Nutrigenomics and Fish Growth Endocrinology Group, Institute of Aquaculture Torre de La Sal (IATS, CSIC), 12595, Castellón, Spain.
Grupo de Investigación en Acuicultura (GIA), IU-ECOAQUA, Universidad de Las Palmas de Gran Canaria, Ctra. Taliarte S/N, 35214, Telde, Las Palmas, Canary Islands, Spain.
BMC Genomics. 2023 Nov 7;24(1):670. doi: 10.1186/s12864-023-09759-7.
Broodstock nutritional programming improves the offspring utilization of plant-based diets in gilthead sea bream through changes in hepatic metabolism. Attention was initially focused on fatty acid desaturases, but it can involve a wide range of processes that remain largely unexplored. How all this can be driven by a different genetic background is hardly underlined, and the present study aimed to assess how broodstock nutrition affects differentially the transcriptome and genome-wide DNA methylome of reference and genetically selected fish within the PROGENSA® selection program.
After the stimulus phase with a low fish oil diet, two offspring subsets of each genetic background received a control or a FUTURE-based diet. This highlighted a different hepatic transcriptome (RNA-seq) and genome-wide DNA methylation (MBD-seq) pattern depending on the genetic background. The number of differentially expressed transcripts following the challenge phase varied from 323 in reference fish to 2,009 in genetically selected fish. The number of discriminant transcripts, and associated enriched functions, were also markedly higher in selected fish. Moreover, correlation analysis depicted a hyper-methylated and down-regulated gene expression state in selected fish with the FUTURE diet, whereas the opposite pattern appeared in reference fish. After filtering for highly represented functions in selected fish, 115 epigenetic markers were retrieved in this group. Among them, lipid metabolism genes (23) were the most reactive following ordering by fold-change in expression, rendering a final list of 10 top markers with a key role on hepatic lipogenesis and fatty acid metabolism (cd36, pitpna, cidea, fasn, g6pd, lipt1, scd1a, acsbg2, acsl14, acsbg2).
Gene expression profiles and methylation signatures were dependent on genetic background in our experimental model. Such assumption affected the magnitude, but also the type and direction of change. Thus, the resulting epigenetic clock of reference fish might depict an older phenotype with a lower methylation for the epigenetically responsive genes with a negative methylation-expression pattern. Therefore, epigenetic markers will be specific of each genetic lineage, serving the broodstock programming in our selected fish to prevent and mitigate later in life the risk of hepatic steatosis through changes in hepatic lipogenesis and fatty acid metabolism.
通过改变肝代谢，亲鱼营养编程可提高真鲷幼鱼对植物性饮食的利用。最初的注意力集中在脂肪酸去饱和酶上，但它可能涉及到广泛的仍在很大程度上未被探索的过程。这种情况如何能被不同的遗传背景所驱动，几乎没有被强调，本研究旨在评估亲鱼营养如何在 PROGENSA®选择计划中，以不同的方式影响参考鱼和遗传选择鱼的转录组和全基因组 DNA 甲基化组。
在用低鱼油饮食进行刺激阶段后，每个遗传背景的两个后代亚组分别接受对照或基于 FUTURE 的饮食。这突出了不同的肝转录组（RNA-seq）和全基因组 DNA 甲基化（MBD-seq）模式，这取决于遗传背景。在挑战阶段后，差异表达的转录本数量从参考鱼的 323 个变化到遗传选择鱼的 2009 个。在选择鱼中，差异表达转录本的数量和相关富集功能也明显更高。此外，相关性分析描绘了在 FUTURE 饮食下，选择鱼的高甲基化和下调基因表达状态，而参考鱼则出现相反的模式。在对选择鱼中高代表性功能进行过滤后，在该组中检索到 115 个表观遗传标记。其中，脂质代谢基因（23 个）在按表达倍数排序后的反应最为强烈，最终得到了 10 个具有关键作用的肝内生脂和脂肪酸代谢的顶级标记基因列表（cd36、pitpna、cidea、fasn、g6pd、lipt1、scd1a、acsbg2、acsl14、acsbg2）。
在我们的实验模型中，基因表达谱和甲基化特征取决于遗传背景。这种假设影响了变化的幅度，但也影响了变化的类型和方向。因此，参考鱼的表观遗传钟可能描绘了一种较老的表型，具有较低的甲基化水平，对具有负甲基化-表达模式的表观遗传反应基因。因此，表观遗传标记将是每个遗传谱系所特有的，服务于我们选择鱼的亲鱼编程，以通过改变肝内生脂和脂肪酸代谢来预防和减轻以后生活中肝脂肪变性的风险。
