Novel metabolomic strategies to understand phenotypic perturbations

对生理学和细胞生物学的分子机制的科学理解对于理解正常的人类生理学至关重要，提供了理解扰动的最有力手段。需要发展这样的理解是特别重要的心脏生理学和细胞生物学，以防止和了解不利的表型变化。对心电图波形中涉及的关键离子通道的识别是理解心脏生理学和细胞生物学的高价值的有力证据。然而，除了关键细胞器的含义外，对其他非ECG相关心脏生理学和细胞生物学事件（特别是心脏细胞生物学和形态学变化）之间的分子联系了解甚少（Cross et al. 2015; Laverty et al. 2011）。代谢组学提供了机会，为识别预后和反射扰动心脏细胞生物学和形态表型促进定量翻译理解的发展。这些代谢组学指纹整合并反映了基因组、转录组和蛋白质组的变化，提供了代谢组学指纹变化将产生对心脏细胞生物学表型和表型扰动影响的理解的切实证据。此外，代谢组学改变与各种心血管疾病表型相关，包括心力衰竭和心肌梗死（Dunn et al. 2011; Kordalewska and Markuzewski 2015）。这有力地表明，使用代谢组学来理解心脏细胞生物学和形态学表型变化期间的扰动是可行的。这将提供一个公正的方法，发现可翻译的分子扰动，反过来可以用来产生生物学假说，更详细的调查。为了实现这一愿景，需要将具有更大复杂性和生理相关性的先进模型系统（例如微组织和微生理系统（MPS））与来自临床前物种和临床样本的生物流体/组织一起应用于代谢组学领域。然而，由于样本量小，这在分析方面提出了挑战。通过调整已针对临床应用优化的当代代谢组学技术，我们需要开发更高灵敏度的代谢组学方法，以使代谢组学应用于这一前沿研究。最终，这将汇集模型系统和分析方面的新方法，以深入了解表型扰动。最后，这些进展比心血管领域具有更广泛的适用性，一旦在该领域开发，其他关键器官领域可以进行研究。

Scientific understanding of molecular mechanisms underlying physiology and cell biology is critical in understanding normal human physiology, providing the most powerful means of understanding perturbations. The need to develop such an understanding is particularly important in cardiac physiology and cell biology in order to prevent and understand adverse phenotypic changes. Tangible evidence for the high value of understanding cardiac physiology and cell biology is the identification of the key ion channels involved in the ECG waveform. However the molecular links between other, non ECG-related cardiac physiology and cell biology events, in particular changes in cardiac cell biology and morphology, are poorly understood beyond the implication of key organelles (Cross et al. 2015; Laverty et al. 2011). Metabolomics offers the opportunity for the identification of prognostic and reflective perturbations in cardiac cell biology and morphology phenotypes facilitating the development of quantitative translational understanding. These metabolomic fingerprints integrate and reflect changes in the genome, transcriptome and proteome, providing tangible evidence that changes in the metabolomic fingerprint will generate understanding of cardiac cell biology phenotypes and the impact of phenotypic perturbations. In addition, metabolomic alterations have been linked to various cardiovascular disease phenotypes including heart failure and myocardial infarction (Dunn et al. 2011; Kordalewska and Markuzewski 2015). This strongly suggests that using metabolomics to understand perturbations during phenotypic changes in cardiac cell biology and morphology would be feasible. This would provide an unbiased approach for discovering translatable molecular perturbations that can in turn be used to generate biological hypotheses for more detailed investigations. In order for this vision to be realised, advanced model systems with greater complexity and thus physiological relevance (e.g. microtissues and microphysiological systems (MPS)) need to be applied to the metabolomics field in conjunction with biofluids/tissue from preclinical species and clinical samples. However, this presents challenges in terms of the analytics, due to the small sample masses. By adapting contemporary metabolomics technologies that have been optimised for clinical applications we need to develop much higher sensitivity metabolomics methods to enable the application of metabolomics to this cutting edge research. Ultimately this will bring together new novel approaches in terms of model systems and analytics to develop deep understanding of phenotypic perturbations. Finally, these advancements have wider applicability than the cardiovascular area, once developed in this space other key organ areas could be investigated.