Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-29T10:51:42.994Z Has data issue: false hasContentIssue false
  • English
  • Français

Integrated regulatory network reveals novel candidate regulators in the development of negative energy balance in cattle

Published online by Cambridge University Press:  28 December 2017

Z. Mozduri ,
M. R. Bakhtiarizadeh  and
A. Salehi
Z. Mozduri
Affiliation:
Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, 33916-53775, Tehran, Iran
M. R. Bakhtiarizadeh*
Affiliation:
Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, 33916-53775, Tehran, Iran
A. Salehi
Affiliation:
Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, 33916-53775, Tehran, Iran
*
†E-mail: [email protected]
Get access

Abstract

Negative energy balance (NEB) is an altered metabolic state in modern high-yielding dairy cows. This metabolic state occurs in the early postpartum period when energy demands for milk production and maintenance exceed that of energy intake. Negative energy balance or poor adaptation to this metabolic state has important effects on the liver and can lead to metabolic disorders and reduced fertility. The roles of regulatory factors, including transcription factors (TFs) and micro RNAs (miRNAs) have often been separately studied for evaluating of NEB. However, adaptive response to NEB is controlled by complex gene networks and still not fully understood. In this study, we aimed to discover the integrated gene regulatory networks involved in NEB development in liver tissue. We downloaded data sets including mRNA and miRNA expression profiles related to three and four cows with severe and moderate NEB, respectively. Our method integrated two independent types of information: module inference network by TFs, miRNAs and mRNA expression profiles (RNA-seq data) and computational target predictions. In total, 176 modules were predicted by using gene expression data and 64 miRNAs and 63 TFs were assigned to these modules. By using our integrated computational approach, we identified 13 TF-module and 19 miRNA-module interactions. Most of these modules were associated with liver metabolic processes as well as immune and stress responses, which might play crucial roles in NEB development. Literature survey results also showed that several regulators and gene targets have already been characterized as important factors in liver metabolic processes. These results provided novel insights into regulatory mechanisms at the TF and miRNA levels during NEB. In addition, the method described in this study seems to be applicable to construct integrated regulatory networks for different diseases or disorders.

Keywords

integrated regulatory networkmodule networknegative energy balancesequence-based informationtranscription factor
Type
Research Article
Information
animal , Volume 12 , Issue 6 , June 2018 , pp. 1196 - 1207
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Andrews, S 2010. FastQC: a quality control tool for high throughput sequence data.Google Scholar
Arner, P 2003. The adipocyte in insulin resistance: key molecules and the impact of the thiazolidinediones. Trends in Endocrinology & Metabolism 14, 137–145.Google Scholar
Bakhtiarizadeh, MR, Moradi-Shahrbabak, M and Ebrahimie, E 2014. Transcriptional regulatory network analysis of the over-expressed genes in adipose tissue. Genes & Genomics 36, 105–117.CrossRefGoogle Scholar
Barabási, A-L, Gulbahce, N and Loscalzo, J 2011. Network medicine: a network-based approach to human disease. Nature Reviews Genetics 12, 56–68.CrossRefGoogle ScholarPubMed
Barturen, G, Rueda, A, Hamberg, M, Alganza, A, Lebron, R, Kotsyfakis, M, Shi, B-J, Koppers-Lalic, D and Hackenberg, M 2014. sRNAbench: profiling of small RNAs and its sequence variants in single or multi-species high-throughput experiments. Methods in Next Generation Sequencing 1, 21–31.CrossRefGoogle Scholar
Behdani, E and Bakhtiarizadeh, MR 2017. Construction of an integrated gene regulatory network link to stress-related immune system in cattle. Genetica 145, 441–454.Google Scholar
Bolger, AM, Lohse, M and Usadel, B 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120.Google Scholar
Bonnet, E, Calzone, L and Michoel, T 2015. Integrative multi-omics module network inference with Lemon-Tree. PLoS Computational Biology 11, e1003983.Google Scholar
Cartharius, K, Frech, K, Grote, K, Klocke, B, Haltmeier, M, Klingenhoff, A, Frisch, M, Bayerlein, M and Werner, T 2005. MatInspector and beyond: promoter analysis based on transcription factor binding sites. Bioinformatics 21, 2933–2942.Google Scholar
Chang, L-W, Viader, A, Varghese, N, Payton, JE, Milbrandt, J and Nagarajan, R 2013. An integrated approach to characterize transcription factor and microRNA regulatory networks involved in Schwann cell response to peripheral nerve injury. BMC Genomics 14, 1.CrossRefGoogle ScholarPubMed
Chen, J, Wang, G, Lu, C, Guo, X, Hong, W, Kang, J and Wang, J 2012. Synergetic cooperation of microRNAs with transcription factors in iPS cell generation. PloS One 7, e40849.CrossRefGoogle ScholarPubMed
Chen, K and Rajewsky, N 2007. The evolution of gene regulation by transcription factors and microRNAs. Nature Reviews Genetics 8, 93–103.Google Scholar
Cho, DY, Kim, YA and Przytycka, TM 2012. Chapter 5: network biology approach to complex diseases. PLoS Computational Biology 8, e1002820.Google Scholar
De Smet, R and Marchal, K 2010. Advantages and limitations of current network inference methods. Nature Reviews Microbiology 8, 717–729.Google Scholar
Fatima, A, Lynn, DJ, O’Boyle, P, Seoighe, C and Morris, D 2014a. The miRNAome of the postpartum dairy cow liver in negative energy balance. BMC Genomics 15, 279.Google Scholar
Fatima, A, Waters, S, O’Boyle, P, Seoighe, C and Morris, DG 2014b. Alterations in hepatic miRNA expression during negative energy balance in postpartum dairy cattle. BMC Genomics 15, 1.Google Scholar
Garraway, LA, Widlund, HR, Rubin, MA, Getz, G, Berger, AJ, Ramaswamy, S, Beroukhim, R, Milner, DA, Granter, SR and Du, J 2005. Integrative genomic analyses identify MITF as a lineage survival oncogene amplified in malignant melanoma. Nature 436, 117–122.Google Scholar
Guo, Y, Alexander, K, Clark, AG, Grimson, A and Yu, H 2016. Integrated network analysis reveals distinct regulatory roles of transcription factors and microRNAs. RNA 22, 1663–1672.CrossRefGoogle Scholar
Herdt, TH 2000. Ruminant adaptation to negative energy balance: Influences on the etiology of ketosis and fatty liver. Veterinary Clinics of North America: Food Animal Practice 16, 215–231.Google Scholar
Hu, X, Chi, L, Zhang, W, Bai, T, Zhao, W, Feng, Z and Tian, H 2015. Down-regulation of the miR-543 alleviates insulin resistance through targeting the SIRT1. Biochemical and Biophysical Research Communications 468, 781–787.CrossRefGoogle ScholarPubMed
Iancu, OD, Kawane, S, Bottomly, D, Searles, R, Hitzemann, R and McWeeney, S 2012. Utilizing RNA-Seq data for de novo coexpression network inference. Bioinformatics 28, 1592–1597.Google Scholar
John, B, Enright, AJ, Aravin, A, Tuschl, T, Sander, C and Marks, DS 2004. Human microRNA targets. PLoS Biology 2, e363.Google Scholar
Kaur, K, Pandey, AK, Srivastava, S, Srivastava, AK and Datta, M 2011. Comprehensive miRNome and in silico analyses identify the Wnt signaling pathway to be altered in the diabetic liver. Molecular Biosystems 7, 3234–3244.Google Scholar
Kertesz, M, Iovino, N, Unnerstall, U, Gaul, U and Segal, E 2007. The role of site accessibility in microRNA target recognition. Nature Genetics 39, 1278–1284.Google Scholar
Le, TD, Liu, L, Zhang, J, Liu, B and Li, J 2015. From miRNA regulation to miRNA–TF co-regulation: computational approaches and challenges. Briefings in Bioinformatics 16, 475–496.CrossRefGoogle ScholarPubMed
Lee, A-H, Scapa, EF, Cohen, DE and Glimcher, LH 2008. Regulation of hepatic lipogenesis by the transcription factor XBP1. Science 320, 1492–1496.Google Scholar
Lhakhang, TW and Chaudhry, MA 2012. Current approaches to micro-RNA analysis and target gene prediction. Journal of Applied Genetics 53, 149–158.Google Scholar
Li, H, Zhang, Z, Zhou, X, Wang, Z, Wang, G and Han, Z 2011. Effects of microRNA-143 in the differentiation and proliferation of bovine intramuscular preadipocytes. Molecular Biology Reports 38, 4273–4280.Google Scholar
Lin, Y, Zhang, Q, Zhang, H-M, Liu, W, Liu, C-J, Li, Q and Guo, A-Y 2015. Transcription factor and miRNA co-regulatory network reveals shared and specific regulators in the development of B cell and T cell. Scientific Reports 5, 15215.Google Scholar
Loor, J 2010. Genomics of metabolic adaptations in the peripartal cow. Animal 4, 1110–1139.CrossRefGoogle ScholarPubMed
McCabe, M, Waters, S, Morris, D, Kenny, D, Lynn, D and Creevey, C 2012. RNA-seq analysis of differential gene expression in liver from lactating dairy cows divergent in negative energy balance. BMC Genomics 13, 193.Google Scholar
McCarthy, SD, Waters, SM, Kenny, DA, Diskin, MG, Fitzpatrick, R, Patton, J, Wathes, DC and Morris, DG 2010. Negative energy balance and hepatic gene expression patterns in high-yielding dairy cows during the early postpartum period: a global approach. Physiological Genomics 42, 188–199.CrossRefGoogle Scholar
Muniategui, A, Pey, J, Planes, F and Rubio, A 2012. Joint analysis of miRNA and mRNA expression data. Briefings in Bioinformatics 14, 263–278.CrossRefGoogle ScholarPubMed
Patton, J, Kenny, D, Mee, J, O’mara, F, Wathes, D, Cook, M and Murphy, J 2006. Effect of milking frequency and diet on milk production, energy balance, and reproduction in dairy cows. Journal of Dairy Science 89, 1478–1487.Google Scholar
Pedernera, M, Celi, P, Garcia, SC, Salvin, HE, Barchia, I and Fulkerson, WJ 2010. Effect of diet, energy balance and milk production on oxidative stress in early-lactating dairy cows grazing pasture. Veterinary Journal 186, 352–357.CrossRefGoogle ScholarPubMed
Schulz, MH, Pandit, KV, Cardenas, CLL, Ambalavanan, N, Kaminski, N and Bar-Joseph, Z 2013. Reconstructing dynamic microRNA-regulated interaction networks. In Proceedings of the National Academy of Sciences 110, pp. 15686–15691.CrossRefGoogle Scholar
Shimano, H 2007. SREBP-1c and TFE3, energy transcription factors that regulate hepatic insulin signaling. Journal of Molecular Medicine 85, 437–444.Google Scholar
Sturm, M, Hackenberg, M, Langenberger, D and Frishman, D 2010. TargetSpy: a supervised machine learning approach for microRNA target prediction. BMC Bioinformatics 11, 1.Google Scholar
Tabas-Madrid, D, Muniategui, A, Sánchez-Caballero, I, Martínez-Herrera, DJ, Sorzano, COS, Rubio, A and Pascual-Montano, A 2014. Improving miRNA-mRNA interaction predictions. BMC Genomics 15, 1.CrossRefGoogle ScholarPubMed
Trapnell, C, Pachter, L and Salzberg, SL 2009. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111.CrossRefGoogle ScholarPubMed
Vargas Junior, FMd, Wechsler, FS, Oliveira, MVMd, Seno, LdO, Fernandes, ARM and Camilo, FR 2013. Reproductive efficiency of Nellore cows nursing Nellore or crossbred Simmental× Nellore calves. Revista Brasileira de Zootecnia 42, 475–480.Google Scholar
Wang, H, Zheng, Y, Wang, G and Li, H 2013. Identification of microRNA and bioinformatics target gene analysis in beef cattle intramuscular fat and subcutaneous fat. Molecular BioSystems 9, 2154–2162.Google Scholar
Wang, YR and Huang, H 2014. Review on statistical methods for gene network reconstruction using expression data. Journal of Theoretical Biology 362, 53–61.Google Scholar
Zhao, J, Yang, T-H, Huang, Y and Holme, P 2011. Ranking candidate disease genes from gene expression and protein interaction: a Katz-centrality based approach. PloS One 6, e24306.Google Scholar
Supplementary material: File

Mozduri et al. supplementary material

Mozduri et al. supplementary material 1

Download Mozduri et al. supplementary material(File)
File 15.7 KB
Supplementary material: File

Mozduri et al. supplementary material

Mozduri et al. supplementary material 2

Download Mozduri et al. supplementary material(File)
File 70.9 KB
Supplementary material: File

Mozduri et al. supplementary material

Mozduri et al. supplementary material 3

Download Mozduri et al. supplementary material(File)
File 15.8 KB
Supplementary material: File

Mozduri et al. supplementary material

Mozduri et al. supplementary material 4

Download Mozduri et al. supplementary material(File)
File 33.5 KB
Supplementary material: File

Mozduri et al. supplementary material

Mozduri et al. supplementary material 5

Download Mozduri et al. supplementary material(File)
File 62.8 KB
Supplementary material: File

Mozduri et al. supplementary material

Mozduri et al. supplementary material 6

Download Mozduri et al. supplementary material(File)
File 38.6 KB
Supplementary material: File

Mozduri et al. supplementary material

Mozduri et al. supplementary material 7

Download Mozduri et al. supplementary material(File)
File 16.9 KB