Transcriptomic data reveals MYC as an upstream regulator in laying hen follicular recruitment

Abstract

Understanding the mechanisms of follicular recruitment is essential for improving laying hen and broiler breeder productivity, as it directly influences egg production. Despite advancements in poultry breeding for enhanced egg production, the factors driving successful ovarian follicle maturation remain inadequately understood. This study investigates the genetic drivers mediating the transition of pre-recruitment follicles to the pre-ovulatory phase, a crucial stage before ovulation. Using RNA sequencing and bioinformatics approaches such as a differential gene expression analysis, we compared pre-recruitment follicles with the recently recruited F5 pre-ovulatory follicle to identify key genes and upstream regulators involved in this transition. Further validation through qRT-PCR confirmed these findings. Using Qiagen's Ingenuity Pathway Analysis we identified MYC proto-oncogene (C-Myc) as a pivotal upstream regulator, controlling genes essential for cell proliferation and differentiation. Additionally, TGFβ1 emerged as a key regulator, influencing pathways involving SMAD3, TNF, and TP53. The study highlights the intricate regulatory network involving MYC and other transcription factors such as CTNNB1, crucial for follicular development. These findings provide valuable insights into the molecular mechanisms governing follicular selection and maturation, which are essential for enhancing egg production efficiency. Future research should explore the roles of MYC, CTNNB1, and other driver genes in follicular development to further understand and improve reproductive efficiency in poultry.

Keywords: Broiler breeder; Follicular recruitment; Layer hen; MYC; Ovary.

Fig. 1

Experimental Design. (A) 6 eight-month-old laying hens were used to obtain ovarian follicular samples. (B) RNA was isolated from the F5 pre-ovulatory follicle and a cluster of 3-4 pre-recruitment follicles per hen. (C) RNA-Seq was conducted on the follicular samples. (D) Bioinformatics analyses was performed for the F5 pre-ovulatory vs pre-recruitment follicle samples. Figure was created with BioRender.com using an institutional license sponsored by University of Delaware Office for Research.

Fig. 2

Experimental laying hen ovarian follicles isolated and sorted by size. A group of 3-4 pre-recruitment follicles and the F5 pre-ovulatory follicle were sampled per bird for RNA-Seq analysis. Samples taken are indicated.

Fig. 3

MDS plot across pre-recruitment and pre-ovulatory follicles in layer hens. The pre-recruitment follicles are indicated as R# and the pre-ovulatory samples are indicated as PO#. The numbers correspond to the six birds used in the study.

Fig. 4

Smear plot across samples. Smear Plot of F5 pre-ovulatory vs pre-recruitment follicles. Final log fold change vs average log CPM (counts per million). Red signifies significantly differentially expressed genes and blue lines are indicated at absolute value of 1.

Fig. 5

Heat maps for DEGs for pre-recruitment and pre-ovulatory follicles in layer hen ovaries. Top 20 significantly differentially expressed genes by P-value (P ≤ 1.29E-78; FDR ≤ 7.34E-76). (A) Heat map representing the top 20 up-regulated DEGS according to P-value (does not include CDH11, which has intensity >25000). (B) Heat map representing the top 20 down-regulated DEGS according to P-value (does not include NPC1, which has intensity >10000).

Fig. 6

Classification of gene enrichment in F5 pre-ovulatory follicles vs pre-recruitment in laying hens. Gene Ontology (GO) term enrichment of genes differentially expressed in F5 pre-ovulatory vs pre-recruitment follicles using DAVID pathway analysis. (A)Top 15 molecular functions; P-value ≤ 0.005 (B) Top 15 cellular components; P-value ≤ 0.002 (C) Top 15 biological processes by gene count; P-value ≤ 0.004 (D) Top 15 KEGG Pathways by gene count; P-value ≤ 0.06.

Fig. 7

TGFβ1 IPA upstream regulator interaction pathway. The Qiagen IPA (Ingenuity Pathway Analysis) was employed to identify TGFβ1 as an upstream regulator that interacts with Tgf beta, TNF, beta-estradiol, ESR 1/2, SMAD3, TP53, JUN, and NFKB1A.

Fig. 8

MYC IPA upstream regulator interaction pathway. The Qiagen IPA (Ingenuity Pathway Analysis) was employed to identify MYC as an upstream regulator that interacts with beta-estradiol, CTNNB1, MLXIPL, MAX, ESR 1/2, MYCN, TP53, JUN, TCF, SMAD3, and HDAC1.

Fig. 9

IPA regulator effects analysis for ovarian follicular development in layer hens. The IPA (Ingenuity Pathway Analysis) regulator effects analysis was performed to determine the biological impact of upstream molecules via genes they regulate. Upstream regulators are indicated at the top of the network, genes from our dataset are found in the middle, and the bottom of the network shows predicted disease or function.

Fig. 10

IPA graphical summary of pathways involved in ovarian follicular development/maturation in layer hens. The IPA (Ingenuity Pathway Analysis) graphical summary indicates major biological processes involved based on differentially expressed genes (DEGs) which provides an overview of major biological themes.

Fig. 11

qRT-PCR validation in pre-recruitment and pre-ovulatory follicles of laying hens and broiler breeders. Quantitative real-time PCR (qRT-PCR) validation of genes identified in RNA-Seq for twelve-month-old layer hens and eight-month-old broiler breeder hens. Data was normalized and is relative to the housekeeping gene GAPDH. The significance of grouped results was calculated using Student's t-test; *P < 0.01 and **P < 0.001.

Fig. 12

Western blot validation in pre-recruitment and pre-ovulatory follicles of laying hens. Western blot validation of proteins identified as upstream regulators in Qiagen Ingenuity Pathway Analysis for twelve-month-old layer hens. GAPDH is used as loading control. Hen 1 and Hen 2 are two independent biological replicates from same cohort.

Fig. 13

Simplified upstream regulator networks identified in follicular development/maturation in layer hen ovaries. TGFβ1 and MYC networks are indicated that regulate gene networks that drive successful developmental maturation of pre-recruitment follicles to pre-ovulatory follicles.