Feeding restriction in the pre and postpartum period of hair ewes raised in the semi-arid region: implications on performance and carcass traits of the progeny

Landim, Aline Vieira; Peres, Maria Claudete Rodrigues; Costa, Hélio; Silveira, Robson Mateus Freitas; Costa, A. C.; Parente, Michelle de Oliveira Maia; Mourão, Gérson Barreto; McManus, Concepta

doi:10.1007/s11250-022-03279-4

Cited by 3 publications

(1 citation statement)

References 49 publications

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“…They may play a role in FS with DD disease progression. Linda’s studies have proven that POSTN is upregulated in DD and that it can promote the transformation of fibroblasts into myofibroblasts, thus promoting progression of the disease ( 44 ). This study proves the accuracy of our bioinformatics analysis.…”

Section: Discussionmentioning

confidence: 99%

Exploring the potential relationship between frozen shoulder and Dupuytren’s disease through bioinformatics analysis and machine learning

Ouyang,

Chen,

et al. 2023

Front. Immunol.

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BackgroundFrozen shoulder (FS) and Dupuytren’s disease (DD) are two closely related diseases, but the mechanism of their interaction is unknown. Our study sought to elucidate the molecular mechanism of these two diseases through shared gene and protein interactions.MethodsGSE75152 and GSE140731 data were downloaded from the Gene Expression Omnibus (GEO) database, and shared genes between FS and DD were selected by using R packages. Then, we used Cytoscape software and the STRING database to produce a protein−protein interaction (PPI) network. Important interaction networks and hub genes were selected through MCODE and cytoHubba algorithms. To explore the potential mechanisms of the development of the two diseases, the hub genes were further enriched by GO and KEGG analyses. We predicted the transcription factors (TFs) of hub genes with Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST). Moreover, we identified candidate genes for FS with DD with cytoHubba and machine learning algorithms. Finally, we analyzed the role of immunocyte infiltration in FS and constructed the relationship between candidate genes and immunocytes in FS.ResultsWe identified a total of 321 shared genes. The results of GO and KEGG enrichment of shared genes showed that extracellular matrix and collagen fibril tissue play a certain role in the occurrence and development of disease. According to the importance of genes, we constructed the key PPI network of shared genes and the top 15 hub genes for FS with DD. Then, we predicted that five TFs are related to the hub genes and are highly expressed in the FS group. Machine learning results show that the candidate genes POSTN and COL11A1 may be key for FS with DD. Finally, immune cell infiltration revealed the disorder of immunocytes in FS patients, and expression of candidate genes can affect immunocyte infiltration.ConclusionWe identified a PPI network, 15 hub genes, and two immune-related candidate genes (POSTN and COL11A1) using bioinformatics analysis and machine learning algorithms. These genes have the potential to serve as diagnostic genes for FS in DD patients. Furthermore, our study reveals disorder of immunocytes in FS.

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Section: Discussionmentioning

confidence: 99%