In Panels A-D, representative ovarian sections are presented from WT mice at 1- (A) and 3- (B) months of age, and from Kiss1r KO mice at 3- (C) and 12- (D) months of age.

<p>Boxed areas are shown at higher magnification in the insets. Resting (white arrows), primary (black arrows) and secondary (SF) follicles are indicated. In Panels E-H, quantitative analyses of age-related changes are shown for the population of resting (RF; see E), early growing (primary or EGF; see F) and secondary (SF; see G) follicles, and in the proportion of small growing follicles (H) in WT and Kiss1r KO mice. The later (proportion) was calculated by dividing EGF by the total figure of RF+EGF in each ovary. Different superscript letters indicate statistically significant differences between age-points within each genotype (P<0.05 ANOVA followed by Student-Newman-Keuls test); *, P<0.05 vs. corresponding WT values at the same age-point (Student t-test).</p

Crowding and Follicular Fate: Spatial Determinants of Follicular Reserve and Activation of Follicular Growth in the Mammalian Ovary

<div><p>Initiation of growth of resting ovarian follicles is a key phenomenon for providing an adequate number of mature oocytes in each ovulation, while preventing premature exhaustion of primordial follicle reserve during the reproductive lifespan. Resting follicle dynamics strongly suggest that primordial follicles are under constant inhibitory influences, by mechanisms and factors whose nature remains ill defined. In this work, we aimed to assess the influence of spatial determinants, with special attention to clustering patterns and crowding, on the fate of early follicles in the adult mouse and human ovary. To this end, detailed histological and morphometric analyses, targeting resting and early growing follicles, were conducted in ovaries from mice, either wild type (WT) or genetically modified to lack kisspeptin receptor expression (Kiss1r KO), and healthy adult women. Kiss1r KO mice were studied as model of persistent hypogonadotropism and anovulation. Different qualitative and quantitative indices of the patterns of spatial distribution of resting and early growing follicles in the mouse and human ovary, including the Morisita’s index of clustering, were obtained. Our results show that resting primordial follicles display a clear-cut clustered pattern of spatial distribution in adult mouse and human ovaries, and that resting follicle aggrupation is inversely correlated with the proportion of follicles initiating growth and entering into the growing pool. As a whole, our data suggest that resting follicle <i>crowding</i>, defined by changes in density and clustered pattern of distribution, is a major determinant of follicular activation and the fate of ovarian reserve. Uneven follicle crowding would constitute the structural counterpart of the major humoral regulators of early follicular growth, with potential implications in ovarian ageing and pathophysiology.</p></div

In Panel A, the proportion of small (early) growing follicles is plotted against the number of remaining resting follicles (RF) per ovary.

<p>In addition, mean changes in the proportion of resting follicles with at least one close neighbor with respect to age are shown in Panel B, while the proportion of resting follicles (RF) with at least one neighbor are plotted against the number of remaining resting follicles (RF) in Panel C. In addition, in Panel D the correlation between the proportion of early growing follicles and the percentage of follicles with close neighbors is displayed. In all figures, data from WT and Kiss1r KO mice are shown, as blue and red dots, respectively. Yet, regression analyses were applied only to WT data. Similarly, the confidence intervals (CI, presented as blue dotted lines) and the predictive intervals (PI, presented as grey dotted lines) were calculated for the regression slopes obtained from WT individuals. In Panel E, a schematic figure is presented to illustrate of the method used to evaluate the presence of resting follicle neighbors (at a distance of ≤ 60 μm) for resting and early growing follicles. An early growing follicle (GF) lacking close neighbors is highlighted in green, whereas each of the four resting follicles (RF), which have each three neighbors, is marked in red. In Panel F, a distribution plot of frequencies of the number of neighbors in resting and early growing follicles is presented for 3 moth-old WT mice. Data represent the mean ± SEM for n = 5/group. Finally, in Panel G, the Morisita’s index of clustering in WT and Kiss1r KO mice is shown. Values above the dotted line indicate clustered spatial pattern. Different superscript letters indicate statistically significant differences between age-points within each genotype (P<0.05 ANOVA followed by Student-Newman-Keuls test); *, P<0.05 vs. corresponding WT values at the same age-point (Student t-test). Regression analyses, including r² and P values, as well as CI and PI calculations, were conducted using Graph-Pad Prism software. DF: Degrees of freedom.</p

Association analysis between follicle status and the presence of resting follicle neighbors in human ovaries.

<p>Association analysis between follicle status and the presence of resting follicle neighbors in human ovaries.</p

In Panels A-C, representative ovarian sections are presented from 23- (A), 32- (B) and 39- (C) year-old women, showing decreasing density and increasing clustering of resting follicles (arrows) with age.

<p>Boxed areas are shown at higher magnification in the insets. In Panel D, a schematic figure is shown illustrating the method used to evaluate the presence of resting follicle neighbors (at a distance ≤ 60-μm) for resting and early growing follicles. In this representative image, one early growing follicle (highlighted in green) that has one neighbor is shown, while several resting follicles are also presented (highlighted in red). Of these resting follicles, those labeled with letter A have one neighbor, whereas those marked with letter B have two neighbors. In Panel E, the distributions of the frequency of the number of neighbors in resting and early growing follicles in human ovaries are presented (pooled data from 20 ovaries). Finally, in Panel F, the correlation between the Morisita’s index of clustering and age in human ovaries is shown. Values above the dotted line indicate clustered spatial pattern.</p

Association analysis between follicle status and the presence of resting follicle neighbors in 3-mo-old WT mice.

<p>Association analysis between follicle status and the presence of resting follicle neighbors in 3-mo-old WT mice.</p

MOESM1 of Functional and transcriptomic characterization of carboplatin-resistant A2780 ovarian cancer cell line

Additional file 1: Table S1. Gene list of 156 transcripts

Hypothalamic miR-30 regulates puberty onset via repression of the puberty-suppressing factor, Mkrn3 - Fig 3

Expression profiles of Mkrn3 mRNA and miR-30b in the hypothalamus of female rats neonatally injected (on PND1) with EB (A). Expression analyses were conducted at the expected time of puberty (PND35). Serum LH levels in the same experimental groups are shown in (B). In addition, densitometric quantification and a representative western blot autoradiographic image of hypothalamic Mkrn3 protein from female rats neonatally injected with EB are shown (C). Loading control (β-Actin) is also presented. Animals injected with olive oil (VEH) served as controls (n = 6–8/group). Data are presented as mean ± SEM. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001 versus VEH; Student t test. For underlying data, see S1 Data file. EB, estradiol benzoate; LH, luteinizing hormone; Mkrn3, makorin RING-finger protein-3; PND, postnatal day; VEH, vehicle.</p

Hypothalamic miR-30 regulates puberty onset via repression of the puberty-suppressing factor, Mkrn3 - Fig 1

Potential miRNA regulators for Mkrn3 identified by different bioinformatics tools: MiRtarget2 (A), PicTar (B), and MiRanda (C) in rat (rno), human (hsa), and mouse (mmu) are shown. In addition, the number of conserved binding sites for the potential miRNA candidates predicted by TargetScan (www.targetscan.org/) in the 3′ UTR of Mkrn3 transcript is represented (D). The best miRNAs candidates are highlighted in black. The predicted binding sites for the members of the miR-30 family, including miR-30b, in rat, mouse, and human MKRN3 3′ UTR by using TargetScan (www.targetscan.org/) are represented in (E). For each species, the overall MKRN3 3′ UTR is shown (top), with detailed sequences of the predicted miR-30b target binding sites being presented below. Seed region sequences for miR-30b in the 3′ UTR of Mkrn3 are highlighted in light orange boxes. miRNA, microRNA; Mkrn3, makorin RING-finger protein-3.</p

Hypothalamic miR-30 regulates puberty onset via repression of the puberty-suppressing factor, Mkrn3 - Fig 4

Luciferase activity of mouse Mkrn3 3′ UTR reporter construct after pre–miR-30b overexpression (Luc-Mkrn3-3′ UTR + pre–miR-30b) in HEK 293 cells (A). The following combinations of plasmids were used as controls: Luc-Mkrn3-3′ UTR + pcDNA (empty vector); Luc-Mkrn3-3′ UTR + scrambled pre-miRNA; and scrambled Luc-Mkrn3-3′ UTR + pre–miR-30b (n = 3 replicates/group). In addition, a schematic representation of Luc-Mkrn3-3′ UTR and pre–miR-30b clones is shown (B). Data are presented as mean ± SEM. ***P ≤ 0.001 versus each control group; one-way ANOVA followed by post hoc Tukey test. For underlying data, see S1 Data file. CMV, cytomegalovirus; eGFP, enhanced green fluorescent protein; Luc, luciferase; Mkrn3, makorin RING-finger protein-3; SV40, simian virus 40.</p