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02 02, 2021;11 (1): 2771.

Cerebrum, liver, and muscle regulatory networks uncover maternal nutrition effects in developmental programming of beef cattle during early pregnancy.

Wellison J S Diniz, Matthew S Crouse, Robert A Cushman, Kyle J McLean, Joel S Caton, Carl R Dahlen, Lawrence P Reynolds, Alison K Ward
Author Information
  1. Wellison J S Diniz: Department of Animal Sciences, Center for Nutrition and Pregnancy, North Dakota State University, Fargo, ND, USA. w.dasilvadiniz@ndsu.edu.
  2. Matthew S Crouse: USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, USA.
  3. Robert A Cushman: USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, USA.
  4. Kyle J McLean: Department of Animal Science, University of Tennessee, Knoxville, TN, USA.
  5. Joel S Caton: Department of Animal Sciences, Center for Nutrition and Pregnancy, North Dakota State University, Fargo, ND, USA.
  6. Carl R Dahlen: Department of Animal Sciences, Center for Nutrition and Pregnancy, North Dakota State University, Fargo, ND, USA.
  7. Lawrence P Reynolds: Department of Animal Sciences, Center for Nutrition and Pregnancy, North Dakota State University, Fargo, ND, USA.
  8. Alison K Ward: Department of Animal Sciences, Center for Nutrition and Pregnancy, North Dakota State University, Fargo, ND, USA.
PMID: 33531552 DOI: 10.1038/s41598-021-82156-w

Abstract

The molecular basis underlying fetal programming in response to maternal nutrition remains unclear. Herein, we investigated the regulatory relationships between genes in fetal cerebrum, liver, and muscle tissues to shed light on the putative mechanisms that underlie the effects of early maternal nutrient restriction on bovine developmental programming. To this end, cerebrum, liver, and muscle gene expression were measured with RNA-Seq in 14 fetuses collected on day 50 of gestation from dams fed a diet initiated at breeding to either achieve 60% (RES, n = 7) or 100% (CON, n = 7) of energy requirements. To build a tissue-to-tissue gene network, we prioritized tissue-specific genes, transcription factors, and differentially expressed genes. Furthermore, we built condition-specific networks to identify differentially co-expressed or connected genes. Nutrient restriction led to differential tissue regulation between the treatments. Myogenic factors differentially regulated by ZBTB33 and ZNF131 may negatively affect myogenesis. Additionally, nutrient-sensing pathways, such as mTOR and PI3K/Akt, were affected by gene expression changes in response to nutrient restriction. By unveiling the network properties, we identified major regulators driving gene expression. However, further research is still needed to determine the impact of early maternal nutrition and strategic supplementation on pre- and post-natal performance.

References

  1. Paradis, F. et al. Maternal nutrient restriction in mid-to-late gestation influences fetal mRNA expression in muscle tissues in beef cattle. BMC Genom. 18, 1–14 (2017). [DOI: 10.1186/s12864-017-4051-5]
  2. Reynolds, L. P., Ward, A. K. & Caton, J. S. Epigenetics and developmental programming in ruminants: Long-term iimpacts on growth and development. in Biology of Domestic Animals (eds. Scanes, C. G. & Hill, R. A.) 85–120 (CRC Press, Boca Raton, 2017). https://doi.org/10.1201/9781315152080
  3. Caton, J. S. et al. Maternal nutrition and programming of offspring energy requirements. Transl. Anim. Sci. 3, 976–990 (2019). [PMID: 32704862]
  4. Bazer, F. W., Wang, X., Johnson, G. A. & Wu, G. Select nutrients and their effects on conceptus development in mammals. Anim. Nutr. 1, 85–95 (2015). [PMID: 29767122]
  5. Chavatte-Palmer, P., Tarrade, A., Kiefer, H., Duranthon, V. & Jammes, H. Breeding animals for quality products: Not only genetics. Reprod. Fertil. Dev. 28, 94–111 (2016). [PMID: 27062878]
  6. Long, N. M., Vonnahme, K. A., Hess, B. W., Nathanielsz, P. W. & Ford, S. P. Effects of early gestational undernutrition on fetal growth, organ development, and placentomal composition in the bovine. J. Anim. Sci. 87, 1950–1959 (2009). [PMID: 19213703]
  7. Maloney, C. A. & Rees, W. D. Gene-nutrient interactions during fetal development. Reproduction 130, 401–410 (2005). [PMID: 16183858]
  8. Sookoian, S., Gianotti, T. F., Burgueño, A. L. & Pirola, C. J. Fetal metabolic programming and epigenetic modifications: A systems biology approach. Pediatr. Res. 73, 531–542 (2013). [PMID: 23314294]
  9. Greenwood, P. L., Hearnshwaw, H., Cafe, L. M., Hennessy, D. W. & Harper. Nutrition in utero and pre-weaning has long term consequences for growth and size of Piedmontese and Wagyu-sired steers. J. Anim. Sci. 82 (Suppl. 1), 408 (2004).
  10. Reynolds, L. P. & Vonnahme, K. A. TRIENNIAL REPRODUCTION SYMPOSIUM: Developmental programming of fertility. J. Anim. Sci. 94, 2699–2704 (2016). [PMID: 27482657]
  11. Thompson, R. P., Nilsson, E. & Skinner, M. K. Environmental epigenetics and epigenetic inheritance in domestic farm animals. Anim. Reprod. Sci. https://doi.org/10.1016/j.anireprosci.2020.106316 (2020). [DOI: 10.1016/j.anireprosci.2020.106316]
  12. Du, M. et al. Fetal programming of skeletal muscle development in ruminant animals. J. Anim. Sci. https://doi.org/10.2527/jas.2009-2311 (2009). [DOI: 10.2527/jas.2009-2311]
  13. Rui, L. Energy metabolism in the liver. Compr. Physiol. 176, 177–197 (2014). [DOI: 10.1002/cphy.c130024]
  14. Longman, D., Stock, J. T. & Wells, J. C. K. A trade-off between cognitive and physical performance, with relative preservation of brain function. Sci. Rep. 7, 13709 (2017). [PMID: 29057922]
  15. Hyatt, M. A. et al. Maternal nutrient restriction in early pregnancy programs hepatic mRNA expression of growth-related genes and liver size in adult male sheep. J. Endocrinol. 192, 87–97 (2007). [PMID: 17210746]
  16. Zhu, M. J. et al. Maternal nutrient restriction affects properties of skeletal muscle in offspring. J. Physiol. 575, 241–250 (2006). [PMID: 16763001]
  17. Crouse, M. S. et al. Moderate nutrient restriction of beef heifers alters expression of genes associated with tissue metabolism, accretion, and function in fetal liver, muscle, and cerebrum by day 50 of gestation. Transl. Anim. Sci. 3, 855–866 (2019). [PMID: 32704851]
  18. Zhou, X. et al. Evidence for liver energy metabolism programming in offspring subjected to intrauterine undernutrition during midgestation. Nutr. Metab. 16, 1–14 (2019). [DOI: 10.1186/s12986-019-0346-7]
  19. Hudson, N. J., Dalrymple, B. P. & Reverter, A. Beyond differential expression: The quest for causal mutations and effector molecules. BMC Genomics 13, 356 (2012). [PMID: 22849396]
  20. Gaiteri, C., Ding, Y., French, B., Tseng, G. C. & Sibille, E. Beyond modules and hubs: The potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav. 13, 13–24 (2014). [PMID: 24320616]
  21. van Dam, S., Võsa, U., van der Graaf, A., Franke, L. & de Magalhães, J. P. Gene co-expression analysis for functional classification and gene–disease predictions. Brief. Bioinform. bbw139 (2017). https://doi.org/10.1093/bib/bbw139
  22. McKenzie, A. T., Katsyv, I., Song, W.-M., Wang, M. & Zhang, B. DGCA: A comprehensive R package for differential gene correlation analysis. BMC Syst. Biol. 10, 106 (2016). [PMID: 27846853]
  23. Yanai, I. et al. Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification. Bioinformatics 21, 650–659 (2005). [DOI: 10.1093/bioinformatics/bti042]
  24. Reverter, A., Hudson, N. J., Nagaraj, S. H., Pérez-Enciso, M. & Dalrymple, B. P. Regulatory impact factors: Unraveling the transcriptional regulation of complex traits from expression data. Bioinformatics 26, 896–904 (2010). [PMID: 20144946]
  25. Fuller, T. F. et al. Weighted gene coexpression network analysis strategies applied to mouse weight. Mamm. Genome 18, 463–472 (2007). [PMID: 17668265]
  26. Pérez-Montarelo, D. et al. Porcine tissue-specific regulatory networks derived from meta-analysis of the transcriptome. PLoS ONE 7, e46159 (2012). [PMID: 23049964]
  27. Goenawan, I. H., Bryan, K. & Lynn, D. J. DyNet: Visualization and analysis of dynamic molecular interaction networks. Bioinformatics 32, 2713–2715 (2016). [PMID: 27153624]
  28. Reverter, A. & Chan, E. K. F. Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks. Bioinformatics 24, 2491–2497 (2008). [PMID: 18784117]
  29. Aarstad, J., Ness, H. & Haugland, S. A. In what ways are small-world and scale-free networks interrelated?. Proc. IEEE Int. Conf. Ind. Technol. https://doi.org/10.1109/ICIT.2013.6505891 (2013). [DOI: 10.1109/ICIT.2013.6505891]
  30. Crouse, M. S. et al. Maternal nutrition and stage of early pregnancy in beef heifers: Impacts on hexose and AA concentrations in maternal and fetal fluids. J. Anim. Sci. 97, 1296–1316 (2019). [PMID: 30649334]
  31. Choi, M. et al. Overexpression of human GATA-1 and GATA-2 interferes with spine formation and produces depressive behavior in rats. PLoS ONE 9, e109253 (2014). [PMID: 25340772]
  32. Regal, J. F., Gilbert, J. S. & Burwick, R. M. The complement system and adverse pregnancy outcomes. Mol. Immunol. 67, 56–70 (2015). [PMID: 25802092]
  33. Lawan, A. et al. Hepatic mitogen-activated protein kinase phosphatase 1 selectively regulates glucose metabolism and energy homeostasis. Mol. Cell. Biol. 35, 26–40 (2015). [PMID: 25312648]
  34. Plutzky, J. The PPAR-RXR transcriptional complex in the vasculature. Circ. Res. 108, 1002–1016 (2011). [PMID: 21493923]
  35. Baardman, M. E. et al. The role of maternal-fetal cholesterol transport in early fetal life: Current insights. Biol. Reprod. 88, 1–9 (2013). [DOI: 10.1095/biolreprod.112.102442]
  36. Jones, A. K. et al. Gestational restricted- and over-feeding promote maternal and offspring inflammatory responses that are distinct and dependent on diet in sheep. Biol. Reprod. 98, 184–196 (2018). [PMID: 29272350]
  37. McLennan, I. S. & Koishi, K. Fetal and maternal transforming growth factor-β1 may combine to maintain pregnancy in Mice. Biol. Reprod. 70, 1614–1618 (2004). [PMID: 14766723]
  38. Wang, X. et al. Regulation of hepatic stellate cell activation and growth by transcription factor myocyte enhancer factor 2. Gastroenterology 127, 1174–1188 (2004). [PMID: 15480995]
  39. Kaimori, A. et al. Histone deacetylase inhibition suppresses the transforming growth factor β1-induced epithelial-to-mesenchymal transition in hepatocytes. Hepatology 52, 1033–1045 (2010). [PMID: 20564330]
  40. Saito, S. et al. HDAC8 inhibition ameliorates pulmonary fibrosis. Am. J. Physiol. Cell. Mol. Physiol. 316, L175–L186 (2019). [DOI: 10.1152/ajplung.00551.2017]
  41. Gomez-Pastor, R., Burchfiel, E. T. & Thiele, D. J. Regulation of heat shock transcription factors and their roles in physiology and disease. Nat. Rev. Mol. Cell Biol. https://doi.org/10.1038/nrm.2017.73 (2017). [DOI: 10.1038/nrm.2017.73]
  42. Gauvin, M. C. et al. Poor maternal nutrition during gestation in sheep alters prenatal muscle growth and development in offspring. J. Anim. Sci. 98, 1–15 (2020). [DOI: 10.1093/jas/skz388]
  43. Buckingham, M. & Rigby, P. W. J. Gene regulatory networks and transcriptional mechanisms that control myogenesis. Dev. Cell 28, 225–238 (2014). [PMID: 24525185]
  44. Oikawa, Y. et al. The methyl-CpG-binding protein CIBZ suppresses myogenic differentiation by directly inhibiting myogenin expression. Cell Res. 21, 1578–1590 (2011). [PMID: 21625269]
  45. Ruzov, A. et al. The non-methylated DNA-binding function of Kaiso is not required in early Xenopus laevis development. Development 136, 729–738 (2009). [PMID: 19158185]
  46. Mentch, S. J. & Locasale, J. W. One-carbon metabolism and epigenetics: understanding the specificity. Ann. N. Y. Acad. Sci. 1363, 91–98 (2016). [PMID: 26647078]
  47. Robinson, S. C. et al. The POZ-ZF transcription factor Znf131 is implicated as a regulator of Kaiso-mediated biological processes. Biochem. Biophys. Res. Commun. 493, 416–421 (2017). [PMID: 28882591]
  48. Ebert, S. M., Al-Zougbi, A., Bodine, S. C. & Adams, C. M. Skeletal muscle atrophy: Discovery of mechanisms and potential therapies. Physiology 34, 232–239 (2019). [PMID: 31165685]
  49. Chi, X.-Z. et al. RUNX3 suppresses gastric epithelial cell growth by inducing p21WAF1/Cip1 expression in cooperation with transforming growth factor β-activated SMAD. Mol. Cell. Biol. 25, 8097–8107 (2005). [PMID: 16135801]
  50. Schiaffino, S., Dyar, K. A., Ciciliot, S., Blaauw, B. & Sandri, M. Mechanisms regulating skeletal muscle growth and atrophy. FEBS J. 280, 4294–4314 (2013). [PMID: 23517348]
  51. Yu, J. S. L. & Cui, W. Proliferation, survival and metabolism: the role of PI3K/AKT/mTOR signalling in pluripotency and cell fate determination. Development 143, 3050–3060 (2016). [PMID: 27578176]
  52. Park, J. H., Kim, K. P., Ko, J. J. & Park, K. S. PI3K/Akt/mTOR activation by suppression of ELK3 mediates chemosensitivity of MDA-MB-231 cells to doxorubicin by inhibiting autophagy. Biochem. Biophys. Res. Commun. 477, 277–282 (2016). [PMID: 27301639]
  53. Xing, Y. et al. Reduction of the PI3K/Akt related signaling activities in skeletal muscle tissues involves insulin resistance in intrauterine growth restriction rats with catch-up growth. PLoS ONE 14, e0216665 (2019). [PMID: 31071176]
  54. Roh, E., Song, D. K. & Kim, M. S. Emerging role of the brain in the homeostatic regulation of energy and glucose metabolism. Exp. Mol. Med. 48, e216–e216 (2016). [PMID: 26964832]
  55. Licausi, F. & Hartman, N. W. Role of mTOR complexes in neurogenesis. Int. J. Mol. Sci. 19, 1544 (2018). [>PMCID: ]
  56. Takei, N. & Nawa, H. mTOR signaling and its roles in normal and abnormal brain development. Front. Mol. Neurosci. 7, 28 (2014). [PMID: 24795562]
  57. Gameiro, P. A. & Struhl, K. Nutrient deprivation elicits a transcriptional and translational inflammatory response coupled to decreased protein synthesis. Cell Rep. 24, 1415–1424 (2018). [PMID: 30089253]
  58. Slomnicki, L. P. et al. Requirement of neuronal ribosome synthesis for growth and maintenance of the dendritic tree. J. Biol. Chem. 291, 5721–5739 (2016). [PMID: 26757818]
  59. Bronicki, L. M. & Jasmin, B. J. Emerging complexity of the HuD/ELAVl4 gene; implications for neuronal development, function, and dysfunction. RNA 19, 1019–1037 (2013). [PMID: 23861535]
  60. Civiero, L. & Greggio, E. PAKs in the brain: Function and dysfunction. Biochim. Biophys. Acta Mol. Basis Dis. 1864, 444–453 (2018). [PMID: 29129728]
  61. Fang, F. et al. A distinct isoform of ZNF207 controls self-renewal and pluripotency of human embryonic stem cells. Nat. Commun. 9, 1–14 (2018). [DOI: 10.1038/s41467-018-06908-5]
  62. Nakashima, H. et al. Canonical TGF-β signaling negatively regulates reuronal morphogenesis through TGIF/Smad complex-mediated CRMP2 suppression. J. Neurosci. 38, 4791–4810 (2018). [PMID: 29695415]
  63. Wallen-Mackenzie, Å., Wootz, H. & Englund, H. Genetic inactivation of the vesicular glutamate transporter 2 (VGLUT2) in the mouse: What have we learnt about functional glutamatergic neurotransmission?. Upsala J. Med. Sci. 115, 11–20 (2010). [PMID: 20187846]
  64. Du, T., Xu, Q., Ocbina, P. J. & Anderson, S. A. NKX2.1 specifies cortical interneuron fate by activating Lhx6. Development 135, 1559–1567 (2008). [PMID: 18339674]
  65. Trollmann, R. & Gassmann, M. The role of hypoxia-inducible transcription factors in the hypoxic neonatal brain. Brain Dev. 31, 503–509 (2009). [PMID: 19398180]
  66. Herwartz, C., Castillo-Juárez, P., Schröder, L., Barron, B. L. & Steger, G. The transcription factor ZNF395 is required for the maximal hypoxic induction of proinflammatory cytokines in U87-MG cells. Mediat. Inflamm. 2015, 1–9 (2015). [DOI: 10.1155/2015/804264]
  67. Ma, X. et al. Ets2 suppresses inflammatory cytokines through MAPK/NF-kB signaling and directly binds to the IL-6 promoter in macrophages. Aging (Albany. NY). 11, 10610–10625 (2019). [PMID: 31785145]
  68. Dresselhaus, E. C. & Meffert, M. K. Cellular specificity of NF-κB function in the nervous system. Front. Immunol. 10, 1043 (2019). [PMID: 31143184]
  69. Liu, S. et al. Epigenomics and genotype-phenotype association analyses reveal conserved genetic architecture of complex traits in cattle and human. BMC Biol. 18, 1–16 (2020). [DOI: 10.1186/s12915-020-00792-6]
  70. Caton, J. S. et al. Maternal periconceptual nutrition, early pregnancy, and developmental outcomes in beef cattle. J. Anim. Sci. https://doi.org/10.1093/jas/skaa358/5962128 .
  71. National Academies of Sciences, Engineering, and M. Nutrient Requirements of Beef Cattle, 8th Revised Edition. (National Academies Press, 2016). https://doi.org/10.17226/19014 .
  72. McLean, K. J. et al. Technical note: A new surgical technique for ovariohysterectomy during early pregnancy in beef heifers. J. Anim. Sci. 94, 5089–5096 (2016). [PMID: 28046159]
  73. Gokulakrishnan, P., Kumar, R. R., Sharma, B. D., Mendiratta, S. K. & Sharma, D. Sex determination of cattle meat by polymerase chain reaction amplification of the DEAD box protein (DDX3X/DDX3Y) gene. Asian-Austr. J. Anim. Sci. 25, 733–737 (2012). [DOI: 10.5713/ajas.2012.12003]
  74. Andrews, S. FASTQC. A quality control tool for high throughput sequence data. (2010). Available at: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ .
  75. Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016). [PMID: 27312411]
  76. Rosen, B. D. et al. De novo assembly of the cattle reference genome with single-molecule sequencing. Gigascience 9, 1–9 (2020). [DOI: 10.1093/gigascience/giaa021]
  77. Dobin, A. et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013). [DOI: 10.1093/bioinformatics/bts635]
  78. RStudio Team. RStudio: Integrated Development Environment for R. (2020).
  79. R Core Team. R: A Language and Environment for Statistical Computing. (2018).
  80. Tarazona, S. et al. Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. Nucl. Acids Res. https://doi.org/10.1093/nar/gkv711 (2015). [DOI: 10.1093/nar/gkv711]
  81. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010). [DOI: 10.1093/bioinformatics/btp616]
  82. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). [PMID: 25516281]
  83. Condon, K. Tispec. (2019). Available at: https://github.com/roonysgalbi/tispec/blob/master/vignettes/UserGuide.Rmd .
  84. Kryuchkova-Mostacci, N. & Robinson-Rechavi, M. A benchmark of gene expression tissue-specificity metrics. Brief. Bioinform. 18, 205–214 (2017). [PMID: 26891983]
  85. Bastian, F. et al. Bgee: Integrating and comparing heterogeneous transcriptome data among species. Data Integration in the Life Sciences 124–131 (2008).
  86. Haendel, M. A. et al. Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon. J. Biomed. Semantics 5, 21 (2014). [PMID: 25009735]
  87. Hu, H. et al. AnimalTFDB 3.0: A comprehensive resource for annotation and prediction of animal transcription factors. Nucl. Acids Res. 47, 33–38 (2018). [DOI: 10.1093/nar/gky822]
  88. Alexandre, P. A. et al. Systems biology reveals NR2F6 and TGFB1 as key regulators of feed efficiency in beef cattle. Front. Genet. 10, 230 (2019).
  89. Shannon, P. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003). [PMID: 14597658]
  90. Assenov, Y., Ramírez, F., Schelhorn, S.-E., Lengauer, T. & Albrecht, M. Computing topological parameters of biological networks. Bioinforma. Appl. 24, 282–284 (2008). [DOI: 10.1093/bioinformatics/btm554]
  91. Bindea, G. et al. ClueGO: A cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25, 1091–1093 (2009). [PMID: 19237447]
  92. Ge, S. X., Jung, D. & Yao, R. ShinyGO: A graphical gene-set enrichment tool for animals and plants. Bioinformatics 36, 2628–2629 (2020). [PMID: 31882993]
  93. Kolde, R. pheatmap: Pretty Heatmaps. (2018).
  94. Oliveros, J. C. Venny. An interactive tool for comparing lists with Venn’s diagrams.

MeSH Term

Animal Nutritional Physiological Phenomena
Animals
Cattle
Female
Fetal Development
Fetus
Nutritional Status
Pregnancy
Prenatal Nutritional Physiological Phenomena
Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Links to CNCB-NGDC Resources

GEN: GEND000161 (Transcriptomic profiling of cerebrum, liver, and muscle tissues of 50 days fetuses in response to early maternal nutrient restriction (cattle))

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