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. 2022 Jul 8;89(9):399–412. doi: 10.1002/mrd.23631

RNA‐sequencing reveals genes linked with oocyte developmental potential in bovine cumulus cells

Álvaro Martínez‐Moro 1,2, Leopoldo González‐Brusi 1, Ismael Lamas‐Toranzo 1, Elena O'Callaghan 3, Anna Esteve‐Codina 4, Pat Lonergan 3, Pablo Bermejo‐Álvarez 1,✉
PMCID: PMC9796886  PMID: 35802551

Abstract

Cumulus cells provide an interesting biological material to perform analyses to understand the molecular clues determining oocyte competence. The objective of this study was to analyze the transcriptional differences between cumulus cells from oocytes exhibiting different developmental potentials following individual in vitro embryo production by RNA‐seq. Cumulus cells were allocated into three groups according to the developmental potential of the oocyte following fertilization: (1) oocytes developing to blastocysts (Bl+), (2) oocytes cleaving but arresting development before the blastocyst stage (Bl−), and (3) oocytes not cleaving (Cl−). RNAseq was performed on 4 (Cl−) or 5 samples (Bl+ and Bl−) of cumulus cells pooled from 10 cumulus‐oocyte complexes per group. A total of 49, 50, and 18 differentially expressed genes (DEGs) were detected in the comparisons Bl+ versus Bl−, Bl+ versus Cl− and Bl‐ versus Cl−, respectively, showing a fold change greater than 1.5 at an adjusted p value <0.05. Focussing on DEGs in cumulus cells from Bl+ group, 10 DEGs were common to both comparisons (10/49 from Bl+ vs. Bl−, 10/50 from Bl+ vs. Cl−). These DEGs correspond to 6 upregulated genes (HBE1, ITGA1, PAPPA, AKAP12, ITGA5, and SLC1A4), and 4 downregulated genes (GSTA1, PSMB8, FMOD, and SFRP4) in Bl+ compared to the other groups, from which 7 were validated by quantitative PCR (HBE1, ITGA1, PAPPA, AKAP12, ITGA5, PSMB8 and SFRP4). These genes are involved in critical biological functions such as integrin‐mediated cell adhesion, oxygen availability, IGF and Wnt signaling or PKA pathway, highlighting specific biological processes altered in incompetent in vitro maturation oocytes.

Keywords: granulosa cell, integrin, in vitro maturation, oocyte quality, transcription

The article has compared the transcriptome of cumulus cells obtained from bovine cumulus‐oocyte‐complexes exhibiting different developmental competence: (1) unable to cleavage, (2) able to cleavage but unable to develop to blastocyst, or (3) able to develop to blastocyst.

graphic file with name MRD-89-399-g004.jpg

1. INTRODUCTION

In vitro embryo production (IVP) enables relevant applications for cattle reproductive management, including alleviating the negative effects of heat stress (Baruselli et al., 2020) and accelerating genetic improvement, especially when combined with sexed semen and embryo genomic selection (Ferre et al., 2020). Nevertheless, the general efficiency of the IVP process remains relatively low, as only between 30% and 40% of in vitro matured oocytes reach the blastocyst stage following fertilization and culture. Reduced oocyte competence clearly stands out as a major causative factor for the reduced developmental rates in IVP, as in vivo matured oocytes exhibit significantly higher developmental rates following in vitro fertilization (IVF) than those matured in vitro (Dieleman et al., 2002; Rizos et al., 2002; van de Leemput et al., 1999). In this context, understanding the underlying molecular regulation of oocyte competence is critical to improve IVP, but molecular analyses in oocytes are typically invasive, involve destroying the oocyte and are thus incompatible with subsequent embryo development. One solution is to use the surrounding cumulus cells as proxies of oocyte quality as they constitute an attractive matrix on which to perform molecular analyses, and are closely connected to the oocyte during growth and final maturation, serving essential metabolic and signaling functions.

Previous attempts to discover genes whose transcript abundance in cumulus cells may serve as predictor for bovine oocyte competence have been focused on comparing cumulus cells from groups of oocytes whose developmental competence was indirectly inferred by follicle size (Melo et al., 2017), the use of different in vitro maturation (IVM) media (Assidi et al., 2008), or their origin: in vivo versus in vitro (Salhab et al., 2013; Tesfaye et al., 2009), prepubertal versus adult (Bettegowda et al., 2008), or collected before versus after LH surge (Assidi et al., 2010) or at different times following FSH withdrawal (Bunel et al., 2014). Unfortunately, the candidate genes identified on the microarray‐based experiments mentioned above show very poor correlation between studies. Besides, these candidates genes were not coincident with those identified on a microarray‐based experiment where oocyte developmental competence was directly assessed by performing individual IVP (Bunel et al., 2015). Another study analyzing the expression of candidate genes by quantitative PCR (qPCR) in cumulus cells from oocytes showing different developmental competence also showed results discordant with microarray data (Kussano et al., 2016), indicating that the molecular signature of the developmentally competent oocyte still remains elusive.

The reasons for the lack of agreement between studies may have a biological basis, as the different classification criteria used to indirectly infer oocyte competence dealt with diverse biological processes such as follicle growth or hormonal response which may have a transcriptional effect on their own different from the sought‐after transcriptional signature of developmental competence. Another possible source of inconsistency may be technical, as microarray based experiments rely on a finite number of probes that vary depending on the manufacturer. In contrast to microarray, RNA‐seq provides an unbiased search for candidate transcripts and yield a higher dynamic range, ultimately leading to higher accuracy. The objective of this study was to apply RNA‐seq to uncover the transcriptional differences between cumulus cells enclosing oocytes that exhibit different developmental competence. To that aim, individual IVP was performed to infer directly the developmental potential of each oocyte. Once the developmental potential of each cumulus‐oocyte complexe (COC) was known, the stored cumulus cells were allocated to one of three groups according to the oocyte's developmental potential: (1) Oocytes not cleaving following IVF (Cl−), (2) oocytes cleaving but not developing to blastocysts (Bl−) and (3) oocytes developing to blastocyst (Bl+).

2. RESULTS

To correlate cumulus cell transcription with the developmental competence of the enclosed oocyte, COCs were individually matured. Cumulus cell samples were collected from 396 individual COCs in 7 replicates. Developmental potential of each individual oocyte was assessed at 48 h postinsemination (cleavage rate 52.3%, 207 embryos cleaved) and at day 8 postinsemination (blastocyst rate 13.4%, 53 blastocysts). Once the developmental potential of each COC was known, the stored cumulus cells were allocated to one of three groups according to the oocyte's developmental potential: (1) Oocytes not cleaving following IVF (Cl−), (2) oocytes cleaving but not developing to blastocysts (Bl−) and (3) oocytes developing to blastocyst (Bl+).

RNA‐seq detected the expression of 19,335 genes in bovine cumulus cells samples. Using a raw p value <0.05, inappropriate for RNA‐seq data as described below, the analysis identified 1609, 1466, and 1420 differentially expressed genes (DEGs) for the comparisons Bl+ versus Bl−, Bl+ versus Cl−, and Bl− versus Cl−, respectively (Figure 1). These DEGs were narrowed down to 77, 80, and 32 DEGs for the comparisons Bl+ versus Bl−, Bl+ versus Cl−, and Bl− versus Cl−, respectively, when an adjusted p value < 0.05 was used. Such adjusted p value takes into account the data overdispersion inherently associated with RNA‐seq data, yielding a more reliable result. From these subsets of DEGs obtained at an adjusted p value <0.05, 49, 50, and 15 DEGs, for the comparisons Bl+ versus Bl−, Bl+ versus Cl−, and Bl− versus Cl−, respectively, exhibited a fold change greater than 1.5 (Figure 1 and Table 1).

Figure 1.

Figure 1

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Venn diagram of differentially expressed genes (DEG) for the comparisons of the three groups exhibiting different developmental ability following IVF (Bl+, Bl−, and Cl−). Image on left shows DEG at a p raw <0.05. These lists of DEG are reduced by applying an adjusted p < 0.05 and a fold change greater than 1.5 (right image). 10 DEGs were common to Bl+ versus Bl− and Bl+ versus Cl− comparisons (red circle on right image)

Table 1.

Differentially expressed genes at a p adjusted value <0.05 and fold change >1.5 at the three comparisons

baseMean log2FoldChange shrunkenlfc lfcSE stat filter p value padj Fold change Shrunken Fold change
ENSBTAG00000037815, HBE1, protein_coding 26.4787138 −2.12882978 −0.77751908 0.3854938 −5.5223451 1 3.35E‐08 0.00012695 −4.37362577 −1.71418056
ENSBTAG00000012519, XDH, protein_coding 34.9303151 −1.70602939 −0.61150107 0.39614703 −4.30655603 1 1.66E‐05 0.00980409 −3.26261644 −1.52784805
ENSBTAG00000002319, HMCN2, protein_coding 60.1771415 −1.64057208 −0.50575982 0.4298763 −3.81638177 0 0.00013542 0.0356707 −3.11789443 −1.41987096
ENSBTAG00000015409, STK32B, protein_coding 15.6495314 −1.55173881 −0.53294504 0.4260127 −3.64247075 0 0.00027003 0.04909611 −2.9317027 −1.44687977
ENSBTAG00000035005, protein_coding 17.6713968 −1.52954229 −0.57825979 0.3999492 −3.82434143 0 0.00013112 0.03554952 −2.88694232 −1.49304721
ENSBTAG00000000817, SYNJ2, protein_coding 48.4180788 −1.12361283 −0.55634793 0.29865507 −3.7622426 0 0.0001684 0.03880699 −2.1789194 −1.47054194
ENSBTAG00000017733, CA2, protein_coding 1232.68419 −1.09137678 −0.5943611 0.27937775 −3.90645558 1 9.37E‐05 0.03011709 −2.13077282 −1.50980382
ENSBTAG00000010273, EREG, protein_coding 63.657073 −1.04827932 −0.56793004 0.28411068 −3.6896864 0 0.00022453 0.0463371 −2.06806183 −1.48239512
ENSBTAG00000016525, ITGA1, protein_coding 100.3518 −1.02332528 −0.62989844 0.23029216 −4.44359582 1 8.85E‐06 0.00654253 −2.03259851 −1.54745606
ENSBTAG00000010647, NTRK2, protein_coding 44.2282193 −0.96405071 −0.5662427 0.25350133 −3.80294139 0 0.00014299 0.03649014 −1.9507795 −1.48066237
ENSBTAG00000020895, LOXL4, protein_coding 9013.3992 −0.9606973 −0.5492939 0.2537134 −3.78654545 0 0.00015276 0.03705543 −1.94625035 −1.46336931
ENSBTAG00000008814, ADGRA2, protein_coding 544.473729 −0.94162586 −0.6037932 0.2221158 −4.23934655 1 2.24E‐05 0.01084811 −1.92069157 −1.519707
ENSBTAG00000002888, TMTC2, protein_coding 1371.22778 −0.92371015 −0.56201345 0.24029339 −3.84409312 0 0.000121 0.03485529 −1.89698747 −1.47632817
ENSBTAG00000018255, ACTN1, protein_coding 1406.51659 −0.91290287 −0.646985 0.19274936 −4.73621729 1 2.18E‐06 0.00301075 −1.88283017 −1.56589231
ENSBTAG00000052736, protein_coding 70.0576043 −0.91195359 −0.56554114 0.23423242 −3.89337057 0 9.89E‐05 0.03080768 −1.88159169 −1.47994251
ENSBTAG00000004010, PAPPA, protein_coding 705.782085 −0.89773068 −0.58690686 0.21375835 −4.19974556 1 2.67E‐05 0.01231594 −1.86313302 −1.50202295
ENSBTAG00000005714, ACTC1, protein_coding 1295.44064 −0.8687399 −0.54698365 0.2328306 −3.73121023 0 0.00019056 0.0420548 −1.82606725 −1.46102782
ENSBTAG00000004375, ESRP2, pseudogene 66.9836549 −0.85983042 −0.61682149 0.19224247 −4.47263519 1 7.73E‐06 0.00628409 −1.81482497 −1.5334929
ENSBTAG00000017086, GRB10, protein_coding 468.565501 −0.82402184 −0.53702793 0.21830105 −3.77470396 0 0.0001602 0.03767073 −1.77033432 −1.4509803
ENSBTAG00000014270, UNC5B, protein_coding 1451.4862 −0.80782213 −0.57019158 0.19370083 −4.17046284 0 3.04E‐05 0.01313486 −1.75056682 −1.48472071
ENSBTAG00000014705, HES4, protein_coding 401.094545 −0.80463733 −0.57900276 0.18921065 −4.25260065 0 2.11E‐05 0.01084811 −1.74670664 −1.49381631
ENSBTAG00000014788, AKAP12, protein_coding 3125.78806 −0.80402001 −0.56642493 0.19181549 −4.19163227 0 2.77E‐05 0.01235307 −1.74595939 −1.4808494
ENSBTAG00000013745, ITGA5, protein_coding 8536.22896 −0.80039934 −0.67011624 0.13183965 −6.0710062 1 1.27E‐09 1.76E‐05 −1.74158314 −1.59120117
ENSBTAG00000003955, MYO7A, protein_coding 295.849762 −0.79463621 −0.54856033 0.20508284 −3.87470846 0 0.00010675 0.03208839 −1.73463991 −1.46262541
ENSBTAG00000007763, SLC1A4, protein_coding 731.552183 −0.76493065 −0.51011009 0.20801395 −3.67730452 0 0.00023571 0.0471329 −1.69928831 −1.42415887
ENSBTAG00000007156, AGAP2, protein_coding 502.509705 −0.75188674 −0.53930729 0.18867277 −3.98513651 0 6.74E‐05 0.02453977 −1.68399369 −1.45327456
ENSBTAG00000016685, FMO4, protein_coding 281.168645 −0.74909699 −0.5273272 0.19717086 −3.79922766 0 0.00014515 0.03649014 −1.68074049 −1.44125659
ENSBTAG00000003100, SMTN, protein_coding 1828.20159 −0.73778949 −0.60298346 0.14279815 −5.16665997 1 2.38E‐07 0.0005492 −1.66761874 −1.51885428
ENSBTAG00000010531, CYP1B1, protein_coding 302.952778 −0.73459353 −0.57562419 0.15782087 −4.65460328 0 3.25E‐06 0.00374026 −1.6639286 −1.49032212
ENSBTAG00000051704, TNFRSF10D, protein_coding 2153.66879 −0.71567807 −0.57329256 0.14921157 −4.79639795 0 1.62E‐06 0.00258255 −1.6422549 −1.48791547
ENSBTAG00000046476, IGSF1, protein_coding 53.4421741 −0.71226507 −0.50483777 0.19486683 −3.65513751 0 0.00025704 0.04802905 −1.63837439 −1.41896379
ENSBTAG00000001042, MXD1, protein_coding 412.83263 −0.64709909 −0.49469463 0.16738173 −3.866008 0 0.00011063 0.03254679 −1.56601615 −1.40902249
ENSBTAG00000020650, ADCYAP1, protein_coding 7138.37598 −0.63633302 −0.48586002 0.168616 −3.77385919 0 0.00016074 0.03767073 −1.5543733 −1.40042043
ENSBTAG00000001511, BCL6, protein_coding 5546.65961 −0.58499101 −0.49417875 0.12946284 −4.51860152 0 6.22E‐06 0.00537952 −1.50002964 −1.40851874
ENSBTAG00000021516, GSTA1, protein_coding 5460.78137 0.61970182 0.54315932 0.11335477 5.46692312 0 4.58E‐08 0.00012695 1.53655757 1.45716002
ENSBTAG00000024493, DHRS3, protein_coding 2369.08918 0.65928401 0.50356721 0.16823569 3.9188118 0 8.90E‐05 0.03001018 1.57929864 1.41771467
ENSBTAG00000015086, HSD11B1, protein_coding 1492.89658 0.67415399 0.48931148 0.18314371 3.68101086 0 0.00023231 0.0471329 1.59566079 1.40377477
ENSBTAG00000003039, PSMB8, protein_coding 709.1219 0.68317143 0.52216713 0.16951175 4.03023058 0 5.57E‐05 0.02082352 1.60566556 1.43611087
ENSBTAG00000021964, CDH17, protein_coding 1535.98143 0.68771286 0.58492491 0.12580542 5.46648021 0 4.59E‐08 0.00012695 1.61072797 1.49996092
ENSBTAG00000014912, FMOD, protein_coding 739.722034 0.73701708 0.6215327 0.13030585 5.65605543 1 1.55E‐08 0.00010708 1.66672615 1.5385088
ENSBTAG00000002333, HOPX, protein_coding 1016.20725 0.74721593 0.6000701 0.14865524 5.02650252 1 5.00E‐07 0.00098667 1.67855049 1.51579021
ENSBTAG00000014291, WNT2B, protein_coding 319.903568 0.93282217 0.57235467 0.23656942 3.9431224 0 8.04E‐05 0.0278018 1.9090067 1.48694849
ENSBTAG00000021880, COQ8A, protein_coding 191.22917 0.96592022 0.63707829 0.21754298 4.44013509 1 8.99E‐06 0.00654253 1.95330904 1.55517646
ENSBTAG00000012543, EDA, protein_coding 19.6165426 1.2074367 0.57659555 0.31657913 3.81401229 0 0.00013673 0.0356707 2.30926974 1.49132589
ENSBTAG00000015366, SFRP4, protein_coding 886.750538 1.25803286 0.68623077 0.28545786 4.40707038 1 1.05E‐05 0.00689889 2.39169408 1.60907411
ENSBTAG00000003668, RADX, protein_coding 28.5049993 1.32787195 0.59111845 0.32947259 4.0302957 1 5.57E‐05 0.02082352 2.51032116 1.50641414
ENSBTAG00000022759, PAG11, protein_coding 70.901648 1.83737746 0.43593861 0.49388336 3.720266 0 0.00019901 0.04233467 3.57359827 1.35279066
ENSBTAG00000016030, HOXD10, protein_coding 9.66888124 4.89511024 0.34181762 1.14396661 4.27906743 0 1.88E‐05 0.01038009 29.7560319 1.2673523
ENSBTAG00000033330, HOXD11, protein_coding 22.7369796 5.40528012 0.2810518 1.18585902 4.5581136 0 5.16E‐06 0.00475788 42.3790727 1.21508042
baseMean log2FoldChange shrunkenlfc lfcSE stat filter p value padj Fold change Shrunken fold change
ENSBTAG00000037815, HBE1, protein_coding 26.4787138 −1.91848857 −0.64987859 0.40607055 −4.72452032 1 2.31E‐06 0.00297571 −3.78026813 −1.56903615
ENSBTAG00000005404, MSC, protein_coding 9.47009056 −1.68620844 −0.53638082 0.45222608 −3.72868466 0 0.00019248 0.03691229 −3.2180984 −1.45032961
ENSBTAG00000021846, CELSR3, protein_coding 28.7980484 −1.33691093 −0.70503025 0.28588494 −4.67639503 1 2.92E‐06 0.00312092 −2.52609855 −1.63017884
ENSBTAG00000050986, PXDN, protein_coding 759.097847 −1.28697186 −0.63829334 0.30510584 −4.21811607 1 2.46E‐05 0.01152678 −2.44015343 −1.55648679
ENSBTAG00000008111, ESYT3, protein_coding 35.4964486 −1.28597795 −0.62360821 0.31108815 −4.1338056 1 3.57E‐05 0.01301056 −2.43847291 −1.54072375
ENSBTAG00000016525, ITGA1, protein_coding 100.3518 −1.22846434 −0.74425043 0.24213649 −5.07343748 1 3.91E‐07 0.00135643 −2.3431744 −1.67510373
ENSBTAG00000000575, TNC, protein_coding 1942.73478 −1.18594336 −0.65829251 0.27295316 −4.34486042 1 1.39E‐05 0.00974389 −2.27512113 −1.57821363
ENSBTAG00000013081, PSPH, protein_coding 529.96833 −1.04225433 −0.57189496 0.2726827 −3.82222385 0 0.00013225 0.02976424 −2.0594432 −1.48647475
ENSBTAG00000004010, PAPPA, protein_coding 705.782085 −1.03006758 −0.65821181 0.22648823 −4.54799598 1 5.42E‐06 0.00480358 −2.04211991 −1.57812536
ENSBTAG00000000898,F2RL2, protein_coding 391.727257 −1.01147449 −0.57030922 0.2651496 −3.81473136 0 0.00013633 0.02976424 −2.01597045 −1.48484179
ENSBTAG00000011171, PIEZO2, protein_coding 266.685041 −0.95741882 −0.5973885 0.23464523 −4.08028251 1 4.50E‐05 0.01484478 −1.94183258 −1.51297537
ENSBTAG00000005305, NTS, protein_coding 800.137496 −0.94308085 −0.56728208 0.24642762 −3.82700958 0 0.00012971 0.02976424 −1.9226296 −1.48172948
ENSBTAG00000007763, SLC1A4, protein_coding 731.552183 −0.93784076 −0.61181017 0.2203764 −4.25563146 1 2.08E‐05 0.01152678 −1.91565898 −1.52817543
ENSBTAG00000049806, protein_coding 45.5574402 −0.86036732 −0.55532883 0.22431935 −3.83545739 0 0.00012533 0.02976424 −1.81550049 −1.46950354
ENSBTAG00000014788, AKAP12, protein_coding 3125.78806 −0.84079252 −0.57935931 0.20341816 −4.13332085 0 3.58E‐05 0.01301056 −1.79103374 −1.49418554
ENSBTAG00000012004, TGFB3, protein_coding 492.916789 −0.82260429 −0.52851344 0.22533801 −3.65053501 0 0.00026169 0.04700897 −1.76859571 −1.44244213
ENSBTAG00000006731, SLC7A5, protein_coding 1615.61458 −0.80300952 −0.5712345 0.19264121 −4.16842025 0 3.07E‐05 0.0124073 −1.74473693 −1.48579441
ENSBTAG00000013745, ITGA5, protein_coding 8536.22896 −0.69648492 −0.57619276 0.1398384 −4.98064151 0 6.34E‐07 0.0014989 −1.62055156 −1.49090957
ENSBTAG00000010366, HCRTR1, protein_coding 92.8984086 −0.67396878 −0.50576093 0.1736652 −3.88085101 0 0.00010409 0.02645027 −1.59545595 −1.41987205
ENSBTAG00000006770, MTBP, protein_coding 197.974827 −0.66753843 −0.52635602 0.15623066 −4.27277469 0 1.93E‐05 0.01141521 −1.58836054 −1.4402867
ENSBTAG00000022007, SAMHD1, protein_coding 507.068088 −0.65998868 −0.49619588 0.17198734 −3.83742589 0 0.00012433 0.02976424 −1.58007022 −1.41048946
ENSBTAG00000000446, ATP11A, protein_coding 817.397518 −0.63853304 −0.5112286 0.15082022 −4.23373624 0 2.30E‐05 0.01152678 −1.55674543 −1.42526344
ENSBTAG00000021516, GSTA1, protein_coding 5460.78137 0.58634961 0.5090243 0.12023851 4.87655405 0 1.08E‐06 0.00166247 1.5014429 1.42308743
ENSBTAG00000005814, PSME2, protein_coding 1461.85184 0.59167211 0.46356199 0.15837816 3.73581881 0 0.00018711 0.03637277 1.50699237 1.3789422
ENSBTAG00000037988, ZSCAN31, protein_coding 394.510381 0.59644896 0.46825745 0.15731561 3.79141629 0 0.00014979 0.03166985 1.51199038 1.38343748
ENSBTAG00000014912, FMOD, protein_coding 739.722034 0.59878705 0.50092357 0.1380527 4.33738015 0 1.44E‐05 0.00974389 1.51444276 1.41511919
ENSBTAG00000003458, CDCA7, protein_coding 128.32517 0.6126092 0.47091108 0.16214237 3.7782179 0 0.00015795 0.0322565 1.52902204 1.38598445
ENSBTAG00000017810, EFHC1, protein_coding 255.040583 0.63268455 0.51540401 0.14234854 4.44461552 0 8.80E‐06 0.00657635 1.55044737 1.42939437
ENSBTAG00000014878, COX7A1, protein_coding 448.645919 0.63496081 0.48829927 0.16612315 3.8222294 0 0.00013225 0.02976424 1.55289556 1.40279021
ENSBTAG00000008954, PSMB9, protein_coding 322.288285 0.64130912 0.5126677 0.15221499 4.2131797 0 2.52E‐05 0.01152678 1.55974385 1.42668586
ENSBTAG00000010365, SQOR, protein_coding 297.827243 0.64827557 0.5409818 0.13217637 4.90462525 0 9.36E‐07 0.00166247 1.56729371 1.45496233
ENSBTAG00000019015, IFITM3, protein_coding 17098.1904 0.66380895 0.52294932 0.15690873 4.23054199 0 2.33E‐05 0.01152678 1.58425981 1.4368897
ENSBTAG00000021378,S100A13, protein_coding 410.889638 0.737533 0.54657618 0.17711502 4.16414715 0 3.13E‐05 0.0124073 1.66732229 1.46061523
ENSBTAG00000007389, IFI35, protein_coding 476.508804 0.79118103 0.56433895 0.19007248 4.16252268 0 3.15E‐05 0.0124073 1.7304905 1.4787098
ENSBTAG00000005182, BoLA, protein_coding 1270.66579 0.79322265 0.53057632 0.21296559 3.72465179 0 0.00019559 0.03700729 1.73294114 1.44450613
ENSBTAG00000007602, ITGA8, protein_coding 123.960196 0.82296642 0.54099383 0.22217648 3.70411138 0 0.00021213 0.03909584 1.76903969 1.45497446
ENSBTAG00000052055, PRSS35, protein_coding 926.617163 0.87588891 0.57257477 0.22005409 3.98033467 0 6.88E‐05 0.02170224 1.83513846 1.48717536
ENSBTAG00000003039, PSMB8, protein_coding 709.1219 0.87604555 0.64973839 0.18024151 4.86039847 1 1.17E‐06 0.00166247 1.8353377 1.56888367
ENSBTAG00000012208, protein_coding 641.996258 0.92782533 0.52508522 0.25480351 3.64133656 0 0.00027123 0.04811215 1.90240621 1.43901858
ENSBTAG00000003152, IFI27, protein_coding 12519.18 1.00530287 0.63919636 0.22516773 4.46468453 1 8.02E‐06 0.00651819 2.00736487 1.55746135
ENSBTAG00000017741, HACD4, protein_coding 96.8920071 1.01382425 0.57832167 0.25558096 3.96674396 0 7.29E‐05 0.02199943 2.01925659 1.49311126
ENSBTAG00000007554, IFI6, protein_coding 9514.43329 1.01384156 0.60995997 0.24571896 4.12602094 1 3.69E‐05 0.01309452 2.01928083 1.52621686
ENSBTAG00000037533,C4A, protein_coding 1687.1508 1.04743914 0.62189015 0.24716276 4.23785181 1 2.26E‐05 0.01152678 2.06685781 1.53889004
ENSBTAG00000004155, SPATA20, protein_coding 143.31563 1.15486135 0.81924597 0.18903435 6.10926728 1 1.00E‐09 7.55E‐06 2.22662923 1.76448354
ENSBTAG00000006864, protein_coding 533.66699 1.20916202 0.54071426 0.32344862 3.73834343 0 0.00018524 0.03637277 2.31203306 1.45469254
ENSBTAG00000015366, SFRP4, protein_coding 886.750538 1.57569053 0.79740628 0.30320757 5.19673877 1 2.03E‐07 0.00095938 2.98078131 1.73797374
ENSBTAG00000006846, LGALS9, protein_coding 189.319423 1.57652444 0.53710214 0.41616931 3.78818044 0 0.00015175 0.03166985 2.98250475 1.45105493
ENSBTAG00000020203, TMEM151A, protein_coding 44.2503514 1.8064424 0.61919315 0.41960817 4.30506965 1 1.67E‐05 0.0103706 3.4977869 1.5360159
ENSBTAG00000040244, APOL3, protein_coding 47.3460324 2.10452986 0.5057058 0.53922484 3.90288007 0 9.51E‐05 0.02549885 4.30057588 1.4198178
ENSBTAG00000014529, GBP4, protein_coding 57.7785598 2.58915172 0.41020341 0.62389815 4.14995896 0 3.33E‐05 0.01275406 6.01744781 1.32887316
baseMean log2FoldChange shrunkenlfc lfcSE stat filter p value padj Fold change Shrunken fold change
ENSBTAG00000011538, KIF1A, protein_coding 70.20266 −2.23957319 −0.64752587 0.47159611 −4.74892212 1 2.05E‐06 0.00688155 −4.72257331 −1.56647948
ENSBTAG00000005679, TMEM130, protein_coding 21.2912193 −2.0531094 −0.58276117 0.4826633 −4.25370933 0 2.10E‐05 0.03312846 −4.14999442 −1.49771298
ENSBTAG00000033726, GRIP1, protein_coding 42.6648451 −1.04563585 −0.59317774 0.26370095 −3.96523354 1 7.33E‐05 0.04934713 −2.06427597 −1.50856592
ENSBTAG00000007698, TMEM59L, protein_coding 445.22969 −0.83954751 −0.55161438 0.21807519 −3.8498076 0 0.00011821 0.04972235 −1.7894888 −1.46572493
ENSBTAG00000012012, CYB5A, protein_coding 912.792784 −0.75546844 −0.57766426 0.16723207 −4.51748539 0 6.26E‐06 0.01684609 −1.68817965 −1.49243103
ENSBTAG00000047706, ING2, protein_coding 1370.59626 −0.73789537 −0.54506528 0.17903156 −4.12159382 0 3.76E‐05 0.03376308 −1.66774113 −1.45908636
ENSBTAG00000002699, KIT, protein_coding 3268.693 −0.65390318 −0.52709465 0.14709654 −4.4454016 0 8.77E‐06 0.01968028 −1.57341929 −1.44102428
ENSBTAG00000015248, PLA2G16, protein_coding 1726.79268 −0.63501221 −0.48962246 0.16425182 −3.86608937 0 0.00011059 0.04972235 −1.5529509 −1.40407739
ENSBTAG00000003081, RWDD4, protein_coding 1842.91844 −0.62907124 −0.49540946 0.15522398 −4.05266796 0 5.06E‐05 0.03786511 −1.54656904 −1.4097208
ENSBTAG00000004375, ESRP2, pseudogene 66.9836549 0.79182826 0.55735351 0.20436503 3.87457796 0 0.00010681 0.04972235 1.73126703 1.47156729
ENSBTAG00000004155, SPATA20, protein_coding 143.31563 0.79989979 0.54641291 0.19081576 4.19200067 0 2.77E‐05 0.03312846 1.74098019 1.46044995
ENSBTAG00000011131, NMUR2, protein_coding 302.704748 0.84025472 0.54728294 0.21787477 3.85659483 0 0.00011498 0.04972235 1.79036621 1.46133095
ENSBTAG00000003955, MYO7A, protein_coding 295.849762 0.90394908 0.60207688 0.21824976 4.14181022 1 3.45E‐05 0.03312846 1.87118095 1.51790014
ENSBTAG00000054774, processed_pseudogene 325.949244 1.18039991 0.5864884 0.28826976 4.09477533 1 4.23E‐05 0.03554937 2.26639593 1.50158734
ENSBTAG00000049042,, protein_coding 22.5892647 1.79384363 0.56725143 0.45930221 3.90558458 0 9.40E‐05 0.04972235 3.46737442 1.48169801
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Enrichment analysis failed to find enriched terms in the 15 DEGs in Bl− versus Cl− comparison, but four common enrichment terms (FDR < 0.05) were found in the comparisons Bl+ versus Bl− and Bl+ versus Cl−: “integrin domain superfamily,” “cell surface receptor signaling pathway,” “response to organic substance,” and “response to chemical.” There were also terms exclusive to Bl+ versus Cl− and Bl+ versus Bl− comparisons: “Extracellular region” and “proteosomal complex” were exclusive to the Bl+ versus Cl− comparison and “anatomical structure morphogenesis,” “positive regulation of cell communication,” or “cell communication” were exclusive to Bl+ versus Bl− comparison.

Interaction networks allow to determine if the proteins encoded by DEGs interact directly (physical) or indirectly (functional) with each other, aiming to uncover network properties associated to developmental potential. The interaction network from DEGs in the Bl− versus Cl− comparison did not show a significant connectivity, as the observed interactions (5) were close to the expected random observations (3). In contrast, there were statistically significant relationships for DEGs in Bl+ versus Bl− and Bl+ versus Cl− comparisons. In the case of the 50 DEGs in the Bl+ versus Cl− comparison, there were 59 observed interactions versus 33 expected (Figure 2). Several clusters of genes in the interaction network coded for proteins that were related to enriched terms were selected. such as (1) extracellular matrix organization, including PXDN, TNC, ITGA1, ITGA8, and FMOD), (2) cytokine signaling in immune system including PSME2, SAMHD1, IFI6, GBP4, PSMB9, PSMB8, LGALS9, IFITM3, IFI35, and IFI27, and (3) G alpha (q) signaling events, including NTS, F2RL2, and HCTRC1. Interaction analysis of the 49 DEGs in the Bl+ versus Bl− comparison observed 69 interactions versus 23 expected (Figure 2). Although the connectivity found Bl+ versus Bl− was weaker than for Bl+ versus Cl−, several nodes including NTRK2, CDH17, and ACTN1 or HMCN2 displayed a high number of connections.

Figure 2.

Figure 2

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Interaction networks obtained from the DEGs at a p adjusted value <0.05 and fold change >1.5 at the three comparisons (from left to right Bl− vs. Cl−, Bl+ vs. Cl− and Bl+ vs. Bl−) using STRING. The color of edges and lines represents different types of evidence for protein‐to‐protein interaction: red for fusion evidence, green for neighborhood evidence, blue for co‐occurrence evidence, purple for experimental evidence, yellow for textmining evidence, light blue for database evidence and black for coexpression evidence. DEG, differentially expressed genes.

A more stringent selection of DEGs was obtained through the three‐group experimental design. Such a design constitutes a double check for DEGs potentially associated with oocyte developmental potential as both Bl+ versus Bl− and Bl+ versus Cl− comparisons contrast COCs exhibiting good developmental potential (Bl+) to COCs resulting in developmental arrest (Bl− and Cl−) (Figure 1). At an adjusted p value > 0.05 and fold change >1.5, 10 DEGs were common to both comparisons (10/49 from Bl+ vs. Bl−, 10/50 from Bl+ vs. Bl−). These DEGs correspond to 4 genes upregulated (GSTA1, PSMB8, FMOD, and SFRP4) and 6 genes downregulated (HBE1, ITGA1, PAPPA, AKAP12, ITGA5, and SLC1A4) in Bl+ compared to the other groups and none exhibited statistically significant differences in the Bl− versus Cl− comparison. Nine of those genes were selected for RNA‐seq validation by qPCR, as we were unable to amplify SLC1A4 by qPCR from cumulus cell complementary DNA (cDNA) samples at an efficiency compatible with reliable quantification. qPCR was performed using the same RNA samples than those employed for RNA‐seq, so although this provides a technical validation (using different retrotranscription and technique) no validation in independent biological samples was tested. qPCR confirmed the significant differences observed by RNA‐seq in both comparisons for HBE1, ITGA1, PAPPA, ITGA5, and SFRP4. In the case of AKAP12 and PSMB8 only the Bl+ versus Cl− comparison remained significant, whereas no significant difference between groups were observed for GSTA1 and FMOD (Figure 3).

Figure 3.

Figure 3

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Relative mRNA abundance of nine genes common to the comparisons Bl+ versus Bl− and Bl+ versus Cl− determined by qPCR. Mean ± standard error of the mean. Different letters indicate significant differences based on ANOVA (p < 0.05). ANOVA, analysis of variance; mRNA, messenger RNA.

3. DISCUSSION

The transcriptional analysis of individual COCs requires two critical modifications to conventional bovine IVP procedures: (1) individual IVP (iIVP) and (2) cumulus cell removal before fertilization. As a consequence of these modifications, blastocyst rates are halved compared to conventional IVP, impairing a direct application for commercial IVP. Given that we have previously observed that cumulus cell removal before IVF does not cause a significant reduction in blastocyst rate when IVP is conducted in groups (Lamas‐Toranzo et al., 2019), we believe that the major cause of the reduced developmental rate is iIVP, as previously reported by others (Bunel et al., 2015). In this sense, while current methods allow performing iIVP to elucidate molecular clues of developmental competence (i.e., this experiment) further advances on iIVP are needed to be able to apply oocyte selection markers for commercial IVP purposes.

The transcriptional analysis of cumulus cells obtained from oocytes of known developmental potential has identified different biological pathways that are likely involved in oocyte quality. Among the different DEGs obtained in this analysis, integrins may be a key player involved in oocyte competence, as two of the genes validated by qPCR (Integrin Subunit Alpha 1 and 5, ITGA1 and ITGA5) were downregulated in cumulus cells from oocytes developing to blastocysts (Bl+) compared to other groups, and enrichment analysis of biological annotations also highlighted “integrin domain superfamily” in both Bl+ versus Bl− and Bl+ versus Cl− comparisons. Integrin‐mediated cell adhesions provide dynamic links between the extracellular matrix and the cytoskeleton and, in the context of oocyte maturation, they control both cumulus cell expansion and luteinization, increasing their expression during cumulus expansion (Kitasaka et al., 2018) and ovulation (Wissing et al., 2014). In this sense, the negative correlation between integrin expression in cumulus cells and oocyte quality may indicate that competent IVM oocytes may achieve cumulus cell expansion earlier or to a greater extend, thereby resuming the expression of the integrins required for expansion before the end of IVM. In agreement with this hypothesis, cumulus expansion intensity is positively linked to embryo development (Qian et al., 2003), and ITGA5 was found to be upregulated in cumulus cells from rhesus monkey oocytes matured in vitro compared to those matured in vivo (Lee et al., 2011).

Other genes downregulated in cumulus cells from competent oocytes include HBE1, PAPPA, and AKAP12. HBE1 encodes for Epsilon 1 subunit of hemoglobin. Hemoglobin has been reported to be expressed by mouse preimplantation embryos where it was suggested to play a role on embryonic oxygen regulation (Lim et al., 2019). As oxidative stress exerts a negative impact on oocyte competence (Bennemann et al., 2018; Bermejo‐Alvarez et al., 2010), the lower expression of HBE1 in cumulus cells from competent oocytes may indicate a reduced exposure to oxygen during folliculogenesis, before IVM, or a higher ability to deal with oxidative stress during IVM. Pregnancy‐associated plasma protein‐A (PAPPA) regulates ovarian follicle dominance by degrading IGFBP‐4 in preovulatory follicles of several species, including cattle (Mazerbourg et al., 2001; Rivera & Fortune, 2001). Similarly to integrins, PAPPA expression increases during bovine folliculogenesis (Mazerbourg et al., 2001) and thereby the lower expression in cumulus cells from more competent oocytes may indicate that they have attained full developmental competence by the end of IVM. Finally, A‐Kinase Anchoring Protein 12 (AKAP12) overexpression in the cumulus cells from mice lacking estrogen receptor beta has been suggested to contribute to the sequestration of PKA regulatory units, leading to reduced cAMP levels (Binder et al., 2013). In this perspective, the higher expression of AKAP12 in cumulus cells from incompetent bovine oocytes may be linked to reduced cAMP, diminishing the odds to progress to the blastocyst stage (Luciano et al., 1999).

Other genes (SFRP4, PSMB8, FMOD, and GSTA1) were upregulated in cumulus cells from competent oocytes, although the differences in FMOD and GSTA1 expression were not confirmed by qPCR. Secreted Frizzled Related Protein 4 (SFRP4) inhibits Wnt signaling, a pathway known to play multiple functions during folliculogenesis (Hernandez Gifford, 2015). The role of SFRP4 during folliculogenesis remain under debate, as its ablation in mice has been reported to increase (Zamberlam et al., 2019) or decrease (Christov et al., 2011) litter size, and whereas mRNA content in human cumulus cells was positively associated with in vivo meiotic progression (Devjak et al., 2012), protein content in human follicular fluid has been negatively associated with in vitro meiotic progression (Pla et al., 2021). The differences between studies may be linked to the different hormonal environment of in vitro and in vivo maturation, as SFRP4 expression is modulated by LH in a species‐specific manner (Maman et al., 2011). The agreement between our findings and those obtained in human IVM (Pla et al., 2021) suggest that, in contrast to rodents, bovine and humans may share a similar regulation of SFRP4, although dedicated experiments would be required to test this hypothesis. Proteasome 20 S subunit beta 8 (PSMB8) is a component of the proteasome, which modulates oocyte meiotic maturation (Huo et al., 2004). In agreement with the positive association between PSMB8 expression and oocyte quality observed in bovine cumulus cells, the expression level of its antisense (PSMB8‐AS1) was higher in cumulus cells from old versus young women (Bouckenheimer et al., 2018). Finally, although qPCR data did not detect significant differences between groups in glutathione S‐Transferase Alpha 1 (GSTA1) expression, the positive association between GSTA1 expression and oocyte quality observed by RNA‐seq is consistent with previous findings. GSTA1 catalyzes the conjugation of glutathione to molecules such as prostaglandins A2 and J2 and it has been found to display steroid isomerase activity in bovine (Raffalli‐Mathieu et al., 2007). GSTA1 expression in bovine cumulus cells increases following FSH and/or phorbol myristate addition to IVM media, which resulted in improved rates of blastocyst development (Assidi et al., 2008). GSTA1 transcript abundance is also higher in cumulus cells from in vivo matured bovine oocytes compared to IVM counterparts (Salhab et al., 2013) and in bovine oocytes selected by Brilliant Cresyl Blue staining (Janowski et al., 2012).

In conclusion, the transcriptome of bovine cumulus cells from COCs exhibiting different developmental competence following individual IVP differs in a small subset of genes involved in critical biological functions such as integrin‐mediated cell adhesion, oxygen availability, IGF and Wnt signaling or PKA pathway. Such biological functions are required during folliculogenesis to attain full oocyte competence, thereby highlighting specific processes altered in incompetent IVM bovine oocytes.

4. MATERIALS AND METHODS

4.1. Individual IVP and collection of cumulus cells samples

Bovine embryos were produced from slaughterhouse ovaries following conventional protocols (Lamas‐Toranzo et al., 2020), adapted to individual embryo production. Bovine ovaries were transported at 35–37°C from a local slaughterhouse to the laboratory. COCs were aspirated from surface visible antral follicles (2–8 mm) and selected using conventional morphological criteria (Hawk & Wall, 1994). Individual in vitro production (iIVP) was required to determine the developmental ability of each oocyte. COCs were matured individually in 40 µl drops of TCM‐199 supplemented with 10% fetal calf serum (FCS) and 10 ng/ml epidermal growth factor covered under mineral oil at 39°C and 5% CO2 in air with humidified atmosphere for 24 h. Following maturation, the cumulus cells from each individual COC were removed by pipetting in medium supplemented with 0.1% hyaluronidase. Cumulus cells were collected from the media by centrifugation at 1500 rpm for 5 min, snap frozen in liquid nitrogen and stored at −80°C until analysis. Denuded oocytes were individually inseminated in 40 µl drops of TALP medium covered under mineral oil and containing 1 × 106 frozen‐thawed bull spermatozoa/ml. After 20 h of gamete coincubation, presumptive zygotes were cultured individually in 10 µl drops of synthetic oviduct fluid medium supplemented with 5% FCS at 39°C and in a 5% CO2 and 5% O2 water‐saturated atmosphere. Embryo development was assessed for each individual oocyte at 48 h postinsemination (cleavage, at least 4‐cells) and at day 8 postinsemination (blastocyst formation).

4.2. Transcriptional analysis

RNA extraction was performed on 4 (Cl−) or 5 (Bl− and Bl+) samples per group each of which was composed of cumulus cells obtained from 10 individual COCs. Individual samples (i.e., 40 Cl−, 50 Bl−, and 50 Bl+) were collected from 7 independent replicates. The rationale behind analyzing three experimental groups was to double‐check for the correlation between transcriptional change and developmental competence; for instance a “positive marker” for oocyte competence should be upregulated in Bl+ versus Bl− but also between Bl+ and Cl−. Total RNA was extracted using MagMAX mirVana Total Isolation Kit (Applied Biosystems) following the manufacturer's instruction with minor modifications. Briefly 200 µl of Lysis Binding Mix were added to the sample, followed by gentle pipetting and 5 min incubation at room temperature. Then 20 µl of Binding Beads Mix were added and shaken gently for 5 min. Beads‐mRNA complexes were washed once in each Wash Solution 1 and 2. Following the washing step, samples were treated with 50 µl of Turbo DNAse treatment, 50 µl of Rebinding Buffer and 100 µl of Isopropanol were added to the sample and mixed gently. Finally, following a double wash in Wash Solution 2, total RNA was eluted in 20 µl of Elution Buffer and stored at −80°C until analysis.

RNA samples were quantified by Qubit RNA BR Assay (Thermo Fisher Scientific) and RNA integrity was estimated by using RNA 6000 Nano Bioanalyzer 2100 Assay (Agilent). RNA amount oscillated between 0.285 and 0.676 µg and RIN values between 8 and 9.2. RNA‐seq libraries were prepared with KAPA Stranded mRNA‐Seq Illumina Platforms Kit (Roche) following the manufacturer's recommendations. Briefly, 50–100 ng of total RNA was used for the poly‐A fraction enrichment with oligo‐dT magnetic beads, following the mRNA fragmentation. The strand specificity was achieved during the second strand synthesis performed in the presence of dUTP instead of dTTP. The blunt‐end double stranded cDNA was 3′ adenylated and Illumina platform compatible adaptors with unique dual indexes and unique molecular identifiers (Integrated DNA Technologies) were ligated. The ligation product was enriched with 15 PCR cycles and the final library was validated on an Agilent 2100 Bioanalyzer with the DNA 7500 assay.

The libraries were sequenced on a HiSeq. 4000 system (Illumina) with a read length of 2 × 76 bp + 8 bp + 8 bp using the HiSeq. 4000 SBS kit (Illumina) obtaining >30 M reads/sample. Image analysis, base calling and quality scoring of the run were processed using the manufacturer's software Real Time Analysis (RTA 2.7.7). Differential expression was analysed by DESeq. 2 software obtaining raw p values, adjusted p values, raw fold changes and shrunken fold changes for all genes detected. Enrichment in biological annotations and a network of biological interactions for each of the three comparisons were performed on differentially expressed genes (DEGs) with an adjusted p value <0.05 and fold change >1.5 using STRING (v11; Szklarczyk et al., 2019) through the package “STRINGdb” in R (v4.0.4). Only enriched terms with FDR < 0.05 calculated by the Benjamini‐Hochberg procedure were selected.

RNA‐seq validation was performed on independently retrotranscribed cDNA obtained from the RNA samples mentioned above using qScript cDNA Supermix (Quantabiosciences, containing a blend of random and oligo dT primers optimized to deliver unbiased representation of 5′ and 3′ sequences) in a 20 µl final volume. Relative mRNA abundance of 9 selected DEGs was analyzed in 4 (Cl−) or 5 (Bl− and Bl+) samples by qPCR as previously described (Lamas‐Toranzo et al., 2018) using specific primers detailed in Table 2. Experiments were conducted to contrast the relative levels of each transcript and the housekeeping gene PPIA in each sample. cDNA was diluted to 55 µl in 10 mM Tris‐HCl (pH 7.5) and qPCR was performed in duplicate by adding a 2 µl aliquot of diluted cDNA to the PCR mix containing the specific primers for each DEG. PCR efficiencies were optimized to achieve efficiencies close to 1 and then the comparative cycle threshold (Cq) method was used to quantify expression levels as described in (Schmittgen & Livak, 2008). As primers were designed using the same annealing parameters, all qPCRs were performed using the same cycling conditions: 40 cycles of 94°C 15 s, 56°C 30 s, and 72°C 20 s followed by a final melting assay to assess product identity. Cq value was taken in the log‐linear phase of the reaction, and ΔCq value was determined by substracting the PPIA Cq value for each sample from each gene Cq value of the sample. Calculation of ΔΔCq involved using the highest sample ΔCq value (i.e., the sample with the lowest target expression) as an arbitrary constant to subtract from all other ΔCq sample values. Fold changes in the relative gene expression of the target were determined using the formula 2−ΔΔCq. qPCR data were analysed by one way analysis of variance using the statistical software Sigma Stat (Jandel Scientific).

Table 2.

Details of primers used for qPCR

Gene Primer sequence (5′–3′) Fragment size (bp) GenBank accession no.
PPIA CGGGATTTATGTGCCAGGGT 218 NM_178320.2
CCAAAGTACCACGTGCTTGC
HBE1 CTGAGTGAGCTGCACTGTGA 219 NM_001110507.1
AGGCACTGGGGACACAAAAT
ITGA1 AGGGCAGAACTTCAGAGTGA 100 XM_024981466.1
TGCCTGGTAGCCCATCTTTG
PAPPA CAAGGAGGGCAAGTGGAACA 234 XM_024996354.1
AGGCACATGAGCTCACACAG
AKAP12 AAAACCCGAACCCACGGAAT 154 XM_024997063.1
TGAGCAGTTGACACGTCTGT
ITGA5 AGTGGATCAAGGCAGAAGGC 197 NM_001166500.1
GAGGAATCAGGCATCGGAGG
GSTA1 GTGCCCACCTGCTGAAAAAG 202 NM_001078149.1
GAAGTTGGCCAAAAGGCTGG
PSMB8 TGGCCTTCAAGTTCCAGCAT 210 NM_001040480.1
TACGCTCCCCATTCCTCAGA
FMOD GGCCTGGCCTCAAATACCTT 153 NM_174058.2
GCAGAAGCTGCTGATGGAGA
SFRP4 CCACACATCCTGCCTCATCA 180 NM_001075764.1
TGCTGTTCGCTTCTTGTCCT
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CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

PEER REVIEW

The peer review history for this article is available at https://publons.com/publon/10.1002/mrd.23631

ACKNOWLEDGMENTS

The authors want to acknowledge “Kildare Chilling Company” for kindly providing bovine ovaries to conduct the experiments. This work was funded by the projects IND2017/BIO‐7748 from Madrid Region Government and AGL2017‐84908‐R and PID2020‐117501RB‐I00 by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO). AMM was funded by project IND2017/BIO‐7748 and ILT by a FPI fellowship by MINECO.

Martínez‐Moro, Á. , González‐Brusi, L. , Lamas‐Toranzo, I. , O'Callaghan, E. , Esteve‐Codina, A. , Lonergan, P. , & Bermejo‐Álvarez, P. (2022). RNA‐sequencing reveals genes linked with oocyte developmental potential in bovine cumulus cells. Molecular Reproduction and Development, 89, 399–412. 10.1002/mrd.23631

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

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

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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