SNCA, the first gene associated with Parkinson’s disease, encodes the α-synuclein protein, the predominant component within pathological inclusions termed Lewy bodies. The presence of Lewy bodies is one of the classical hallmarks found in the brain of patients with Parkinson’s disease, and Lewy bodies have also been observed in patients with other synucleinopathies. However, the study of α-synuclein pathology in cells has relied largely on two-dimensional culture models, which typically lack the cellular diversity and complex spatial environment found in the brain. Here, to address this gap, we use 3D midbrain organoids, differentiated from human induced pluripotent stem cells derived from patients carrying a triplication of the SNCA gene and from CRISPR/Cas9 corrected isogenic control iPSCs. These human midbrain organoids recapitulate key features of α-synuclein pathology observed in the brains of patients with synucleinopathies. In particular, we find that SNCA triplication human midbrain organoids express elevated levels of α-synuclein and exhibit an age-dependent increase in α-synuclein aggregation, manifested by the presence of both oligomeric and phosphorylated forms of α-synuclein. These phosphorylated α-synuclein aggregates were found in both neurons and glial cells and their time-dependent accumulation correlated with a selective reduction in dopaminergic neuron numbers. Thus, human midbrain organoids from patients carrying SNCA gene multiplication can reliably model key pathological features of Parkinson’s disease and provide a powerful system to study the pathogenesis of synucleinopathies.

The purpose of this article is to provide a comprehensive review of metabolomics studies for psychosis, as a means of biomarker discovery. Manuscripts were selected for review if they involved discovery of metabolites using high-throughput analysis in human subjects and were published in the last decade. The metabolites identified were searched in Human Metabolome Data Base (HMDB) for a link to psychosis. Metabolites associated with psychosis based on evidence in HMBD were then searched using PubMed to explore the availability of further evidence. Almost all of the studies which underwent full review involved patients with schizophrenia. Ten biomarkers were identified. Six of them were reported in two or more independent metabolomics studies: N-acetyl aspartate, lactate, tryptophan, kynurenine, glutamate, and creatine. Four additional metabolites were encountered in a single metabolomics study but had significant evidence (two supporting articles or more) for a link to psychosis based on PubMed: linoleic acid, D-serine, glutathione, and 3-hydroxybutyrate. The pathways affected are discussed as they may be relevant to the pathophysiology of psychosis, and specifically of schizophrenia, as well as, constitute new drug targets for treatment of related conditions. Based on the biomarkers identified, early diagnosis of schizophrenia and/or monitoring may be possible.

SNCA, the first gene associated with Parkinson′s disease, encodes the α-synuclein (α-syn) protein, the predominant component within pathological inclusions termed Lewy bodies (LBs). The presence of LBs is one of the classical hallmarks found in the brain of patients with Parkinson′s disease, and LBs have also been observed in patients with other synucleinopathies. However, the study of α-syn pathology in cells has relied largely on two-dimensional culture models, which typically lack the cellular diversity and complex spatial environment found in the brain. Here, to address this gap, we use 3D midbrain organoids (hMOs), differentiated from human induced pluripotent stem cells derived from patients carrying a triplication of the SNCA gene and from CRISPR/Cas9 corrected isogenic control iPSCs. These hMOs recapitulate key features of α-syn pathology observed in the brains of patients with synucleinopathies. In particular, we find that SNCA triplication hMOs express elevated levels of α-syn and exhibit an age-dependent increase in α-syn aggregation, manifested by the presence of both oligomeric and phosphorylated forms of α-syn. These phosphorylated α-syn aggregates were found in both neurons and glial cells and their time-dependent accumulation correlated with a selective reduction in dopaminergic neuron numbers. Thus, hMOs from patients carrying SNCA gene multiplication can reliably model key pathological features of Parkinson′s disease and provide a powerful system to study the pathogenesis of synucleinopathies.

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This study analyzed gene expression messenger RNA data, from cases with major depressive disorder (MDD) and controls, using supervised machine learning (ML). We built on the methodology of prior studies to obtain more generalizable/reproducible results. First, we obtained a classifier trained on gene expression data from the dorsolateral prefrontal cortex of post‐mortem MDD cases (n = 126) and controls (n = 103). An average area‐under‐the‐receiver‐operating‐characteristics‐curve (AUC) from 10‐fold cross‐validation of 0.72 was noted, compared to an average AUC of 0.55 for a baseline classifier (p = .0048). The classifier achieved an AUC of 0.76 on a previously unused testing‐set. We also performed external validation using DLPFC gene expression values from an independent cohort of matched MDD cases (n = 29) and controls (n = 29), obtained from Affymetrix microarray (vs. Illumina microarray for the original cohort) (AUC: 0.62). We highlighted gene sets differentially expressed in MDD that were enriched for genes identified by the ML algorithm. Next, we assessed the ML classification performance in blood‐based microarray gene expression data from MDD cases (n = 1,581) and controls (n = 369). We observed a mean AUC of 0.64 on 10‐fold cross‐validation, which was significantly above baseline (p = .0020). Similar performance was observed on the testing‐set (AUC: 0.61). Finally, we analyzed the classification performance in covariates subgroups. We identified an interesting interaction between smoking and recall performance in MDD case prediction (58% accurate predictions in cases who are smokers vs. 43% accurate predictions in cases who are non‐smokers). Overall, our results suggest that ML in combination with gene expression data and covariates could further our understanding of the pathophysiology in MDD.

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