scholarly journals Integrative Analysis of Metabolomic and Transcriptomic Profiles Uncovers Biological Mechanism of Feed Efficiency in Pigs

2020 ◽  
Author(s):  
Priyanka Banerjee ◽  
Victor Adriano Okstoft Carmelo ◽  
Haja N Kadarmideen
Keyword(s):  
Feed Efficiency ◽  
Regulatory Mechanisms ◽  
Rna Seq ◽  
Data Quality Control ◽  
Isoleucine Biosynthesis ◽  
Significant Gene ◽  
Important Trait ◽  
Unique Genes ◽  
Integration Analysis ◽  
Model Approach

ABSTRACTFeed efficiency (FE) is an economically important trait. Thus, reliable predictors would help to reduce the production cost and provide sustainability to the pig industry. We carried out metabolome-transcriptome integration analysis on 40 purebred Duroc and Landrace male uncastrated pigs to identify potential gene-metabolite interactions and explore the molecular mechanism underlying FE. To this end, we applied untargeted metabolomics and RNA-seq approaches to the same animals. After data quality control, we used a linear model approach to integrate the data and find significant differently correlated gene-metabolite pairs separately for the breeds (Duroc and Landrace) and FE groups (low and high FE) followed by a pathway over-representation analysis. We identified 21 and 12 significant gene-metabolite pairs for each group. The valine-leucine-isoleucine biosynthesis/degradation and arginine-proline metabolism pathways were associated with unique metabolites. The unique genes obtained from significant metabolite-gene pairs were associated with sphingolipid catabolism, multicellular organismal process, cGMP, and purine metabolic processes. While some of the genes and metabolites identified were known for their association with FE, others are novel and provide new avenues for further research. Further validation of genes, metabolites, and gene-metabolite interactions in larger cohorts will help us to elucidate the regulatory mechanisms and pathways underlying FE.

scholarly journals Integrative Analysis of Metabolomic and Transcriptomic Profiles Uncovers Biological Pathways of Feed Efficiency in Pigs

Metabolites ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 275
Author(s):  
Priyanka Banerjee ◽  
Victor Adriano Okstoft Carmelo ◽  
Haja N. Kadarmideen
Keyword(s):  
Feed Efficiency ◽  
Molecular Mechanisms ◽  
Regulatory Mechanisms ◽  
Rna Seq ◽  
Data Quality Control ◽  
Isoleucine Biosynthesis ◽  
Significant Gene ◽  
Important Trait ◽  
Integration Analysis ◽  
Model Approach

Feed efficiency (FE) is an economically important trait. Thus, reliable predictors would help to reduce the production cost and provide sustainability to the pig industry. We carried out metabolome-transcriptome integration analysis on 40 purebred Duroc and Landrace uncastrated male pigs to identify potential gene-metabolite interactions and explore the molecular mechanisms underlying FE. To this end, we applied untargeted metabolomics and RNA-seq approaches to the same animals. After data quality control, we used a linear model approach to integrate the data and find significant differently correlated gene-metabolite pairs separately for the breeds (Duroc and Landrace) and FE groups (low and high FE) followed by a pathway over-representation analysis. We identified 21 and 12 significant gene-metabolite pairs for each group. The valine-leucine-isoleucine biosynthesis/degradation and arginine-proline metabolism pathways were associated with unique metabolites. The unique genes obtained from significant metabolite-gene pairs were associated with sphingolipid catabolism, multicellular organismal process, cGMP, and purine metabolic processes. While some of the genes and metabolites identified were known for their association with FE, others are novel and provide new avenues for further research. Further validation of genes, metabolites, and gene-metabolite interactions in larger cohorts will elucidate the regulatory mechanisms and pathways underlying FE.

scholarly journals A Transcriptome Analysis Reveals that Hepatic Glycolysis and Lipid Synthesis Are Negatively Associated with Feed Efficiency in DLY Pigs

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Cineng Xu ◽  
Xingwang Wang ◽  
Zhanwei Zhuang ◽  
Jie Wu ◽  
Shenping Zhou ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Feed Efficiency ◽  
Molecular Mechanisms ◽  
Lipid Synthesis ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Rna Seq ◽  
Go Enrichment ◽  
Group A ◽  
Important Trait

Abstract Feed efficiency (FE) is an important trait in the porcine industry. Therefore, understanding the molecular mechanisms of FE is vital for the improvement of this trait. In this study, 6 extreme high-FE and 6 low-FE pigs were selected from 225 Duroc × (Landrace × Yorkshire) (DLY) pigs for transcriptomic analysis. RNA-seq analysis was performed to determine differentially expressed genes (DEGs) in the liver tissues of the 12 individuals, and 507 DEGs were identified between high-FE pigs (HE- group) and low-FE pigs (LE- group). A gene ontology (GO) enrichment and pathway enrichment analysis were performed and revealed that glycolytic metabolism and lipid synthesis-related pathways were significantly enriched within DEGs; all of these DEGs were downregulated in the HE- group. Moreover, Weighted gene co-expression analysis (WGCNA) revealed that oxidative phosphorylation, thermogenesis, and energy metabolism-related pathways were negatively related to HE- group, which might result in lower energy consumption in higher efficiency pigs. These results implied that the higher FE in the HE- group may be attributed to a lower glycolytic, energy consumption and lipid synthesizing potential in the liver. Furthermore, our findings suggested that the inhibition of lipid synthesis and glucose metabolic activity in the liver may be strategies for improving the FE of DLY pigs.

scholarly journals Transcription Factor Co-expression Networks of Adipose RNA-Seq Data Reveal Regulatory Mechanisms of Obesity

2018 ◽  
Vol 19 (4) ◽  
pp. 289-299 ◽  
Author(s):  
Ruta Skinkyte-Juskiene ◽  
Lisette J.A. Kogelman ◽  
Haja N. Kadarmideen
Keyword(s):  
Transcription Factor ◽  
Regulatory Mechanisms ◽  
Rna Seq

Measuring consistency among gene set analysis methods: A systematic study

2019 ◽  
Vol 17 (05) ◽  
pp. 1940010 ◽  
Author(s):  
Farhad Maleki ◽  
Katie L. Ovens ◽  
Daniel J. Hogan ◽  
Elham Rezaei ◽  
Alan M. Rosenberg ◽  
...  
Keyword(s):  
Gene Set Analysis ◽  
Rna Seq ◽  
Systematic Analysis ◽  
Gene Set ◽  
Large Gene ◽  
Analysis Methods ◽  
Gene Sets ◽  
Significant Gene ◽  
Biological Insight ◽  
Relevant Gene

Gene set analysis is a quantitative approach for generating biological insight from gene expression datasets. The abundance of gene set analysis methods speaks to their popularity, but raises the question of the extent to which results are affected by the choice of method. Our systematic analysis of 13 popular methods using 6 different datasets, from both DNA microarray and RNA-Seq origin, shows that this choice matters a great deal. We observed that the overall number of gene sets reported by each method differed by up to 2 orders of magnitude, and there was a bias toward reporting large gene sets with some methods. Furthermore, there was substantial disagreement between the 20 most statistically significant gene sets reported by the methods. This was also observed when expanding to the 100 most statistically significant reported gene sets. For different datasets of the same phenotype/condition, the top 20 and top 100 most significant results also showed little to no agreement even when using the same method. GAGE, PAGE, and ORA were the only methods able to achieve relatively high reproducibility when comparing the 20 and 100 most statistically significant gene sets. Biological validation on a juvenile idiopathic arthritis (JIA) dataset showed wide variation in terms of the relevance of the top 20 and top 100 most significant gene sets to known biology of the disease, where GAGE predicted the most relevant gene sets, followed by GSEA, ORA, and PAGE.

scholarly journals Exploring the Regulatory Potential of Long Non-Coding RNA in Feed Efficiency of Indicine Cattle

Genes ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 997
Author(s):  
Pâmela A. Alexandre ◽  
Antonio Reverter ◽  
Roberta B. Berezin ◽  
Laercio R. Porto-Neto ◽  
Gabriela Ribeiro ◽  
...  
Keyword(s):  
Feed Efficiency ◽  
Bos Taurus ◽  
Bos Indicus ◽  
Rna Seq ◽  
Complex Phenotypes ◽  
Non Coding Rna ◽  
Genomic Location ◽  
Regulatory Potential ◽  
Long Non Coding Rna ◽  
Pituitary Adrenal Axis

Long non-coding RNA (lncRNA) can regulate several aspects of gene expression, being associated with complex phenotypes in humans and livestock species. In taurine beef cattle, recent evidence points to the involvement of lncRNA in feed efficiency (FE), a proxy for increased productivity and sustainability. Here, we hypothesized specific regulatory roles of lncRNA in FE of indicine cattle. Using RNA-Seq data from the liver, muscle, hypothalamus, pituitary gland and adrenal gland from Nellore bulls with divergent FE, we submitted new transcripts to a series of filters to confidently predict lncRNA. Then, we identified lncRNA that were differentially expressed (DE) and/or key regulators of FE. Finally, we explored lncRNA genomic location and interactions with miRNA and mRNA to infer potential function. We were able to identify 126 relevant lncRNA for FE in Bos indicus, some with high homology to previously identified lncRNA in Bos taurus and some possible specific regulators of FE in indicine cattle. Moreover, lncRNA identified here were linked to previously described mechanisms related to FE in hypothalamus-pituitary-adrenal axis and are expected to help elucidate this complex phenotype. This study contributes to expanding the catalogue of lncRNA, particularly in indicine cattle, and identifies candidates for further studies in animal selection and management.

scholarly journals TED toolkit: a comprehensive approach for convenient transcriptomic profiling as a clinically-oriented application

2017 ◽  
Author(s):  
Thahmina Ali ◽  
Baekdoo Kim ◽  
Carlos Lijeron ◽  
Olorunseun O Ogunwobi ◽  
Raja Mazumder ◽  
...  
Keyword(s):  
Comprehensive Approach ◽  
Transcriptome Data ◽  
Rna Seq ◽  
Data Quality Control ◽  
Biologically Relevant ◽  
Knowledge Database ◽  
Cancer Transcriptome ◽  
The Galaxy ◽  
Sustainable Fashion

In translational medicine, the technology of RNA sequencing (RNA-seq) continues to prove powerful, and transforming the RNA-seq data into biological insights has become increasingly imperative. We present the Transcriptomics profiler for Easy Discovery (TED) toolkit, a comprehensive approach to processing and analyzing RNA-seq data. TED is divided into three major modules: data quality control, transcriptome data analysis, and data discovery, with eleven pipelines in total. These pipelines perform the preliminary steps from assessing and correcting the quality of the RNA-seq data, to the simultaneous analysis of five transcriptomic features (differentially expressed coding, non-coding, novel isoform genes, gene fusions, alternative splicing events, genetic variants of somatic and germline mutations) and ultimately translating the RNA-seq analysis findings into actionable, clinically-relevant reports. TED was evaluated using previously published prostate cancer transcriptome data where we observed previously studied outcomes, and also created a knowledge database of highly-integrated, biologically relevant reports demonstrating that it is well-positioned for clinical applications. TED is implemented on an instance of the Galaxy platform (Galaxy page: http://galaxy.hunter.cuny.edu/u/bioitcore/p/transcriptomics-profiler-for-easy-discovery-ted-toolkit , Documentation Manual: http://ted.readthedocs.io/en/latest/index.html ) as intuitive and reproducible pipelines providing a manageable strategy for conducting substantial transcriptome analysis in a routine and sustainable fashion for bioinformatics researchers and clinicians alike.

scholarly journals Characterization and Duodenal Transcriptome Analysis of Chinese Beef Cattle With Divergent Feed Efficiency Using RNA-Seq

2021 ◽  
Vol 12 ◽  
Author(s):  
Chaoyun Yang ◽  
Liyun Han ◽  
Peng Li ◽  
Yanling Ding ◽  
Yun Zhu ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Differentially Expressed Genes ◽  
Feed Intake ◽  
Feed Efficiency ◽  
Muscle Development ◽  
Dietary Factors ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Biological Regulation ◽  
Key Genes

Residual feed intake (RFI) is an important measure of feed efficiency for agricultural animals. Factors associated with cattle RFI include physiology, dietary factors, and the environment. However, a precise genetic mechanism underlying cattle RFI variations in duodenal tissue is currently unavailable. The present study aimed to identify the key genes and functional pathways contributing to variance in cattle RFI phenotypes using RNA sequencing (RNA-seq). Six bulls with extremely high or low RFIs were selected for detecting differentially expressed genes (DEGs) by RNA-seq, followed by conducting GO, KEGG enrichment, protein-protein interaction (PPI), and co-expression network (WGCNA, n = 10) analysis. A total of 380 differentially expressed genes was obtained from high and low RFI groups, including genes related to energy metabolism (ALDOA, HADHB, INPPL1), mitochondrial function (NDUFS1, RFN4, CUL1), and feed intake behavior (CCK). Two key sub-networks and 26 key genes were detected using GO analysis of DEGs and PPI analysis, such as TPM1 and TPM2, which are involved in mitochondrial pathways and protein synthesis. Through WGCNA, a gene network was built, and genes were sorted into 27 modules, among which the blue (r = 0.72, p = 0.03) and salmon modules (r = −0.87, p = 0.002) were most closely related with RFI. DEGs and genes from the main sub-networks and closely related modules were largely involved in metabolism; oxidative phosphorylation; glucagon, ribosome, and N-glycan biosynthesis, and the MAPK and PI3K-Akt signaling pathways. Through WGCNA, five key genes, including FN1 and TPM2, associated with the biological regulation of oxidative processes and skeletal muscle development were identified. Taken together, our data suggest that the duodenum has specific biological functions in regulating feed intake. Our findings provide broad-scale perspectives for identifying potential pathways and key genes involved in the regulation of feed efficiency in beef cattle.

scholarly journals TED toolkit: a comprehensive approach for convenient transcriptomic profiling as a clinically-oriented application

2017 ◽  
Author(s):  
Thahmina Ali ◽  
Baekdoo Kim ◽  
Carlos Lijeron ◽  
Olorunseun O Ogunwobi ◽  
Raja Mazumder ◽  
...  
Keyword(s):  
Comprehensive Approach ◽  
Transcriptome Data ◽  
Rna Seq ◽  
Data Quality Control ◽  
Biologically Relevant ◽  
Knowledge Database ◽  
Cancer Transcriptome ◽  
The Galaxy ◽  
Sustainable Fashion

In translational medicine, the technology of RNA sequencing (RNA-seq) continues to prove powerful, and transforming the RNA-seq data into biological insights has become increasingly imperative. We present the Transcriptomics profiler for Easy Discovery (TED) toolkit, a comprehensive approach to processing and analyzing RNA-seq data. TED is divided into three major modules: data quality control, transcriptome data analysis, and data discovery, with eleven pipelines in total. These pipelines perform the preliminary steps from assessing and correcting the quality of the RNA-seq data, to the simultaneous analysis of five transcriptomic features (differentially expressed coding, non-coding, novel isoform genes, gene fusions, alternative splicing events, genetic variants of somatic and germline mutations) and ultimately translating the RNA-seq analysis findings into actionable, clinically-relevant reports. TED was evaluated using previously published prostate cancer transcriptome data where we observed previously studied outcomes, and also created a knowledge database of highly-integrated, biologically relevant reports demonstrating that it is well-positioned for clinical applications. TED is implemented on an instance of the Galaxy platform (Galaxy page: http://galaxy.hunter.cuny.edu/u/bioitcore/p/transcriptomics-profiler-for-easy-discovery-ted-toolkit , Documentation Manual: http://ted.readthedocs.io/en/latest/index.html ) as intuitive and reproducible pipelines providing a manageable strategy for conducting substantial transcriptome analysis in a routine and sustainable fashion for bioinformatics researchers and clinicians alike.

scholarly journals Regulatory Mechanisms of L-Lactic Acid and Taste Substances in Chinese Acid Rice Soup (Rice-Acid) Fermented With a Lacticaseibacillus paracasei and Kluyveromyces marxianus

2021 ◽  
Vol 12 ◽  
Author(s):  
Na Liu ◽  
Likang Qin ◽  
Song Miao
Keyword(s):  
Lactic Acid ◽  
Lactate Dehydrogenase ◽  
Organic Acids ◽  
Tca Cycle ◽  
Regulatory Mechanisms ◽  
Rna Seq ◽  
Acid Biosynthesis ◽  
Pyruvate Metabolism ◽  
Dynamic Changes ◽  
Acid Accumulation

Rice-acid has abundant taste substances and health protection function due to the various bioactive compounds it contains, including organic acids. L-lactic acid is the most abundant organic acid in rice-acid, but the regulatory mechanisms of L-lactic acid accumulation in rice-acid are obscure. In this study, we analyzed the dynamic changes in organic acids and taste substances in rice-acid in various fermentation phases and different inoculation methods. We identified the key genes involved in taste substance biosynthesis by RNA-Seq analysis and compared the data of four experimental groups. We found that the interaction of the differences in key functional genes (L-lactate dehydrogenase and D-lactate dehydrogenase) and key metabolism pathways (glycolysis, pyruvate metabolism, TCA cycle, amino acid biosynthesis, and metabolism) might interpret the accumulation of L-lactic acid, other organic acids, and taste substances in rice-acid fermented with Lacticaseibacillus paracasei. The experimental data provided the basis for exploring regulatory mechanisms of taste substance accumulation in rice-acid.

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