. Data availability statement The data presented in the study (sequencing raw data) were uploaded to GEO repository accession number GEO24488221 ([135]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GEO24488221). CRediT authorship contribution statement Yanan Zhang: Funding acquisition, Formal analysis, Data curation. Yufen Liu: Data curation. Yanyan Shi: Formal analysis. Chunyu Bai: Data curation. Ting Wang: Data curation. Fang Ruan: Data curation. Chuanbing Hu: Funding acquisition, Formal analysis, Data curation. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Contributor Information Ting Wang, Email: jiningwangting@163.com. Fang Ruan, Email: ruanfang820206@163.com. Chuanbing Hu, Email: xiaobing1028@163.com. References