Abstract

Introduction

Polycystic ovary syndrome (PCOS) is a common endocrine disorder among women of reproductive age, which is characterized by biochemical or clinical hyperandrogenism, irregular cyclicity and polycystic ovarian morphology (Norman et al., 2007; Teede et al., 2018). Metabolic dysfunction is evident in women with PCOS, and manifests as impaired glucose homeostasis, dyslipidemia and abdominal obesity (Azziz et al., 2016). Women with PCOS have epigenetic and transcriptional changes in skeletal muscle that, in part, can explain the metabolic abnormalities seen in these women (Skov et al., 2007; Nilsson et al., 2018). In addition, defects in early insulin signaling in combination with fibrosis in the skeletal muscle, likely contribute to insulin resistance in women with PCOS (Stepto et al., 2020).

There is an increasing body of publications supporting a role for both innate and adaptive immunity in response to changes in metabolic status. This new field of immunometabolism builds on evidence for activation of immune-derived signals in metabolically relevant tissues and explores how immune cells support tissues to adapt to environmental challenges (Man et al., 2017). Adipose tissue is one of the most explored tissues in the field, and the expression and activation of various immune cell types and anti-inflammatory cytokines have been studied in both lean and obese states (Ferrante, 2013; Man et al., 2017). Skeletal muscle is another key metabolically active organ considering that it is the most effective organ for insulin-stimulated glucose uptake in the body (~80%) (Thiebaud et al., 1982). It also plays an important role in the development and progression of type 2 diabetes (Petersen and Shulman, 2018). However, there is scarce knowledge on how immune cells support the metabolic function in the skeletal muscle and on the role of inflammation in modulating skeletal muscle metabolism. The possible impact of immune cells on whole body glucose uptake and insulin sensitivity is a relatively recent appreciation.

Insulin resistance is a central feature of PCOS, with 30–40% of women with the syndrome having glucose intolerance, and 10% of them develop type 2 diabetes before the age of 40 (Legro et al., 1999; Rubin et al., 2017). Clinical studies exploring the molecular pathways of PCOS-insulin resistance in the skeletal muscle are limited. We have previously performed a wide-scale transcriptomic analysis in the skeletal muscle of women with PCOS and reported that many enriched pathways were involved in immune function or immune diseases (Nilsson et al., 2018). Due to the limited sample size of our study and the need to better understand the skeletal muscle metabolism in PCOS, we aim here to perform an integrated meta-analysis of the three available gene expression arrays in skeletal muscle of overweight/obese women with impaired glucose homeostasis. We further explore whether immunometabolism pathways are dysregulated in the skeletal muscle of these women.

Methods

Results

Meta-Analysis of mRNA Expression in Skeletal Muscle From Women With PCOS and Controls

A total of 24 studies were identified, 12 were assessed for eligibility, and three studies were included in the meta-analysis as they fulfilled the eligibility criteria; published articles, published in English, and baseline samples (no treatment) (Skov et al., 2007, 2008; Nilsson et al., 2018). A PRISMA flow diagram is included in Supplementary Figure 1 (Prisma checklist in Supplementary Table 1). Only 3 samples in GSE8157 were exclusive for this study, while the others were also included in GSE6798. This generated a total number of 27 controls and 36 PCOS cases in the meta-analysis. Table 1 shows the most relevant clinical characteristics of the patients from each cohort. All women were over-weight or obese (mean BMI > 30 kg/m²), and premenopausal. Women with PCOS were hyperandrogenic with metabolic aberration and insulin resistance. PCOS was diagnosed according to 2 criteria; irregular periods with cycle length > 35 days, and biochemical or clinical signs of hyperandrogenism in Skov et al. (2007, 2008), and according to Rotterdam criteria in Nilsson et al. (2018) fulfilling 2 of 3 criteria; ultrasound-verified polycystic ovaries, oligomenorrhea/amenorrhea, and/or clinical signs of hyperandrogenism.

Table 1

Clinical characteristics of the patients in the 3 cohorts included in the meta-analysis.

Characteristic	Skov et al. (2007) Control/PCOS	Skov et al. (2008) Control/PCOS	Nilsson et al. (2018) Control/PCOS
Total subjects	13/16	13/10	14/17
Included subjects	13/16	0/3	14/17
Age (years)	34.7 ± 2.0/30.8 ± 1.8	34.7 ± 2.0/30.3 ± 2.1	29.7 ± 5.85/31.2 ± 5.4
BMI (kg/m2)	34.0 ± 1.8/34.1 ± 1.1	34.0 ± 1.8/33.2 ± 0.9	30.2 ± 3.6/31.3 ± 4.3
Insulin (pmol/L)	43 ± 4/116 ± 16*	51 ± 6/125 ± 23*	63 ± 27/89 ± 55
Glucose (mmol/L)	5.5 ± 0.1/5.5 ± 0.1	5.6 ± 0.1/5.9 ± 0.2	5.1 ± 0.4/4.9 ± 0.3
Free testosterone (nmol/L)	0.021 ± 0.000/0.059 ± 0.010*	0.025 ± 0.003/0.053 ± 0.009*	0.015 ± 0.005/0.029 ± 0.019*
LH/FSH ratio	0.68 ± 0.06/1.55 ± 0.17*	No data	1.18 ± 0.84/1.80 ± 1.13

Open in a separate window

In total, 27 controls and 36 women with PCOS were included. ^*P < 0.05 PCOS vs. control subjects in each cohort. Data presented as mean ± SEM in Skov et al. (2007, 2008), and as mean ± SD in Nilsson et al. (2018).

In total, of the 8,351 analyzed transcripts, 1,228 were differentially expressed in skeletal muscle from women with PCOS vs. controls (P < 0.05). After correction for multiple testing by FDR (5%, q < 0.05), 114 unique transcripts remained significant (Figure 1A, Table 2). In total, 82% of the transcripts were upregulated and 18% were downregulated in women with PCOS. The three genes with the largest differential increase in expression were ING2, CDKAL1, and AKTIP, and the three genes with the largest differential decrease in expression were TSHZ2, FKBP2, and OCEL1. Ninety-nine of the 114 identified transcripts have not been previously shown to be altered in skeletal muscle arrays from women with PCOS (Skov et al., 2007, 2008; Nilsson et al., 2018) (marked in bold in Table 2).

Open in a separate window

Figure 1

Comparison of individual analysis in dataset 1 (Skov et al., 2007, 2008) and 2 (Nilsson et al., 2018). (A) Heat map of the 114 differentially expressed genes (q < 0.05), (B) Venn diagram of the individual analysis; Illumina, Affymetrix, and integrated meta-analysis (q < 0.05). (C) Forest plot of the effect size of the two genes, IRX3, and CDKAL1, consistently upregulated in the three studies.

Table 2

Differentially expressed transcripts in skeletal muscle from women with PCOS vs. controls in this meta-analysis (q < 0.05).

Gene	ES	q	Gene	ES	q	Gene	ES	q
ING2	1.32	0.002	MYLK4	0.93	0.004	AES	0.78	0.050
CDKAL1	1.27	0.002	USP53	0.93	0.017	INPP5A	0.77	0.047
AKTIP	1.17	0.020	NFIA	0.92	0.033	MOCS2	0.75	0.030
RAPH1	1.16	0.004	STXBP3	0.92	0.019	UBR3	0.75	0.030
UBC	1.15	0.040	RRM2B	0.92	0.013	ANXA7	0.75	0.044
ADK	1.13	0.004	PSMC4	0.92	0.050	NEDD1	0.74	0.031
HFE2	1.13	0.004	PPP1R3B	0.92	0.009	COL4A3	0.74	0.038
CSPP1	1.11	0.007	RBM24	0.91	0.017	FAM129A	0.74	0.033
SCP2	1.11	0.005	PLCL1	0.91	0.017	MSRB3	0.74	0.049
MAPKAP1	1.11	0.004	HIST1H2AC	0.90	0.011	C1ORF43	0.74	0.047
CACNB1	1.10	0.013	VCL	0.89	0.042	ENSA	0.74	0.030
ABLIM1	1.10	0.005	KRR1	0.89	0.042	CLK1	0.73	0.047
IRX3	1.09	0.001	NGDN	0.89	0.042	BPGM	0.73	0.050
UNC13B	1.09	0.012	FBXW7	0.88	0.010	FBXO32	0.72	0.047
SHISA2	1.08	0.002	MCL1	0.88	0.022	CALML6	0.70	0.017
HSF2	1.08	0.012	PGK1	0.87	0.023	MLF1	0.69	0.048
DUSP13	1.07	0.026	PHKG1	0.87	0.009	LRRC3B	0.68	0.030
IREB2	1.07	0.005	PDE4DIP	0.87	0.017	LPL	−0.71	0.046
SYNC	1.05	0.005	EGF	0.86	0.030	HMOX1	−0.76	0.038
ADPRHL1	1.04	0.008	GRB14	0.86	0.007	ADM	−0.77	0.049
ATP2A1	1.02	0.002	CTNNB1	0.85	0.030	UCP2	−0.78	0.026
MORC3	1.01	0.004	SERPINE2	0.85	0.019	MT2A	−0.81	0.030
RCN2	1.01	0.017	FAM184B	0.85	0.038	SKAP2	−0.81	0.030
ATP2C1	1.00	0.005	TSPAN3	0.85	0.018	HSPB6	−0.82	0.030
HOMER1	1.00	0.007	CHAF1B	0.85	0.031	MT1E	−0.85	0.031
OAT	1.00	0.008	PTBP2	0.84	0.033	PC	−0.86	0.033
MSTN	0.99	0.002	PITX2	0.84	0.045	ATOH8	−0.89	0.030
CTBP1	0.98	0.010	ARMC8	0.84	0.049	NBPF20	−0.89	0.045
ATP5G2	0.97	0.030	NCKAP1	0.83	0.012	SNX21	−0.89	0.033
EGFLAM	0.96	0.019	PDZRN3	0.83	0.035	AGPAT9	−0.89	0.030
TSC22D3	0.96	0.030	ACVR1	0.83	0.021	LDHB	−0.92	0.008
TMEM182	0.96	0.017	TRDN	0.83	0.013	IFITM3	−0.93	0.046
PRR16	0.95	0.004	USP46	0.81	0.047	THY1	−0.95	0.017
H2AFY	0.95	0.030	GPD2	0.81	0.043	RSPO3	−0.96	0.012
FRMD6	0.95	0.009	RAN	0.81	0.040	BST2	−1.08	0.016
CLTCL1	0.93	0.030	ALDOA	0.79	0.030	OCEL1	−1.15	0.013
DCAF6	0.93	0.042	RB1	0.79	0.041	FKBP2	−1.26	0.013
ZBTB16	0.93	0.020	PPTC7	0.79	0.042	TSHZ2	−1.29	0.006

Open in a separate window

Transcripts not previously shown to be altered in PCOS muscle arrays (Skov et al., 2007, 2008; Nilsson et al., 2018) are marked in bold. ES, Enrichment score.

In Nilsson et al. (2018), we have previously investigated the overlap between the differentially expressed genes identified in that study (p < 0.05) and those found in Skov et al. (2007) (q < 0.1), and found four transcripts (i.e., RAPH1, INO80D, PPIE, and AKTIP) that were upregulated in both studies. Two of these genes, RAPH1 and AKTIP were significant at q < 0.05 (effect size 1.16 and 1.17, respectively) and PPIE at q < 0.1 (effect size 0.86) in the integrated meta-analysis, while INO80D was not significantly altered. The meta-analysis findings were also compared with those obtained by individual analyses in both datasets (Supplementary Table 2) to evaluate bias and reproducibility across the microarray studies. As a result, 34 genes identified in the Illumina data set were also highlighted by our meta-analysis, whereas 20 genes were shared with the Affymetrix data set (Figure 1B). Two of these genes, IRX3 (effect size 1.09) and CDKAL1 (effect size 1.27) were consistently upregulated (q < 0.05), by the three studies (Affymetrix, Illumina and integrated meta-analysis) (Figure 1C).

Gene Set Enrichment Analysis and Pathway Analysis

Next, we tested whether sets of biologically-related genes were altered in women with PCOS compared with controls. According to the GSEA, there were significant (q < 0.05) gene expression alterations in 9 downregulated pathways in women with PCOS (Table 3). The majority of these downregulated pathways are involved in inflammatory/immune response while myogenesis and MYC signaling, a transcription factor for growth-related genes for glycolysis and mitochondrial metabolism in T cells (Wang et al., 2011), were upregulated (p < 0.05). Finally, GO analysis including the 114 differently expressed transcripts (q < 0.05) showed 205 enriched signaling pathways (Figure 2A, Supplementary Table 3). Genes involved in metabolic and carbohydrate biosynthetic processes, positive regulation of I-kappaB kinase/NF-kappaB signaling, and negative regulation of cell proliferation and positive regulation of cell death appeared in the enriched pathways (Figure 2B). Muscle system processes and calcium ion transport were also enriched together with the androgen receptor signaling pathway (Figure 2B, Supplementary Table 3). All three genes in the androgen receptor signaling pathway; RAN, RB1, and CTNNB1, were significantly upregulated in women with PCOS compared to controls (q < 0.05, Table 2).

Table 3

Significantly differentially expressed gene sets in skeletal muscle from women with PCOS compared with controls (GSEA, q 0.25).

Gene set name	Size	ES	NES	p	q
Upregulated enriched gene sets
MYC TARGETS V1	165	0.37	1.71	0.000	0.023
SPERMATOGENESIS	46	0.45	1.71	0.000	0.043
G2M CHECKPOINT	99	0.34	1.47	0.019	0.109
HEME METABOLISM	124	0.32	1.43	0.013	0.124
MYOGENESIS	163	0.29	1.34	0.035	0.20
Downregulated enriched gene sets
INTERFERON ALPHA RESPONSE	67	−0.57	−2.45	0.000	0.000
INTERFERON GAMMA RESPONSE	127	−0.44	−2.14	0.000	0.001
CHOLESTEROL HOMEOSTASIS	50	−0.49	−2.00	0.000	0.001
XENOBIOTIC METABOLISM	117	−0.42	−2.00	0.000	0.001
COAGULATION	66	−0.45	−1.94	0.000	0.001
EPITHELIAL MESENCHYMAL TRANSITION	130	−0.39	−1.89	0.000	0.002
APOPTOSIS	114	−0.32	−1.54	0.003	0.048
IL2 STAT5 SIGNALING	117	−0.32	−1.52	0.000	0.047
APICAL JUNCTION	111	−0.31	−1.51	0.003	0.047
COMPLEMENT	106	−0.31	−1.45	0.008	0.067
HEDGEHOG SIGNALING	16	−0.43	−1.31	0.125	0.19
INFLAMMATORY RESPONSE	86	−0.29	−1.30	0.070	0.20
HYPOXIA	134	−0.26	−1.27	0.063	0.21
TNFA SIGNALING VIA NFKB	116	−0.26	−1.24	0.083	0.25
P53 PATHWAY	136	−0.25	−1.24	0.065	0.23
ADIPOGENESIS	164	−0.24	−1.23	0.078	0.22
ALLOGRAFT REJECTION	87	−0.27	−1.22	0.130	0.24
IL6 JAK STAT3 SIGNALING	40	−0.31	−1.20	0.192	0.25
ANGIOGENESIS	22	−0.35	−1.20	0.208	0.24

Open in a separate window

The enrichment score (ES) was calculated for each gene set, the primary outcome of GSEA. NES, Enrichment score normalized for differences in gene set size.

Open in a separate window

Figure 2

Functional annotation clustering determined using DAVID Bioinformatics Resources with respect to the 114 differently expressed genes (q < 0.05) in the skeletal muscle from women with PCOS compared with controls. (A) The representative groups with an enrichment score of 1.3 or above are presented. The x-axis represents the number of genes, while the y-axis represents the ontology categories. (B) Selected GO terms of biological processes enriched by the differently expressed genes (P < 0.05).

Discussion

Taking an integrated meta-analysis approach, this study showed an overall downregulation of transcripts involved in the immune system and several alterations in metabolic pathways in skeletal muscle of women with PCOS. Previous data suggests that insulin signaling defects in skeletal muscle partly account for the insulin resistance in obese women with PCOS. However, our data indicate that additional molecular mechanisms are involved in the PCOS-insulin resistance in skeletal muscle. Fibrosis might be one key player (Stepto et al., 2020), together with immunometabolic alterations as supported by our data.

Fibrosis in skeletal muscle can impair muscle function and affect muscle fiber regeneration after injury (Delaney et al., 2017; Mahdy, 2019). Tissue fibrosis is often initiated and maintained through TGF-beta signaling and has been suggested to be associated with insulin resistance and steatosis in PCOS (Petta et al., 2017; Stepto et al., 2019). This association is supported by a recent paper, investigating TGF-beta ligand induced fibrosis in obese women with PCOS (Stepto et al., 2020). Moreover, we have previously shown an enrichment of extracellular matrix signaling pathways and a decreased collagen gene expression in our previous study (Nilsson et al., 2018), supporting a link between dysregulated TGF-beta signaling and extracellular matrix deposition with insulin resistance in PCOS. Surprisingly, fibrosis, extracellular matrix-, or TGF-beta signaling pathways were not enriched in women with PCOS in this meta-analysis. However, the most upregulated gene in PCOS muscle, inhibitor of growth family member 2 (ING2), promotes TGF-beta-induced transcription and cellular responses (Sarker et al., 2008), which leave the issue open for further investigation.

We have previously shown that the majority of the significant gene expression pathways (GSEA) in women with PCOS were involved in immune responses or were related to immune diseases (Nilsson et al., 2018). A distinct pattern was a downregulation of human leukocyte antigen (HLA) genes in women with PCOS. This finding was not validated in this meta-analysis, although two HLA genes were downregulated (p < 0.05), but they did not survive FDR <5%. However, here we show a dysregulated expression of many genes involved in immune pathways, including a downregulation of gene sets associated with inflammatory response, interferon alpha and gamma response and the proinflammatory cytokines interleukin-2, interleukin-6 and TNF-alpha. Among the dysregulated genes, ADK and BST2 are thought to play a role in the immune system, identified as having anti-inflammatory and anti-viral actions, respectively (Douglas et al., 2010; Boison, 2013). BST2 was one of the top downregulated genes, and is known to be an interferon-inducible gene, and to play a role in innate immunity (Douglas et al., 2010). FKBP2 was also one of the top downregulated genes, which plays a role in immunoregulation by binding the immunosuppressive compound rapamycin that inhibits mTOR signaling (Hendrickson et al., 1993). This is in line with the downregulation of enriched gene sets associated with interferon alpha and gamma responses. Looking into the most upregulated genes, ADK is a phosphotransferase that converts the purine ribonucleotide adenosine into 5′-adenosine-monophosphate (Boison, 2013). This reaction controls largely the adenosine tone, and consequently affects a wide range of functions. One of the functions that ADK and adenosine exert, is the regulation of the immune system function. Adenosine is evidenced to be generated as a result of stress response in damaged tissues and to display immunosuppressive properties (Hasko and Cronstein, 2004). A downregulation of inflammatory pathways in skeletal muscle is in contrast to the chronic low-grade pro-inflammatory state in adipose tissue (Dimitriadis et al., 2016). There is a lack of conclusive evidence, but androgens likely have direct effects on the adipose tissue resident immune cells (Huang et al., 2013). Changes in adipocyte function impair the secretion of adipokines, leading to decreased adiponectin secretion (Manneras-Holm et al., 2011), which promotes susceptibility to low grade inflammation. Despite the different immune responses in skeletal muscle and adipose tissue, it is evident that immune function is impaired in women with PCOS.

Inflammatory and metabolic pathways in skeletal muscle are closely tied to cell signaling and differentiation, which leads to tissue specific adaptations in response to different stimuli e.g., androgens, obesity and exercise. The GSEA showed an upregulated myogenesis and the GO showed an enrichment of genes involved in negative regulation of cell proliferation, positive regulation of cell death, and negative regulation of response to stimuli, supporting skeletal muscle specific adaptations in response to PCOS. Furthermore, genes involved in MYC signaling were the most upregulated enriched gene set in the GSEA. MYC is a transcription factor that drives metabolic reprogramming in activated, primary T lymphocytes, and this may be one of the key targets responsible for metabolic reprogramming in response to PCOS conditions (Wang et al., 2011). As expected, many metabolic pathways were found to be enriched in the GO, with a negative regulation of the majority of the metabolic processes. ALDOA, encoding aldolase, was upregulated in women with PCOS and seems to be a key gene involved in many carbohydrate metabolic processes. Aldolase is an insulin stimulated glycolytic enzyme that catalyzes the conversion of fructose-1,6-bisphosphate during glycolysis, but it is also involved in actin remodeling, possibly modulating both metabolic and structural tissue adaptations (Hu et al., 2016).

Lastly, two genes were consistently upregulated in all three studies, IRX3 and CDKAL1. Both genes have been associated with body mass index and type 2 diabetes in genome-wide association studies (Steinthorsdottir et al., 2007; Ragvin et al., 2010; Yengo et al., 2018). IRX3 is a transcription and neuronal progenitor factor, and acts as a regulator of energy metabolism. It is shown to be controlled by a non-coding region of the fat mass and obesity-associated (FTO) gene, which is one of the strongest obesity-associated genes found in humans (Frayling et al., 2007). Many studies have investigated the role of BMI-associated variants in women with PCOS (Jones and Goodarzi, 2016). The results are controversial, but a meta-analysis showed that FTO has an increased effect on the BMI of women with PCOS, but it is not a PCOS susceptibility locus (Wojciechowski et al., 2012). Moreover, data for CDKAL1 and FTO suggest that polymorphisms of these genes are not associated with insulin resistance or insulin secretory capacity in Asian women with PCOS (Kim et al., 2012).

The interpretation of this meta-analysis is limited by a fairly low number of women with PCOS and controls (n < 40/group), the inclusion of a single ethnicity (Caucasian), and a mix of PCOS phenotypes (Rotterdam criteria). Further, our findings cannot distinguish whether the identified dysregulated pathways are responses to a stimulus or the causal effectors. Another limitation is the fact that we cannot identify the gene expression changes within specific target cells i.e., myocytes, as the muscle biopsy is a bulk of many different cell types and structures, e.g., immune cells, vessels and connective tissue.

Conclusion

Clinical studies and transcriptomics data clearly demonstrate that molecular dysfunction in skeletal muscle contributes to insulin resistance in women with PCOS. Intracellular insulin-signaling pathways, mitochondrial function and fat oxidation, with a possible contribution of reduced adiponectin levels, have all been in focus for the mechanism of insulin resistance in PCOS (Stepto et al., 2019). Here, we identify 99 genes not previously shown to be altered in PCOS muscle, two of them consistently upregulated in all three studies. We show that immunometabolism can be added to the list of dysfunctional pathways and present genes with large effect size that warrant further investigation. However, gene expression does not always translate to alterations in biological function and data on total and phosphorylated protein levels in skeletal muscle is limited. Therefore, functional studies and proteomics analysis is needed to validate and advance our understanding of insulin resistance in skeletal muscle in these women.

Data Availability Statement

All datasets presented in this study are included in the article/Supplementary Material.

Author Contributions

MM, ES-V, and AB designed the study and contributed to the interpretation of data. MM and AB performed data analyses and statistical analyses, and wrote the manuscript. All authors critically revised and approved the manuscript. All authors contributed to the article and approved the submitted version.

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.

Acknowledgments

Footnotes

References