Department of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK.
Department of Psychiatry and MRC Unit on Anxiety and Stress Disorders, Groote Schuur Hospital (J-2), University of Cape Town, Cape Town, South Africa.
Transl Psychiatry. 2016 May 10;6(5):e802. doi: 10.1038/tp.2016.67.
The clinical presentation, course and treatment of methamphetamine (METH)-associated psychosis (MAP) are similar to that observed in schizophrenia (SCZ) and subsequently MAP has been hypothesized as a pharmacological and environmental model of SCZ. However, several challenges currently exist in diagnosing MAP accurately at the molecular and neurocognitive level before the MAP model can contribute to the discovery of SCZ biomarkers. We directly assessed subcortical brain structural volumes and clinical parameters of MAP within the framework of an integrative genome-wide RNA-Seq blood transcriptome analysis of subjects diagnosed with MAP (N=10), METH dependency without psychosis (MA; N=10) and healthy controls (N=10). First, we identified discrete groups of co-expressed genes (that is, modules) and tested them for functional annotation and phenotypic relationships to brain structure volumes, life events and psychometric measurements. We discovered one MAP-associated module involved in ubiquitin-mediated proteolysis downregulation, enriched with 61 genes previously found implicated in psychosis and SCZ across independent blood and post-mortem brain studies using convergent functional genomic (CFG) evidence. This module demonstrated significant relationships with brain structure volumes including the anterior corpus callosum (CC) and the nucleus accumbens. Furthermore, a second MAP and psychoticism-associated module involved in circadian clock upregulation was also enriched with 39 CFG genes, further associated with the CC. Subsequently, a machine-learning analysis of differentially expressed genes identified single blood-based biomarkers able to differentiate controls from methamphetamine dependents with 87% accuracy and MAP from MA subjects with 95% accuracy. CFG evidence validated a significant proportion of these putative MAP biomarkers in independent studies including CLN3, FBP1, TBC1D2 and ZNF821 (RNA degradation), ELK3 and SINA3 (circadian clock) and PIGF and UHMK1 (ubiquitin-mediated proteolysis). Finally, focusing analysis on brain structure volumes revealed significantly lower bilateral hippocampal volumes in MAP subjects. Overall, these results suggest similar molecular and neurocognitive mechanisms underlying the pathophysiology of psychosis and SCZ regardless of substance abuse and provide preliminary evidence supporting the MAP paradigm as an exemplar for SCZ biomarker discovery.
（METH）-相关精神病（MAP）的临床表现、病程和治疗与精神分裂症（SCZ）相似，随后MAP 被假设为 SCZ 的药理学和环境模型。然而，在 MAP 模型能够为 SCZ 生物标志物的发现做出贡献之前，目前在分子和神经认知水平上准确诊断 MAP 仍然存在一些挑战。我们直接评估了在 MAP 模型中诊断 MAP 的分子和神经认知水平，研究了在 MAP 患者（N=10）、无精神病的 METH 依赖者（MA；N=10）和健康对照组（N=10）中，亚皮质脑结构体积和临床参数。首先，我们确定了离散的共表达基因（即模块），并对其进行了功能注释，并测试了它们与脑结构体积、生活事件和心理测量测量的表型关系。我们发现一个与 MAP 相关的模块，涉及泛素介导的蛋白水解下调，该模块富集了 61 个基因，这些基因在独立的血液和死后大脑研究中使用收敛的功能基因组（CFG）证据发现与精神病和 SCZ 有关。该模块与脑结构体积，包括前连合和伏隔核，存在显著的关系。此外，另一个与 MAP 和精神病性相关的模块涉及昼夜节律钟的上调，也富集了 39 个 CFG 基因，与连合进一步相关。随后，对差异表达基因的机器学习分析，能够以 87%的准确率区分对照组和 METH 依赖者，以 95%的准确率区分 MAP 和 MA 患者。CFG 证据在包括 CLN3、FBP1、TBC1D2 和 ZNF821（RNA 降解）、ELK3 和 SINA3（昼夜节律钟）以及 PIGF 和 UHMK1（泛素介导的蛋白水解）在内的独立研究中验证了这些 MAP 候选生物标志物的显著比例。最后，对脑结构体积的分析表明，MAP 患者双侧海马体积明显较低。总之，这些结果表明，无论是否存在物质滥用，精神病和 SCZ 的发病机制都存在类似的分子和神经认知机制，并提供了初步证据支持 MAP 范式作为 SCZ 生物标志物发现的范例。
