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. 2024 Jun 6;19(6):e0293688. doi: 10.1371/journal.pone.0293688

Differential effects of follicle-stimulating hormone glycoforms on the transcriptome profile of cultured rat granulosa cells as disclosed by RNA-seq

Teresa Zariñán 1,#, Jesús Espinal-Enriquez 2,#, Guillermo De Anda-Jáuregui 2,#, Saúl Lira-Albarrán 3,¤, Georgina Hernández-Montes 1, Rubén Gutiérrez-Sagal 1, Rosa G Rebollar-Vega 1, George R Bousfield 4, Viktor Y Butnev 4, Enrique Hernández-Lemus 2, Alfredo Ulloa-Aguirre 1,*
Editor: Satish Rojekar5
PMCID: PMC11156319  PMID: 38843139

Abstract

It has been documented that variations in glycosylation on glycoprotein hormones, confer distinctly different biological features to the corresponding glycoforms when multiple in vitro biochemical readings are analyzed. We here applied next generation RNA sequencing to explore changes in the transcriptome of rat granulosa cells exposed for 0, 6, and 12 h to 100 ng/ml of four highly purified follicle-stimulating hormone (FSH) glycoforms, each exhibiting different glycosylation patterns: a. human pituitary FSH18/21 (hypo-glycosylated); b. human pituitary FSH24 (fully glycosylated); c. Equine FSH (eqFSH) (hypo-glycosylated); and d. Chinese-hamster ovary cell-derived human recombinant FSH (recFSH) (fully-glycosylated). Total RNA from triplicate incubations was prepared from FSH glycoform-exposed cultured granulosa cells obtained from DES-pretreated immature female rats, and RNA libraries were sequenced in a HighSeq 2500 sequencer (2 x 125 bp paired-end format, 10–15 x 106 reads/sample). The computational workflow focused on investigating differences among the four FSH glycoforms at three levels: gene expression, enriched biological processes, and perturbed pathways. Among the top 200 differentially expressed genes, only 4 (0.6%) were shared by all 4 glycoforms at 6 h, whereas 118 genes (40%) were shared at 12 h. Follicle-stimulating hormone glycocoforms stimulated different patterns of exclusive and associated up regulated biological processes in a glycoform and time-dependent fashion with more shared biological processes after 12 h of exposure and fewer treatment-specific ones, except for recFSH, which exhibited stronger responses with more specifically associated processes at this time. Similar results were found for down-regulated processes, with a greater number of processes at 6 h or 12 h, depending on the particular glycoform. In general, there were fewer downregulated than upregulated processes at both 6 h and 12 h, with FSH18/21 exhibiting the largest number of down-regulated associated processes at 6 h while eqFSH exhibited the greatest number at 12 h. Signaling cascades, largely linked to cAMP-PKA, MAPK, and PI3/AKT pathways were detected as differentially activated by the glycoforms, with each glycoform exhibiting its own molecular signature. These data extend previous observations demonstrating glycosylation-dependent distinctly different regulation of gene expression and intracellular signaling pathways triggered by FSH in granulosa cells. The results also suggest the importance of individual FSH glycoform glycosylation for the conformation of the ligand-receptor complex and induced signalling pathways.

Introduction

Follicle-stimulating hormone is produced by the anterior pituitary gland (AP) as different glycoforms, defined by the presence or abscence of glycans on the hormone-specific β-subunit. As other glycoprotein hormones, this gonadotropin is composed of two subunits, the α and β subunits, associated with each other through non-covalent interactions; the α-subunit, is common to all glycoprotein hormones [luteinizing hormone (LH), choriogonadotropin (CG) and thyroid-stimulating hormone (TSH)], whereas the β-subunit, confers specificity for binding and action to each gonadotropin at its cognate receptor [1]. In human FSH (hFSH), four Asn residues, two in FSHα (αN52 and αN78) and two in FSHβ (βN7 and βN24) are targets for N-linked glycosylation [2] (Fig 1). In glycoprotein hormones, the oligosaccharide in position αAsn52 is involved in the activation of the receptor/signal transducer (G protein) system and biological response [36], an effect mediated through the stabilization of the conformation of the FSH dimer bound to the FSH receptor (FSHR) [79]. Meanwhile, glycans attached to FSHβ play a major role in defining the circulatory half-life and in vivo bioactivity of the gonadotropin [10], albeit it has been shown that they also impact on FSH-mediated intracellular signaling [1114].

Fig 1. Typical glycans attached to human pituitary FSH (hFSH) glycoforms, human recombinant FSH produced by Chinese hamster ovary cells (recFSH), and equine FSH (eFSH).

Fig 1

The green and cyan bars and ribbons (3-D structures at right) indicate the common-α and hormone-specific FSHβ subunits, respectively. N-glycosylation sites are indicated by the numbers below the bars. eFSHα subunit has 4 additional amino acid residues at the N-terminus, accounting for the difference in numbering. The glycan at position β7 in eqFSH (black dotted square) is absent in the bulk (90%) of the molecules contained in highly purified preparations [20]. Glycans in recFSH were taken from Mastrangeli et al [21]. Human FSH glycoform models created with the GLYCAM web tool are shown on the right with the subunits rendered as cartoons using PyMol. The same color scheme is employed for subunits and glycans, which are rendered as spheres. Partially obscured Asn24 glycan is colored dark blue to distinquish it from Asn7 glycan (light blue).

Human FSH macroheterogeneity occurs at either one or both FSHβ N-glycosylation sites (Fig 1), determining differences in apparent molecular weights by gel electrophoresis and immunoblotting [1517]. In fact, Western blots of FSH recovered after gel electrophoresis, yielded two FSHβ bands, a 24 kDa band exhibiting both FSHβ-subunit Asn7 and Asn24 N-linked glycans (which corresponds to fully- o tetra-glycosylated FSH heterodimer or FSH24) and a 21kDa band in which the Asn24 glycan is absent (hypo- or tri-glycosylated FSH heterodimer or FSH21) [15, 18]. Purified Hypo-glycosylated preparations include an additional hypo-glycosylated variant lacking the Asn7 glycan in FSHβ, with its corresponding heterodimer designated as FSH18 (18 kDa-FSH) (Fig 1c), hence the designation FSH18/21. Although an additional hypo-glycosylated FSH variant also has been detected in human pituitaries (exhibiting a 15 kDa-FSHβ), the corresponding β-subunit assembles poorly with FSHα and consequently very low levels of secretion of the corresponding heterodimer (FSH15 glycoform) are detected [19]. Thus, three hFSH glycoforms (FSH24, FSH21, and FSH18) appear to be the physiologically relevant FSH variants in humans.

Similar to the influence of microheterogeneity (i.e. variations in the structure of the carbohydrates and complexity of the oligosaccharides on gonadotropins) on the ability of the gonadotropins to activate the FSHR and trigger intracellular signaling [2224], FSH macroheterogeneity also contributes to its bioactivity [14, 25, 26]. In fact, in vitro and in vivo sgtudies have shown that pituitary and recombinant hFSH glycoforms display differential FSHR binding kinetics and bioactivity [12, 14, 27, 28]. Further, in a recent study employing cAMP accumulation, 𝝱-arrestin-mediated ERK1/2 activation, and intracellular calcium (iCa2+) accumulation as read-outs in FSH-stimulated HEK-293 cells, we found that in addition to determining the intensity of the biological response at the target cell, the presence or abscence of glycans attached to FSHβ conferred some degree of biased agonism to the different FSH glycoforms [26].

Follicle-stimulating hormone stimulation triggers activation of a complex array of diverse signaling cascades mediated not only by the canonical Gs/cAMP/PKA pathway but also by other G proteins and receptor interacting proteins [29, 30]. Activation of this signaling network and particular signaling modules most probably occurs through stabilization of different FSHR conformations by FSH glycoforms possessing distinct glycosylation patters [28]. Given the differential effects of FSH glycoforms on particular FSH-stimulated read outs [26], we here applied next generation sequencing (NGS) to assess as primary objective whether different FSH perturbogens exhibiting differential glycosylation led to distinct gene expression patterns and biological processes across time points, which might allow to better understand the physiological relevance of differential FSH glycosylation (particularly on those naturally occurring variants synthesized and secreted by the anterior pituitary gland) during the human menstrual cycle. To this end, cultured rat granulosa cells were exposed during different times to a fixed dose of four highly purified FSH glycoform preparations, each exhibiting different glycosylation patterns: a. human pituitary FSH18/21 (hypo-glycosylated); b. human pituitary FSH24 (tetra- or fully-glycosylated); c. Equine FSH (eqFSH, 90% hypo-glycosylated); and d. Chinese-hamster ovary cell-derived human recombinant FSH (recFSH; 80% tetra-glycosylated) (Fig 1). These four preparations also differ to varying extent in microheterogeneity [15, 20, 25, 26, 3138]. Our analysis was focused on the effects of each FSH preparation at three levels: gene expression, enriched biological processes, and perturbed pathways.

Material and methods

Hormones

Human pituitary FSH24 and FSH18/21 were purified after extraction of human pituitary tissue (a generous gift of Dr. James A. Dias, University at Albany, Albany, NY, USA), employing Superdex G75 chromatography of fractions obtained after Sephacryl S-100 and immunoaffinity chromatography, as described in detail previously [24]. The fractions possessing largely 24 kDa-FSHβ were pooled to generate FSH24 and those possessing largely 18kDa- and 21 kDa-FSHβ were pooled to obtain FSH18/21. Recombinant human FSH produced in Chinese hamster ovary cells (Follitropin Alfa, batches AU012310 and BA024393), was a gift of Merck Serono (Mexico City, Mexico); according to the manufacturer [34], the batch-to-batch consistency for this recombinant FSH compound exhibits coefficients of variation that ranges from 7% to 15% for its most and least abundant isoforms. Equine FSH (batch VB-I-171) was purified from horse pituitaries obtained from Animal Technologies, Inc., (Tyler, TX, USA), as previously described [39]. Kds for the FSH preparations employed in the present study have been reported previously [26].

Rat granulosa cells culture

Granulosa cells (GC) from immature (21 days old) Wistar rats pretreated for 4 days with 10 mg diethylstilbestrol (DES) through an implanted 10 mm x 1.5 mm silastic capsule, were collected and cultured following the method described by Jia and Hsue [40] with some modifications [41]. Briefly, rats were anesthetized with 120 mg/kg pentobarbital administered IP, and rapidly euthanized by cervical dislocation performed by a trained technician, followed by laparotomy to remove their ovaries. Granulosa cells were then collected by puncturing the ovarian follicles, pooled, counted, and added to 6-well (34 mm diameter) polystyrene culture dishes (Nunc, Roskilde, Denmark) at a density of 1.0 x 106 viable cells/well in serum- and insulin-free 2 ml McCoy’s 5a medium (Life Technologies, Grand Island, NY, USA), pH 7.0, supplemented with 2 nM glutamine (Sigma-Aldrich Inc., St. MO, USA), antibiotic (penicillin plus streptomycin) reagent (Invitrogen, Waltham, MA, USA), and 0.1% bovine serum albumin (Sigma), and cultured for 24h at 37ºC in a humidified atmosphere of 95% air-5% CO2. At the end of the culture period, cells were washed, redissolved in insulin- and serum-free, supplemented McCoy’s 5a medium, incubated for an additional 24h in the absence of androgens, and then exposed during 0, 6, and 12 h to 100 ng/ml of the different FSH preparations or control medium (no FSH) in triplicate wells for each preparation and incubation time. The 100 ng/ml dose was chosen based on preliminary experiments assessing the aromatization response of cultured GC to increasing doses of each FSH preparation, in which maximal estrogen production was achieved with the 100 ng/ml dose (not shown). The project was approved by the Internal Research Committee for the Care and Use of Laboratory Animals of the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (Project CINVA-RAI-1864-16/19-1).

RNA isolation and sequencing

Total RNA was isolated from individual GC wells using the TRIzol TM reagent (Thermo Fisher Scientific, Waltham, MA USA)) and the Direct-zol RNA kit (Zymo Research, Irving, Ca, USA), following the instructions provided by the manufacturer [42]. Total RNA concentration was assessed using the NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and the quality of RNA in each sample was determined in an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Only samples with a RNA Integrity Number (RIN) >8.0 were used. RNA libraries were prepared from 500 ng RNA samples using the TruSeq Stranded mRNA Kit (Illumina Inc., San Diego, CA, US) following the manufacturer’s instructions. Libraries were sequenced using an Illumina HiSeq2500 equipment (Illumina) in paired-end (2x125 pb) read runs. Depth of sequencing was 10–15 million reads.

Data bioinformatics analysis

The main objective of the computational pipeline was to identify differences among the four FSH glycoforms tested at three distinct levels: a. gene expression; b. active biological processes; and c. perturbed pathways. To achieve this goal, we applied three different computational approaches: differential gene expression, over-representation analysis and pathway perturbation analysis.

Data pre-processing

Prior to data analysis, a series of pre-processing procedures were applied to the data in order to eliminate/diminish technical errors, biases and other sources of undesired variation Quality parameters of the FASTQ files were checked using the quality control FASTQC tool (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). FASTQ reads were filtered and trimmed using the AfterQC tool [43]; default parameters were used to discard low-quality reads, trim adaptor sequences, and to eliminate poor-quality bases. The reads passing quality control parameters were aligned and quantified to the Rattus norvegicus reference transcriptome Rnor_6.0 using Salmon [44] version 0.8.2. Transcript-level quantifications were imported using the tximport package [45] version 1.22.0.

Differential expression

Differential gene expression analysis was performed with the edgeR package [46], by comparing each FSH treatment at 0, 6, and 12 h. Since the number of replicates was limited, it was decided to conserve the top-200 differentially expressed genes (FDR < 0.01). Additionally, the similarities and differences between groups of genes reported as differentially expressed were compared under conditions: a. All FSH glycoform preparations at the same incubation time; and b. Different incubation times (0, 6, and 12 h) for the same FSH glycoform (eg. eqFSH, recFSH, FSH18/21, and FSH24). Each of these comparisons followed distinct scientific questions. On the one hand, the first comparison was useful to analyze the behavior of granulosa cells under the action of four distinctly different FSH glycoforms, on the other hand the latter comparison served to analyze the dynamics of a given FSH glycoform at different times, providing a set of snapshots of the temporal effects of the molecules.

Over-representation analysis

By taking separately the overexpressed and underexpressed gene sets, over-representation analysis to detect whether the differentially expressed genes for each contrast were associated to a specific set of biological processes was performed. We implemented independent analyses for over- and underexpressed genes, based on the idea that sets of overexpresed genes may exacerbate a given process. Conversely, underexpressed genes may indicate a depleted/diminished behavior in a particular biological event. We used the Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes (KEGG) [47] as databases for biological categories. Significance threshold was set at p<0.01 to consider a biological category as significant.

Pathway perturbation analysis

Pathway perturbation was assessed using the gene expression data previously described. A subset of KEGG pathways was selected based on the information previously described [29]. Pathways were collected using the Graphite package [48]. Contrasts were made between the experimental conditions described above and the baseline cell culture using the Generally Applicable Gene-set Enrichment for Pathway Analysis (GAGE) package [49]. Multiple test correction was performed using the Benjamini-Hochberg post hoc method [50]. Top 4 pathways by q-value (corrected p value) for each contrast were selected, and their network representation was merged into a single metapathway. Betweenness centrality (a measure for the relative importance of molecules for the communication in the corresponding pathways) for each gene in the metapathway was calculated. For each contrast, key genes were identified as those that had above-median logfold change in the differential expression analysis, and above-median betweenness centrality in the metapathway network.

Validation of FSH glycoforms-sensitive transcripts by real time (RT)-PCR

To validate differentially expressed genes in the RNAseq data, we analysed the mRNA level by RT-PCR of five selected FSH glycoform-sensitive genes (Pld1, Npy1R, Amh, Vegf-B, and Bcl2l1). cDNA synthesis was performed in 2.5 μg total RNA with 260/280 nm ratio >1.8 following the manufacturer’as instructions for the Maxima First Strand cDNA Synthesis Kit for RT-qPCR (Thermo Fisher), employing random primers and oligo dT.

RT-PCR was performed employing 10 ng cDNA in a 20 μl reaction mixture containing 1 μl TaqMan Gene Expression Assay®, 1μl TaqMan Gene Expression Assay for Actin or GAPDH reference gene, and 10 μl Master Mix TaqMan Universal PCR® (all from Applied Biosystems, Waltham, MA, USA). All reactions were performed under the following conditions: 2 min at 50°C (UNG incubation), 10 min at 95°C (AmpliTaq Gold activation), followed by 40 cycles at 95°C for 15 sec (denaturation) and at 60°C for 1 min (anealing and amplification), employing a Step One plus thermocycler (Applied Biosystems). The results were normalized and expressed as fold changes in gene expression levels relative to control cDNA (2-ΔΔCT method). The TaqMan gene expression assays employed were: Rn01493709_m1, Rn02769337_s1, Rn00563731_g1, Rn01454585_g1, Rn06267811_g1 (for the above described test genes, respectively) and Rn01426628_g1 and Rn01775763_g1 (for actin and GAPDH, respectively). Differences in expression levels of selected genes expressed in response to FSH stimulation as assesed by RT-PCR, were analyzed employing the Student’s t test at an alpha level for significance <0.05.

Results

General contrast between 6 and 12 hours

By comparing the four glycoforms at 6 and 12 h vs control (no FSH added) it was possible to determine how many genes were shared among these glycoforms. Fig 2A and 2B show the top200 differentially expressed genes for the four glycoforms at 6 h (A) and 12 h (B). The whole set of differentially expressed genes for each glycoform and contrasts can be found in the S1 to S4 Tables. This analysis allowed to identify particular changes in gene expression induced by each glycoform. At 6 h, the number of genes shared among the glycoforms (center of Fig 2A) were only 4 genes, whereas at 12 h this number increased to 118 genes (center in Fig 2B). Thus, at 12 h the four glycoforms behaved more similarly to each other regarding the induction of differential gene expression.

Fig 2. Venn diagrams of the top-200 differentially expressed genes between FSH glycoforms at 6 h (A) and 12 h (B).

Fig 2

Numbers inside the figure represent the number of genes shared in the corresponding set. Gray scale is proportional to the number of genes in the subset compared to the total of genes. Note that the number of genes shared between the four groups (center of the figure) at 6 h is only 4 genes; meanwhile, at 12 h, the very same set is 118 genes, meaning that at 12 h, the four compounds behave much similar than at 6 h.

Regarding those genes clasically regulated by FSH in granulosa cells, only after 12 h of exposure to FSH glycoforms was fold increase in the Lhcgr and Cyp191a genes clearly detectable (S2 Table); in the case of recFSH, expression of Cyp191a increased from -0.38 at 6h to 0.87 (log fold increase) at 12h. In contrast, the highest increase in expression of this latter gene after eqFSH exposure was observed at 6h (log fold increase, 0.92). Although Ccnd2 expression could not be detected in our study, one Ccnd2 transcript (ENSRNOT00000086440) was detectable under the conditions employed; the trend in log fold change identified for this particular transcript after exposure to the pituitary FSH glycoforms, but not to recFSH and eqFSH, was of modestly increased values with time (log fold changes at 6h and 12h, respectively: FSH18/21, from -0.25 to 0.03; FSH24, from -0.37 to -0.08 at 12h).

Contrasts among each FSH glycoform vs control at 6 and 12 hours

Fig 3A–3D shows the contrast between 6 h and 12 h for each FSH glycoform. In all cases, the number of top200 differentially expressed genes induced at 6 h was higher than at 12h. recFSH showed the strongest difference between 6 h and 12 h since the intersections between these incubation times were only 34 genes (Fig 3D). The results with all comparisons, as well as the statistics of each contrast can be found at https://osf.io/57j3f/.

Fig 3. Contrast between 6 and 12 hours for eachFSH glycoform.

Fig 3

In the four cases, the top200 more differentially expressed genes are more different between 6 hours and control than 12 hours vs control. The contrast of recombinant FSH shows that there is a strong difference at 6 and 12 hours, since the intersection between 12 and 6 hours is only 34 genes.

Gene ontology enrichment

Each contrast was enriched by means of the DAVID online tool [51]. Gene Ontology (GO) terms were obtained with the DAVID-standard p-values of enrichment (at p<0.01). This method was applied to explore those processes in which differentially expressed genes participate. The four different treatments were analyzed at 6 h and 12 h time points, using the list of overexpressed and underexpressed genes separately.

Processes associated with overexpressed genes after 6 hours of FSH stimulation (Tables 1 and 2 and Fig 4)

Table 1. Processes associated with overexpressed genes after exposure for 6 hours to FSH18/21 and FSH24.
Processes associated with overexpressed genes at 6 hours
FSH18/21 FSH24
Exclusive Associated Exclusive Associated
Cellular response to lipopoly-saccharide
Cellular response to gonado-tropin stimulus
Celullar response to interferon gamma
Response to gonadotropin
Cellular response to organic cyclic compound
Steroid biosynthetic process
Response to drug
Cellular response to cAMP
Cellular response to follicle-stimulating hormone stimulus
Response to estrogen
Response to peptide hormone
Ovulation from ovarian follicle
Cellular response to epinephrin stimulus
None Cellular response to cAMP
Table 2. Processes associated with overexpressed genes after 6 hours of cell exposure to eqFSH and recFSH.
Processes associated with overexpressed genes at 6 hours
eqFSH recFSH
Exclusive Associated Exclusive Associated
Response to cAMP
Positive regulation of interleukine-2 production
Cellular response to cAMP
Cellular response to follicle-stimulating hormone stimulus
Response to estrogen
Response to peptide hormone
Ovulation from ovarian follicle
Cellular response to epinephrin stimulus
Cellular response to interleukin-1
Cellular response to calcium ion
Cellular response to amino acid stimulus
Response to estradiol
Response to hypoxia
Angiogenesis
Microtubule depolymerization
Extracellular matriz organization
Chromosome segregation
Mitotic sister chromatid segregation
Response to drug
Fig 4. Enriched processes of overexpressed genes for the four FSH glycoforms at 6 h, with the nodes corresponding to glycoforms in yellow.

Fig 4

In this network representation, red squares represent significantly (p<0.01) enriched biological processes in the treatment in which there is a link. There are some shared processes i.e. biological functions that appear enriched in more than one phenotype, which is indicated by more than one link. It is important to consider the number of associated processes for each isoform: eqFSH, 8 processes (2 exclusive); recFSH, 11 processes (only 1 exclusive); FSH18/21 13 processes, from which 6 are exclusive, and FSH24 only one associated process. In this figure, the intersection between FSH18/21 at 6 h is depicted against the other 3 isoforms at the same time.

Exposure of granulosa cells to recFSH and FSH18/21 was followed by induction of several enriched processes that included response to estradiol and calcium, angiogenesis (in the case of recFSH), and response to the gonadotropin stimulus and steroid biosynthetic process (for FSH18/21) (Tables 1 and 2). For these two FSH compounds the only category shared was reponse to drugs. In contrast, FSH18/21 shared several processes with eqFSH, including response to estrogen, ovulation, and cAMP response, the latter also shared with FSH24. This finding suggests that eqFSH and FSH18/21 behaved more similarly in the early (6 h) response. Interestingly, and in contrast to FSH18/21, FSH24 did not exhibit any exclusively enriched process after stimulation during 6 h and showed only one shared biological process (cellular response to cAMP, shared with eqFSH and FSH18/21). Fig 4 shows the enriched processes of overexpressed genes for the four glycoforms at 6 h, with the nodes corresponding to each glycoform colored in yellow.

Processes associated with overexpressed genes at 12 hours of FSH stimulation (Tables 3 and 4, and Fig 5)

Table 3. Processes associated with overexpressed genes after 12 hours exposure to FSH18/21 and FSH24.
Processes associated with overexpressed genes at 12 hours
FSH18/21 FSH24
Exclusive Associated Exclusive Associated
Phospholipid biosynthetic process Response to nutrient
Steroid biosynthetic process
Male gonad development
Cellular response to cholesterol
Sterol biosynthetic process
Extracellular matrix organization
Response to hypoxia
Response to drug
Cholesterol metabolic process
Response to estradiol
Response to organic cyclic compound
Metabolic process
Oxidation-reduction process
Cholesterol synthetic process
Cellular response to follicle-stimulating hormone stimulus
Response to estrogen
Cellular response to hypoxia
Cellular response to
cadmium ion
Cellular response to
starvation
Male gonad development
Isoprenoid biosynthetic process
Cellular response to cholesterol
Sterol biosynthetic process
Extracellular matrix organization
Response to hypoxia
Response to drug
Cholesterol metabolic process
Response to estradiol
Response to organic cyclic compound
Metabolic process
Oxidation-reduction process
Cholesterol biosynthetic process
Cellular response to follicle-stimulating hormone stimuluss
Response to estrogen
Table 4. Processes associated with overexpressed genes after 12 hours exposure to eqFSH and recFSH.
Processes associated with overexpressed genes at 12 hours
eqFSH recFSH
Exclusive Associated Exclusive Associated
Cell Adhesion Cellular response to cAMP
Male gonad development
Isoprenoid biosynthetic process
Extracellular matrix organization
Response to hypoxia
Response to drug
Cholesterol metabolic process
Response to estradiol
Response to organic cyclic compound
Metabolic process
Oxidation-reduction process
Cholesterol biosynthetic process
Cellular response to follicle-stimulating hormone stimulus
Response to estrogen
Female gonad development
Adrenal gland development
Response to ethanol
Negative regulation of osteoclast differentiation
Response to go-nadotropin
Liver development
Cellular response to cAMP
Response to nutrient
Steroid biosynthetic process
Isoprenoid biosynthetic process
Cellular response to interleukin-1
Cellular response to calcium ion
Cellular response to amino acid stimulus
Response to estradiol
Response to hypoxia
Cholesterol metabolic process
Response to estradiol
Response to organic cyclic compounds
Metabolic process
Oxidation-reduction process
Cholesterol synthetic process
Cellular response to follicle-stimulating hormone stimulus
Response to estrogen
Fig 5. Biological processes associated with overexpressed genes at 12 hours after FSH glycoforms addition.

Fig 5

Color code is the same as in Fig 4. It is evident that the number of shared processes was larger at 12 h than at 6 h of exposure. See also legend of Fig 4.

Similar to the data shown by the Venn diagrams shown in Fig 2, all compounds exhibited more shared processes at 12 h than at 6 h, as illustrated in Fig 5 and Tables 3 and 4. In fact, eleven processes (including response to estrogen and cholesterol biosynthetic process), were shared by all FSH phenotypes. At this time, there were fewer treatment-specific processes than after 6 h of FSH exposure (Tables 1 and 2 and Fig 5), being recFSH and FSH24 (both tetra-glycosylated FSH molecules) the glycoforms exhibiting a larger number of exclusive processes. FSH18/21 and eqFSH showed only one exclusive process at this time.

Processes associated with underexpressed genes at 6 hours of FSH stimulation (Tables 5 and 6, and Fig 6)

Table 5. Processes associated with underexpressed genes after 6 hours exposure to FSH18/21 and FSH24.
Processes associated with underexpressed genes at 6 hours
FSH18/21 FSH2
Exclusive Associated Exclusive Associated
Response to wounding
Positive regulation of gene expression
Positive regulation of smooth muscle cell migration
Response to lipopolysacch-aride
Wound healing
Positive regulation of angiog-enesis
Positive regulation of smooth muscle cell proliferation
Positive regulation of cell proliferation
Positive regulation of cell migration
None Cellular response to cAMP
Table 6. Processes associated with underexpressed genes after 6 hours exposure to eqFSH and recFSH.
Processes associated with underexpressed genes at 6 hours
eqFSH recFSH
Exclusive Associated Exclusive Associated
Regulation of cell growth Positive regulation of cell
proliferation
Positive regulation of cell
migration
Thyroid hormone metabolic process Cellular response to cAMP
Fig 6. Biological processes associated with underexpressed genes at 6 hours of exposure to each of the four FSH treatments with the nodes representing each glycoform in yellow.

Fig 6

In this representation, blue color represents the enriched processes.

For the underexpressed genes, there was a lower number of significantly enriched processes (n = 12) despite the gene sets of overexpressed and underexpressed genes were of the same size (i.e. 200). FSH18/21 was the preparation exhibiting more negatively regulated exclusive processes (n = 7), including positive regulation of angiogenesis, whereas for eqFSH and recFSH there was only one exclusively enriched process, regulation of cell growth and thyroid hormone metabolic process, respectively. Positive regulation of cell proliferation and cell migration were negatively associated processes between FSH18/21 and eqFSH at this time. For FSH24 we only identified cell response to cAMP as a negatively regulated shared process (with recFSH).

Processes associated with underexpressed genes at 12 hours of FSH stimulation (Fig 7 and Tables 7 and 8)

Fig 7. Biological processes associated with underexpressed genes at 12 hours of exposure to each of the four treatments.

Fig 7

In this representation, blue color represents the enriched processes.

Table 7. Processes associated with underexpressed genes after 12 hours exposure to FSH18/21 and FSH24.
Processes associated with underexpressed genes at 12 hours
FSH18/21 FSH24
Exclusive Associated Exclusive Associated
None DNA replication
Chromosome segregation
Microtubule-based movement
Response to toxic substance
Inactivation of MAPK activity
Bone mineralization
Regulation of circadian rhythm
Mitotic cytokinesis
Peptidyl-serine phosphorylation
Cellular response to DNA
damage stimulus
Mitotic nuclear division
DNA replication
Mitotic sister chromatid
segregation
Chromosome segregation
Microtubule-based movement
Response to toxic substance
Cell division
Table 8. Processes associated to underexpressed genes after 12 hours exposure to eqFSH and recFSH.
Processes associated to underexpressed genes at 12 hours
eqFSH recFSH
Exclusive Associated Exclusive Associated
Response to organic substance
Mitotic spindle organization
DNA replication initiation
Cellular response to diacyl bacterial lipopeptide
Response to estradiol
Positive regulation of fibroblast proliferation
Positive regulation of cell migration
Mitotic metaphase plate congresssion
Activation of protein kinase activity
Positive regulation of proteasomal ubiquitin-dependent proteinkinase activity
Positive regulation of cell proliferation
Cellular response to DNA damage stimulus
Mitotic nuclear division
DNA replication
Mitotic sister chromatic segregation
Chromosome segregation
Microtubule-based movement
Response to toxic substance
Cell division
Intracellular signal transduc-
tion
Positive regulation of smooth
muscle cell migration
Positive regulation of smooth
muscle cell proliferation
Positive regulation of cell proliferation
Chromosome segregation
Microtubule-based movement
Cell division

For underexpressed genes, a larger number of significantly enriched biological processes were identified at 12 h than at 6 h (Fig 7). At this time, eqFSH was the preparation showing the largest number of significantly underregulated processes (both exclusive and associated) (n = 18 processes vs 3 processes at 6 h), from which 9 were shared and 10 were exclusive; the latter were more abundant than those detected with the exclusively overexpressed genes at the same time, during which only 1 exclusive process (cell adhesion) was detected (see Fig 5). In contrast to the findings at 6 h, FSH18/21 showed fewer shared biological processes at 12 h (n = 4), including DNA replication, chromosome segregation, microtubule-based movement, and response to toxic substances, probably related to decreased cell growth, movement, and proliferation at this time) and no exclusive processes. Meanwhile, exposure to FSH24 resulted in negative regulation of eight shared processes and five exclusive processes, including enriched inactivation of MAPK activity, bone mineralization, mitosis cytokinesis, and peptidyl-serine phosphorylation. Finally, exposure to recFSH for 12 h yielded 4 shared- and 3 exclusive processes, including cell smooth muscle migration and proliferation, and intracellular signal transduction (Tables 7 and 8).

Timeline for each FSH phenotype (S1 to S4 Figs)

FSH18/21. As shown in S1 Fig, overexpressed gene-associated processes exhibited exclusive biological processes at each time (6 and 12 h), with a subset of shared processes at both times. At 6 h, all FSH18/21-stimulated exclusive processes were associated with cell responses, whereas the four shared processes present at 12 h included responses to steroid hormones and drugs, as well as biosynthetic steroid process. At 12 h, 13 processes, including responses to estradiol, cholesterol, hypoxia, nutrient, and organic cyclic compound, as well as male gonad development appeared as significantly enriched. Meanwhile, among underexpressed genes-related processes, positive regulation of proliferation and migration, wound healing, and regulation of gene expression appeared as exclusive at 6 h, and DNA replication, chromosome segregation, microtubule-based movement, and response to substances were enriched at 12 h. Shared underexpressed processes between 6 and 12 h were not evident. Contrary to those corresponding to overexpressed genes, there were fewer underexpressed genes- associated processes at 12h than at 6h.

FSH24. Interestingly, exposure to FSH24 for 6 h yielded the same associated process based on overexpressed and underexpressed genes, i.e. cellular response to cAMP (S2 Fig bottom). This was the only process enriched at 6 h for this particular compound. On the other hand, several processes associated with cellular response to different stimuli (in the case of overexpressed genes), and cell division, DNA replication, and cell movement (for underexpressed genes), among others, were observed after 12 h of exposure to this glycoform.

eqFSH. For overexpressed genes-related processes stimulated after 6 h exposure to eqFSH, the response to cAMP and processes associated with response to FSH stimulus (e.g. ovulation, response to peptide hormone) were exclusive, whereas response to estrogen and cAMP were shared with those processes stimulated after exposure during 12 h (S3 Fig). Among the exclusive processes detected after 12 h of exposure were cholesterol metabolic and biosynthetic processes, extracellular matrix organization, and cell adhesion. For the case of underexpressed genes-related processes, regulation of cell growth was the only exclusive function, and cell migration and proliferation were shared with processes stimulated at 12 h by this glycoform. Biological processes associated with cell proliferation were among the 12 h exclusive ones. Interestingly enough was finding that the response to estradiol was an enriched process associated with overexpressed and underexpressed genes at 12 h, similar to the cellular response to cAMP observed for FSH24 at 6 h. In both cases, stimulation followed by inhibition of these processes may be related to their selective (biased) desensitization following stimulation with FSH24 and eqFSH.

recFSH. At 6 h, microtubule depolymerization, mitotic sister segregation, and angiogenesis were among the exclusive, overexpressed gene-related processes (S4 Fig). Elements shared between 6 h and 12 h were response to drug, hypoxia and estradiol, as well as extracellular matrix organization. Comparatively, more exclusive processes were detected after 12 h of recFSH stimulation, including female gonad development, steroid biosynthetic process, and response to estrogen, among others.

The only process significantly enriched from underexpressed genes at 6 h was thyroid hormone metabolic process (S4 Fig, bottom left). Microtubule-based movement, cell proliferation and intracellular signaling appeared as processes enriched after 12 h of recFSH exposure. The case of chromosome segregation was interesting in that this process was significantly enriched for overexpressed genes at 6 h and at 12 h for the underexpressed ones, perhaps pointing to a transitional process. Another interesting finding was that cellular response to cAMP appeared as an enriched process related to underexpressed genes at 6 h but also to overexpressed genes at 12 h. This could mean that this particular process changed its behavior from one pole to the other during a 12 h exposure to recFSH.

Pathway perturbation analysis

Pathway perturbation analyses allow linking the global effect of gene perturbation observed in a given set of experiments with known functional and molecular processes. Based on previous knowledge of the effects of FSH on FSHR-mediated intracellular signaling [29, 30], we focused our analysis on cAMP-PKA, MAPK-, and PI3K/AKT-mediated signaling. Our analyses identified these pathways as altered in virtually all experimental comparisons.

Behavior of the cAMP signaling pathway in response to each FSH compound (S5 and S6 Figs)

A closer inspection on the cAMP pathway, allowed us to identify that said dysregulation is driven by different sets of perturbed genes. S5 and S6 Figs show the differentially expressed genes in KEGG-resolved cAMP signaling pathway for each treatment at 6 and 12 h. As shown, each compound exhibited a time-dependent individual molecular signature. For example, 6 h after FSH18/21 and FSH24 exposure, membrane-bound FSH-stimulated FSHR expression was found generally underexpressed, most probably due to internalization and subsequent degradation of the receptor. Simultaneously, phosphodiesterase was overexpressed to prevent prolonged activation of the cAMP/PKA pathway. In the case of FSH18/21, CREB was overexpressed at 6 h, whereas no changes in this transcription factor were observed in the case of FSH24 at this time. After 6 h of FSH24 exposure, G protein-coupled receptors (GPCRs) that responded to colinergic and GABAergic stimuli were modestly overexpressed whereas no change was observed for FSH18/21 at this time. Changes similar to those provoked by FSH18/21 exposure for 6 h were observed for eqFSH, whereas for recFSH no GPCR exhibited dysregulation.

After 12 h of FSH exposure, the effects of gene expression on pathway molecules were more consistent. Most molecules in the cAMP-PKA pathway (eg. CREB) exhibited underexpression at this time point, regardless of their cellular or pathway position. The exception was AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) receptor, which is stimulated by glutamic acid present in follicular fluid [52] and in turn regulates signaling molecules (presumably via calcium influx) involved in ovulation [53]; AMPAR showed overexpression with all treatments. At this time, signaling was also very similar between FSH18/21 and eqFSH with the exception of GPCRs for glycoprotein hormones and c-Jun N-terminal kinase (JNK; a kinase involved in cell cycle progression, mitosis and apoptosis [54, 55]) signaling overexpression stimulated by eqFSH. It is interesting to note the contrast between recFSH- and FSH24- (both fully-glycosylated human FSH compounds) stimulated signaling. JNK-mediated signaling and CREB-regulated gene expression (at 6 h) as well as MEK and JNK signaling (at 12 h), were markedly overexpressed after exposure to recFSH but not to FSH24. In contrast, after exposure to FSH24 both CREB-regulated gene expression and JNK signaling remained unmodified or were underexpressed at 6 h and 12 h, respectively, whereas MAPK signaling was slightly overexpressed at both times.

Behavior of the MAPK and PI3K-AKT signaling pathways in response each FSH compound (S7 to S10 Figs)

The effects of the FSH compounds tested on the MAPK signaling pathway also differed among the glycoforms (S7 and S8 Figs). Notably, MEK1 and MEK2 pathways were upregulated by FSH18/21 at 6 h but not by FSH24, whereas at 12 h, ERK signaling was modestly upregulated in response to both glycoforms. Meanwhile, in the case of eqFSH, MEK1 and 2 as well as ERK-signaling were modestly overexpressed at 6 h and 12 h, respectively, whereas after exposure to recFSH, both MEK2 and ERK were overexpressed at 12 h but not at 6 h. Worthy of note in the PI3-AKT signaling pathway (S9 and S10 Figs) was the overexpression of SGK (serum and glucocorticoid-regulated kinase, which is involved in survival signaling, growth, and proliferation [56]) after 12 h of cell exposure to FSH18/21, recFSH, and eqFSH, whereas this signaling pathway remained unaltered or markedly underexpressed at 6 h for all glycoforms.

Validation of FSH glycoforms-regulated gene expression by RT-PCR

To validate the results obtained from the RNA-seq experiment, we analyzed by RT-PCR the relative mRNA expression of four over-expressed and one under-expressed genes under the stimulation of distinct glycoforms (Fig 8). Genes examined that were putatively up-regulated by FSH glycoforms included: a) Phospholipase D1 (Pld1), which encodes an enzyme implicated in a number of cellular pathways, including signal transduction mediated by GPCRs and receptor tyrosine kinases, subcellular trafficking, and regulation of mitosis [57]; b) Neuropeptide Y (NPY) receptor Y1 (Npy1R), which encodes for a GPCR involved in Npy-regulated granulosa cell proliferation and apoptosis [58, 59]; c) Antimüllerian hormone gene (Amh), encoding a glycoprotein belonging to the transforming growth factor beta superfamily, which is involved in follicular development [60]; and d) Vascular endothelial growth factor B (Vegf-B), a gene that encodes the VEGF-B protein, a factor involved in angiogenesis [61]. The FSH down-regulated gene examined was BCL2 like 1 (Bcl2l1) which acts as anti- or pro-apoptotic regulators that are involved in a wide variety of cellular activities [62], including prolactin signaling [63]. The selection of those genes for validation was based on the role they play in FSH-stimulated follicular maturation, rather than on fold change variation, and their corresponding mRNAs were quantified in three independent biological replicates (samples coming from three different culture wells) from granulosa cells exposed to different FSH glycoforms during 6h or 12h.

Fig 8. Validation of differentially expressed genes in the RNA-seq data by RT-PCR of five selected FSH glycoform-sensitive genes (Pld1, Npy1R, Amh, Vegf-B, and Bcl2l1).

Fig 8

Genes examined that were putatively up-regulated by FSH glycoforms included: phospholipase D1 (Pld1); neuropeptide Y (NPY) receptor Y1 (Npy1R), antimüllerian hormone gene (Amh), and vascular endothelial growth factor B (Vegf-B), whereas the FSH down-regulated gene examined was BCL2 like 1 (Bcl2l1).

The mRNA expression of the 4 up-regulated genes in response to particular glycoforms vs zero hours [with statistically significant (p<0.01 or p<0.05) differences] was the following: Amh and Npy1R for recFSH and FSH18/21 at 6 h, respectively; Pld1 for FSH18/21 and eqFSH at 12 h; and Vegf-B for recFSH at 12 h. For the down-regulated gene (Bcl2l1) a statistically significant difference (p<0.05) was found for FSH24 at 6 h. These results indicated the existence of a positive validation (Fig 8). The use of only 3 replicates was justified by the difficulties in generating more material from such experiments. It is clear that using more replicates may better support the PCR vs RNAseq results where statistical power is increased by the measurement of thousand of targets at the same time.

Discussion

Previous studies have demonstrated that hypoglycosylated FSH (FSH18/21) is the predominant form secreted by women in reproductive age and that a shift to fully-glycosylated glycoforms occurs as age advances, which is probably due to the progressive decrease in the production of ovarian estrogens as menopause approaches [15, 6466]. In vitro and in vivo studies also have shown that hypoglycoslated FSH exhibits higher FSHR binding activity and receptor affinity as well as potency and efficiency to trigger intracellular signaling than fully glycosylated FSH [12, 14, 15, 26, 27, 65, 67]) despite its relatively shorter plasma half-life [67, 68]. In fact, when injected to Fshb null female mice, both hypo- and fully glycosylated FSH stimulated ovarian weight response and induced particular ovarian genes in a similar fashion, whereas in Fshb null male mice, hypoglycosylated FSH18/21 was more active than fully-glycosylated FSH24 in inducing FSH-responsive genes and Sertoli cell proliferation [27]. More recently, short-term in vivo studies in prepubertal mice showed that FSH18/21 was more effective than FSH24 in promoting follicle development and health, as well as in rescuing granulosa cells from apoptosis [67]. Although ovarian gene transcription in vivo was stimulated by both glycoforms, FSH18/21 induced greater activation of Gαs-mediated cAMP-PKA signaling as well as PI3/K and MAPK/ERK signaling pathways, with greater expression of early response genes upon administration of this particular glycoform [67]. These data were concordant with findings from previous studies showing that in vivo administration of more basic, short-lived FSH isoforms to hypophysectomized rats, mantained equally or even more efficiently granulosa cell proliferation than their more acidic counterparts [22].

Of note, it was interesting to find that mainly after 12h exposure to the FSH glycoforms, was fold increase in Lhcgr (and also Cyp19a1, in the case of the pituitary FSH glycoforms) clearly detectable. This may be explained by the fact that estradiol production (and also presumably Cyp19a1 mRNA expression) in culture conditions similar to that employed in the present study occurs late during the incubation period (24–48 h) [40, 69]. A similar expression profile has also been observed for the Lhcgr [70, 71]; the finding that mRNA expression of the Lhcgr occurs quite late in cultured granulosa cells (peak levels at 48 h) [71], may explain the relatively modest increase in the transcript of this particular gene after 12 h of FSH exposure. eqFSH and recFSH genes also exhibited a strong expression of the Lhcgr at 12 h, but not of that of Cyp19a1. This latter finding may suggest an even slower expression of this particular gene when stimulated by these particular glycoforms, which could not be detected with the time resolution of the present study. The same occurred in the case of the Ccnd2 gene, underlying the need to perform experiments with a more frequent time resolution. A complete explanation to this latter finding remains to be established. Nevertheless, either differences in culture conditions between ours and a previous study on Ccnd2 mRNA expression [72] or a low time resolution for clearly detect expression of this particular gene in our study, might explain these apparent discrepancies.

In the present study, we analyzed whether FSH glycoforms exhibiting distinct glycosylation patterns differentially induce gene transcription and activation of biological processes and signaling pathways in cultured rat granulosa cells exposed during 6h and 12h to equal doses of the glycoforms. Analysis of differentially expressed genes identified particular changes in gene expression induced by each glycoform, with a limited number of genes shared among the glycoforms at 6 h, followed by a sharp increase at 12 h, time at which the four glycoforms behaved more similarly for inducing differential gene expression. These data reflect the relatively early differential effects of the glycoforms on gene expression, with a platau reached at 12 hours. Analysis of individual FSH glycoforms revealed the expression of more FSH-dependent genes at 6 h than at 12 hours, particularly for the hypo-glycosylated preparations (FSH18/21 and eqFSH), which was expected given that FSH-mediated signaling occurs rapidly after receptor activation in in vitro conditions and that hypoglycosylated FSH exhibits a more favorable FSHR binding profile than its fully-glycosylated counterpart from kinetic and thermodynamic points of view, as disclosed by molecular dynamics simulations [12, 28]. Nevertheless, the data also showed that even after 12 h of FSH exposure the effects of FSH stimulation on gene expression persisted, independenly of the kinetics by which each glycosylation variant activates the FSHR and stimulates intracellular signaling.

Gene ontology analysis revealed that exposure of granulosa cells to each FSH compound during 6 h or 12 h was differentially associated with induction or inhibition of one or several genes linked to distinct biological processes. Some of these processes were exclusive or unique for a given FSH compound whereas others where shared by or associated with several FSH glycoforms, demonstrating the distinctly different ability of each glycoform to activate/inhibit FSH-dependent biological processes. In this analysis, it was interesting to find that in contrast to hypo-glycosylated and recFSH, stimulation with FSH24 and eqFSH for 6 h, was poorly associated with exclusive overexpressed processes. In fact, in the case of FSH24 no exclusive biological processes and only one associated process (cAMP response) were clearly identified at this time. This observation contrasted with the findings after 12 hours of FSH exposure, where multiple associated processes where shared among all glycoforms, again emphasizing the long-term effects of FSH on biological effects in vitro. Here it is important to emphasize that at this time (12 h) the exclusive processes stimulated by FSH18/21 where virtually absent, underlying the short and acute effects of this particular glycoform on granulosa cell responsiveness, an observation that is in agreement with a previous study on the effects of FSH18/21 and FSH24 on ovarian global trascriptomics in vivo [67]. In this scenario it is possible to conclude that recFSH (tetra-glycosylated), was the most potent preparation to evoke particular/exclusive biological processes at 6 h and 12 h and that the effects of all FSH compounds converged in a number of similar processes in a time-dependent fashion, as it probably occurs in vivo during follicular maturation.

Meanwhile, individual differences in time also were observed in processes associated to underegulated genes, with exposure to FSH18/21 for 6 h resulting in more underegulated genes that in those by other glycoforms but not later, at 12 h, time at which FSH18/21 did not show any exclusive process, an observation that was in sharp contrast with the effects of the remaining glycoforms. These data indicates that different glycoforms also have distinct abilities to turn off selective genes in a time dependent manner, with the FSH18/21 hypo-glycosylated glycoform acting faster than the other compounds. This observation is in line with the ability of this particular glycoform to efficiently trigger intracellular signaling and also to rapidly evoke down-regulation of stimulated signaling and also probaby resensitization of the FSH18/21/FSHR complex [12, 14, 26]. In fact, it has been shown that FSH glycosylation can influence the turn-on time of FSHR-mediated signaling [26, 73, 74].

Pathway perturbation analysis unveiled interesting differential effects of the FSH glycoforms on intracellular FSH-regulated signaling. We analyzed by this bioinformatics procedure three well-known pathways stimulated by gonadotropins, the canonical cAMP-PKA as well as the MAPK and PI3K-AKT signaling pathways [29, 30, 75]. It is believed that these signaling cascades lead to fine-tuning regulation of the FSH stimulus, where activation and/or inhibition of their downstream activated components vary depending on the cell context, cell developmental stage, and concentration of the ligand and the receptor. Simultaneous activation of these signaling modules eventually converges on the ligand/receptor-integrated biological responses, which include cell proliferation, differentiation and survival of responsive cells and, at the molecular level, differential gene expression, as observed in the present study. In the cAMP pathway, it was interesting to find that for FSH18/21 but not for the fully glycosylated FSH24, the transcription factor CREB was overactivated at 6 hours indicating a rapid activation of the cAMP-PKA pathway by this particular naturally occurring glycoform, whereas after 12 h stimulation of this pathway persisted active for both glycoforms, albeit to a lesser extent after FSH24 exposure. The overall data underline the differential activation of the cAMP signaling pathway by these two glycoforms.

The MAPK and PI3K-AKT cascades are also part of the intertwined signals regulated by the FSH/FSHR complex and both are involved in controlling follicle development as well as in regulating gene transcription [29, 7678]. In these pathways, the effects of the FSH compounds tested on the MAPK signaling pathway also exhibited time-related differences. For example, MEK1 and MEK2 pathways were upregulated by FSH18/21 at 6 h but not by FSH24, whereas at 12 h, ERK signaling was modestly upregulated in response to both glycoforms. Worthy of note is the fact that activation of FSHR involves cross-talk with pathways regulated by growth factors and the estrogen GPCR (GPER) [56, 79, 80]). In the former, RTK plays an important role in folliculogenesis and ovulation [81, 82], and it is known that this kinase may activate JNK in a Src and PI3K dependent fashion, whereas in the latter FSHR/GPER heterodimers trigger anti-apoptotic/proliferative signaling through the Gβγ dimer [79, 83]. In this vein, it was interesting to find that all FSH glycoforms activated RTK and MEK after 6 h of FSH exposure, but not later, underlying the important role of this pathways on FSH-evoked cell proliferation and angiogenesis.

Regarding all these signaling pathway analyses, it is important to emphasize on that pathway perturbation captures a static representation of what is essentially a dynamic process involving several signaling cascades regulated by distinct factors either directly by the FSHR or via cross-talk with other membrane receptors. As such, unexpected behaviors such as under- and overexpression of certain molecules can be capturing dynamic feedback effects. With this in mind, the differences and similarities observed between different pathways can be thought to be capturing different kinetic behaviors by the distinct FSH treatments. In any case, these data underline the distinct time- and glycosylation-dependent fingerprints for each particular FSH compound.

In summary, the present transcriptomic analysis allowed dissection of some distinctly different glycosylation-dependent effects of the human FSH glycoforms after exposure of cultured rat granulosa to these compounds at different times, comparing these effects with those of eqFSH, which albeit differing in amino acid sequence and glycosylation pattern, exerts potent effects at the human FSHR [26]. Of particular interest are the data on the transcriptomic effects of the naturally-occurring FSH glycoforms, which are consistent with previous in vivo and in vitro studies showing that hypoglycosylated FSH18/21 exhibits greater biological activity at the target cell level and that both FSH21/18 and FSH24 initiate different FSHR-mediated intracellular signaling activation, eventually leading to differential impacts on follicle development [67, 84]. The overall findings are important to better understand the physiological significance of FSH glycoforms in follicular maturation and ovulation in naturally-occurring cycles in women.

Supporting information

S1 Table. Representative subset of overexpressed genes at 6 hours of FSH glycoform exposure.

(PDF)

pone.0293688.s001.pdf (64.4KB, pdf)
S2 Table. Representative subset of overexpressed genes at 12 hours of FSH glycoform exposure.

(PDF)

pone.0293688.s002.pdf (64.1KB, pdf)
S3 Table. Representative subset of underexpressed genes at 6 hours of FSH glycoform exposure.

(PDF)

pone.0293688.s003.pdf (56KB, pdf)
S4 Table. Representative subset of underexpressed genes at 12 hours of FSH glycoform exposure.

(PDF)

pone.0293688.s004.pdf (62.9KB, pdf)
S1 Fig. Enriched processes for FSH18/21 at 6 and 12 hours.

In this representation, the red color corresponds to overexpressed enriched processes and the blue color to underexpressed processes. The intersection of the overexpressed processes at 6 h is against the underexpressed processes at 12 h, that is, the comparasion is performed between the two incubation times. This is why the exclusive processes may be different than those shown in Fig 4.

(PDF)

pone.0293688.s005.pdf (377.5KB, pdf)
S2 Fig. Enriched processes for FSH24 at 6 and 12 hours.

In this representation, the red colored squares corresponds to overexpressed enriched processes and the blue color to underexpressed processes.

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pone.0293688.s006.pdf (426KB, pdf)
S3 Fig. Enriched processes for eqFSH at 6 and 12 hours.

The red squares correspond to overexpressed enriched processes and the blue color to underexpressed processes.

(PDF)

pone.0293688.s007.pdf (360.4KB, pdf)
S4 Fig. Enriched processes for recFSH at 6 and 12 hours.

The red squares correspond to overexpressed enriched processes and the blue color to underexpressed processes.

(PDF)

pone.0293688.s008.pdf (354.5KB, pdf)
S5 Fig. Cyclic AMP signaling pathway (KEGG hsa04024), with differentially expressed genes colored.

This pathway is significantly perturbed by both FSH18/21 and FSH24 at 6h. Note the activation of CREB followed by an inhibition of c-fos induced by FSH18/21, not observed with FSH24. In eqFSH and recFSH, this pathway is significantly perturbed at 6 h. Note the difference in phosphodiesterase (PDE) states: induced by eFSH, and repressed by recFSH. [Pathway perturbation detected with GAGE. Pathway visualization rendered with Pathview].

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pone.0293688.s009.pdf (613.3KB, pdf)
S6 Fig. cAMP signaling pathway (KEGG hsa04024) at 12 h, with differentially expressed genes colored.

At this time, this pathway was significantly perturbed by both FSH18/21 and FSH24. Note the activation of PAR1 (coagulation factor II thrombin receptor) by FSH24, which was not induced by FSH18/21. This pathway was significantly perturbed by both recFSHand eFSH at this time. In addition, note the activation of adenylate cyclase (AC) and lipase E (HSL) by recFSH, not induced by eFSH. [Pathway perturbation detected with GAGE. Pathway visualization rendered with Pathview].

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pone.0293688.s010.pdf (614.6KB, pdf)
S7 Fig. Within the context of the MAPK signaling pathway (KEGG hsa04010), it can be observed distinct effects of the different FSH glycoforms at the 6-hour time point.

Notably, receptor signaling proteins presented varying expression levels: FSH24 induced a more profound overexpression than FSH18/21 and eqFSH, while recFSH elicited comparatively lower expression. Furthermore, our analysis revealed that highly central molecules within the MAPK pathway, such as p38, responded differentially to the FSH glycoforms. Under the influence of FSH24, p38 and similar central molecules exhibited underexpression. In contrast, when exposed to all other FSH glycoforms, these central molecules (mainly p38) exhibited a consistent pattern of overexpression. [Pathway perturbation was detected using GAGE, and the pathway visualization was generated through Pathview].

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pone.0293688.s011.pdf (494.1KB, pdf)
S8 Fig. In the context of the MAPK signaling pathway (KEGG hsa04010), the comparison of the effects among the FSH glycoforms after 12-hour exposure with those at 6 h revealed significant differences.

Receptor signaling proteins are now predominantly underexpressed, regardless of the particular FSH treatment applied, with a marked shift from the earlier (6 h) overexpression. Notably, at this time central molecules, like p38, consistently exhibited overexpression across all FSH glycoforms perturbations. [Pathway perturbation was detected using GAGE, and the pathway visualization was generated through Pathview].

(PDF)

pone.0293688.s012.pdf (495.6KB, pdf)
S9 Fig. Distinct findings emerged in the PI3K signaling pathway (KEGG hsa04151) at the 6 h time point.

Receptor signaling proteins expression showed a balanced distribution of both up- and down regulation across all FSH glycoforms, with no significant differences. PI3K consistently exhibited underexpression across all glycoforms, indicating a shared regulation. Notably, downstream effectors within the PI3K pathway showed unique responses; recFSH upgregulated effectors such as CCND1, CDK, and cyclin; and eqFSH, FSH18/21 and FSH24 downregulated BCl-2 and c-Myb. [Pathway perturbation was detected using GAGE, and the pathway visualization was generated through Pathview].

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pone.0293688.s013.pdf (492.4KB, pdf)
S10 Fig. In the PI3K pathway (KEGG hsa04151) at the 12 h mark, different features were detected.

First, expression of signaling molecules exhibited a more varied pattern which was distinctly influenced by each FSH glycoform, suggesting differential regulatory effects. For instance, in the case of the proteins involved in focal adhesion, ECM, ITGA, and ITGB were, respectively: a) all upregulated by recFSH; b) up-, down-, and down-regulated, respectively, by eqFSH; and c) down-, up-, and down-regulated, respectively by FSH18/21 and FSH24. In the case of downstream effectors, the patterns of expression of molecules such as GYS and PEPCK were unique for each glycoform: a) both upregulated by recFSH; b) down- and upregulated, respectively, by eqFSH; c) down- and up-regulated, respectively, by FSH18/21,; and c) up-regulated and without change, respectively, by FSH24. All glycoforms consistently showed underexpression of PI3K persisted across all compounds, indicating a sustained (and shared) impact on this key element. [Pathway perturbation was detected using GAGE, and the pathway visualization was generated through Pathview].

(PDF)

pone.0293688.s014.pdf (492.8KB, pdf)

Acknowledgments

The authors would like to thank Dr. James A. Dias, from the State University of New York, Albany, NY, USA, for his carefull reading and comments on this study. Dr. Viktor Y. Butnev passed away before the submission of the final version of this manuscript. Dr. Alfredo Ulloa-Aguirre accepts responsibility for the integrity and validity of the data collected and analyzed.

Data Availability

All relevant data are within the paper and its Supporting information files. All statistical database are available at https://osf.io/57j3f/.

Funding Statement

This study was supported by grants from CONACyT, Mexico (grant no. 240619) and the Coordinación de la Investigación Científica-UNAM, Mexico (to A.U-A). G.R.B. and VYB were supported by NIH grant P01AG-029531". The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Satish Rojekar

2 Feb 2024

PONE-D-23-33249Differential effects of follicle-stimulating hormone glycoforms on the transcriptome profile of cultured rat granulosa cells as disclosed by RNA-seqPLOS ONE

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Reviewer #1: No

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #2: Yes

**********

5. Review Comments to the Author

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Reviewer #1: There are multiple concerns that the authors need to address.

Lines 48-50: It is easier for readers to understand if the FSH glycoforms are numbered. Though it is clear in Methods that four glycoforms were used, with the use of “and” and commas in this sentence it is not obvious how many different glycoforms, that is groups of treatment, are used in this study.

Lines 129-130: FSH is glycosylated at 2 Ns in alpha and beta chains each. Why is FSH-beta is identified as tetra- and tri-glycosylated? Shouldn’t it be mono- and di-glycosylated FSH-beta?

Line 166: diverssignaling should be changed to “diverse signaling”.

Line 174: It should be FSH18/21

Line 199-209: How many rats were used? How many granulosa cells were obtained from each rat? Were granulosa cells from all rats pooled before seeding to plates? Granulosa cells were seeded at 1M per well and cultured for 48h before FSH treatment. Did this culture increase the cell number?

Line 213: What is triplicate incubation? Was each FSH prep for each time-point added to three wells? Was each biological replicate run on different day? How were technical and biological replications considered?

Line 227-228: If only samples with >8 RIN were used, how many samples per treatment per time-point reached this quality threshold? With only 3 replicates, how many ended up being sequenced for each group? How was statistical analysis considered if the number was too low?

Line 231: 10-15 million reads doesn’t appear to be deep. What is the rationale for this depth?

Line 237: how did authors test if the biological processes were indeed active? Mere enrichment of a process among DEGs doesn’t mean active.

Lines 320-350: These two sections are confusing and not well justified. Why did authors run these comparisons? What physiological relevance do these comparisons address? While the first section title suggests “contrast between 6 and 12h”, the text seems to describe contrasts among FSH glycoforms at each time-point! The second section title suggests contrasts among each FSH glycoforms, but the text seems to describe how one glycoform is different from others.

Lines 357-358: It is not recommended to separate up and downregulated genes to discover enriched BPs and pathways as some of the genes involved in a particular pathway may be upregulated while other of that pathway may be down regulated (as in Lines 514-515).

Lines 496-556 : Each of the FSH glycoform appears to have regulated dramatically different set of biological processes. FSH18/21 induced steroid biosynthetic process; FSH24 did not but response to cAMP was both up and down-regulated; eqFSH stimulated processes like ovulation as well as ECM organization that is mostly associated with ovulation; recFSH induced angiogenesis and ECM remodeling processes. Of these, only FSH18/21 appears to have regulated biologically relevant process for follicular granulosa cells. Therefore, it is important for authors to first check which glycoform regulates the usual suspects of granulosa cell genes (Cyp19, Lhcgr, Ccnd2) first before starting to compare different glycoforms.

Authors should first prove that all FSH treatment stimulated Cyp19a1 and Lhcgr in granulosa cells, to ensure that results obtained using the cell culture model of this study are relevant to in vivo biology.

Reviewer #2: In this study the authors have addressed an important question has to how variations in glycosylation of glycoprotein hormones may impart distinct biological features which is largely ignored on studies on signaling effects of hormones on the target tissues. The paper is potential useful because glycosylation variants of hormone is produced in pituitary and which in turn can vary with age leading to possible differential effects. The approach they have used to get to this problem is by a doing global RNA seq analysis on rat granulosa cells after treating them with four different glycoforms of FSH. Furthermore, they relate the differentially expressed genes to intracellular signaling by carrying extensive pathway analysis. The study is very strong in RNA seq analysis and validation of the key genes that are differentially expressed and at times rather heavy technically. The methodology in the paper is well detailed for replication of the study. The results are supported by figures that are clear although with rather wordy legends. The paper is discussed well and futher supported by relevant references. However, I must say that manuscript is not organized well because the figure legends and tables that describe are embedded within text which makes it hard for the reader. Overall the paper is a significant contribution to our understanding of glycoprotein variation and their physiological effects. I have the following comments

1) The author’s observer a rather robust change in the number of the differentially expressed genes between 6hrs and 12hrs. What is the logic in choosing the 6 and 12hrs time period? How is the effect of non-synchronized target cells ruled as cause of this difference?

2) It not clear in the discussion as to what is cause of these changed glycoforms with age?

3) Do the different glycoform have similar affinity to the cognate receptor? Affinity measurements data would have clarified it.

**********

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Reviewer #1: No

Reviewer #2: Yes: Rauf Latif

**********

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PLoS One. 2024 Jun 6;19(6):e0293688. doi: 10.1371/journal.pone.0293688.r002

Author response to Decision Letter 0


27 Feb 2024

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Authors’ responses to reviewers’ comments

PONE-D-23-33249

Differential effects of follicle-stimulating hormone glycoforms on the transcriptome profile of cultured rat granulosa cells as disclosed by RNA-seq

Review Comments/Answers to/from the Author

Reviewer #1: There are multiple concerns that the authors need to address.

- Lines 48-50: It is easier for readers to understand if the FSH glycoforms are numbered. Though it is clear in Methods that four glycoforms were used, with the use of “and” and commas in this sentence it is not obvious how many different glycoforms, that is groups of treatment, are used in this study.

Answer:

Thanks for this observation. We have employed the accepted nomeclature familiar to those interested in the gonadotropin field. All glycoforms from the pituitary are known as FSH24 and FSH18/21, whereas the abbreviations of recFSH and equine FSH are also well-known. The identification of the compounds with letters, as suggested by the reviewer is now included in the Abstract (lines 49-51) and Introduction (lines 178-181) of the new ms version. This undoubtedly will facilitate the readers to identify the preparations studied.

- Lines 129-130: FSH is glycosylated at 2 Ns in alpha and beta chains each. Why is FSH-beta is identified as tetra- and tri-glycosylated? Shouldn’t it be mono- and di-glycosylated FSH-beta?

Answer:

Reviewer is right on this observation. However, for the identification of tetra- and tri-glycosylated, the heterodimer is taken into account, rather than the isolated subunits. Text has been modified to make clearer and less confusing this issue (lines 129-138 in new version).

- Line 166: diverssignaling should be changed to “diverse signaling”.

Answer:

“divers signaling” has been corrected in the revised version.

- Line 174: It should be FSH18/21

Answer:

This mistake has been corrected in the revised version. Thanks to the reviewer for picking this typo.

- Line 199-209: How many rats were used? How many granulosa cells were obtained from each rat? Were granulosa cells from all rats pooled before seeding to plates? Did this culture increase the cell number?

Answer:

A total of 22 rats were used to obtain the granulosa cells. We did not count for granulosa cell number from each follicle/rat as after puncturing the follicles cells were were pooled, counted for viable cells and then seeded at 1 million per well and cultured for 48h before FSH treatment. We did not control for changes in cell number, as it was assumed that changes in this variable, if any, would be minimal, and would be equal for all incubations, particularly because pre-incubations and incubations were done in the absence of mitogenic factors (eg. insulin and serum) as well as estrogens. On the other hand, cells were immediately frozen after adding Trizol reagent to prevent any RNA degradation, so there was no available material to perform cell counting at the end of each incubation period (0, 6h and 12h, for 4 FSH preparations at each time). The authors recognize that although a previous study detected changes in the number of cultured granulosa cells (Han et al., Biol Reprod 88 (3):57, 2013; Liu et al., Mol Cell Endocrinol 21:63, 1981, see below), others have not even controlled for this potential effect (see Jia and Hsueh, Endocrinology 119(4):1580, 1976; Shi and Segaloff, Mol Endocrinol 9:734, 1995). The only study we are aware that FSH-stimulated DES-primed granulosa cell proliferation was Wan Shum's (Liu et al., Mol Cell Endocrinol 21:63, 1981) FSH assay using these cells in M199 with 10% chicken serum without 0.5 µM testosterone; growth was only observed after 5-6 days.

-Line 213: What is triplicate incubation? Was each FSH prep for each time-point added to three wells ? Was each biological replicate run on different day? How were technical and biological replications considered?

Answer:

Cells were exposed to the different FSH preparations in triplicate wells for each compound and incubation time (including control incubations in the absence of FSH) (ie. for each time point, each FSH preparation was added to three independent wells). For all glycoforms and experimental conditions, the complete set of biological replicates were included in the same NGS run, and all biological replicates were considered for the analysis. Technical replicates were not necessary because the technique employed (NGS) is considered quite robust and sensitive, as per the recommendation of the manufacturer (Illumina).

- Line 227-228: If only samples with >8 RIN were used, how many samples per treatment per time-point reached this quality threshold? With only 3 replicates, how many ended up being sequenced for each group? How was statistical analysis considered if the number was too low?

Answer:

In all cases we have ensured to have 3 sequenced replicates for every treatment/time-point, following standard practice in the field, based on a Bayesian criterion supported by B-statistic distributions. By following this guideline in the case a well known organism sequenced by a reliable technology (Illumina NextSeq protocols) statistical robustness enough to allow for differential expression analysis of coding transcripts is expected.

-Line 231: 10-15 million reads doesn’t appear to be deep. What is the rationale for this depth?

Answer:

The number of reads required for RNA-Seq will depend on how sensitive the experiment needs to be, the complexity of your organism and the project goals (in our case, determining differential gene expression of coding genes, without splice variant analysis, in a well known model organism with a mature technology). In general 5 M mapped reads is a good bare minimum for a differential gene expression (DGE) analysis in humans and mammals. In many cases 5 M – 15 M mapped reads are sufficient. This allows us to get a good snapshot of highly expressed genes. A higher sequencing depth generates more informational reads, which increases the statistical power to detect differential expression also among genes with lower expression levels.

See for instance, Liu, Y., Zhou, J. & White, K. P. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30, 301–304 (2014).

- Line 237: how did authors test if the biological processes were indeed active? Mere enrichment of a process among DEGs doesn’t mean active.

Answer:

The reviewer's query regarding the assessment of activity for biological processes is pertinent, considering that mere enrichment among Differentially Expressed Genes (DEGs) doesn't inherently imply activity. While acknowledging the limitations of protein-level measurement within our study, we focused on gene expression as a proxy for activity, recognizing its coarse-grained yet generally reliable approximation. Despite caveats such as the discrepancy between gene expression and protein concentration, and variability in overall proteic activity, high gene expression levels within our study suggest active transcriptional processes driving these genes. Thus, while gene expression serves as an imperfect marker for activity, it remains a valuable tool for assessing the dynamics of biological processes under investigation in our study.

- Lines 320-350: These two sections are confusing and not well justified. Why did authors run these comparisons? What physiological relevance do these comparisons address? While the first section title suggests “contrast between 6 and 12h”, the text seems to describe contrasts among FSH glycoforms at each time-point! The second section title suggests contrasts among each FSH glycoforms, but the text seems to describe how one glycoform is different from others.

Answer:

The reviewer's concerns regarding the clarity and justification of the two sections are valid, and we appreciate the opportunity to provide clarification. What we are presenting here aligns with the concept commonly known as a "difference in differences" comparison. This method entails examining changes in outcomes between two or more groups that have been exposed to different treatments over time. In the context of our study, which involved a perturbation assay, we sought to assess whether different perturbogens (in this case, different FSH glycoforms) led to distinct gene expression patterns across time points. Thus, our objective was to evaluate the similarities and differences in gene set expression patterns among the various glycoforms and time points. While the section titles may not have accurately conveyed this intention, we ensured that the text was revised (lines 170 to 176 of Introduction) to provide a clearer explanation of the rationale behind these comparisons and their physiological relevance within the context of our study.

-Lines 357-358: It is not recommended to separate up and downregulated genes to discover enriched BPs and pathways as some of the genes involved in a particular pathway may be upregulated while other of that pathway may be down regulated (as in lines 514-515).

Answer:

Separating a list of differentially expressed genes into two categories, overexpressed and underexpressed, to perform functional enrichment analysis on each list separately can be a valid and useful strategy in certain contexts. However, as the reviewer correctly pointed out, it's important to note that separating differentially expressed genes into two groups may lead to a loss of important information about interactions between these genes and how they collectively contribute to specific biological processes. However, we decided to use those two lists of genes based on the following considerations:

Overexpression and underexpression of genes MAY have different biological effects and contribute to different aspects of cellular function or observed phenotype. Therefore, analyzing these two categories separately can help better understand the underlying mechanisms. It is also worth noting that, by separating differentially expressed genes into overexpressed and underexpressed categories, it is possible to obtain more detailed information about the biological pathways or cellular functions being regulated in each case. This can facilitate the interpretation of functional enrichment analysis results.

Additionally, separating differentially expressed genes into more homogeneous categories can help reduce biological noise in the analysis, which may improve sensitivity to detect significant associations between genes and biological functions. It is also important to compare and contrast the results obtained from overexpressed and underexpressed gene lists to identify common patterns and differences between them. This can provide a more comprehensive understanding of the underlying biological processes.

Based on the above, we decided to construct two parallel analyses, each one for a differential expression trend. The results were broadly analyzed. The findings of the analysis are also consistent with the experimental results and current knowledge.

- Lines 496-556 : Each of the FSH glycoform appears to have regulated dramatically different set of biological processes. FSH18/21 induced steroid biosynthetic process; FSH24 did not but response to cAMP was both up and down-regulated; eqFSH stimulated processes like ovulation as well as ECM organization that is mostly associated with ovulation; recFSH induced angiogenesis and ECM remodeling processes. Of these, only FSH18/21 appears to have regulated biologically relevant process for follicular granulosa cells. Therefore, it is important for authors to first check which glycoform regulates the usual suspects of granulosa cell genes (Cyp19, Lhcgr, Ccnd2) first before starting to compare different glycoforms.

Answer:

We have checked for Cyp19a1, Lhcgr, and Ccnd2 transcript expression, and the levels of log fold change for the former genes are now included in the supplementary tables. See also the below answer on the expression of these particular transcripts.

- Authors should first prove that all FSH treatment stimulated Cyp19a1 and Lhcgr in granulosa cells, to ensure that results obtained using the cell culture model of this study are relevant to in vivo biology.

Answer:

This reviewer’s concerns are quite pertinent and we apologize for the omission of mentioning these important genes. Fold increase in these genes expression are now included in the supplementary tables. It was interesting to note that mainly after 12 h of exposure to the pituitary glycoforms was fold increase in the Lhcgr and Cyp191a genes clearly detectable. This may be explained by the fact that estradiol production (and presumably Cyp191a mRNA expression) in culture conditions similar to those employed in the present study, occurs late during the incubation period (24-48 h) (Parakh et al., PNAS 103 (33):12435, 2006; Jia and Hsueh, Endocrinology 119(4):1580, 1976). A similar expression profile has also been observed for the Lhcgr (Shi and Segaloff, Mol Endocrinol 9:734, 1995; Gulappa et al., Endocrinology 158(8):2672, 2017). In fact mRNA expression of Lhcgr occurs quite late during the incubation period (peak levels at 48 h; Shi and Segaloff, Mol Endocrinol 9:734, 1995), which may explain the relatively modest fold increase identif

Attachment

Submitted filename: Authors response PONE-D-23-33249.docx

pone.0293688.s015.docx (27.3KB, docx)

Decision Letter 1

Satish Rojekar

17 Apr 2024

Differential effects of follicle-stimulating hormone glycoforms on the transcriptome profile of cultured rat granulosa cells as disclosed by RNA-seq

PONE-D-23-33249R1

Dear Dr. Alfredo,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: All my comments have been adequately addressed. The revised manuscript reads well. The conclusions are sound based on the data presented.

Reviewer #3: In This work, Alfredo and colleagues have presented the data to provide information regarding the Importance of glycosylation/ glycoforms of the glycoprotein harmones through Next generation RNA sequencing analysis. The study was well designed and executed. The most important finding of the study include

1. Analysis of the effect of FSH glycoforms in all three possible ways like variations in gene expression, variations in the biological processes and the pathways affected. As these are the 3 most important parameters to delineate any pathways or ligand-receptor interactions of a biological molecules. Authors are succeeded in these by the design of the experiment.

2. This study provides a useful information for further investigation of the therapeutic molecules effecting the signaling pathways in various diseases.

3. Authors are provided the sufficient scientific explanation for the methodology and the results obtained.

**********

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: No

**********

Acceptance letter

Satish Rojekar

20 May 2024

PONE-D-23-33249R1

PLOS ONE

Dear Dr. Ulloa-Aguirre,

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Representative subset of overexpressed genes at 6 hours of FSH glycoform exposure.

    (PDF)

    pone.0293688.s001.pdf (64.4KB, pdf)
    S2 Table. Representative subset of overexpressed genes at 12 hours of FSH glycoform exposure.

    (PDF)

    pone.0293688.s002.pdf (64.1KB, pdf)
    S3 Table. Representative subset of underexpressed genes at 6 hours of FSH glycoform exposure.

    (PDF)

    pone.0293688.s003.pdf (56KB, pdf)
    S4 Table. Representative subset of underexpressed genes at 12 hours of FSH glycoform exposure.

    (PDF)

    pone.0293688.s004.pdf (62.9KB, pdf)
    S1 Fig. Enriched processes for FSH18/21 at 6 and 12 hours.

    In this representation, the red color corresponds to overexpressed enriched processes and the blue color to underexpressed processes. The intersection of the overexpressed processes at 6 h is against the underexpressed processes at 12 h, that is, the comparasion is performed between the two incubation times. This is why the exclusive processes may be different than those shown in Fig 4.

    (PDF)

    pone.0293688.s005.pdf (377.5KB, pdf)
    S2 Fig. Enriched processes for FSH24 at 6 and 12 hours.

    In this representation, the red colored squares corresponds to overexpressed enriched processes and the blue color to underexpressed processes.

    (PDF)

    pone.0293688.s006.pdf (426KB, pdf)
    S3 Fig. Enriched processes for eqFSH at 6 and 12 hours.

    The red squares correspond to overexpressed enriched processes and the blue color to underexpressed processes.

    (PDF)

    pone.0293688.s007.pdf (360.4KB, pdf)
    S4 Fig. Enriched processes for recFSH at 6 and 12 hours.

    The red squares correspond to overexpressed enriched processes and the blue color to underexpressed processes.

    (PDF)

    pone.0293688.s008.pdf (354.5KB, pdf)
    S5 Fig. Cyclic AMP signaling pathway (KEGG hsa04024), with differentially expressed genes colored.

    This pathway is significantly perturbed by both FSH18/21 and FSH24 at 6h. Note the activation of CREB followed by an inhibition of c-fos induced by FSH18/21, not observed with FSH24. In eqFSH and recFSH, this pathway is significantly perturbed at 6 h. Note the difference in phosphodiesterase (PDE) states: induced by eFSH, and repressed by recFSH. [Pathway perturbation detected with GAGE. Pathway visualization rendered with Pathview].

    (PDF)

    pone.0293688.s009.pdf (613.3KB, pdf)
    S6 Fig. cAMP signaling pathway (KEGG hsa04024) at 12 h, with differentially expressed genes colored.

    At this time, this pathway was significantly perturbed by both FSH18/21 and FSH24. Note the activation of PAR1 (coagulation factor II thrombin receptor) by FSH24, which was not induced by FSH18/21. This pathway was significantly perturbed by both recFSHand eFSH at this time. In addition, note the activation of adenylate cyclase (AC) and lipase E (HSL) by recFSH, not induced by eFSH. [Pathway perturbation detected with GAGE. Pathway visualization rendered with Pathview].

    (PDF)

    pone.0293688.s010.pdf (614.6KB, pdf)
    S7 Fig. Within the context of the MAPK signaling pathway (KEGG hsa04010), it can be observed distinct effects of the different FSH glycoforms at the 6-hour time point.

    Notably, receptor signaling proteins presented varying expression levels: FSH24 induced a more profound overexpression than FSH18/21 and eqFSH, while recFSH elicited comparatively lower expression. Furthermore, our analysis revealed that highly central molecules within the MAPK pathway, such as p38, responded differentially to the FSH glycoforms. Under the influence of FSH24, p38 and similar central molecules exhibited underexpression. In contrast, when exposed to all other FSH glycoforms, these central molecules (mainly p38) exhibited a consistent pattern of overexpression. [Pathway perturbation was detected using GAGE, and the pathway visualization was generated through Pathview].

    (PDF)

    pone.0293688.s011.pdf (494.1KB, pdf)
    S8 Fig. In the context of the MAPK signaling pathway (KEGG hsa04010), the comparison of the effects among the FSH glycoforms after 12-hour exposure with those at 6 h revealed significant differences.

    Receptor signaling proteins are now predominantly underexpressed, regardless of the particular FSH treatment applied, with a marked shift from the earlier (6 h) overexpression. Notably, at this time central molecules, like p38, consistently exhibited overexpression across all FSH glycoforms perturbations. [Pathway perturbation was detected using GAGE, and the pathway visualization was generated through Pathview].

    (PDF)

    pone.0293688.s012.pdf (495.6KB, pdf)
    S9 Fig. Distinct findings emerged in the PI3K signaling pathway (KEGG hsa04151) at the 6 h time point.

    Receptor signaling proteins expression showed a balanced distribution of both up- and down regulation across all FSH glycoforms, with no significant differences. PI3K consistently exhibited underexpression across all glycoforms, indicating a shared regulation. Notably, downstream effectors within the PI3K pathway showed unique responses; recFSH upgregulated effectors such as CCND1, CDK, and cyclin; and eqFSH, FSH18/21 and FSH24 downregulated BCl-2 and c-Myb. [Pathway perturbation was detected using GAGE, and the pathway visualization was generated through Pathview].

    (PDF)

    pone.0293688.s013.pdf (492.4KB, pdf)
    S10 Fig. In the PI3K pathway (KEGG hsa04151) at the 12 h mark, different features were detected.

    First, expression of signaling molecules exhibited a more varied pattern which was distinctly influenced by each FSH glycoform, suggesting differential regulatory effects. For instance, in the case of the proteins involved in focal adhesion, ECM, ITGA, and ITGB were, respectively: a) all upregulated by recFSH; b) up-, down-, and down-regulated, respectively, by eqFSH; and c) down-, up-, and down-regulated, respectively by FSH18/21 and FSH24. In the case of downstream effectors, the patterns of expression of molecules such as GYS and PEPCK were unique for each glycoform: a) both upregulated by recFSH; b) down- and upregulated, respectively, by eqFSH; c) down- and up-regulated, respectively, by FSH18/21,; and c) up-regulated and without change, respectively, by FSH24. All glycoforms consistently showed underexpression of PI3K persisted across all compounds, indicating a sustained (and shared) impact on this key element. [Pathway perturbation was detected using GAGE, and the pathway visualization was generated through Pathview].

    (PDF)

    pone.0293688.s014.pdf (492.8KB, pdf)
    Attachment

    Submitted filename: Authors response PONE-D-23-33249.docx

    pone.0293688.s015.docx (27.3KB, docx)

    Data Availability Statement

    All relevant data are within the paper and its Supporting information files. All statistical database are available at https://osf.io/57j3f/.


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