Unveiling Circular RNA-Mediated Regulatory Mechanisms in Necroptosis in Premature Ovarian Failure

Background : Necroptosis is a programmed necrotic cell death, in which dying cells rupture and release intracellular components that trigger a proinflammatory response. The current study aimed at probing the circular RNA (circRNA)-mediated regulatory mechanisms in necroptosis in premature ovarian failure (POF). Methods : CircRNA sequencing analysis was conducted in ovarian tissues of control and POF rats and transcriptome microarrays were acquired from the GSE33423 dataset. Differential expression analysis of circRNAs and mRNAs was executed between the POF and control data. Both a necroptosis-based circRNA–microRNA (miRNA)–mRNA network and a protein–protein interaction (PPI) network were established. Then, the functional annotation and immunological traits were analyzed. Results : Totally, 1266 upregulated and 1283 downregulated circRNAs as well as 1101 upregulated and 1168 downregulated mRNAs were determined in the POF rats versus the controls. The differentially expressed mRNAs predominantly correlated with necroptosis. The circRNA–miRNA–mRNA networks of downregulated necroptosis genes (comprising rno_circRNA_004995-rno-miR-148b-5p-H2afy2, rno_circRNA_016998-rno-miR-29a-5p-Hmgb1, and rno_circRNA_017593-rno-miR-29a-5p-Hmgb1) and upregulated necroptosis genes (comprising rno_circRNA_015900-rno-miR-935-Stat1, rno_circRNA_007946-rno-miR-328a-3p-Stat5a, rno_circRNA_007947-rno-miR-328a-3p-Stat5a, rno_circRNA_005064-rno-miR-18a-5p-Stat1, rno_circRNA_005064-rno-miR-18a-5p-Stat5a, rno_circRNA_005115-rno-miR-22-3p-Stat1, rno_circRNA_009028-rno-miR-342-5p-Stat1, rno_circRNA_011240-rno-miR-1224-Stat5a, rno_circRNA_016078-rno-miR-711-Stat5a) were built. POF-specific necroptosis genes ( STAT1, STAT5A, PLA2G4A, HMG1L1, HMGB1, AGER, EGFR, HDAC7, IFNA1, IL10RB, IL27RA, PYGL, SOCS1, TRADD, CXCL10, DDX5, EZH2, FADS2, FER, H2AFY2, HIST1H2AF, IFI44L, IL27, IRGM, MX1, NFKB2, PAFAH2, PEMT, PGM2L1, PGR , PHKA2 , and PLB1 ) were selected since they displayed notable associations with most immune cells, immune checkpoints, chemokines, human leukocyte antigen (HLA) molecules, and immune receptors. Conclusions : Altogether, we proposed the presence of widespread regulatory mechanisms of circR-NAs in necroptosis and demonstrated that altered circRNA biogenesis might contribute to POF by affecting necroptosis.


Introduction
Premature ovarian failure (POF) is a gynecological endocrine disease, which is characterized by mature follicle deficiency in women under 40 years of age, who also have reduced estrogen levels and elevated gonadotropins [1].The global incidence of POF is 1%-3%, while there has been a gradual increase in recent years [2].Despite the influence of POF in a small percentage of women, evidence shows that it appears to correlate to mortality [3].Chemotherapy-induced granulosa apoptosis is a primary etiology of POF [4], which remains one of the most prevalent causes of female infertility.Currently, hormone replacement treatment represents the major therapeutic option for POF; however, it is accompanied by severe side effects [5].Hence, more potent therapeutic approaches are required.
Circular RNAs (circRNAs) are a novel class of RNA molecules that possess a covalently closed circular structure and the absence of a 5 ′ -cap and 3 ′ -poly(A) tail, thereby offering resistance to RNA exonuclease-induced degradation [6].CircRNAs are specifically abundant in cell types and tissues during development [7].Despite the synthesis of cir-cRNAs in the nucleus, they are principally localized in the cytoplasm [8].CircRNAs are capable of binding distinct forms and numbers of microRNAs (miRNAs) as well as negatively modulate modulating miRNA activity through competitive miRNA-mRNA binding [9].Accumulated evidence demonstrates that circRNAs display notable regulatory functions through (i) binding to the host genes at their synthesis locus and causing transcriptional pausing or ter-mination; (ii) combining with U1 snRNP and interacting with Pol II to improve parental gene expression; (iii) act-ing as miRNA sponges and regulating the miRNA-targeted mRNAs; (iv) interacting with proteins; (v) directly recruiting ribosomes and triggering translation, etc.; thus, participating in multiple physiological and pathophysiological processes in a variety of human diseases [10][11][12].Nonetheless, to date, no studies have unveiled the roles of circRNAs in POF.
Necroptosis is a caspase-independent form of programmed necrotic cell death, which presents the features of an increase in cell volume, swelling of organelles, and the rupturing of the plasma membranes together with the loss of intracellular content [13].Necroptosis is regulated by RIPK1 kinase, together with RIPK3 kinase and MLKL pseudo-kinase, and is accompanied by the release of damage-associated molecular patterns and cytokines, which triggers a 2proinflammatory response [14].Evidence proves that necroptosis is a highly regulated and orchestrated process, which plays fundamental physiological functions in development and tissue homeostasis [15].Dysregulation of necroptosis has been detected in diverse human diseases, including POF [16].However, the molecular mechanisms underlying necroptosis in POF are still indistinct.Several circRNAs have been discovered to participate in necroptosis [17].For instance, circRNA CNEACR affects necroptosis of cardiomyocytes via attenuating Foxa2 activity [17].Nonetheless, the regulatory functions of circRNAs in necroptosis during POF remain indistinct.Herein, comprehensive research was implemented to probe for the circRNA regulatory mechanisms involved in necroptosis.

Experimental Animals and POF Establishment
All experimental procedures were approved by the Animal Ethical and Welfare Committee of Zhejiang Chinese Medical University (IACUC-20210503-05).A total of 6 female Sprague Dawley (SD) rats (3-week-old, and 50 ± 5 g in weight) had free access to adequate food and water and were adaptively fed for 1 week.All rats were randomly separated into POF and control groups.To establish POF models, rats were administered intraperitoneally with 80 mg/kg 4-vinylcyclohexene diepoxide (VCD) solution, which had been dissolved in 2.5 mL/kg/day sesame oil, for 15 consecutive days.Control rats did not receive any treatment.The ovaries were collected under anesthesia 45 days after the initial treatment, and stored at -80 °C.

CircRNA Sequencing
RNA-seq analysis of the ovaries was conducted by Aksomics Inc. (Shanghai, China).Briefly, total RNA was extracted using TRIzol reagent (Invitrogen, catalog number15596026, Thermo Fisher, New York, NY, USA), and twice treated with DNase I (Ambion, catalog number EN0521, Thermo Fisher, New York, NY, USA) for 30 min at 37 °C.Next, 3 µg extracted RNA was subjected to RiboMinus Eukaryote Kit (catalog number A15020, life technologies, New York, NY, USA) to remove any ribosomal RNA, and subsequently treated with RNase R (catalog number RNR07250, epicentre, Epicentre Biotechnologies, Madison, WI, USA).Purified RNA was treated with RNase R and purified with Trizol.Next, a RNA sequencing library was constructed, and raw sequencing data were achieved on the Illumina NovaSeq 6000.Quantile normalization and data preprocessing were performed using the limma computational approach [18].Our circRNA RNAseq data were uploaded into the Gene Expression Omnibus repository with the accession number GSE221728.

Dataset Acquisition
Transcriptome microarrays from POF granulosa cells (n = 3) and controls (n = 3) were acquired from the GSE33423 dataset in the Gene Expression Omnibus repository [19].This dataset was based on the Affymetrix platform.

Differential Expression Analysis
Expression levels of circRNAs and mRNAs were compared between POF and control groups using limma [18].For differentially expressed circRNAs, the criteria were set as |fold change (FC)| >1.5 and p-value < 0.05.Furthermore, mRNAs with differential expressions were screened based upon |FC| >1.2 and p-value < 0.05.

Prediction of circRNA-miRNA and miRNA-mRNA Interactions
The CircAtlas 2.0 database, which is an integrated resource of 1,007,087 circRNAs from 1070 RNA-seq profiles acquired from 19 normal tissue specimens among 6 vertebrate species [20], was adopted to infer the interactions between circRNAs and miRNAs.Through utilizing three online databases comprising miRanda [21], miRDB [22], and miTarBase [23], miRNAs that targeted down-or upregulated mRNAs were estimated and intersected through any two databases.After integrating the circRNA-miRNA and miRNA-miRNA relationships, Cytoscape software (ver-sion3.7.2,The Cytoscape Consortium, San Diego, CA, USA) was used to establish the competitive endogenous RNA (ceRNA) networks [24].

Protein-Protein Interaction
To evaluate the interactions between proteins derived from mRNAs with differential expression, they were imported into the STRING online tool using the criteria of required confidence (combined score) >0.2 [25].Proteinprotein interaction (PPI) network and subnetwork were constructed using Cytoscape software [24].

Functional Annotation Analysis
Gene Ontology [26] and Kyoto Encyclopedia of Genes and Genomes (KEGG) [27] analyses were used to analyze the functional annotations of mRNAs with differential expression or those from the ceRNA network.A pvalue was computed by Fisher's exact test.KEGG pathways were integrated and visualized using the Pathview web tool [28].

Gene Set Enrichment Analysis
To gain insights into the biological processes as well as to predict the signaling pathways underlying POF-specific necroptosis genes, gene set enrichment analysis (GSEA) was performed to determine the differences in predefined gene sets between low and high expression groups using GSEA software (GSEA4.3.2,Broad Institute, Massachusetts, USA), which was acquired from the Broad Institute website, to divide the median expression value of each gene [29].Terms with a false discovery rate (FDR) <0.05 were regarded as being statistically different.

Analysis of Immunological Traits
The abundance levels of 28 immune cell types were estimated by conducting single-cell GSEA (ssGSEA) [30].Marker genes of immune signatures were acquired from previously reported literature [31,32].Then, the transcriptional levels of immune checkpoints, chemokines, HLA molecules, and immune receptors were measured.

Statistical Analysis
Student's t test was performed to compare the two groups.Pearson's test was executed for correlation analysis.All statistical analyses were implemented utilizing appropriate R packages, and a p-value < 0.05 was regarded as being statistically different.

Characterization of circRNAs with Differential Expression in POF
To identify the circRNAs linked to POF, the current study implemented circRNA sequencing analysis of POF (n = 3) and control (n = 3) rats.CircRNAs with differential expression in POF versus controls were screened based upon the criteria of |FC| >1.5 and p-value < 0.05.Consequently, 1266 circRNAs displayed notable upregulation in POF, with 1283 displaying downregulation (Fig. 1A-C).Especially, according to |FC|, we listed the top twenty upand downregulated circRNAs in POF versus the controls (Fig. 1D; Table 1).

Characterization of mRNAs with Differential Expression in POF
Using the GSE33423 dataset, we acquired transcriptome microarrays of granulosa cells from POF rats (n = 3) and controls (n = 3).In accordance with the criteria of |FC| >1.2 and p-value < 0.05, 1101 upregulated and 1168 downregulated mRNAs were determined in POF versus the controls (Fig. 2A-C).Fig. 2D and Table 2 exhibit the top twenty up-and downregulated mRNAs in POF.
Here, H2AFY2, HMGB1, STAT1, and STAT5A exhibited positive associations with the negative regulation of mitotic nuclear division and female meiotic nuclear division, while it also regulated the centrosome duplication processes and Fanconi anemia, homologous recombination, and the DNA replication pathways.Furthermore, negative correlations were found relating to the receptor signaling pathway, via JAK-STAT, in skeletal muscle fiber development, and cellular-modified amino acid catabolic processes alongside steroid biosynthesis, glycosaminoglycan degradation, and 2-Oxocarboxylic acid metabolism (Fig. 6C-J).

Landscape of POF Immunological Features
Utilizing the ssGSEA algorithm, we estimated the abundance of 28 immune cell types across POF and control specimens (Fig. 7A).Significant relationships between immune cells were observed (Fig. 7B).A higher abundance of most immune cells was exhibited in POF samples compared to the control, such as activated B cell, activated CD8 + T cell, activated dendritic cell, CD56 bright and CD56 dim natural killer cells, central memory CD4 + and CD8 + T cell, effector memory CD8 + T cell, macrophage, mast cell, T follicular helper cell, and Type 1 T helper cells (Fig. 7C).In addition, the current study observed differences between the POF and control samples in the transcriptional levels of immune checkpoints, chemokines, HLA molecules, and immune receptors (Fig. 7D-G).

Discussion
POF pathogenesis is attributed to premature follicle loss owing to expedited atresia as well as mature or recruited primordial follicles that result in a folliculogenesis deficiency, which is an organized and complex process where small primordial follicles continue to mature into large ovulations anterior follicle [34].Nonetheless, the molecular mechanisms underlying POF remain indistinct.CircRNAs are a form of single-stranded noncoding RNAs, which are circular in conformation owing to noncanonical splicing as well as back-splicing events [35].Evidence has demonstrated that circRNAs participate in modulating granulosa cell functions [36].For instance, Pan et al. [37] proposed that circRNA-induced inhibin-activin balance modulation in the apoptosis of ovarian granulosa cells as well as follicular atresia.Circular DDX10 exhibits an association with ovarian function via the modulation of proliferative capacity and steroidogenesis in granulosa cells [38].Circ-ANKHD1 mediates granulosa cell apoptosis by targeting miR-27a-3p/SFRP1 signaling [39].Circ-SLC41A1 is capable of resisting apoptosis in porcine granulosa cells and follicular atresia through miR-9820-5p/SRSF1 signaling [40].These findings reveal that altered circRNAs participate in the onset of POF.
However, the limitations of our study should be highlighted.
Although we identified the circRNAmediated post-transcriptional regulatory mechanisms underlying necroptosis in POF, further experimental validation is required.Thus, in future studies, we will further validate our findings on the regulatory mechanisms of cir-cRNAs in necroptosis.Furthermore, we found that necroptosis genes were remarkably linked to immunity and immune responses in POF.Therefore, our future research will conduct an in-depth analysis of the interactions between necroptosis and immunity and immune responses as well as their interactions in POF.

Conclusions
In summary, the present research proposed the widespread regulatory mechanisms of circRNAs in necroptosis during POF, demonstrating that abnormal circRNA biogenesis can potentially affect necroptosis, which might result in the onset of POF.

Fig. 1 .
Fig. 1.Characterization of circular RNAs (circRNAs) with differential expression in premature ovarian failure (POF) rats (n = 3) versus controls (n = 3).(A,B) Scatter and volcano plots illustrate the circRNAs with differential expression in POF compared to controls based upon the criteria of |FC| >1.5 together with p-value < 0.05.Blue denotes downregulated circRNA, while red indicates upregulated circRNA.(C) Heatmap illustrates the expression levels of circRNAs with differential expression across POF and control specimens.Colors from blue to red represent downregulated to upregulated circRNA levels.(D) Heatmap depicts the top twenty up-or downregulated circRNAs in POF compared to controls.

Fig. 2 .
Fig. 2. Characterization of mRNAs with differential expression in POF (n = 3) versus controls (n = 3) in the GSE33423 dataset.(A,B) Scatter and volcano plots displaying the mRNAs with differential expression in POF compared to control specimens after applying the criteria of |FC| >1.2 together with p-value < 0.05.Blue represents downregulated mRNA and red indicates upregulated mRNA.(C) Heatmap exhibits the expression levels of mRNAs with differential expression across POF and controls.Colors from blue to red denote downregulated to upregulated mRNA levels.(D) Heatmap illustrates the top twenty up-or downregulated mRNAs in POF versus control specimens.

Fig. 3 .
Fig. 3. Exploration of the biological significance of differentially expressed mRNAs in POF.(A) Biological process, (B) cellular component, (C) molecular function, and (D) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway terms for mRNAs with differential expression.(E) Visualization of necroptosis pathway enriched by mRNAs with differential expression.

Fig. 5 .
Fig. 5. Biological implications of mRNAs derived from the circRNA-miRNA-mRNA network.(A) Biological process, (B) molecular function, (C) cellular component, and (D) KEGG pathway terms for mRNAs derived from the circRNA-miRNA-mRNA network.(E) Visualization of the tyrosine metabolism pathway enriched by mRNAs derived from the circRNA-miRNA-mRNA network.

Fig. 6 .
Fig. 6.Identification of POF-specific necroptosis genes and any underlying molecular mechanisms.(A) Protein-protein interaction (PPI) subnetwork depicts POF-specific necroptosis genes.(B) Heatmap exhibits the transcriptional levels of POF-specific necroptosis genes across POF and control specimens.Colors from blue to red indicate downregulated to upregulated levels of POF-specific necroptosis genes.(C,D) Gene set enrichment analysis (GSEA unveils the biological processes and KEGG pathways significantly correlated to H2AFY2.(E,F) GSEA unveils the biological processes and KEGG pathways that exhibit significant correlations to HMGB1.(G,H) GSEA predicts the biological processes and KEGG pathways significantly associated with STAT1.(I,J) GSEA reveals the biological processes and KEGG pathways significantly linked to STAT5A.

Fig. 7 .
Fig. 7. Immunological features in POF.(A) Heatmap illustrates the abundance of diverse immune cell types across POF and control specimens.Colors from blue to red denote low to high infiltration of immune cells.(B) Heatmap exhibits the relationships between diverse immune cell types across POF and controls.Blue denotes the negative correlation coefficient, while red is the positive correlation coefficient.The shade of the colors is proportional to the correlation.p-value is marked in boxes.(C) Box plot illustrates the abundance score of diverse immune cell types in POF versus control specimens.(D) Heatmap visualizes the transcriptional levels of immune checkpoints across POF and controls.Colors from blue to red denote downregulated to upregulated transcriptional levels.(E-G) Heatmaps depict the transcriptional levels of (E) chemokines, (F) Human leukocyte antigen (HLA) molecules, and (G) immune receptors across POF and control specimens.

Fig. 8 .
Fig. 8. Associations between POF-specific necroptosis genes and POF immunological features.(A) Heatmap illustrates the relationships between POF-specific necroptosis genes and diverse immune cell types in POF and control specimens.Blue denotes the negative correlation coefficient, while red is the positive correlation coefficient.The shade of colors is proportional to the correlation.(B-E) Heatmaps exhibit the associations between POF-specific necroptosis genes and (B) immune checkpoints, (C) chemokines, (D) HLA molecules, and (E) immune receptors in POF and control samples.