Lipid metabolism is a complex and dynamic system involving numerous enzymes at the junction of multiple metabolic pathways. Disruption of these pathways leads to systematic dyslipidemia, a hallmark of many pathological developments, such as nonalcoholic steatohepatitis and diabetes. Recent advances in computational tools can provide insights into the dysregulation of lipid biosynthesis, but limitations remain due to the complexity of lipidomic data, limited knowledge of interactions among involved enzymes, and technical challenges in standardizing across different lipid types. Here, we present a low-parameter, biologically interpretable framework named Lipid Synthesis Investigative Markov model (LipidSIM), which models and predicts the source of perturbations in lipid biosynthesis from lipidomic data. LipidSIM achieves this by accounting for the interdependency between the lipid species via the lipid biosynthesis network and generates testable hypotheses regarding changes in lipid biosynthetic reactions. This feature allows the integration of lipidomics with other omics types, such as transcriptomics, to elucidate the direct driving mechanisms of altered lipidomes due to treatments or disease progression. To demonstrate the value of LipidSIM, we first applied it to hepatic lipidomics following knockdown and found that changes in mRNA expression of the lipid pathways were consistent with the LipidSIM-predicted fluxes. Second, we used it to study lipidomic changes following intraperitoneal injection of CCl to induce fast NAFLD/NASH development and the progression of fibrosis and hepatic cancer. Finally, to show the power of LipidSIM for classifying samples with dyslipidemia, we used a -knockdown study dataset. Thus, we show that as it demands no knowledge of enzyme kinetics, LipidSIM is a valuable and intuitive framework for extracting biological insights from complex lipidomic data.

中文翻译：

---

LipidSIM：使用灵活的低参数马尔可夫建模框架从脂质组学中推断脂质生物合成的扰动

脂质代谢是一个复杂且动态的系统，涉及多个代谢途径交汇处的众多酶。这些途径的破坏会导致系统性血脂异常，这是许多病理发展的标志，例如非酒精性脂肪性肝炎和糖尿病。计算工具的最新进展可以深入了解脂质生物合成的失调，但由于脂质组学数据的复杂性、对所涉及酶之间相互作用的了解有限以及不同脂质类型标准化方面的技术挑战，仍然存在局限性。在这里，我们提出了一个低参数、生物学可解释的框架，称为脂质合成研究马尔可夫模型（LipidSIM），该框架根据脂质组学数据对脂质生物合成中的扰动来源进行建模和预测。LipidSIM 通过脂质生物合成网络解释脂质种类之间的相互依赖性来实现这一目标，并生成有关脂质生物合成反应变化的可检验假设。这一功能允许脂质组学与其他组学类型（例如转录组学）的整合，以阐明由于治疗或疾病进展而改变的脂质组学的直接驱动机制。为了证明 LipidSIM 的价值，我们首先将其应用于敲低后的肝脏脂质组学，发现脂质途径 mRNA 表达的变化与 LipidSIM 预测的通量一致。其次，我们用它来研究腹腔注射 CCl 诱导 NAFLD/NASH 快速发展以及纤维化和肝癌进展后的脂质组学变化。最后，为了展示 LipidSIM 对血脂异常样本进行分类的能力，我们使用了 -knockdown 研究数据集。因此，我们表明，由于 LipidSIM 不需要酶动力学知识，因此它是一个有价值且直观的框架，可用于从复杂的脂质组学数据中提取生物学见解。