Integration of transcriptome and machine learning to identify the potential key genes and regulatory networks affecting drip loss in pork DOI
Wen Yang, Liming Hou, Binbin Wang

и другие.

Journal of Animal Science, Год журнала: 2024, Номер 102

Опубликована: Янв. 1, 2024

Abstract Low level of drip loss (DL) is an important quality characteristic meat with high economic value. However, the key genes and regulatory networks contributing to DL in pork remain largely unknown. To accurately identify affecting muscles postmortem, 12 Duroc × (Landrace Yorkshire) pigs extremely (n = 6, H group) low L at both 24 48 h postmortem were selected for transcriptome sequencing. The analysis differentially expressed weighted gene co-expression network (WGCNA) performed find overlapping using data, functional enrichment protein–protein interaction (PPI) conducted genes. Moreover, we used machine learning related based on interactive PPI network. Finally, nine potential (IRS1, ESR1, HSPA6, INSR, SPOP, MSTN, LGALS4, MYLK2, FRMD4B) mainly associated MAPK signaling pathway, insulin calcium pathway identified, a single-gene set (GSEA) was further annotate functions these GSEA results showed that are ubiquitin-mediated proteolysis oxidative reactions. Taken together, our indicate influencing mediated differences glycolysis changes muscle structure improve understanding expression regulation contribute future molecular breeding improving quality.

Язык: Английский

Integrated analysis of muscle lncRNA and mRNA of Chinese indigenous breed Ningxiang pig in four developmental stages DOI Creative Commons
Wenwu Chen, Fengtang Yang, Sui Liufu

и другие.

Frontiers in Veterinary Science, Год журнала: 2024, Номер 11

Опубликована: Окт. 21, 2024

Meat and its derivatives serve as crucial sources of protein, vitamins, minerals, other essential nutrients for humans. Pork stands China’s primary animal-derived food product consumed widely across diverse dietary structures; evaluating intramuscular fat content becomes pivotal in assessing quality standards. Nonetheless, the intricate molecular mechanisms governing deposition remain elusive. Our study utilized sequencing technology to scrutinize longitudinal development stages within Ningxiang pig’s longest dorsal muscles aiming unravel these underlying mechanisms. In three distinct comparisons (30d vs. 90d, 90d 150d 210d) there were 578, 1,000 3,238 differentially expressed mRNA, along with 16, 158 85 lncRNAs identified. STEM analysis unveiled six enriched model profiles while seven such emerged mRNAs; notably, multiple shared existed between both RNA types. Enriched highlighted numerous genes from mRNA profile8 lncRNA profile7 significantly associated pathways linked deposition. Weight Gene Co-Expression Network Analysis (WGCNA) revealed that differential expression modules (DMEs) & primarily clustered cyan, dark slate blue pale turquoise modules. Furthermore, target PKD2 (MSTRG21592.MTRSG8859 MTRSG18175), COL5A1 (MTRSG9969 MTRSG180) SOX13 (MTRSG21592 MTRSG9088) core components all intricately tied into processes related This lays groundwork deeper exploration LDM traits, it also presents candidate future marker-assisted breeding.

Язык: Английский

Процитировано

1
Integration of transcriptome and machine learning to identify the potential key genes and regulatory networks affecting drip loss in pork DOI
Wen Yang, Liming Hou, Binbin Wang

и другие.

Journal of Animal Science, Год журнала: 2024, Номер 102

Опубликована: Янв. 1, 2024

Abstract Low level of drip loss (DL) is an important quality characteristic meat with high economic value. However, the key genes and regulatory networks contributing to DL in pork remain largely unknown. To accurately identify affecting muscles postmortem, 12 Duroc × (Landrace Yorkshire) pigs extremely (n = 6, H group) low L at both 24 48 h postmortem were selected for transcriptome sequencing. The analysis differentially expressed weighted gene co-expression network (WGCNA) performed find overlapping using data, functional enrichment protein–protein interaction (PPI) conducted genes. Moreover, we used machine learning related based on interactive PPI network. Finally, nine potential (IRS1, ESR1, HSPA6, INSR, SPOP, MSTN, LGALS4, MYLK2, FRMD4B) mainly associated MAPK signaling pathway, insulin calcium pathway identified, a single-gene set (GSEA) was further annotate functions these GSEA results showed that are ubiquitin-mediated proteolysis oxidative reactions. Taken together, our indicate influencing mediated differences glycolysis changes muscle structure improve understanding expression regulation contribute future molecular breeding improving quality.

Язык: Английский

Процитировано

0