Liver proteomics unravel the metabolic pathways related to Feed Efficiency in beef cattle

Fonseca, Leydiana Duarte; Eler, Joanir Pereira; Pereira, Mikaele Alexandre; Rosa, Alessandra Fernandes; Moncau, Cristina Tschorny; Salvato, Fernanda; Rosa-Fernandes, Lívia; Palmisano, Giuseppe; Ferraz, José Bento Sterman; Fukumasu, Heidge

doi:10.1038/s41598-019-41813-x

Cited by 46 publications

(32 citation statements)

References 73 publications

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“…The genetic modulation of the innate immune system supports our previous studies that demonstrate the relationship between feed efficiency and hepatic inflammation (5,9). Along with the genes related to complement cascade regulation, other genes with potential functional variations enriched for the innate immune system as BOLA, ECSIT, GLB1, ADA2, RIPK3, LILRA4, SIRPB1 and YEAR6.…”

Section: Discussionsupporting

confidence: 83%

“…Our group was the first to described a pathophysiological mechanism associated with FE in beef cattle: liver inflammation due to altered metabolism and/or bacterial translocation/infection (5), which was in part corroborated by others (6)(7)(8). We also unravel the metabolic pathways related to FE in Nellore cattle showing an increased bacterial load in low feed efficient animals (LFE) which is in part responsible for the hepatic lesions and inflammation in these animals (9).…”

Section: Introductionsupporting

confidence: 68%

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Potential Functional Variants in Innate Immune Response Genes Associated with Feed Efficiency in Beef Cattle

Ribeiro

César

Ferraz

et al. 2020

Preprint

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Background: Identifying and selecting animals for feed efficiency (FE) is extremely important for the beef production chain. Currently, the most common parameter to access the FE animals is residual feed intake (RFI), which is the residual of the linear regression that estimates DMI based on average daily gain and mid-test metabolic body weight. However, it relies on costly and time-consuming data collection, creating a growing demand for alternative approaches to identify genetically superior animals for FE. This study aimed to detect potential liver-specific functional variants from RNA-seq data of 16 Nellore bulls divergently selected for FE. Results: The variant call analysis detected 247 missense SNPs and nine insertion-deletions (INDELs) that alter the protein functions. These variants were found within 190 genes differentially found (P < 0.05) in liver tissue between high FE (HFE) and low FE (LFE) animals. To better understand the role of these variants in biological pathways, we performed a functional enrichment analysis, which highlighted six genes involved in complement cascade and cascade complement regulation pathways, and 20 genes involved in the regulation of the innate immune system. They had four different significant variants in the complement factor H (CFH) family genes, and all were homozygous in HFE animals rather than some degree of heterozygous in LFE animals. Conclusion: We developed a pipeline to detect potential liver-specific functional variants from RNA-seq data from animals divergently selected for feed efficiency. With this approach, we found potential functional variants in innate immune response genes associated with feed efficiency in beef cattle.

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Section: Discussionsupporting

confidence: 83%

Section: Introductionsupporting

confidence: 68%

Potential Functional Variants in Innate Immune Response Genes Associated with Feed Efficiency in Beef Cattle

Ribeiro

César

Ferraz

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…As an example, for the cattle feed efficiency dataset, we found significant enrichment for genes falling in shapes 1 to 4 ( Supplementary Files 2 to 5 ), each shape capturing specific Biological Processes. The animals on the feed efficiency dataset have been already extensively characterized [ 9 , 10 , 25 , 26 , 27 , 28 ], with low feed efficiency being associated with inflammatory/immune response and altered lipid metabolism in liver. Interestingly, shape 4, the one presenting the higher number of genes and reflecting a higher number of positive correlations, is not enriched specifically for inflammatory/immune response, but for transcription-related terms, such as mRNA processing (FDR = 7.13 × 10 −19 ) and mRNA splicing (FDR = 4.54 × 10 −15 ).…”

Section: Resultsmentioning

confidence: 99%

Genome-Wide Co-Expression Distributions as a Metric to Prioritize Genes of Functional Importance

Hudson

Lehnert

Fortes

et al. 2020

Genes

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Genome-wide gene expression analysis are routinely used to gain a systems-level understanding of complex processes, including network connectivity. Network connectivity tends to be built on a small subset of extremely high co-expression signals that are deemed significant, but this overlooks the vast majority of pairwise signals. Here, we developed a computational pipeline to assign to every gene its pair-wise genome-wide co-expression distribution to one of 8 template distributions shapes varying between unimodal, bimodal, skewed, or symmetrical, representing different proportions of positive and negative correlations. We then used a hypergeometric test to determine if specific genes (regulators versus non-regulators) and properties (differentially expressed or not) are associated with a particular distribution shape. We applied our methodology to five publicly available RNA sequencing (RNA-seq) datasets from four organisms in different physiological conditions and tissues. Our results suggest that genes can be assigned consistently to pre-defined distribution shapes, regarding the enrichment of differential expression and regulatory genes, in situations involving contrasting phenotypes, time-series, or physiological baseline data. There is indeed a striking additional biological signal present in the genome-wide distribution of co-expression values which would be overlooked by currently adopted approaches. Our method can be applied to extract further information from transcriptomic data and help uncover the molecular mechanisms involved in the regulation of complex biological process and phenotypes.

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“…It was reported that antigen processing and presentation play a key role in adaptive immunity in teleost fish (47,48). The antigen processing and presentation pathway was also enriched in beef cattle fed a mixture containing dry corn grain, corn silage, soybean, citrus pulp pellets, urea, calcareous, mineral salt, and potassium chloride (49). Our results showed that most of the proteins involved in antigen processing and presentation were significantly upregulated in Mix-based diets (Mix0.08 and Mix0.5), while those protein expressions were inhibited in Pg0.08.…”

Section: Effect Of Extract-based Diets On Regulating Immune System Fumentioning

confidence: 99%

Single or Combined Dietary Supply of Psidium guajava and Phyllanthus amarus Extracts Differentially Modulate Immune Responses and Liver Proteome in Striped Catfish (Pangasianodon hyphophthalmus)

et al. 2020

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and NLRP3) were synergistically upregulated in mixture of Pg and Pa-based diets compared to control and single dietary treatments. Taken together, the results revealed that single Pg and Pa extracts at 0.2 and 0.5% and their mixture at 0.08 and 0.5% have the potential to modulate the immune mechanisms and disease resistance of striped catfish. Moreover, the combination of Pg and Pa in diets suggested positive synergistic effects liver proteome profile related to immune system processes.

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Liver proteomics unravel the metabolic pathways related to Feed Efficiency in beef cattle

Cited by 46 publications

References 73 publications

Potential Functional Variants in Innate Immune Response Genes Associated with Feed Efficiency in Beef Cattle

Potential Functional Variants in Innate Immune Response Genes Associated with Feed Efficiency in Beef Cattle

Genome-Wide Co-Expression Distributions as a Metric to Prioritize Genes of Functional Importance

Single or Combined Dietary Supply of Psidium guajava and Phyllanthus amarus Extracts Differentially Modulate Immune Responses and Liver Proteome in Striped Catfish (Pangasianodon hyphophthalmus)

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