Multi-omics analyses highlight molecular differences between clinical and neuropathological diagnoses in Alzheimer's disease.

Both clinical diagnosis and neuropathological diagnosis are commonly used in literature to categorize individuals as Alzheimer's disease (AD) or non-AD in omics analyses. Whether these diagnostic strategies result in distinct profiles of molecular abnormalities is poorly understood. Here, we analysed one of the most commonly used AD omics datasets in the literature from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort and compared the two diagnosis strategies using brain transcriptome and metabolome by grouping individuals as non-AD and AD according to clinical or neuropathological diagnosis separately. Differentially expressed genes, associated pathways related with AD hallmarks and AD-related genes showed that the categorization based on neuropathological diagnosis more accurately reflects the disease state at the molecular level than the categorization based on clinical diagnosis. We further identified consensus biomarker candidates between the two diagnosis strategies such as 5-hydroxylysine, sphingomyelin and 1-myristoyl-2-palmitoyl-GPC as metabolite biomarkers and sphingolipid metabolism as a pathway biomarker, which could be robust AD biomarkers since they are independent of diagnosis strategies. We also used consensus AD and consensus non-AD individuals between the two diagnostic strategies to train a machine-learning based model, which we used to classify the individuals who were cognitively normal but diagnosed as AD based on neuropathological diagnosis (asymptomatic AD individuals). The majority of these individuals were classified as consensus AD patients for both omics data types. Our study provides a detailed characterization of both diagnostic strategies in terms of the association of the corresponding multi-omics profiles with AD.

Sphingolipid and Phospholipid Levels Are Altered in Human Brain in Chorea-Acanthocytosis.

BACKGROUND: Chorea-acanthocytosis (ChAc) is associated with mutations of VPS13A, which encodes for chorein, a protein implicated in lipid transport at intracellular membrane contact sites. OBJECTIVES: The goal of this study was to establish the lipidomic profile of patients with ChAc. METHODS: We analyzed 593 lipid species in the caudate nucleus (CN), putamen, and dorsolateral prefrontal cortex (DLPFC) from postmortem tissues of four patients with ChAc and six patients without ChAc. RESULTS: We found increased levels of bis(monoacylglycerol)phosphate, sulfatide, lysophosphatidylserine, and phosphatidylcholine ether in the CN and putamen, but not in the DLPFC, of patients with ChAc. Phosphatidylserine and monoacylglycerol were increased in the CN and N-acyl phosphatidylserine in the putamen. N-acyl serine was decreased in the CN and DLPFC, whereas lysophosphatidylinositol was decreased in the DLPFC. CONCLUSIONS: We present the first evidence of altered sphingolipid and phospholipid levels in the brains of patients with ChAc. Our observations are congruent with recent findings in cellular and animal models, and implicate defects of lipid processing in VPS13A disease pathophysiology. © 2023 International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Genomic data resources of the Brain Somatic Mosaicism Network for neuropsychiatric diseases.

Somatic mosaicism is defined as an occurrence of two or more populations of cells having genomic sequences differing at given loci in an individual who is derived from a single zygote. It is a characteristic of multicellular organisms that plays a crucial role in normal development and disease. To study the nature and extent of somatic mosaicism in autism spectrum disorder, bipolar disorder, focal cortical dysplasia, schizophrenia, and Tourette syndrome, a multi-institutional consortium called the Brain Somatic Mosaicism Network (BSMN) was formed through the National Institute of Mental Health (NIMH). In addition to genomic data of affected and neurotypical brains, the BSMN also developed and validated a best practices somatic single nucleotide variant calling workflow through the analysis of reference brain tissue. These resources, which include >400 terabytes of data from 1087 subjects, are now available to the research community via the NIMH Data Archive (NDA) and are described here.

Comprehensive identification of somatic nucleotide variants in human brain tissue.

BACKGROUND: Post-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells. RESULTS: Here, the Brain Somatic Mosaicism Network conducts a coordinated, multi-institutional study to examine the ability of existing methods to detect simulated somatic single-nucleotide variants (SNVs) in DNA mixing experiments, generate multiple replicates of whole-genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual, devise strategies to discover somatic SNVs, and apply various approaches to validate somatic SNVs. These efforts lead to the identification of 43 bona fide somatic SNVs that range in variant allele fractions from ~ 0.005 to ~ 0.28. Guided by these results, we devise best practices for calling mosaic SNVs from 250× whole-genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrate that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees. CONCLUSIONS: This study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.