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Open Access

Peer-reviewed

Research Article

Comprehensive transcriptomic analysis of long non-coding RNAs in bovine ovarian follicles and early embryos

  • Pengmin Wang,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Département des sciences animales, Faculté des sciences de l’agriculture et de l’alimentation, Université Laval, Québec City, Québec, Canada

    ⨯
  • Éric R. Paquet ,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    eric.paquet@fsaa.ulaval.ca (ERP); claude.robert@fsaa.ulaval.ca (CR)

    Affiliation Département des sciences animales, Faculté des sciences de l’agriculture et de l’alimentation, Université Laval, Québec City, Québec, Canada

    ⨯
  • Claude Robert

    Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing

    eric.paquet@fsaa.ulaval.ca (ERP); claude.robert@fsaa.ulaval.ca (CR)

    Affiliation Département des sciences animales, Faculté des sciences de l’agriculture et de l’alimentation, Université Laval, Québec City, Québec, Canada

    ⨯

Abstract

Long non-coding RNAs (lncRNAs) have been the subject of numerous studies over the past decade. First thought to come from aberrant transcriptional events, lncRNAs are now considered a crucial component of the genome with roles in multiple cellular functions. However, the functional annotation and characterization of bovine lncRNAs during early development remain limited. In this comprehensive analysis, we review lncRNAs expression in bovine ovarian follicles and early embryos, based on a unique database comprising 468 microarray hybridizations from a single platform designed to target 7,724 lncRNA transcripts, of which 5,272 are intergenic (lincRNA), 958 are intronic, and 1,524 are antisense (lncNAT). Compared to translated mRNA, lncRNAs have been shown to be more tissue-specific and expressed in low copy numbers. This analysis revealed that protein-coding genes and lncRNAs are both expressed more in oocytes. Differences between the oocyte and the 2-cell embryo are also more apparent in terms of lncRNAs than mRNAs. Co-expression network analysis using WGCNA generated 25 modules with differing proportions of lncRNAs. The modules exhibiting a higher proportion of lncRNAs were found to be associated with fewer annotated mRNAs and housekeeping functions. Functional annotation of co-expressed mRNAs allowed attribution of lncRNAs to a wide array of key cellular events such as meiosis, translation initiation, immune response, and mitochondrial related functions. We thus provide evidence that lncRNAs play diverse physiological roles that are tissue-specific and associated with key cellular functions alongside mRNAs in bovine ovarian follicles and early embryos. This contributes to add lncRNAs as active molecules in the complex regulatory networks driving folliculogenesis, oogenesis and early embryogenesis all of which are necessary for reproductive success.

Citation: Wang P, Paquet ÉR, Robert C (2023) Comprehensive transcriptomic analysis of long non-coding RNAs in bovine ovarian follicles and early embryos. PLoS ONE 18(9): e0291761. https://doi.org/10.1371/journal.pone.0291761

Editor: Xiaoyong Sun, Shandong Agricultural University, CHINA

Received: May 17, 2023; Accepted: September 5, 2023; Published: September 19, 2023

Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: This manuscript is a comprehensive re-analysis of datasets available at NCBI. All dataset are published and freely available.

Funding: The authors acknowledge the following funding agencies for their support: Natural Sciences and Engineering Research Council of Canada (Grant RGPIN-2017–04775) and PW is supported by a scholarship from the Fonds Québécois de la Recherche sur la Nature et les Technologies. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

High-throughput genome sequencing technology has revealed that most transcribed RNA encodes little or no protein [1, 2]. These RNA transcripts are referred to as non-coding RNAs (ncRNAs), which may be housekeeping or regulatory. Housekeeping ncRNAs like transfer RNAs (tRNAs), ribosomal RNAs (rRNAs), small nuclear RNAs (snRNAs), and small nucleolar RNAs (snoRNAs) are generally expressed constitutively and are required for normal cell function and viability. Regulatory ncRNAs are grouped into two broad classes based on length: short ncRNAs and long ncRNAs (lncRNAs). Short ncRNAs are less than 200 nucleotides in length and includes small interfering RNAs (siRNAs), microRNAs (miRNAs) and piwi-interacting RNAs (piRNAs), which are usually highly conserved across species and are involved in transcriptional and post-transcriptional gene silencing [3]. LncRNAs exceed 200 nucleotides in length and have emerged as a major class of transcript [4]. In terms of biogenesis and structure, lncRNAs share many features with messenger RNAs, often transcribed by RNA polymerase II and spliced [5], bearing a 7-methylguanosine cap at the 5’ end and a polyadenylated tail at the 3’ end. On the other hand, lncRNAs have fewer but slightly longer exons, are expressed in lower copy numbers and with more cell and tissue specificity [1], have shorter half-lives [6], and are generally less conserved across species [7].

The more lncRNAs are studied, the more they are shown to be functional RNA molecules. For example, they have been implicated in regulation of gene expression through a variety of mechanisms such as regulation of transcription [8], alternative splicing [9], mRNA translation [10–12], and epigenetic regulation [13, 14]. LncRNAs are also involved in diverse cellular processes including proliferation [15], apoptosis [16], pluripotency and differentiation [17], and in embryonic growth and development [18, 19]. Current knowledge about lncRNAs comes from variety of animals, from mice to humans [20, 21].

Cattle provide a suitable model for studying ovarian function in humans since they are mono-ovulatory with a similar endocrine cycle. However, surprisingly few characterizations of lncRNAs in early embryonic development in cattle have been published [22–25].

Bovine folliculogenesis, oogenesis and early pre-hatching development are studied mainly to understand the succession of key events that include follicular support leading to the formation of a developmentally competent oocyte, fertilization, embryonic genome activation, and initiation of cell lineage differentiation. The study of early development is important for perfecting assisted reproductive technologies such as in vitro oocyte maturation and fertilization for embryo production and transfer [26]. RNA sequencing has shown that the transcriptome is more complex than expected [27]. While most microarrays are designed solely to study mRNAs and are blind to lncRNAs, the EmbryoGENE microarray platform was designed using a comprehensive transcript catalog derived from an RNA sequencing survey that included novel transcripts [28]. This microarray was used in concordance with standard operation procedures for sample preparation, hybridization, and processing, making all independent studies that used it fully compatible. However, due to the absence of functional information associated with lncRNAs, downstream analyses were focused on mRNAs. This now provides a unique database to perform a comprehensive analysis to start filling this knowledge gap by describing the extent of their expression in bovine ovarian follicles and early embryos alongside expressed mRNAs.

Therefore, we assembled and curated a compendium of 468 transcriptomes from 47 different experimental conditions to characterize lncRNAs expression during bovine early development. These uncharacterized transcripts were classified according to their genomic position in relation to protein-coding genes. We then defined lncRNAs that are specifically associated with folliculogenesis, oogenesis and early embryogenesis, and analyzed the joint expression of lncRNAs and mRNAs across tissues to associate potential functions to lncRNAs based on co-expression networks.

Materials and methods

Reannotating, assembling and curating the datasets

The 43,794 probe sequences present on the EmbryoGENE microarray were downloaded from the website (http://emb-bioinfo.fsaa.ulaval.ca/) on April 2020 and were mapped on the ARS-UCD1.2/bosTau9 (Apr.2018) reference genome using BLAT with the following parameters: stepSize = 5, repMatch = 2253, minScore = 20, minIdentity = 0, and mismatch = 2 [29]. Of these, 34,780 (79%) were mapped on the latest genome. Using bovine genome annotations obtained from UCSC, Ensembl, and NCBI, probes were then reannotated separately, as mRNAs if they overlapped with at least one exon on the same strand, as intronic lncRNAs if they were within introns of protein-coding genes on the same strand, as antisense lncRNAs if they overlapped with protein-coding genes on the opposite strand, and finally, as intergenic lncRNAs if they did not overlap with protein-coding genes. Probes annotated ambiguously as mRNAs or lncRNAs in the same database were discarded. To lower the risk of errors in identifying lncRNAs, we kept 30,575 probes left after this annotation procedure and assigned them to lncRNAs if all three databases concurred. Only one database was required to assign a probe to mRNA. To reduce redundancy when a plurality of probes targeted the same gene, the probe with the highest mean signal was selected, which reduced the total to 21,840. The entire workflow leading to the final annotation is represented in Fig 1.

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Fig 1. Workflow by which the EmbryoGENE compendium of gene expression for this study was assembled.

https://doi.org/10.1371/journal.pone.0291761.g001

All raw microarray data associated with the EmbryoGENE platform (GPL13226) were retrieved from the NCBI Gene Expression Omnibus on August 2020 [30]. This platform uses standardized samples processing procedures. Tests were done during the platform development phase to minimize method-induced variance including parameters to extract total RNA, the global amplification step and the microarray hybridization conditions [31]. It has been used for 74 Gene Expression Omnibus series comprising 663 individual samples. Some of these 74 series are repeated and some are also listed on a different platform (GPL18384). We retained 605 unique samples from 62 expression series and used the signal from the Cy3 (green) channel for all downstream analyses. All raw signal intensities were log2 transformed and quantile normalization was applied across all data from all arrays to uniformize the distribution and thereby make all expression levels comparable [32]. Spearman correlation analysis then allowed us to note relationships between the different datasets and individual samples. The datasets and samples that did not cluster with the others were called outliers and removed from downstream analysis, which led to a final dataset of 468 samples (S1 Fig).

The EmbryoGENE platform is composed of 604 DarkCorner plus 153 structural negative probes (3xSLv1) usable as negative controls to measure if expression level is above the background. We considered a probe to be expressed on an array if its raw intensity exceeded the 95th percentile of the raw intensities of these negative controls. Based on this cutoff, 31 probes were considered not expressed at all in any sample and were discarded from downstream analyses. We thus retained a final set of 14,085 probes targeting mRNA and 7,724 probes targeting lncRNA from 468 quantile-normalized samples from different embryo developmental stages.

Weighted gene co-expression network analysis (WGCNA)

Network analysis was performed using R package WGCNA protocol version 1.70 [33]. We chose a soft thresholding power (β) of 12 to construct the network, based on the criterion of approximate scale-free topology R2 = 0.9 and mean connectivity < 100 (S2A and S2B Fig). The co-expression modules were then constructed using automatic module detection with the blockwiseModules function as follows: β = 12, corType = “pearson”, networkType = “signed”, TOMType = “signed”, minModuleSize = 30, maxBlockSize = 21809, mergeCutHeight = 0.25, and reassignThreshold = 0. Gene modules were distinguished by color and appeared as branches on the clustering tree. To identify tissue-related modules, the correlation between tissue type (each tissue type as a column and each sample as a row with binary variables) and module eigengenes (1st principal component explaining most variation in the module expression levels) was calculated using Pearson correlation. We also identified potential transcription factors present in individual modules using the AnimalTFDB3.0 database [34].

Functional enrichment analysis

To elucidate the putative biological functions of lncRNAs in each module, mRNAs annotated by the enricher function in R package clusterProfiler [35] version 4.4.4 were analyzed using Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) downloaded from http://geneontology.org/ (goa_cow.gaf, last updated July 1, 2022) and https://www.genome.jp/kegg/ (bta00001.keg, last updated July 17, 2022) respectively. Pathway analysis was restricted to metabolism, genetic information processing, environmental information processing, cellular processes, and organismal systems. The enricher function uses a hypergeometric test to assess significance and we used a Benjamini-Hochberg adjusted p-value < 0.05 to select enriched functions associated with individual modules.

Construction of lncRNA-mRNA-TF co-expression networks and finding hub transcription factors and hub genes

A Pearson correlation coefficient and p-value were calculated for each mRNA/lncRNA pair of transcripts in modules using the corAndPvalue function in the R package WGCNA. Pairs with correlation coefficient > 0.7 and p-value < 0.05 were selected to build the correlation network and identify the transcription factors and genes that had the largest correlation coefficient sums were designated as pathway hubs.

Results

Assembling a large compendium of bovine early development RNA abundance data

The following analysis enabled to characterize the expression of lncRNAs in the bovine ovarian follicle and early embryos. Data gathered over several years from the EmbryoGENE gene expression platform allowed us to assemble a curated compendium of 468 transcriptomes obtained from ovarian cells and early bovine development. Briefly, this platform was created to study coding and non-coding gene expression in the context of bovine early development. To create this platform, an exhaustive survey of all RNA species present during folliculogenesis and bovine early development was first performed using total RNA-seq and subsequently used to design a dedicated array and standardized protocols to be used by all consortium researchers [28].

All probes listed on this platform were reannotated using the most recent bovine genome ARS-UCD1.2/bosTau9 (Apr.2018)). A total of 14,085 probes target mRNAs and 7,724 probes target lncRNAs. Based on proximity to nearby protein-coding genes, lncRNAs were subdivided into 5,272 intergenic (lincRNAs, 68%) and 2,452 genic lncRNAs (32%) probes. LincRNAs were further categorized using the distance and the strand of their nearest genes. About 9% of them might be products of gene 5’ or 3’ UTR extensions since they are located within 1 kb on the same strand. However, most lincRNAs (82%) are located more than 1 kb from known protein-coding genes and 21% are located more than 50 kb away (S1 Table).

Our study of the probes distribution throughout the bovine genome indicates that the reannotated compendium covers most chromosomes uniformly except for 18, 19, and 25 that have higher densities of both lncRNAs and mRNAs (Fig 2). The number of lncRNAs per chromosome is strongly correlated with mRNAs (Pearson’s ρ = 0.89, p-value = 3.4e-11). Furthermore, we also performed several quality controls of the data used in the dataset and performed a global normalization of the dataset to be able to jointly analyze the data (see Material and methods). Given the coverage by the probes, the dataset size and spanning of different bovine developmental stages, this assembled and curated compendium is a good resource to characterize the joint gene expression profile of mRNAs and lncRNAs.

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Fig 2. Location of the 21,809 selected and reannotated mRNA and lncRNA probes on the bovine genome.

(A) Probe distribution and approximate abundance (color intensity) on each chromosome based on the Refseq annotation from bosTau9. (B) Number of probes per chromosome and per 106 (1Mb) nucleotides.

https://doi.org/10.1371/journal.pone.0291761.g002

Developmental stage specificity of mRNAs, genic lncRNAs and lincRNAs

Gene expression during early development is quite heterogenous and dynamic and progresses in stages [36]. To understand how lncRNAs and mRNAs expression patterns might also be stage specific, we performed multiple principal component analyses using different proportions of mRNAs, genic lncRNAs, and lincRNAs (Fig 3). Since mRNAs, genic lncRNAs and lincRNAs expression profiles differ in numbers, the three classes were balanced beforehand to ensure equal contribution. A PCA combining mRNAs and lncRNAs distinguishes between the early developmental stages (oocyte through 2-cell), the blastocyst or somatic, cumulus, and granulosa cells (PC1 in Fig 3A). If mRNAs alone are used, the oocyte and 2-cell stages still differ from the others (PC1 and PC2), but blastocysts now differ from cumulus and granulosa cells (PC2), suggesting that mRNAs are more specific than lncRNAs for characterizing embryonic development (Fig 3B). If only genic and lincRNAs are analyzed, the oocyte and 2-cell stages remain distinguishable whereas the other stages are harder to distinguish, suggesting that lncRNAs expression is more distinctive for the early stages (Fig 3C–3E). Overall, this analysis reveals that lncRNAs abundance is a distinctive feature of early development at stages still under the control of maternal reserves.

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Fig 3. Joint and individual principal component analyses of mRNAs, lincRNAs and genic lncRNAs expression in early developmental stages of bovine embryos.

(A) Of a random set of 2,000 mRNAs, 1,000 genic lncRNAs, and 1,000 lincRNAs. (B) Of all mRNAs (n = 14,085). (C) Of a random set of 1,000 genic lncRNAs and 1,000 lincRNAs. (D) Of all genic lncRNAs (n = 2,452). (E) Of all lincRNAs (n = 5,272). Ellipses contain 95% of the samples of the given cell or tissue type.

https://doi.org/10.1371/journal.pone.0291761.g003

Tissue-wide and tissue-specific expressions of lncRNAs and mRNAs

Among the 468 samples in the dataset, 217 were from blastocysts, 159 from granulosa cells, 52 from oocytes, 28 from cumulus cells, and 12 from 2-cell embryos. Tissue-specific expression was identified for both lncRNAs and mRNAs. A probe was considered expressed in a tissue if detected in more than 65% of samples (Fig 4A). In general, oocytes expressed larger numbers of mRNAs and lncRNAs in comparison to cumulus, granulosa, 2-cell and blastocysts, which is consistent with the oocyte transcribing and storing of a large amount of maternal RNA needed during the early stages of development [37]. In terms of the number of transcripts expressed (Fig 4B), the tissues ranked as follows: oocyte (16,545) > cumulus (15,284) > granulosa (15,186) > blastocyst (14,623) > 2-cell (14,246). Fig 4B also shows that a small majority of transcripts (11,792 or 54%) detected by the microarray are expressed in all five tissues, and fewer are tissue-specific or expressed in only one tissue. The oocyte contains more tissue-specific transcripts than any other tissue and more than half of these (563/907) are lncRNAs, which is the highest proportion observed. Careful examination of the proportions of tissue-specific lncRNAs shows a decrease as development progresses.

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Fig 4. Abundance of mRNAs and lncRNAs in bovine embryonic cells and tissues.

(A) Number of transcripts expressed in each cell or tissue type versus the proportion of samples expressing the transcripts. (B) ComplexUpset plot of transcript numbers and percentages categorized according to expression status (yes or no) in each cell or tissue type. (C) Frequency of lncRNAs and mRNAs expression categorized according to the number of cell or tissue types for which at least 65% of samples gave a positive signal. (D) Overall expression intensity of mRNAs and lncRNAs expressed in each cell or tissue type. (E) Expression intensity of mRNAs and lncRNAs expressed in all five tissues (n = 11,792). (F) Expression intensity of the tissue-specific transcripts.

https://doi.org/10.1371/journal.pone.0291761.g004

The tissue distributions of lncRNAs and mRNAs expression are shown in Fig 4C. LncRNAs have a more tissue-specific pattern of expression while mRNAs tend to be expressed more broadly. About 63% of mRNAs were detected in all five tissues compared with 38% of lncRNAs while the opposite was observed when a single tissue is considered (5% of mRNAs versus 11% of lncRNAs).

In terms of overall expression level, lncRNAs were less abundant than mRNAs (Fig 4D–4F). We also note that most transcripts detected in all five tissues were highly expressed, whereas transcripts detected in only one tissue tended to be less expressed (Fig 4E and 4F). On the other hand, when considering only tissue-specific transcripts, lncRNAs and mRNAs were almost equally abundant (Fig 4F).

LncRNAs and mRNAs co-expression analysis using WGCNA

As means of inferring indirectly the roles potentially played by lncRNAs in the ovary and in early embryos, co-expression analyses using WGCNA was performed on the profiles of the 14,085 mRNAs and 7,724 lncRNAs molecules detected by the microarray and 25 modules were obtained. The grey module contains all transcripts that could not be assigned to any other module (S2C Fig). The number of transcripts per module varied widely from 3,514 (2,151 mRNAs, 1,149 lncRNAs, and 214 transcription factors, turquoise) to 49 (43 mRNAs, 2 lncRNAs, and 4 transcription factors, dark grey). The number of identified transcription factors totaled 985, which is 70% of the number in the bovine genome. Ranking the modules by their respective proportions of lncRNAs (Fig 5A) revealed substantial differences. In most modules, mRNAs were found in higher numbers than lncRNAs, as expected. The blue module contains the largest contingent of lncRNAs, accounting for about 56% of all transcripts whereas the proportion of lncRNAs was only 3% in the grey60 module. Moreover, modules showed unequal expression levels and unique expression patterns across tissues (Fig 5B, S3 Fig).

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Fig 5. Co-expression of mRNAs, lncRNAs, transcription factors (TF) and housekeeping genes (HKG) as determined using weighted gene co-expression network analysis.

The expression profiles of the 14,085 mRNAs and 7,724 lncRNAs detected by the microarray are grouped into 25 modules. (A) Module size (number of transcripts) and the percentage of lncRNAs. (B) RNA expression level. (C) Heatmap of co-expression in each cell or tissue type. Red is perfect positive correlation and blue is perfect negative correlation. Correlation coefficients and p (in parentheses) are given. (D) The fraction of housekeeping genes versus the fraction of lncRNAs. The area of the dot is proportional to the number of transcripts in the module.

https://doi.org/10.1371/journal.pone.0291761.g005

The relationship between tissue specificity and co-expression modules is apparent in Fig 5C. Turquoise, dark green, green, and brown modules were the most significant, associated respectively with oocytes, 2-cell embryos, blastocysts, and granulosa cells based on correlation coefficients. Although lncRNAs were more tissue-specific, the modules containing the most lncRNAs (on the left in Fig 5A–5D) were not those most strongly associated with tissue specificity. In fact, the tissue-related modules are in the middle of Fig 5A–5D and contain a lower proportion of lncRNAs (< 40%, Fig 5C).

We then investigated whether lncRNA-riched modules that were not strongly associated with any tissues could be associated with common general cellular functions. Using the HRT Atlas of housekeeping genes and reference transcripts [38], 1,509 housekeeping genes were identified in our dataset. Associating these with the different modules, we can see that their proportion in a module is negatively correlated (Pearson’s ρ = -0.57, P = 0.003) with the proportion of lncRNAs (Fig 5D), which corroborates previous research showing that lncRNAs have fewer functions associated with housekeeping genes and have more specific functions [39].

Functional enrichment analysis

Gene ontology and KEGG enrichment analyses focused on the protein-coding genes in each module were performed to infer indirectly the functions of co-expressed lncRNAs. Based on this analysis, 78 biological processes, 53 cellular components, and 27 molecular functions obtained from gene ontology as well as 61 KEGG pathways were significantly enriched within at least one of the 24 modules (adjusted p-value < 0.05, q-value < 0.2, Fig 6A–6E, S1 Dataset). The first 10 lncRNA-enriched modules reported less enrichment than the others (Fig 6A), which might be due in part to their lower proportions of mRNAs, although their numbers of mRNAs were nevertheless considerable (Fig 5A). Interestingly, mRNAs in modules with a high proportion of lncRNAs tend to be associated with fewer GO and KEGG annotations (S4 Fig). The lesser enrichment in those modules could be due to lncRNAs tending to be co-expressed with mRNAs that are less known or involved in a broad spectrum of different functions. The modules on the left are associated with visual perception, olfactory and taste transduction, axon, glutamatergic synapse, and neuroactive ligand-receptor interaction, suggesting neuronal functions. This apparent relationship between ovarian physiology and neuronal function has been reported previously [40, 41].

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Fig 6. Summary of the gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results.

(A) Number of biological processes (BP), cellular component (CC), molecular function (MF) and KEGG pathway gene expressions enriched in each module. (B-D) Gene Ontology enrichment results. (E) KEGG enrichment results.

https://doi.org/10.1371/journal.pone.0291761.g006

Where lncRNAs are less abundant, as in the turquoise module (associated with the oocyte, Fig 5C), enrichments are related to meiosis, protein kinase and ubiquitin-dependent turnover, RNA helicase and DNA binding/RNA transcription, and beta-catenin binding. The light cyan module (associated with blastocysts) is enriched for mitochondrial respiration and initiation of translation functions, elements known to be key features of pre-hatching development following activation of the embryonic genome [42]. The light green and brown modules (associated with granulosa cells) are enriched for immune-related functions and transforming growth factor signaling. We have shown here that most enrichments that are associated to the different modules are relevant for ovarian follicles and early embryos development and could be associated as putative functions to the lncRNAs.

Construction of lncRNAs-mRNAs-TFs co-expression network and detection of hub transcription factors and hub genes

Another way to infer potential functions for lncRNAs is to test networks to identify hub genes, that is, genes that are functionally interconnected with numerous other genes and therefore important because of their centrality within a network of genes. We calculated the Pearson correlation coefficient of each pair of transcripts in each module to reveal interactions between lncRNAs, mRNAs and transcription factors, defined as significant if ρ > 0.7 and p-value < 0.05. The numbers of significant pairings are shown in Table 1. This also allowed us to identify the transcription factor connected to the largest number of lncRNAs (lncRNA-TF) and mRNAs (mRNA-TF) as well as the most connected lncRNA and mRNA. The hub transcription factors and hub genes found in the present study will be discussed in more detail below.

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Table 1. WGCNA modules, number of transcripts with significant correlations, hub transcription factors and hub genes.

https://doi.org/10.1371/journal.pone.0291761.t001

Discussion

In this study, we built a bioinformatic workflow to reannotate all probes on the EmbryoGENE microarray, which contains a comprehensive probe set designed to query a large contingent of long non-coding RNAs [28]. All probes were categorized as protein-coding genes or lncRNAs. This integration process is generally very challenging due to batch effects, that is, technical sources of variation due to differences in sample preparation protocol and data generation on different technological platforms [43, 44]. The advantage of our dataset is that all samples were processed using the same protocols and technological platform, making datasets more homogeneous and compatible and less confounded when merged.

After reannotating, assembling and curating all 468 datasets, 21,809 probes were found to be expressed, of which 7,724 are lncRNAs, making the EmbryoGENE microarray very suitable for studying these uncharacterized elements. Reannotations of microarrays, not originally designed for lncRNA studies, have proven valuable in uncovering useful information on these transcripts. For example, reannotating Affymetrix Mouse Genome Array probes led to the prediction of function for 340 lncRNAs [45]. The present study demonstrates that analyzing microarray data from multiple experiments is a powerful method for investigating lncRNAs function.

Using the EmbryoGENE microarray platform to study physiological contrasts has previously revealed lncRNAs among differentially expressed transcripts [46–49] and one unexpected function whereby the reduction of abundance by RNA silencing increased the rate of embryonic development [50]. However, the dataset has yet to be integrated entirely.

As shown previously and by the present results, lncRNAs expression is lower in all tissues compared to mRNAs and tissue-specific lncRNAs are often less abundant than ubiquitously expressed lncRNAs [7, 51]. The finding that lncRNAs could be better than mRNAs as a marker of specific tissue types (Fig 3) was unexpected. Such specificity has been proposed previously [52]. The evidence that brain and testis express more tissue-specific lncRNAs [53, 54] suggests that such transcripts might be important for the acquisition of specific phenotypic traits. Altered lncRNAs expression observed in numerous diseases [55] and the high tissue specificity suggest important regulatory roles rather than non-specific artefacts of leaky transcription.

Physiological functions of lncRNAs may be inferred by examining co-expressed mRNAs under the assumption that highly co-expressed transcripts are more likely to be functionally associated [56]. The module with the highest number of co-expressed transcripts (turquoise) is highly correlated with the oocyte stage. The enrichment of gene ontology terms suggests the participation of these lncRNAs in meiosis, ubiquitin-dependent protein catabolism, centrosome, helicase activity and DNA-binding transcription activator activity (RNA polymerase II-specific), which are all related to DNA remodeling events. These are essential for oogenesis, during which transcriptional activity is increased until later stages of folliculogenesis (e.g., late antral phase) when oocyte DNA condenses, transcription ceases, and meiosis is underway leading to the formation of the metaphase II oocyte awaiting fertilization [57, 58]. The ubiquitin-proteasome pathway reportedly can control oocyte meiotic maturation, oocyte-sperm binding, and early embryo development [59, 60]. Several key pathway enrichments shown in Fig 6E are related to oocyte maturation, including “FoxO signaling” [61], “Wnt signaling” [62] and “progesterone-mediated oocyte maturation”. Progesterone triggers oocyte maturation in Xenopus. In mammals, the LH surge (Fig 6E) does this [63]. All core cellular processes known to be key for the production of the female gamete are highlighted (turquoise module, S2 Dataset), namely the known oocyte-specific genes PHACTR3, PTPRQ, WEE2, KPNA7, DAZL, MOS, ZAR1, FIGLA, GDF9, BMP15, the zona pellucida genes ZP3 and ZP4 [64–69] and aurora kinases (AURKB, AURKC) which are required for oocyte meiotic maturation [70] and the NLR family (NLRP2, 5, 7, 8, 9, and 13) which are important in oogenesis and early embryo development [71–73].

FIGLA, identified as the hub transcription factor associated with the largest number of lncRNA transcripts in the turquoise module (lncRNA-TF in Table 1), is folliculogenesis specific, expressed preferentially in oocytes and regulates multiple oocyte-specific genes. Expression of Sirena1, the most abundant lncRNA in mouse oocytes, depends on the FIGLA, LHX8, and NOBOX maternal transcription factors network [67].

Enrichment in the light cyan module (Fig 6B) indicates possible involvement of lncRNA in translation, mitochondrial respiration, and mitochondrial gene expression. Based on the module-tissue relationship (Fig 5C), the light cyan module was closely related to the blastocyst stage. Overrepresented functional categories such as ribosomes, mitochondria and oxidative phosphorylation have been found prevalent among highly expressed genes in bovine blastocysts, indicating that these cells are rapidly synthesizing proteins to sustain high rates of growth and division [74]. Likewise, the hub gene in the light cyan module is EIF3D (Table 1), which is required for initiating translation [75]. Similarly, enrichment analyses pointed to involvement of the light green module lncRNAs in the immune system. The hub transcription factors linking the most lncRNAs and mRNAs in this module were respectively IKZF1 and SPI1 (Table 1), suggesting that both types of transcripts could be regulated by different factors. Also found in this module, the hub gene LAPTM5 is expressed preferentially in immune cells [76]. Based on the module-tissue relationship, the light green module is closely related to granulosa cells, corroborating the finding that granulosa cells can identify danger signals and undertake immune cell functions [77]. These results indicate that lncRNAs could be involved in these essential steps along with key proteins. Several lncRNAs expressed in cumulus and granulosa cells are reportedly involved in essential pathways that contribute to oocyte maturation and embryo development. For example, AK124742, an antisense of PSMD6, was upregulated in high-quality embryos compared to poor-quality embryos [78]. In addition, NEAT1, MALAT1, ANXA2P2, MEG3, IL6STP1, and VIM-AS1 might be involved in apoptosis and extracellular matrix-related functions and appear to be essential for oocyte growth [79]. Our results highlight that lncRNAs are co-expressed strongly with key genes that might be involved in folliculogenesis and early embryogenesis (Fig 7). Although regulatory roles of lncRNAs need to be validated experimentally, for example by knockdown or overexpression, computational annotation based on correlated expression remains the most intuitive and commonly used method of imputing function to lncRNAs. This should at least guide investigations into function.

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Fig 7. (A) Examples of potential lncRNAs involvement in folliculogenesis and (B) blastocyst formation.

EPI: pluripotent epiblast; PE: primitive endoderm; TE: trophectoderm. RNA is in bold type. Correlation coefficients are in red.

https://doi.org/10.1371/journal.pone.0291761.g007

Conclusions

It has been proposed that changes in lncRNAs expression are equal in importance to mRNAs for understanding folliculogenesis, oogenesis and early development [46, 80, 81]. The results of the present study globally support this claim and reveal that a large contingent of lncRNAs are present during folliculogenesis, oogenesis and early embryonic development in cattle. The strong correlations with key protein-coding genes known to be essential for reproduction suggest that some lncRNAs must play essential roles. How these transcripts affect their physiological functions are still largely unknown. In the nucleus, several lncRNAs are known to be involved in RNA processing hence exerting some control over gene expression [82] but their role in the cytoplasm is much less understood. It has been proposed that some may act as adaptor molecules in protein complexes alongside RNA binding proteins [83]. Given that different classes of lncRNAs exist (e.g., lincRNA, intron derived, antisense, etc.), they should be expected to be involved in various cellular functions.

Supporting information

S1 Fig.

(A) Correlation analysis across datasets (N = 62). (B) Correlation analysis across samples (N = 473). The arrow represents outlier samples.

https://doi.org/10.1371/journal.pone.0291761.s001

(TIF)

S2 Fig.

(A) Scale-free topology model fit for determining the optimal soft threshold. (B) mean connectivity analysis for selecting the optimal soft threshold. (C) The cluster dendrogram and module colors of 21809 transcripts.

https://doi.org/10.1371/journal.pone.0291761.s002

(TIF)

S3 Fig. Expression patterns of lncRNAs and mRNAs across tissues in modules.

The lines represent the mean expression levels. Modules are arranged by decreasing fraction of lncRNAs. The bars above each graph show the proportion of lncRNAs and mRNAs in each module.

https://doi.org/10.1371/journal.pone.0291761.s003

(TIF)

S4 Fig.

(A) the fraction of mRNAs with BP annotations. (B) the fraction of mRNAs with CC annotations. (C) the fraction of mRNAs with MF annotations. (D) the fraction of mRNAs with KEGG annotations. The blue line represents the linear regression. Abbreviations: BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes.

https://doi.org/10.1371/journal.pone.0291761.s004

(TIF)

S1 Table. Definition and number of probes for different types.

https://doi.org/10.1371/journal.pone.0291761.s005

(DOCX)

S1 Dataset. The complete functional enrichment results in 24 modules.

https://doi.org/10.1371/journal.pone.0291761.s006

(XLSX)

S2 Dataset. Probe information.

https://doi.org/10.1371/journal.pone.0291761.s007

(XLSX)

Acknowledgments

We thank all members of the EmbryoGENE project for giving public access to all the data used in this publication.

References

  1. 1. Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T, Mortazavi A, et al. Landscape of transcription in human cells. Nature. 2012;489(7414):101–8. pmid:22955620
  2. 2. Liu G, Mattick JS, Taft RJ. A meta-analysis of the genomic and transcriptomic composition of complex life. Cell Cycle. 2013;12(13):2061–72. pmid:23759593
  3. 3. Ipsaro JJ, Joshua-Tor L. From guide to target: molecular insights into eukaryotic RNA-interference machinery. Nat Struct Mol Biol. 2015;22(1):20–8. pmid:25565029
  4. 4. Nagano T, Fraser P. No-nonsense functions for long noncoding RNAs. Cell. 2011;145(2):178–81. pmid:21496640
  5. 5. Jarroux J, Morillon A, Pinskaya M. History, Discovery, and Classification of lncRNAs. Adv Exp Med Biol. 2017;1008:1–46. pmid:28815535
  6. 6. Clark MB, Johnston RL, Inostroza-Ponta M, Fox AH, Fortini E, Moscato P, et al. Genome-wide analysis of long noncoding RNA stability. Genome Res. 2012;22(5):885–98. pmid:22406755
  7. 7. Derrien T, Johnson R, Bussotti G, Tanzer A, Djebali S, Tilgner H, et al. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res. 2012;22(9):1775–89. pmid:22955988
  8. 8. Chen LL, Carmichael GG. Long noncoding RNAs in mammalian cells: what, where, and why? Wiley Interdiscip Rev RNA. 2010;1(1):2–21. pmid:21956903
  9. 9. Tripathi V, Ellis JD, Shen Z, Song DY, Pan Q, Watt AT, et al. The nuclear-retained noncoding RNA MALAT1 regulates alternative splicing by modulating SR splicing factor phosphorylation. Mol Cell. 2010;39(6):925–38. pmid:20797886
  10. 10. Carrieri C, Cimatti L, Biagioli M, Beugnet A, Zucchelli S, Fedele S, et al. Long non-coding antisense RNA controls Uchl1 translation through an embedded SINEB2 repeat. Nature. 2012;491(7424):454–7. pmid:23064229
  11. 11. Yoon JH, Abdelmohsen K, Srikantan S, Yang X, Martindale JL, De S, et al. LincRNA-p21 suppresses target mRNA translation. Mol Cell. 2012;47(4):648–55. pmid:22841487
  12. 12. Aleshkina D, Iyyappan R, Lin CJ, Masek T, Pospisek M, Susor A. ncRNA BC1 influences translation in the oocyte. RNA Biol. 2021;18(11):1893–904. pmid:33491548
  13. 13. Lee JT. Epigenetic regulation by long noncoding RNAs. Science. 2012;338(6113):1435–9. pmid:23239728
  14. 14. Mercer TR, Mattick JS. Structure and function of long noncoding RNAs in epigenetic regulation. Nat Struct Mol Biol. 2013;20(3):300–7. pmid:23463315
  15. 15. Li J, Tian H, Yang J, Gong Z. Long Noncoding RNAs Regulate Cell Growth, Proliferation, and Apoptosis. DNA Cell Biol. 2016;35(9):459–70. pmid:27213978
  16. 16. Hu W, Yuan B, Flygare J, Lodish HF. Long noncoding RNA-mediated anti-apoptotic activity in murine erythroid terminal differentiation. Genes Dev. 2011;25(24):2573–8. pmid:22155924
  17. 17. Guttman M, Donaghey J, Carey BW, Garber M, Grenier JK, Munson G, et al. lincRNAs act in the circuitry controlling pluripotency and differentiation. Nature. 2011;477(7364):295–300. pmid:21874018
  18. 18. Pauli A, Rinn JL, Schier AF. Non-coding RNAs as regulators of embryogenesis. Nat Rev Genet. 2011;12(2):136–49. pmid:21245830
  19. 19. Kuang L, Lei M, Li C, Guo Z, Ren Y, Zhang X, et al. Whole transcriptome sequencing reveals that non-coding RNAs are related to embryo morphogenesis and development in rabbits. Genomics. 2020;112(3):2203–12. pmid:31881265
  20. 20. Li L, Chang HY. Physiological roles of long noncoding RNAs: insight from knockout mice. Trends Cell Biol. 2014;24(10):594–602. pmid:25022466
  21. 21. Jiang S, Cheng SJ, Ren LC, Wang Q, Kang YJ, Ding Y, et al. An expanded landscape of human long noncoding RNA. Nucleic Acids Res. 2019;47(15):7842–56. pmid:31350901
  22. 22. Huang W, Long N, Khatib H. Genome-wide identification and initial characterization of bovine long non-coding RNAs from EST data. Anim Genet. 2012;43(6):674–82. pmid:22497321
  23. 23. Qu Z, Adelson DL. Bovine ncRNAs are abundant, primarily intergenic, conserved and associated with regulatory genes. PLoS One. 2012;7(8):e42638. pmid:22880061
  24. 24. Koufariotis LT, Chen YP, Chamberlain A, Vander JC, Hayes BJ. A catalogue of novel bovine long noncoding RNA across 18 tissues. PLoS One. 2015;10(10):e0141225. pmid:26496443
  25. 25. Wang J, Koganti PP, Yao J. Systematic identification of long intergenic non-coding RNAs expressed in bovine oocytes. Reprod Biol Endocrinol. 2020;18(1):13. pmid:32085734
  26. 26. Sirard MA. Folliculogenesis and acquisition of oocyte competence in cows. Anim Reprod. 2019;16(3):449–54. pmid:32435288
  27. 27. St LG, Wahlestedt C, Kapranov P. The Landscape of long noncoding RNA classification. Trends Genet. 2015;31(5):239–51. pmid:25869999
  28. 28. Robert C, Nieminen J, Dufort I, Gagne D, Grant JR, Cagnone G, et al. Combining resources to obtain a comprehensive survey of the bovine embryo transcriptome through deep sequencing and microarrays. Mol Reprod Dev. 2011;78(9):651–64. pmid:21812063
  29. 29. Kent WJ. BLAT—the BLAST-like alignment tool. Genome Res. 2002;12(4):656–64. pmid:11932250
  30. 30. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 2013;41(Database issue):D991–5. pmid:23193258
  31. 31. Gilbert I, Scantland S, Sylvestre EL, Dufort I, Sirard MA, Robert C. Providing a stable methodological basis for comparing transcript abundance of developing embryos using microarrays. Mol Hum Reprod. 2010;16(8):601–16. pmid:20479066
  32. 32. Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003;19(2):185–93. pmid:12538238
  33. 33. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559. pmid:19114008
  34. 34. Hu H, Miao YR, Jia LH, Yu QY, Zhang Q, Guo AY. AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors. Nucleic Acids Res. 2019;47(D1):D33–D8. pmid:30204897
  35. 35. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2(3):100141. pmid:34557778
  36. 36. Assou S, Boumela I, Haouzi D, Anahory T, Dechaud H, De Vos J, et al. Dynamic changes in gene expression during human early embryo development: from fundamental aspects to clinical applications. Hum Reprod Update. 2011;17(2):272–90. pmid:20716614
  37. 37. Vallee M, Dufort I, Desrosiers S, Labbe A, Gravel C, Gilbert I, et al. Revealing the bovine embryo transcript profiles during early in vivo embryonic development. Reproduction. 2009;138(1):95–105. pmid:19383715
  38. 38. Hounkpe BW, Chenou F, de Lima F, De Paula EV. HRT Atlas v1.0 database: redefining human and mouse housekeeping genes and candidate reference transcripts by mining massive RNA-seq datasets. Nucleic Acids Res. 2021;49(D1):D947–D55. pmid:32663312
  39. 39. Sarropoulos I, Marin R, Cardoso-Moreira M, Kaessmann H. Developmental dynamics of lncRNAs across mammalian organs and species. Nature. 2019;571(7766):510–4. pmid:31243368
  40. 40. D’Albora H, Anesetti G, Lombide P, Dees WL, Ojeda SR. Intrinsic neurons in the mammalian ovary. Microsc Res Tech. 2002;59(6):484–9. pmid:12467023
  41. 41. Macaulay AD, Gilbert I, Caballero J, Barreto R, Fournier E, Tossou P, et al. The gametic synapse: RNA transfer to the bovine oocyte. Biol Reprod. 2014;91(4):90. pmid:25143353
  42. 42. Graf A, Krebs S, Heininen-Brown M, Zakhartchenko V, Blum H, Wolf E. Genome activation in bovine embryos: review of the literature and new insights from RNA sequencing experiments. Anim Reprod Sci. 2014;149(1–2):46–58. pmid:24975847
  43. 43. Lazar C, Meganck S, Taminau J, Steenhoff D, Coletta A, Molter C, et al. Batch effect removal methods for microarray gene expression data integration: a survey. Brief Bioinform. 2013;14(4):469–90. pmid:22851511
  44. 44. Goh WWB, Wang W, Wong L. Why Batch Effects Matter in Omics Data, and How to Avoid Them. Trends Biotechnol. 2017;35(6):498–507. pmid:28351613
  45. 45. Liao Q, Liu C, Yuan X, Kang S, Miao R, Xiao H, et al. Large-scale prediction of long non-coding RNA functions in a coding-non-coding gene co-expression network. Nucleic Acids Res. 2011;39(9):3864–78. pmid:21247874
  46. 46. Plourde D, Vigneault C, Lemay A, Breton L, Gagne D, Laflamme I, et al. Contribution of oocyte source and culture conditions to phenotypic and transcriptomic variation in commercially produced bovine blastocysts. Theriogenology. 2012;78(1):116–31.e1-3. pmid:22494684
  47. 47. Labrecque R, Vigneault C, Blondin P, Sirard MA. Gene expression analysis of bovine oocytes with high developmental competence obtained from FSH-stimulated animals. Mol Reprod Dev. 2013;80(6):428–40. pmid:23559376
  48. 48. Labrecque R, Lodde V, Dieci C, Tessaro I, Luciano AM, Sirard MA. Chromatin remodelling and histone m RNA accumulation in bovine germinal vesicle oocytes. Mol Reprod Dev. 2015;82(6):450–62. pmid:25940597
  49. 49. Labrecque R, Fournier E, Sirard MA. Transcriptome analysis of bovine oocytes from distinct follicle sizes: Insights from correlation network analysis. Mol Reprod Dev. 2016;83(6):558–69. pmid:27127921
  50. 50. Caballero J, Gilbert I, Fournier E, Gagne D, Scantland S, Macaulay A, et al. Exploring the function of long non-coding RNA in the development of bovine early embryos. Reprod Fertil Dev. 2014;27(1):40–52. pmid:25472043
  51. 51. Jiang C, Li Y, Zhao Z, Lu J, Chen H, Ding N, et al. Identifying and functionally characterizing tissue-specific and ubiquitously expressed human lncRNAs. Oncotarget. 2016;7(6):7120–33. pmid:26760768
  52. 52. Cabili MN, Trapnell C, Goff L, Koziol M, Tazon-Vega B, Regev A, et al. Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev. 2011;25(18):1915–27. pmid:21890647
  53. 53. Ward M, McEwan C, Mills JD, Janitz M. Conservation and tissue-specific transcription patterns of long noncoding RNAs. J Hum Transcr. 2015;1(1):2–9. pmid:27335896
  54. 54. Hong SH, Kwon JT, Kim J, Jeong J, Kim J, Lee S, et al. Profiling of testis-specific long noncoding RNAs in mice. BMC Genomics. 2018;19(1):539. pmid:30012089
  55. 55. Kumar V, Westra HJ, Karjalainen J, Zhernakova DV, Esko T, Hrdlickova B, et al. Human disease-associated genetic variation impacts large intergenic non-coding RNA expression. PLoS Genet. 2013;9(1):e1003201. pmid:23341781
  56. 56. Sheybani N, Bakhtiarizadeh MR, Salehi A. An integrated analysis of mRNAs, lncRNAs, and miRNAs based on weighted gene co-expression network analysis involved in bovine endometritis. Sci Rep. 2021;11(1):18050. pmid:34508138
  57. 57. Fair T, Hyttel P, Greve T. Bovine oocyte diameter in relation to maturational competence and transcriptional activity. Mol Reprod Dev. 1995;42(4):437–42. pmid:8607973
  58. 58. Lodde V, Modina S, Maddox-Hyttel P, Franciosi F, Lauria A, Luciano AM. Oocyte morphology and transcriptional silencing in relation to chromatin remodeling during the final phases of bovine oocyte growth. Mol Reprod Dev. 2008;75(5):915–24. pmid:17948251
  59. 59. Yu C, Ji SY, Sha QQ, Sun QY, Fan HY. CRL4-DCAF1 ubiquitin E3 ligase directs protein phosphatase 2A degradation to control oocyte meiotic maturation. Nat Commun. 2015;6:8017. pmid:26281983
  60. 60. Wu Y, Li M, Yang M. Post-Translational Modifications in Oocyte Maturation and Embryo Development. Front Cell Dev Biol. 2021;9:645318. pmid:34150752
  61. 61. Kuscu N, Celik-Ozenci C. FOXO1, FOXO3, AND FOXO4 are differently expressed during mouse oocyte maturation and preimplantation embryo development. Gene Expr Patterns. 2015;18(1–2):16–20. pmid:25929834
  62. 62. Harwood BN, Cross SK, Radford EE, Haac BE, De Vries WN. Members of the WNT signaling pathways are widely expressed in mouse ovaries, oocytes, and cleavage stage embryos. Dev Dyn. 2008;237(4):1099–111. pmid:18351675
  63. 63. Arroyo A, Kim B, Yeh J. Luteinizing Hormone Action in Human Oocyte Maturation and Quality: Signaling Pathways, Regulation, and Clinical Impact. Reprod Sci. 2020;27(6):1223–52. pmid:32046451
  64. 64. Vallee M, Gravel C, Palin MF, Reghenas H, Stothard P, Wishart DS, et al. Identification of novel and known oocyte-specific genes using complementary DNA subtraction and microarray analysis in three different species. Biol Reprod. 2005;73(1):63–71. pmid:15744023
  65. 65. Zhang P, Kerkela E, Skottman H, Levkov L, Kivinen K, Lahesmaa R, et al. Distinct sets of developmentally regulated genes that are expressed by human oocytes and human embryonic stem cells. Fertil Steril. 2007;87(3):677–90. pmid:17126334
  66. 66. Tejomurtula J, Lee KB, Tripurani SK, Smith GW, Yao J. Role of importin alpha8, a new member of the importin alpha family of nuclear transport proteins, in early embryonic development in cattle. Biol Reprod. 2009;81(2):333–42. pmid:19420384
  67. 67. Ganesh S, Horvat F, Drutovic D, Efenberkova M, Pinkas D, Jindrova A, et al. The most abundant maternal lncRNA Sirena1 acts post-transcriptionally and impacts mitochondrial distribution. Nucleic Acids Res. 2020;48(6):3211–27. pmid:31956907
  68. 68. Hanna CB, Mudaliar D, John K, Allen CL, Sun L, Hawkinson JE, et al. Development of WEE2 kinase inhibitors as novel non-hormonal female contraceptives that target meiosisdagger. Biol Reprod. 2020;103(2):368–77.
  69. 69. Wassarman PM, Litscher ES. Female fertility and the zona pellucida. Elife. 2022;11. pmid:35076396
  70. 70. Nguyen AL, Drutovic D, Vazquez BN, El YW, Gentilello AS, Malumbres M, et al. Genetic Interactions between the Aurora Kinases Reveal New Requirements for AURKB and AURKC during Oocyte Meiosis. Curr Biol. 2018;28(21):3458–68.e5. pmid:30415701
  71. 71. Tian X, Pascal G, Monget P. Evolution and functional divergence of NLRP genes in mammalian reproductive systems. BMC Evol Biol. 2009;9:202. pmid:19682372
  72. 72. Li G, Tian X, Lv D, Zhang L, Zhang Z, Wang J, et al. NLRP7 is expressed in the ovine ovary and associated with in vitro pre-implantation embryo development. Reproduction. 2019;158(5):415–27. pmid:31505467
  73. 73. Innocenti F, Fiorentino G, Cimadomo D, Soscia D, Garagna S, Rienzi L, et al. Maternal effect factors that contribute to oocytes developmental competence: an update. J Assist Reprod Genet. 2022. pmid:35165782
  74. 74. Chitwood JL, Rincon G, Kaiser GG, Medrano JF, Ross PJ. RNA-seq analysis of single bovine blastocysts. BMC Genomics. 2013;14:350. pmid:23705625
  75. 75. Lee AS, Kranzusch PJ, Doudna JA, Cate JH. eIF3d is an mRNA cap-binding protein that is required for specialized translation initiation. Nature. 2016;536(7614):96–9. pmid:27462815
  76. 76. Ouchida R, Yamasaki S, Hikida M, Masuda K, Kawamura K, Wada A, et al. A lysosomal protein negatively regulates surface T cell antigen receptor expression by promoting CD3zeta-chain degradation. Immunity. 2008;29(1):33–43. pmid:18619870
  77. 77. Poulsen LC, Englund ALM, Wissing MLM, Yding AC, Borup R, Grondahl ML. Human granulosa cells function as innate immune cells executing an inflammatory reaction during ovulation: a microarray analysis. Mol Cell Endocrinol. 2019;486:34–46. pmid:30802528
  78. 78. Li J, Cao Y, Xu X, Xiang H, Zhang Z, Chen B, et al. Increased New lncRNA-mRNA Gene Pair Levels in Human Cumulus Cells Correlate With Oocyte Maturation and Embryo Development. Reprod Sci. 2015;22(8):1008–14. pmid:25670720
  79. 79. Bouckenheimer J, Fauque P, Lecellier CH, Bruno C, Commes T, Lemaitre JM, et al. Differential long non-coding RNA expression profiles in human oocytes and cumulus cells. Sci Rep. 2018;8(1):2202. pmid:29396444
  80. 80. Karlic R, Ganesh S, Franke V, Svobodova E, Urbanova J, Suzuki Y, et al. Long non-coding RNA exchange during the oocyte-to-embryo transition in mice. DNA Res. 2017;24(2):129–41. pmid:28087610
  81. 81. Yang CX, Liang H, Wu ZW, Huo LJ, Du ZQ. Identification of lncRNAs involved in maternal-to-zygotic transition of in vitro-produced porcine embryos by single-cell RNA-seq. Reprod Domest Anim. 2022;57(1):111–22. pmid:34725864
  82. 82. Sun Q, Hao Q, Prasanth KV. Nuclear Long Noncoding RNAs: Key Regulators of Gene Expression. Trends Genet. 2018;34(2):142–57. pmid:29249332
  83. 83. Dangelmaier E, Lal A. Adaptor proteins in long noncoding RNA biology. Biochim Biophys Acta Gene Regul Mech. 2020;1863(4):194370. pmid:30951902