Spatial Expression of Long Non-Coding RNAs in Human Brains of Alzheimer's Disease.

Long non-coding RNAs (lncRNAs) are highly versatile in their modes of action and play critical roles in both normal physiological and disease processes. The dysregulation of lncRNA expression has been implicated in aging and many neurological disorders, including Alzheimer's disease (AD). Here, we report a spatial expression atlas of 7,634 lncRNA genes in aged human brains, covering 258,987 microdomains from 78 postmortem brain sections of 21 ROSMAP participants.

The YTHDF Proteins Shape the Brain Gene Signatures of Alzheimer's Disease.

The gene signatures of Alzheimer's Disease (AD) brains reflect an output of a complex interplay of genetic, epigenetic, epi-transcriptomic, and post-transcriptional regulations. To identify the most significant factor that shapes the AD brain signature, we developed a machine learning model (DEcode-tree) to integrate cellular and molecular factors explaining differential gene expression in AD. Our model indicates that YTHDF proteins, the canonical readers of N6-methyladenosine RNA modification (m6A), are the most influential predictors of the AD brain signature.

SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning.

Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for transcriptome-wide association studies (TWAS). To leverage expression imputation models (i.e.

Unravelling undiagnosed rare disease cases by HiFi long-read genome sequencing.

Solve-RD is a pan-European rare disease (RD) research program that aims to identify disease-causing genetic variants in previously undiagnosed RD families. We utilised 10-fold coverage HiFi long-read sequencing (LRS) for detecting causative structural variants (SVs), single nucleotide variants (SNVs), insertion-deletions (InDels), and short tandem repeat (STR) expansions in extensively studied RD families without clear molecular diagnoses. Our cohort includes 293 individuals from 114 genetically undiagnosed RD families selected by European Rare Disease Network (ERN) experts.

Unraveling Axonal Transcriptional Landscapes: Insights from iPSC-Derived Cortical Neurons and Implications for Motor Neuron Degeneration.

Neuronal function and pathology are deeply influenced by the distinct molecular profiles of the axon and soma. Traditional studies have often overlooked these differences due to the technical challenges of compartment specific analysis. In this study, we employ a robust RNA-sequencing (RNA-seq) approach, using microfluidic devices, to generate high-quality axonal transcriptomes from iPSC-derived cortical neurons (CNs).

Dissecting the human leptomeninges at single-cell resolution.

Emerging evidence shows that the meninges conduct essential immune surveillance and immune defense at the brain border, and the dysfunction of meningeal immunity contributes to aging and neurodegeneration. However, no study exists on the molecular properties of cell types within human leptomeninges. Here, we provide single nuclei profiling of dissected postmortem leptomeninges from aged individuals.

Differential gene expression analysis based on linear mixed model corrects false positive inflation for studying quantitative traits.

Differential gene expression (DGE) analysis has been widely employed to identify genes expressed differentially with respect to a trait of interest using RNA sequencing (RNA-Seq) data. Recent RNA-Seq data with large samples pose challenges to existing DGE methods, which were mainly developed for dichotomous traits and small sample sizes. Especially, existing DGE methods are likely to result in inflated false positive rates.

SR-TWAS: Leveraging Multiple Reference Panels to Improve TWAS Power by Ensemble Machine Learning.

Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for TWAS. To leverage expression imputation models (i.e.

The kinesin motor KIF1C is a putative transporter of the exon junction complex in neuronal cells.

Neurons critically depend on regulated RNA localization and tight control of spatio-temporal gene expression to maintain their morphological and functional integrity. Mutations in the kinesin motor protein gene KIF1C cause Hereditary Spastic Paraplegia, an autosomal recessive disease leading to predominant degeneration of the long axons of central motoneurons. In this study we aimed to gain insight into the molecular function of KIF1C and understand how KIF1C dysfunction contributes to motoneuron degeneration.

Inferring protein expression changes from mRNA in Alzheimer's dementia using deep neural networks.

Identifying the molecular systems and proteins that modify the progression of Alzheimer's disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic data, has limited our ability to advance candidate targets that are mainly based on gene expression. Therefore, by using a deep neural network that predicts protein abundance from mRNA expression, here we attempt to track the early protein drivers of ADRD.

Atlas of RNA editing events affecting protein expression in aged and Alzheimer's disease human brain tissue.

RNA editing is a feature of RNA maturation resulting in the formation of transcripts whose sequence differs from the genome template. Brain RNA editing may be altered in Alzheimer's disease (AD). Here, we analyzed data from 1,865 brain samples covering 9 brain regions from 1,074 unrelated subjects on a transcriptome-wide scale to identify inter-regional differences in RNA editing.

Enrichment of alleles encoding variants with constitutively hyperactive NADase in patients with ALS and other motor nerve disorders.

SARM1, a protein with critical NADase activity, is a central executioner in a conserved programme of axon degeneration. We report seven rare missense or in-frame microdeletion human variant alleles in patients with amyotrophic lateral sclerosis (ALS) or other motor nerve disorders that alter the SARM1 auto-inhibitory ARM domain and constitutively hyperactivate SARM1 NADase activity. The constitutive NADase activity of these seven variants is similar to that of SARM1 lacking the entire ARM domain and greatly exceeds the activity of wild-type SARM1, even in the presence of nicotinamide mononucleotide (NMN), its physiological activator.

Stem cell-derived neurons reflect features of protein networks, neuropathology, and cognitive outcome of their aged human donors.

We have generated a controlled and manipulable resource that captures genetic risk for Alzheimer's disease: iPSC lines from 53 individuals coupled with RNA and proteomic profiling of both iPSC-derived neurons and brain tissue of the same individuals. Data collected for each person include genome sequencing, longitudinal cognitive scores, and quantitative neuropathology. The utility of this resource is exemplified here by analyses of neurons derived from these lines, revealing significant associations between specific Aβ and tau species and the levels of plaque and tangle deposition in the brain and, more importantly, with the trajectory of cognitive decline.

Imaging-AMARETTO: An Imaging Genomics Software Tool to Interrogate Multiomics Networks for Relevance to Radiography and Histopathology Imaging Biomarkers of Clinical Outcomes.

Purpose: The availability of increasing volumes of multiomics, imaging, and clinical data in complex diseases such as cancer opens opportunities for the formulation and development of computational imaging genomics methods that can link multiomics, imaging, and clinical data.

Methods: Here, we present the Imaging-AMARETTO algorithms and software tools to systematically interrogate regulatory networks derived from multiomics data within and across related patient studies for their relevance to radiography and histopathology imaging features predicting clinical outcomes.

Results: To demonstrate its utility, we applied Imaging-AMARETTO to integrate three patient studies of brain tumors, specifically, multiomics with radiography imaging data from The Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and low-grade glioma (LGG) cohorts and transcriptomics with histopathology imaging data from the Ivy Glioblastoma Atlas Project (IvyGAP) GBM cohort.

An UNC5C Allele Predicts Cognitive Decline and Hippocampal Atrophy in Clinically Normal Older Adults.

Background: The UNC5C rs3846455G allele has been linked to poor cognitive resilience against age-related neuropathologies, but this association remains to be replicated, and the allele's effect on hippocampal neurodegeneration needs to be examined.

Objective: To further validate the association between rs3846455G and faster cognitive decline, especially among cognitively normal older adults, and to assess whether rs3846455G predicts accelerated hippocampal volume loss in older adults.

Methods: We assessed participants in the Harvard Aging Brain Study (HABS), a longitudinal cohort study of older adults who were clinically normal at baseline.

Epigenome-wide study uncovers large-scale changes in histone acetylation driven by tau pathology in aging and Alzheimer's human brains.

Accumulation of tau and amyloid-β are two pathologic hallmarks of Alzheimer's disease. We conducted an epigenome-wide association study using the histone 3 lysine 9 acetylation (H3K9ac) mark in 669 aged human prefrontal cortices; in contrast with amyloid-β, tau protein burden had a broad effect on the epigenome, affecting 5,990 of 26,384 H3K9ac domains. Tau-related alterations aggregated in large genomic segments reflecting spatial chromatin organization, and the magnitude of these effects correlated with the segment's nuclear lamina association.

Candidate-based screening via gene modulation in human neurons and astrocytes implicates FERMT2 in Aβ and TAU proteostasis.

Large-scale 'omic' studies investigating the pathophysiological processes that lead to Alzheimer's disease (AD) dementia have identified an increasing number of susceptibility genes, many of which are poorly characterized and have not previously been implicated in AD. Here, we evaluated the utility of human induced pluripotent stem cell-derived neurons and astrocytes as tools to systematically test AD-relevant cellular phenotypes following perturbation of candidate genes identified by genome-wide studies. Lentiviral-mediated delivery of shRNAs was used to modulate expression of 66 genes in astrocytes and 52 genes in induced neurons.

A multi-omic atlas of the human frontal cortex for aging and Alzheimer's disease research.

We initiated the systematic profiling of the dorsolateral prefrontal cortex obtained from a subset of autopsied individuals enrolled in the Religious Orders Study (ROS) or the Rush Memory and Aging Project (MAP), which are jointly designed prospective studies of aging and dementia with detailed, longitudinal cognitive phenotyping during life and a quantitative, structured neuropathologic examination after death. They include over 3,322 subjects. Here, we outline the first generation of data including genome-wide genotypes (n=2,090), whole genome sequencing (n=1,179), DNA methylation (n=740), chromatin immunoprecipitation with sequencing using an anti-Histone 3 Lysine 9 acetylation (H3K9Ac) antibody (n=712), RNA sequencing (n=638), and miRNA profile (n=702).

A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer's disease.

There is a need for new therapeutic targets with which to prevent Alzheimer's disease (AD), a major contributor to aging-related cognitive decline. Here we report the construction and validation of a molecular network of the aging human frontal cortex. Using RNA sequence data from 478 individuals, we first build a molecular network using modules of coexpressed genes and then relate these modules to AD and its neuropathologic and cognitive endophenotypes.

A transcriptomic atlas of aged human microglia.

With a rapidly aging global human population, finding a cure for late onset neurodegenerative diseases has become an urgent enterprise. However, these efforts are hindered by the lack of understanding of what constitutes the phenotype of aged human microglia-the cell type that has been strongly implicated by genetic studies in the pathogenesis of age-related neurodegenerative disease. Here, we establish the set of genes that is preferentially expressed by microglia in the aged human brain.

A human microglia-like cellular model for assessing the effects of neurodegenerative disease gene variants.

Microglia are emerging as a key cell type in neurodegenerative diseases, yet human microglia are challenging to study in vitro. We developed an in vitro cell model system composed of human monocyte-derived microglia-like (MDMi) cells that recapitulated key aspects of microglia phenotype and function. We then used this model system to perform an expression quantitative trait locus (eQTL) study examining 94 genes from loci associated with Alzheimer's disease, Parkinson's disease, and multiple sclerosis.