DOI: 10.31038/IGOJ.2018122

Abstract

Aim: The aim of this study was to investigate the association between follicular fluid (FF) AMH and follicular fluid (FF) soluble receptor of Advanced Glycation End products (sRAGEs) in women with Polycystic Ovarian Syndrome (PCOS) regarding IVF outcome parameters.

Methods: FF AMH, serum AMH and FF sRAGEs were measured in 49 women undergoing IVF in the IVF Department of University Hospital of Alexandroupolis from December 2014 to May 2015. Women were divided in two groups, the PCOS women and the non PCOS women (Group A 38 non PCOS women and Group B 12 PCOS women). FF AMH, serum AMH and FF sRAGE values of Group A were compared to those of group B. The correlation of these values with IVF outcome parameters was investigated in both groups.

Results: We did not find a correlation among the factors studied (serum AMH, FF AMH and FF sRAGES). FF sRAGE levels were found to be higher in PCOS women (p-value <0,01). No correlation of FF sRAGE concentration and number of oocytes retrieved during IVF or number of embryos was noted in PCOS women. FF sRAGE levels were only found to be negatively correlated with the quality of the oocytes retrieved in non PCOS women (r= -0.484, p-value= 0.003). The higher the FF sRAGE concentration, the lower the quality of the oocytes was found to be. A negative correlation was also noted between FF sRAGE levels and BMI in non PCOS women (r= -0.401, p-value=0.014).

Conclusion: These findings support that FF sRAGEs could not be used combined with AMH measurements (serum and FF) to predict IVF outcome but could be used separately and only in non PCOS women.

Keywords

AMH, oxidative stress, sRAGEs, PCOS, ovarian reserve, IVF outcome

Introduction

Firstly described in 1935 by Stein Leventhal 1], Polycystic Ovary Syndrome (PCOS),  is the most common endocrine disorder among reproductive-aged women and the main cause of female infertility due to anovulation, affecting the 19,9 % of Caucasian women under the Rotterdam criteria [2]. It is, therefore, understood that determining ovarian reserve- the quality and quantity of ovarian follicles at a given point in time and being able to predict total IVF outcome in women with PCOS is of great importance in Assisted Reproduction Technology (ART) [3].

Ovarian reserve markers

Up to date, many markers including FSH, estradiol, inhibin B, anti-Müllerian hormone (AMH), the antral follicle count (AFC), the ovarian volume (OVVOL) and the ovarian blood flow, have been studied and proposed as ovarian reserve markers [3].

Among them, the use of AMH seems to be widely accepted and has gained value in everyday clinical practice. AMH is a member of TGF-b glycoprotein superfamily, mainly expressed in granulosa cells of growing antral and pre-antral follicles in the gonadotropin independent phase [4]. AMH inhibits follicle sensitivity to follicle-stimulating hormone (FSH) and therefore affects follicular growth [5]. In clinical practice, AMH has been found to have high specificity and sensitivity in predicting ovarian response to Controlled Ovarian Stimulation (COS). Positive correlation with oocyte retrieval, role in poor responders’ prediction and a relation with life birth rate after IVF have also been noted [6–9]. AMH declines during reproductive age from puberty to menopause until it is almost undetectable [10, 11]. The idea of age related ovarian reserve diminishment and subsequent decline of ovarian function and reproductive potential have led scientist to seek for ovarian aging markers that could be used in practice as ovarian reserve markers.

Oxidative stress and ovarian aging

It is known that aerobic organisms develop the ability to use oxygen to efficiently emit energy. The prevention of oxidation during this procedure is controlled by antioxidant defense mechanisms and is of great importance. Any imbalance of the available antioxidant mechanisms combined with uncontrolled oxidation of biomolecules, mainly by the reactive oxygen species (ROS), results in oxidative stress. Many types of molecules like nucleic acids, proteins, lipids and carbohydrates may be damaged by oxidative stress suggesting that proteins, membranes and carbohydrate complexes could be victims of oxidative stress resulting in unrepaired DNA damage, telomerase loss, altered proteins and lipid destruction. These catastrophic oxidative events accumulate with age, cause tissue damage and in humans plays a significant role in the pathophysiology of many pathological situations and diseases, including the age related ovarian quality and quantity decline [12,13]. Thus, oxidative stress has been proposed to be involved in the pathogenesis of infertility. ROS concentrations have been proposed by many researchers to affect negatively fertilization and implantation [14]. More recently, the Advanced Glycation End products (AGEs) have been proposed to get involved in ovarian function decline [15, 16].

Advanced Glycation end products (AGEs), are the products of one of the most important modifications that occurs after translation, the nonenzymatic glycation of proteins, lipids, and nucleic acids [17, 18]. AGEs may either cause the formation of cross links between key molecules in the basement membrane of the extracellular matrix (ECM) or act via binding to receptors. Two types of receptors have been described. The first one is the multi-ligand transmembrane receptor, the RAGE (receptor for advanced glycation end products), known as the inflammatory receptor [17, 18]. RAGEs are expressed by several cell types, like endothelium, smooth muscle cells and ovaries and their activation may lead to unfavorable cellular conditions like proinflammation, cellular toxicity, and cellular damage via activating nuclear factor-κB [19, 20]. The second receptor that has been described is the soluble RAGE (sRAGE). sRAGE is an extracellular form of RAGE, lacking the cytosolic and transmembrane domains. It is able to adverse intracellular effects AGEs can cause and therefore is known as anti-inflammatory receptor [17, 18].

Prolonged exposure to AGEs, during reproductive life, has been shown to be able to cause subtle oxidative damage to the follicles of the ovary by altering ovarian microenvironment. These changes in the ovarian microenvironment may have adverse effects on granulosa cells’ metabolism, in antioxidant defense, and the development of inadequate follicle vascularization with subsequent follicle hypoxia. Follicle health and maturation may be negatively affected and age related oocyte dysfunction events may occur [21]. Additionally, it has been found that ff sRAGE levels were higher in younger women than in advanced aged women undergoing IVF treatment [22].The idea that sRAGEs may have a protective role in these unfavorable events have led scientist to investigate the correlation of sRAGEs with ovarian reserve markers.

Ovarian reserve markers in PCOS

AMH levels in women with PCOS have been shown to be two to three times higher than in healthy controls and levels of AMH are on average 75 times higher in granulosa cells from PCOS ovaries, compared to levels in granulosa cells from normal ovaries because of the increased development of antral follicles compared to normal women [23,24]. Serum AMH has been shown to be a diagnostic marker of high specificity (92%) and sensitivity (67%) for PCOS [24].

Serum AGE levels and AGE-RAGE expression in theca and granulosa cells have been found to be higher in PCOS women than in healthy controls. This has been attributed to hyperglycemia, oxidative stress and insulin resistance in PCOS women [25, 26] and is linked to diminished ovarian reserve and abnormal folliculogenesis [27]. On the contrary, follicular fluid sRAGEs have been found to be lower in women with PCOS compared to non-PCOS women [28, 29].

It has been shown, in vitro, that the inappropriate prolonged activation of ERK1/2 path, which is critical for normal follicle growth and the beginning of ovulation, by AGEs via intervention in the action of LH, could be responsible for the impaired follicle growth and the subsequent ovulation dysfunction that characterizes PCOS women. Moreover, it is understood that AGEs accumulation in FF of PCOS women could lead to premature ovarian aging [30].

Methods & Materials

49 women from different regions of Greece, mainly from Thrace, who received IVF treatment in the IVF Department of University Hospital of Alexandroupolis from December 2014 to May 2015 were recruited for this study and were divided in two groups (Group A and Group B) . Group A consisted of 38 non PCOS women, while Group B of 12 PCOS women diagnosed under the Rotterdam criteria. Written consent was signed by all women.

Controlled Ovarian Stimulation (COS) was offered to all women, after preliminary control and appropriate COS protocol was chosen for each after study and evaluation of parameters like age, day 3 serum FSH, day 3 serum LH, serum AMH and others. During COS period all women were offered serial ultrasound examinations and serum Estradiol (E2) measurements in order to evaluate respond to stimulation. I.M b-HCG administration was decided when at least 3 of developing follicles reached diameter of 18 mm and oocyte retrieval (OR) was planned for 36 hours post. During OR, FF needed to measure AMH and s RAGEs was collected.

Follicular Fluid collection

During oocyte retrieval, follicular fluid was aspired and collected in tubes. The embryologist separated the oocytes in FF and placed them in a plate with culture substances. Meanwhile, FF was collected in a falcon and labeled with woman’s name. Following FF was centrifuged for 15 minutes in 3000 rpm, to remove any unnecessary element, like cumulus cells. After centrifugation was completed, supernatant serum was transferred to another falcon (labeled with woman’s name) and refrigerated in -70 degree of Celsius. This procedure was followed in all cases in order to collect the samples.

Moreover, in order to be able to distinguish PCOS from non PCOS samples, falcons were labeled with respective labels. Therefore, the samples of Groups A and B could easily be evaluated and compared. AMH and sRAGE measurements were carried out simultaneously by the same kit, special for each parameter. AMH was measured by Elisa method, according to kit’s manufacturer (Anshlab) instructions (UltraSensitive AMH/MIS ELISA AL-105-i). The kit Quantikine ELISA/ Human RAGE was used for the measurements for the FF sRAGE levels according to manufacturer’s instructions(R & D Systems).

Statistical Analysis

The IBM SPSS Statistics for Windows Program, Version 21.0. Released 2012, Armonk, NY: IBM Corp. was used for the statistical analysis in our study.

Demographic characteristics and clinical data of women recruited were evaluated by descriptive statistic methods. The ratio of MII oocytes to the total number of oocytes retrieved for each woman was defined as simple quantitative expression of Oocyte quality. We consider that this ratio adequately represents oocyte quality and were used as quantitative variable during analysis. T-test for independent variables was used to compare the quantitative variables between two groups while χ² test was used to compare the qualitative variables. Significance levels for both cases was set at 0, 05.

The concentrations of the parameters studied (serum and FF AMH, FF sRAGE) were calculated and presented as mean values ± standard deviation for each group separately. The comparison of these concentrations between the two groups was made by t-test for independent variables with significance level of 0.05.

In each group, quantitative variables were examined for possible monofactor linear correlation among them by the Pearson correlation coefficient r and the relative p-values. Multifactor linear regression was applied to whole sample, in order to study the correlation of ff sRAGE, ff AMH and serum AMH levels, taking into consideration women’s age, BMI and PCOS presence. The results of this multifactor analysis were expressed via βήτα coefficient (βήτα mean value± standard error) and the relative p-values. In the mono factor and in the multifactor analysis the significance level was of 0.05.

Results

In the beginning, we completed the above analysis between the two groups, including all qualitative and quantitative variables we had collected. The demographic and clinical characteristics of all women are presented in Table 1. Infertility in non PCOS women was attributed to tubal factor (21, 6%), to male factor (24, 3%), to premature ovarian failure (8, 1%), to other factors (8, 1%), to more than one factors (18, 9%) or was unknown (18, 9%). PCOS women presented in great percentage more than one infertility factors (50%). PCOS women had higher E₂ levels when compared to non PCOS women, had more growing follicles during stimulation, more oocytes after OR. They also had more MII oocytes and more embryos available for embryo transfer (p-value <0, 05). There were no other statistical significant differences between the two groups in the clinical and laboratory characteristics studied.

Table 1. Demographic and clinical characteristics of 49 recruited women (12 PCOS and 37 non PCOS). Quantitative variables: mean value± standard deviation, comparison with t-test for independent samples. Quantitative variables: number of patients, percentage within parenthesis, comparison with χ² test. NS: statistically non significant, significance level 0.05.

Following, we made the below analysis and the graphic with the data of serum AMH, FF AMH and FF sRAGEs.

Serum AMH, FF AMH and FF sRAGEs levels of PCOS and non PCOS women are presented in Table 2 and in Figure 1. PCOS women were found to have higher FF sRAGE levels compared to non PCOS women (p-value <0, 01). On the contrary, the differences in serum and FF AMH levels were not found to be statistically significant between the two groups. Moreover, the deviation of the values (standard deviation) of the three factors studied is larger in PCOS women. This could be attributed to the small sample size (12 women) or could reflect the real heterogeneity of PCOS women, indeed.

Table 2. Serum AMH, FF AMH and FF sRAGE studied in 12 PCOS and in 27 non PCOS women. Mean values ± standard deviations are presented, comparison with t-test for independent samples. NS: statically non significant with significance value 0.05.

Figure 1. AMH and sRAGEs concentrations studies in serum and FF of 12 PCOS and 37 non PCOS. Mean values with 95% confidential intervals are presented.

This data is presented in the graph below (Figure 1).

Additionally, a multifactor analysis, regarding the three variables was made. The results were as following:

The most significant results of the monofactor analysis are presented summarized in Table 3. In PCOS women serum AMH, ff AMH and FF sRAGE levels were not found to be correlated with the number of the oocytes retrieved, the number of MII oocytes, with the embryo number or the ratio of MII oocytes to the total number of the oocytes retrieved. Serum AMH but not ff AMH was found to be a better marker of ovarian response in non PCOS women because it was found to be correlated with the number of MII oocytes and the number of the embryos available for embryo transfer. Only in non PCOS women, ff sRAGE levels were found to be negatively correlated with the quality of the oocytes, as it is expressed in the above mentioned ratio (r= -0.484, p-value= 0.003). This correlation is the most important finding of our study and is presented as a graph in Figure 2. Specifically, elevated ff sRAGE concentrations have been found to lead to lower Oocyte quality in non PCOS women. In PCOS women a high degree linear correlation of serum AMH and ff AMH, something that is not seen in non PCOS women, is noted (r= 0,965, p-value <0.001) (Figure 3). Neither serum AMH, nor ff AMH was found to be correlated with ff sRAGE levels in the sample studied. Lastly, ff sRAGE was found to have a negative correlation with BMI in non PCOS women (r= -0.401, p-value=0.014, Figure 4).

Figure 2. Graph of the correlation of number of MII oocytes/total Oocyte number with the concentration of ff sRAGE in 12 PCOS and 37 non PCOS women. Statistical significant correlation was noted only in non PCOS women (Pearson r = -0.484, p-value = 0.003).

Figure 3. Graph of the correlation of serum AMH with ff AMH in 12 PCOS and in 37 non PCOS women. Statistical significant correlation only in PCOS women (Pearson r=0.965, p-value <0.001).

Figure 4. Graph of the correlation of ff sRAGE with the BMI of 12 PCOS and 37 non PCOS women. Statistically significant correlation is noted only in non PCOS women (Pearson r = -0.484, p-value = 0.003).

Table 3. Results of correlation of serum AMH and FF AMH with oxidative stress factors (ff SRAGEs) and with other clinical and laboratory factors in 12 PCOS and in 37 non PCOS women. Mono factor linear correlation based on Pearson’s r. NS: statistically non significant with significance levels 0.05%.

Lastly, with multifactor linear regression in all women, taking into consideration the age, serum AMH and ff AMH levels,  we found that ff sRAGE levels is independently correlated only with the BMI (β= -136.1, p-value =0.036) and the presence of PCOS(β= 1563.9, p-value = 0,045).

Discussion

In our study we tried to determine whether FF sRAGEs are correlated to serum AMH or FF AMH levels and to evaluate the association of these markers with IVF outcome parameters, in PCOS women receiving IVF treatment.

AHM has been widely used as an ovarian reserve marker in prediction of IVF outcome [8]. Additionally, many researchers have found that the severity of PCOS is positively correlated with the number of small antral follicles and that AMH plays a significant role in the pathogenesis of anovulation in women with PCOS. However, in our study, AMH values between PCOS and non PCOS women were not found to be statistically significant. Additionally, we did not find a correlation between ff AMH and serum AMH. Regarding the predictive value of AMH in IVF outcome, serum AMH was found to be correlated with the number of the number of MII oocytes and the number of the embryos only in non PCOS women. We did not find a correlation of ff AMH with the number of the oocytes retrieved or the number of the embryos available for embryo transfer neither in PCOS, nor in non PCOS women.

As above mentioned, AGEs have been incriminated for age-related ovarian dysfunction due to the effect of oxidative stress mechanisms in the ovarian microenvironment that results in insufficient vascularization, hypoxia of the ovary and malnutrition of the granulosa cells [21]. It is known that folliculogenesis is an inflammatory procedure and when combined with COS during IVF cycles large amounts of cytokines are produced and secreted into the follicular fluid. This environment could cause a huge AGE production and effect negatively oocyte quality. Jinno et al, in a study of 157 women undergoing IVF treatment (71 PCOS women included) reported that elevated AGE levels play a significant role in ovarian dysfunction and are associated with poor IVF outcome [31]. While, AGEs have been found to have negative correlation with follicular growth and IVF outcome, in 2014 Merhi et al was the first to report that FF sRAGEs are positively correlated with ovarian reserve, as measured by FF AMH (r=0.5, P = 0.0085) and the number of the oocytes retrieved (r=0,57, P=0.02) but only in non-PCOS women [16]. Recently Li et al, who studied 124 women undergoing IVF treatment, divided into two age groups, found that ff sRAGE levels are positively correlated with folliculogenesis and IVF outcome only in women of advanced age [22].

In our study, we did not find a statistically significant correlation between ff sRAGE levels and the number of the oocytes retrieved or the number of the embryos available for embryo transfer after the IVF treatment in none of the groups studied. But we did found that ff sRAGE levels seem to have an effect on the quality of the oocytes retrieved. In fact, we found that increased ff sRAGE levels are negatively correlated with the quality of the oocytes retrieved, as defined by the ratio of MII oocytes to the total number of the oocytes retrieved but only in non PCOS women(r= -0.484, p-value= 0.003). Given that the number of the embryos available for embryo transfer is correlated with the number and the quality of the oocytes retrieved, it seems that ff sRAGEs play a role in the IVF outcome, at least in non PCOS women. On the contrary to this hypothesis, it has been shown by Malickova et al, that FF sRAGE concentrations in women undergoing IVF were significantly higher in those having a positive IVF outcome [31].

We also found that PCOS women have higher FF sRAGE levels when compared to non PCOS women (p-value <0,01)but no correlation of these high FF sRAGE values with IVF outcome parameters were noted in this group as above mentioned. On the contrary to our study, Wang et al found that FF sRAGEs are significantly decreased in PCOS females compared to control groups and associated with lower total gonadotrophin doses. However, they did also not found a correlation o FF sRAGEs with other IVF outcome parameters (number of oocytes retrieved, fertilization rate, number of high quality embryos) [28].

Lastly, we found a negative correlation of FF sRAGE levels with BMI(r= -0.401, p-value=0.014). No correlation with FF sRAGE and women’s age was noted.

Therefore combining the above mentioned information and taking into consideration the small sample size, we can conclude that there is no correlation between the AMH protein and the oxidative stress agents. However, each of these factors could be used separately for the improvement and the prediction of IVF outcome. AMH can be used as an ovarian reserve marker and as a predictive marker of ovarian response during ovarian stimulation. On the other hand, FF oxidative stress agents (sRAGEs) could consist of a great marker of the quality of the oocytes retrieved during IVF treatment and an indirect marker of IVF outcome. The combination of both could be used, therefore, in order to improve the IVF outcome.

References

1. Stein I, Leventhal M (1935) Amenorrhea associated with bilateral polycystic ovaries. Am J Obstet Gynecol 29: 181–5
2. Yildiz BO, Bozdag G, Yapici Z, Esinler I, et al. (2012) Prevalence, phenotype and cardiomatabolic risk of polycystic ovary syndrome under different diagnostic criteria. Hum. Reprod 27: 3067–73.
3. Broekmans FJ, Kwee J, Hendriks DJ, Mol BW, Lambalk CB (2006) A systematic review of tests predicting ovarian reserve and IVF outcome. Hum Reprod Update 12: 685–718. [crossref] 
4. Dewailly D, Andersen CY, Balen A, Broekmans F, Dilaver N, et al. (2014) The physiology and clinical utility of anti-Mullerian hormone in women. Hum Reprod Update 20: 370–385. [crossref] 
5. Pellatt L, Rice S, Dilaver N, et al. (2011) Anti-Müllerian hormone reduces follicle sensitivity to follicle-stimulating hormone in human granulosa cells. Fertil Steril 96: 1246–12451.e,
6. RiggsRM, DuranEH, BakerMW, KimbleTD, et al. (2008) Assessment of ovarian reserve with anti-Müllerian hormone: a comparison of the predictive value of anti-Müllerian hormone, follicle-stimulating hormone, inhibin B, and age. Am J Obstet Gynecol 199: 202.e1–8.
7. Broer SL, van Disseldorp J, Broeze KA, et al. (2013) IMPORT study group. Added value of ovarian reserve testing on patient characteristics in the prediction of ovarian response and ongoing pregnancy: an individual patient data approach. Hum Reprod Update 19: 26–36,
8. La Marca A, Sighinolfi G, Radi D, Argento C et al. (2010) Anti-Mullerian hormone (AMH) as a predictive marker in assisted reproductive technology (ART). Hum Reprod Update 16: 113–30.
9. Nelson SM, Yates RW, Fleming R (2007) Serum anti-Müllerian hormone and FSH: prediction of live birth and extremes of response in stimulated cycles–implications for individualization of therapy. Hum Reprod 22: 2414–2421. [crossref]
10. La Marca A, Orvieto R, Giulini S, Jasonni VM et al. (2004) Mullerian-inhibiting substance in women with polycystic ovary syndrome: relationship with hormonal and metabolic characteristics. Fertil Steril 82: 970–972.
11. Laven JS, Mulders AG, Visser JA, Themmen AP et al. (2004) Anti-Mullerian hormone serum concentrations in normoovulatory and anovulatory women of reproductive age. J Clin Endocrinol Metab 89: 318–323
12. Betteridge DJ (2000) What is oxidative stress? Metabolism 49: 3–8. [crossref]
13.  Kirkwood TB (2008) Understanding ageing from an evolutionary perspective. J Intern Med 263: 117–127. [crossref]
14. Ashok Agarwal, Sajal Gupta, Rakesh K. Sharma (2005) Role of oxidative stress in female reproduction, Reprod Biol Endocrinol 3: 28
15. Stensen MH, Tanbo T, Storeng R, Fedorcsak P (2014) Advanced glycation end products and their receptor contribute to ovarian ageing. Hum Reprod 29: 125–134. [crossref] 
16. Merhi Z, Irani M, Doswell AD, Ambroggio J. Follicular fluid soluble receptor for advanced glycation end-products (sRAGE): a potential indicator of ovarian reserve. J Clin Endocrinol Metab 99: E226–33.
17. Inagi R (2011) Inhibitors of advanced glycation and endoplasmic reticulum stress. Methods Enzymol 491: 361–380. [crossref] 
18. Piperi Ch, Adamopoulos Ch, Dalagiorgou E, Diamanti-Kandarakis E et al. (2012) Crosstalk between Advanced Glycation and Endoplasmic Reticulum Stress: Emerging Therapeutic Targeting for Metabolic Diseases. The Journal of Clinical Endocrinology & Metabolism 97: 2231–2242.
19. Schmidt AM, Yan SD, Yan SF, Stern DM (2000) The biology of the receptor for advanced glycation end products and its ligands. Biochim Biophys Acta 1498: 99–111. [crossref] 
20. Fujii EY, Nakayama M (2010) The measurements of RAGE, VEGF, and AGEs in the plasma and follicular fluid of reproductive women: the influence of aging. Fertil Steril 94: 694–700. [crossref] 
21. Tatone C, Amicarelli F, Carbone MC, Monteleone P, Caserta D, et al. (2008) Cellular and molecular aspects of ovarian follicle ageing. Hum Reprod Update 14: 131–142. [crossref]
22. Li YJ, Chen JH, Sun P, Li JJ1 et al. Intrafollicular soluble RAGE benefits embryo development and predicts clinical pregnancy in infertile patients of advanced maternal age undergoing in vitro fertilization. J Huazhong Univ Sci Technolog Med Sci. 2017 Apr;37(2): 243–247. doi: 10.1007/s11596–017–1722-z. Epub 2017 Apr 11.
23. Pellatt L, Hanna L, Brincat M, Galea R, Brain H, et al. (2007) Granulosa cell production of anti-Müllerian hormone is increased in polycystic ovaries. J Clin Endocrinol Metab 92: 240–245. [crossref] 
24. Pigny P, Jonard S, Robert Y, Dewailly D. Serum anti-Mullerian hormone as a surrogate for antral follicle count for definition of the polycystic ovary syndrome. J Clin Endocrinol Metab 91: 941–5.
25. Pertynska-Marczewska M, Diamanti-Kandarakis E, Zhang J, Merhi Z (2015) Advanced glycation end products: a link between metabolic and endothelial dysfunction in polycystic ovary syndrome? Metab Clin Exp 64: 1564–73.
26. Hu H, Jiang H, Ren H, Hu X, et al. (2015) AGEs and chronic subclinical inflammation in diabetes: disorders of immune system. Diabetes Metab Res Rev 31: 127–37.
27. Merhi Z (2014) Advanced glycation end products and their relevance in female reproduction. Hum Reprod 29: 135–145. [crossref] 
28. Wang B, Li J, Yang Q, Zhang F, et al. (2017) Decreased levels of sRAGE in follicular fluid from patients with PCOS. Reproduction 153: 285–292. [crossref] 
29. Garg D, Grazi R, Lambert-Messerlian GM, Merhi Z (2017) Correlation between follicular fluid levels of sRAGE and vitamin D in women with PCOS. J Assist Reprod Genet.
30. Diamanti-Kandarakis E, Piperi C, Livadas S, Kandaraki E, et al. (2013) Interference of AGE-RAGE Signaling with Steroidogenic Enzyme Action in Human Ovarian Cells. San Francisco, CA: Endocrine Society.
31. Jinno M, Takeuchi M, Watanabe A, Teruya K et al. (2011) Advanced glycation end-products accumulation compromises embryonic development and achievement of pregnancy by assisted reproductive technology. Hum Reprod 26: 604–10.
32. Malickova K, Jarosova R., Rezabek K, Fait T, et al. (2010) Concentrations of sRAGE in serum and follicular fluid in assisted reproductive cycles – a preliminary study. Clin Lab 56: 377–38

DOI: 10.31038/IGOJ.2018121

Abstract

We report on development of a rapid, quantitative analysis technique of collagen fibers in cross-linked structures to assess remodeling of the cervix during the transition from soft to ripening in preparation for birth. Optical density analysis of picrosirius red stain tissue using circular polarized birefringence light from fixed paraffin-embedded or frozen cervix from pregnant mice during phases of remodeling prior to birth. Data were analyzed using NIH Image J and extended recently to include studies of prepartum cervix in peripartum women. Our results, developed a rapid, consistent, technique to quantify cervical organization. This approach assesses the structure of collagen organization (the principle component of the cervix) and is essential for analysis of experimental outcomes that disrupt cervical morphology in rodent models of preterm birth. The technique, in this report has, for the first time permitted rapid, accurate assessment of the stages that define cervical ripening with large numbers of slides from individual animals. The approach integrates analysis of collagen organization, with distensability and inflammation, processes associated with cervical change before birth. This analysis further holds promise to evaluate other tissues, but also fibrolytic and fibrogenic changes in collagen associated with physiological or pathophysiological conditions.

Key words

pregnancy, remodeling, ripening, inflammation

Introduction

Identification of large protein using various stains have been used as early as the 17^th century to label extracellular, cellular, and sub-cellular organ structures in tissue [1]. More recently, improved fixation and specificity of stains has allowed quantification of small cellular components and their structural organization. Collagens are the most abundant proteins in humans with type 1 the majority (98%) of the 28 identified types [2, 3], and common in all vertebrate species. The human cervix, composed predominately of collagen fibers, serves as a barrier to the vaginal biome and protects the developing contents of the uterus during pregnancy [4]. Junqueira’s group was the first to use picrosirius red stain to identify a reduction in collagen in cervix from intrapartum compared to nonpregnant women. Picrosirius red stains the principal forms of collagen in the cervix, mostly type 1, though type 3 is present to a lesser extent [5]. As Lattouf more recently concluded, picrosirius red stain is “simple, sensitive and specific for collagen staining….particularly useful to reveal the molecular order, organization and/or heterogeneity of collagen fiber orientation in different connective tissues” [6].

Evidence supporting this conclusion led our lab, well over a decade ago, to develop a protocol that uses birefringence of circular polarized light from picrosirius red stained cervix sections to study the progression of remodeling during the progression from phases of softening to ripening [7–11]. In multiple strains of mice and rats, and more recently in women at term and preterm delivery, this technique is reliable, consistent and essential to assess degradation of collagen organization during late term normal pregnancy or experimental manipulations.

Given the utility of picrosirius red stain with birefringence, physiological remodeling and inflammation-induced premature ripening of the cervix, the goal of this report was to document the current state of our method, which is likely to have broad value to accurately study collagen of other tissues and assess pathological or healing of fibrotic processes.

Methods

Cervix from pregnant mice were processed by immersion fixation in 4% paraformaldehyde, paraffin embedded, sectioned at 10 µm, heated at 60^o C for 45 minutes using a slide warmer, then subjected to xylene incubations to remove paraffin, and rehydrated through a graded series of ethanol. Sections were counterstained with hematoxylin to identify cell nuclei (for cell counting), and washed in distilled water to remove background stain. These tissue processing procedures have been previously detailed [7]. Collagen in cervix sections was stained using a picrosirius red kit (Polysciences Kit #24901-500, Warrington, PA, USA). Following instructions, slides were first placed into Solution-A (a phosphomolybdic acid hydrate solution from the kit) for two minutes and then into Solution-B (Picrosirius Red-F3BA) for 60 minutes. Variations in incubation times of ±20 min were not found to improve staining. Slides were placed into Solution-C (0.01 N HCL) for 2 minutes, dehydrated through an ascending series of ethanol, and placed in xylene before coverslipped with Permount (Fisher Scientific, #SP15-100).

For analysis of collagen structural organization during phases of cervix remodeling, a Zeiss Axio Imager A1 microscope with circular polarized light filters was used to evaluate sections at 250x. In early development of this technique an additional microscope (Nikon Optiphot, with Plan apochromat objectives and Nomarski optics) was similarly validated for this method. In both cases, a Spot Pursuit 4MP digital camera was used for micrographs (Diagnostic Instruments, Sterling Heights, MI). In dim room illumination, initial regions of interest were visually identified. A representative sample of 4–6 grey scale photomicrographs were taken from 2 sections/mouse (8–12 total). A similar process was used for human tissue. Care was taken to avoid distorted morphology, including blood vessels, epithelial lumen, and section artifacts, i.e. tissue folds or tears. To ensure consistency in analysis across sections, as well as individuals and groups, microscope properties (field aperture, condenser, light intensity) were optimized and unchanged across viewing each study session. Cells counts were verified using two to three independent observers.

For image analyses, grey scale photomicrographs were filed into a date-labeled folder and batch processed to evaluate optical density using Image J software (NIH, Bethesda, MD; macro plug-in attached in Appendix). At the outset, a global calibration macro was set with the first photomicrograph. The macro first digitized the photomicrograph into an 8-bit grayscale image (Figure. 1B). The program automatically and without bias centered a 9-square box cross region for analysis. An average optical density (OD) was calculated with Rodbard transformation (performed by the program) for each of the 9 boxed regions together with total OD then exported as an output file to a Excel worksheet for statistical analyses. Data were analyzed by ANOVA (p<0.05 level of significance) with Tukey’s post-hoc analysis following Levene’s test (GraphPad Prism Software, Inc., La Jolla, CA).

Results and Conclusions

In the cervix from a nonpregnant mouse, collagen fibers were observed as a dense, uniform fascicle, stained by picrosirius red collagen fibers (Figure. 1A). The birefringence values further quantified this initial impression with values indicative of aligned and organized cross-linked fibers and bundles/fascicles (i.e., low light transmission values) characteristic of an unremodeled cervix. In contrast, by day18 postbreeding (gestation term is 19 days, (Figure 1B), birefringence values were significantly altered, correlating with the presence of numerous gaps and disorganized, nonparallel collagen fibers. This morphology is characteristic of cervical ripening and consistent with early electron microscope studies [12, 13], as well as, consistent with ripening processes of hypertrophy, edema, and increased distensability of the cervix near term. Digitizing this image in grayscale combined with Rodbard transformation (performed by Image J), increased the sensitivity to discriminate the decline in picrosirius red-stained cross-linked collagen was enhanced (Figure 1C).

The utility of picrosirius red stain to understand structural remodeling of the cervix prior to parturition is illustrated by studies of two genetically modified (knockout, KO) mouse models (Figure 2). In mice lacking the progesterone receptor B isoform, picrosirius red stain identified reduced extracellular cross-linked collagen in a temporal pattern that was similar as ripening found in wild type controls before term [14]. Moreover, in a second modified strain of mice lacking the prostaglandin F2 α receptor, pregnancy progressed while collagen structure was reduced similar to wild-type pregnant controls, however, neither labor nor term birth occurred in these animals [11]. The softening and ripening phases of cervix remodelling occurred in similar phases for both knockout models. Moreover, the use of our picrosirius red protocol critically distinguished, for the first time, the phases of cervical ripening from labor to birth in genetic modified animals in which progesterone mechanisms, thought to be essential for normal delivery, were deleted. In both models, the ability to assess cervical phases and collagen organization in processes thought as essential for normal inflammatory processes for parturition, were rapidly, quantitatively, easily, and determined.

These findings demonstrate the value of a rapid and replicable technique in which assessment of picrosirius red birefringence light can benefit analysis of critical biological questions about cross-linked collagen organization in tissue undergoing physiological change. Although used in the cervix, this technique has potential applications in other tissues or pathologies where fiber organization is disrupted. For example, this analysis may be useful to track the progression of fibrotic diseases of lung, kidney, or liver, as well as, to quantify therapeutic interventions of relevance for diverse pathologies including atherosclerosis, tumor growth, and metastatic processes, e.g. cardiovascular, scleroderma [7, 15]. Additionally, the use of transmitted light to determine collagen organization has been usefully employed in a variety of tissues, including human optic nerve [16], biological cells [17], and human skin [18].

Beyond tissue histology or biology, industrial applications would similarly benefit from a rapid assessment of other fibrous structures such as fabrics including Kevlar [19, 20], colloidal organization in liquid suspensions where knowledge of density, quantitative measurement, or dispersion properties are required.

Although useful in many biological situations, limitations of the use of picrosirius red stain to quantify collagen include its use in aqueous permeable fixed material and relatively thick (~10um) sections [20, 21]. Additionally, oral mucous and oral cavity fibrosis [22, 23], luminal crypts [21], or understandably, nonhomogeneous tissues with differing sub-layers encountered within the same region are problematic for analysis with this technique.

Figure 1A. Illustrative photomicrographs of Picrosirius Red stained, 10µm thick paraffin sections from the cervix of a non-pregnant (NP) and a day18 (d18) pregnant mouse cervix. Term gestation is 19–20 days. Note the ability to visualize individual collagen fibers and fascicles in both sections at this relatively low magnification (250X, using a 20 power objective). The NP section is substantially darker reflecting the denser collagen fiber organization and corresponding reduced light transmission of cross-polarized light through the tissue. In contrast, at d18, as term gestation approaches, the collagen fiber organization is less dense and less organized. Individual fascicles have increased spacing, allowing greater light transmission through the tissue. Within Individual fascicles there is less organization and numerous irregularities as denoted by the overall lighter appearance (increased transmitted light and increased disorganization of individual fibers. Scale bar=50µm.

Figure 1 B and C. Photomicrographs of from nonpregnant (NP) and late term (d18) cervix from wild-type (WT) and progesterone receptor B knockout mice (PRBko). White areas illustrate birefringent illumination and more easily depicts differences in collagen organization. Fibers from non-pregnant animals have more linear structure, in contrast to late term D18 fibers which are dispersed and lack defined structure. Scale bar is 50µm for the two color images and 50µm for all grey scale images and is the same for all grey scale images.

Figure 2. Histograms obtained from photomicrographs of Figure 1 A and B. Cervix optical density histograms (mean±SEM) from pregnant Prostaglandin Receptor 2 knockout (Ptgfr^-/-) and PRB ^-/-) mice following picrosirius red processing (see methods). Optical density values denote an inverse relationship to collagen structure and organization. The more structured a tissue, the lower optical density values for that tissue. All statistical comparisons were performed using analysis of variance (ANOVA) with Tukey’s post-hoc analysis. “a” equals comparison to non-pregnant, “b” denotes comparison to D15. [11, 14]

In summary, the technique we have developed combining picrosirius red stain and birefringence analysis using circular cross polarized light has evolved over several years into an easy, rapid and essential technique, for our continued analysis in experimental manipulations of the rodent, human, and potentially other non-human primate cervical tissue. This technique is essential for rapid, accurate analysis of several hundred sections we commonly employ for each cervix. Our experimental manipulations are pivotal for understanding cervical processes promoting normal term delivery in rodent experimental models with our ultimate goal to understand the central and sometimes subtle process in humans.

Acknowledgements

This work was supported, in part, by NIH grant # R01-HD054931 (SY), and grateful support from the Department of Pediatrics, School of Medicine, Loma Linda University, and the John Mace Pediatric Research fund (MAK). All animal work was performed in accordance with an approved animal care protocol of the Loma Linda University Animal Care and Use Committee (IACUC), #89002.

Competing Interests: All authors declare and affirm the lack of competing interests, finical or otherwise, in this manuscript.

Abbreviations

ANOVA – analysis of variance

CA – State of California

DIH₂O – double distilled water

DXX – estimated day post-conception

Inc. – incorporated business

MI – State of Michigan

NP – nonpregnant

OD – optical density

PP – postpartum, day of delivery animal

PRB ko – Progesterone receptor, gene deleted animal

Ptgfr ko – prostaglandin-F2-genetic gene-deleted animal

ROI – region of interest

WT – wild type

250X – total optical magnification as viewed through microscope objective

References

1. Bracegirdle B (1977) The History of Histology: A Brief Survey of Sources. History of Science 15: 77–101.
2. Mescher AL, Junqueira LCU (2016) Junqueira’s basic histology: Text and atlas (Fourteenth edition.). New York: McGraw-Hill Education.
3. Sabiston DC, Townsend CM (2012) Sabiston Textbook of Surgery: The Biological Basis of Modern Surgical Practice. Elsevier Saunders.
4. Junqueira LC, Zugaib M, Montes GS, Toledo OM, Krisztan RM, Shigihara KM (1980) Morphologic and histochemical evidence for the occurrence of collagenolysis and for the role of neutrophilic polymorphonuclear leukocytes during cervical dilation. Am J Obstet Gynecol. 138: 273–81.
5. Uldbjerg N, Ekman G, Malmstrom A, Olsson K, Ulmsten U (1983) Ripening of the human uterine cervix related to changes in collagen, glycosaminoglycans, and collagenolytic activity. Am J Obstet Gynecol 147: 662–6.
6. Lattouf R, Younes R, Lutomski D, Naaman N, Godeau G, et al. (2014) Picrosirius red staining: a useful tool to appraise collagen networks in normal and pathological tissues. J Histochem Cytochem 62: 751–758. [crossref]
7. Kirby MA, Heuerman AC, Custer M, et al. (2016) Progesterone Receptor-Mediated Actions Regulate Remodeling of the Cervix in Preparation for Preterm Parturition. Reprod Sci 23: 1473–83.
8. Dubicke A, Ekman-Ordeberg G, Mazurek P, Miller L, Yellon SM (2016) Density of Stromal Cells and Macrophages Associated With Collagen Remodeling in the Human Cervix in Preterm and Term Birth. Reprod Sci 23: 595–603.
9. Dobyns AE, Goyal R, Carpenter LG, Freeman TC, Longo LD, et al. (2015) Macrophage gene expression associated with remodeling of the prepartum rat cervix: microarray and pathway analyses. PLoS One 10: e0119782. [crossref]
10. Yellon SM, Burns AE, See JL, Lechuga TJ, Kirby MA (2009) Progesterone withdrawal promotes remodeling processes in the nonpregnant mouse cervix. Biol Reprod 81: 1–6.
11. Yellon SM, Ebner CA, Sugimoto Y (2008) Parturition and recruitment of macrophages in cervix of mice lacking the prostaglandin F receptor. Biol Reprod 78: 438–444. [crossref]
12. Feltovich H, Ji H, Janowski JW, Delance NC, Moran CC, Chien EK (2005) Effects of selective and nonselective PGE2 receptor agonists on cervical tensile strength and collagen organization and microstructure in the pregnant rat at term. Am J Obstet Gynecol 192: 753–60.
13. Clark K, Ji H, Feltovich H, Janowski J, Carroll C, Chien EK (2006) Mifepristone-induced cervical ripening: structural, biomechanical, and molecular events. Am J Obstet Gynecol. 194: 1391–8.
14. Yellon SM, Oshiro BT, Chhaya TY, Lechuga TJ, Dias RM, et al. (2011) Remodeling of the cervix and parturition in mice lacking the progesterone receptor B isoform. Biol Reprod 85: 498–502. [crossref]
15. Kirby LS, Kirby MA, Warren JW, Tran LT, Yellon SM (2005) Increased innervation and ripening of the prepartum murine cervix. J Soc Gynecol Investig 12: 578–585. [crossref]
16. Cense B, Chen TC, Park BH, Pierce MC, de Boer JF (2002) Invivo depth-resolved birefringence measurements of the human retinal nerve fiber layer by polarization-sensitive optical coherence tomography. Optics letters 27: 1610–2.
17. Mourant JR, Freyer JP, Hielscher AH, Eick AA, Shen D, Johnson TM (1998) Mechanisms of light scattering from biological cells relevant to noninvasive optical-tissue diagnostics. Applied optics. 37: 3586–93.
18. Pierce MC, Strasswimmer J, Park BH, Cense B, de Boer JF (2004) Advances in optical coherence tomography imaging for dermatology. The Journal of investigative dermatology 123: 458–63.
19. Penn L, Larsen F (1979) Physicochemical properties of kevlar 49 fiber. Journal of Applied Polymer Science 23: 59–73.
20. Rich L, Whittaker P (2005) Collagen and Picrosirius Red Staining: A Polarized Light Assessment of Fibrillar Hue and Spatial Distribution Braz. J Morphol Sci 22: 97–104.
21. Nazac A, Bancelin S, Teig B, et al. (2015) Optimization of Picrosirius red staining protocol to determine collagen fiber orientations in vaginal and uterine cervical tissues by Mueller polarized microscopy. Microscopy research and technique 78: 723–30.
22. Marcos-Garces V, Harvat M, Molina Aguilar P, Ferrandez Izquierdo A, Ruiz-Sauri A (2017) Comparative measurement of collagen bundle orientation by Fourier analysis and semiquantitative evaluation: reliability and agreement in Masson’s trichrome, Picrosirius red and confocal microscopy techniques. J Microsc 267: 130–42.
23. Kamath VV, Satelur K, Komali Y (2013) Biochemical markers in oral submucous fibrosis: A review and update. Dent Res J (Isfahan) 10: 576–584. [crossref]

DOI: 10.31038/IGOJ.2018115

Introduction

Haemorrhage killed more women than any other complications of pregnancy in the history of mankind. Placenta previa, abruption placenta and uterine rupture are in three important causes of ante partum haemorrhage seen frequently at tertiary level care hospital claiming high maternal mortality and morbidity till date present existing surgical technique to tackle major degree placenta previa is found to be not effective method to control haemorrhage during LUCS causing high incidence of maternal mortality and morbidity. Hence to prevent uncontrolled haemorrhage due to major degree placenta previa, author has advocated new surgical technique (Dutta’s) to prevent uncontrolled haemorrhage during LUCS.

Methodology

New technique (Dutta’s) were undertaken during LUCS operation in a stepwise Manner > delivery of baby following lower segment incision > bilateral uterine artery ligation > inj. Tranexamic acid (1000 gm) intramuscular > oxytocin infusion ( 10 unit) > delivery of placenta and its membrane and checked properly > if tear or laceration interrupted suture by catgut 1–0 > uterine wound were closed in two layers by catgut no 1 after securing bleeding from placental site or uterine wound > abdominal wall closed, after toileting the abdominal cavity, in presence of good uterine contraction. Main objective of the study to find out how to reduce maternal mortality and morbidity, after advocating (Dutta’s) new technique, during LUCS operation, for major degree placenta previa.

Benefits: Operative findings: good effectiveness to control bleeding, caesarean hysterectomy not required, immediate post operative bleeding – less. Maternal mortality – nil, maternal morbidity – less, good fetal outcome. Follow up up to two years: mentrual cycle normal, future fertility – good

Conclusion

Hence by adopting the new surgical technique (Dutta’s) during LUCS it was found to be simple, safe, quick procedure, reduce perfusion pressure, permits time for further steps thereby avoiding unnecessary ligation of hypogastric, bill and caesarean hysterectomy. Maternal mortality and morbidity were also found to be reduced. It is a suitable technique for rural based tertiary care hospital in absence of adequate blood transfusion facility.

Reference

1. Damania KR, Salvi VS, Walvekar Vs. A Study of maternal mortality over 20 years. J Obst Gyn India 1989; 39: 61–5.
2. Motwani MN, Sheeth J. Maternal Mortality from APH: Review of 20 years death. J Obst Gyn India 1990; 39: 364–6.
3. Bowie JD, Rochester D, Cadkin AV, et al. Accuracy of placental localization by ultrasound. Radiology 1978; 128: 177–80.
4. Cotton D, Read J, Paul R, et al. The conservative aggressive management of placenta praevia. Am J Obstet Gynecol 1989; 137: 687–95.
5. Davis ME, Campbell A. The management of placenta praevia in the Chicago Lyingin Hospital. Surg Gynaec and Obst 1946; 83: 777.
6. Evans, McShane. The efficacy of hypogastric artery ligation in obstetric haemorrhage. Surg Gynaecol Obstet 1985; 160: 250–3.
7. Hill DJ, Beischer NA. Placenta praevia without antepartum haemorrhage Aust N Z J Obstet Gynaeclo 1980; 20: 21–3.
8. Macafee CHG. Modern views on the management of placenta praevia. Post Card, Ded Journ 1949; 25: 297.
9. McClure N, Dornal JC. Early identification of placenta praqevia (see comments). Br J Obstet Gynaecol 1990; 98–625.
10. Render S. Placenta preavia and previous lower segment caesarean secton. Surg Gynaec and Obst 1954; 98: 625.
11. Weiser EB. Managing second trimester placenta praevia. Contrib Gynecol Obstet 1980; 15: 187.