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. Of these, 21 families were affected by so-called 'unsolvable' syndromes for which genetic causes remain unknown, and 93 families with at least one individual affected by a rare neurological, neuromuscular, or epilepsy disorder without genetic diagnosis despite extensive prior testing. Clinical interpretation and orthogonal validation of variants in known disease genes yielded thirteen novel genetic diagnoses due to de novo and rare inherited SNVs, InDels, SVs, and STR expansions. In an additional four families, we identified a candidate disease-causing SV affecting several genes including an MCF2 / FGF13 fusion and PSMA3 deletion. However, no common genetic cause was identified in any of the 'unsolvable' syndromes. Taken together, we found (likely) disease-causing genetic variants in 13.0% of previously unsolved families and additional candidate disease-causing SVs in another 4.3% of these families. In conclusion, our results demonstrate the added value of HiFi long-read genome sequencing in undiagnosed rare diseases.

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). We achieve high specificity of axonal fractions, ensuring sample purity without contamination. Comparative analysis revealed a unique and specific transcriptional landscape in axonal compartments, characterized by diverse transcript types, including protein-coding mRNAs, ribosomal proteins (RPs), mitochondrial-encoded RNAs, and long non-coding RNAs (lncRNAs). Previous works have reported the existence of transcription factors (TFs) in the axon. Here, we detect a subset of previously unreported TFs specific to the axon and indicative of their active participation in transcriptional regulation. To investigate transcripts and pathways essential for central motor neuron (MN) degeneration and maintenance we analyzed KIF1C-knockout (KO) CNs, modeling hereditary spastic paraplegia (HSP), a disorder associated with prominent length-dependent degeneration of central MN axons. We found that several key factors crucial for survival and health were absent in KIF1C-KO axons, highlighting a possible role of these also in other neurodegenerative diseases. Taken together, this study underscores the utility of microfluidic devices in studying compartment-specific transcriptomics in human neuronal models and reveals complex molecular dynamics of axonal biology. The impact of KIF1C on the axonal transcriptome not only deepens our understanding of MN diseases but also presents a promising avenue for exploration of compartment specific disease mechanisms.

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., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies showed that SR-TWAS improved power, due to increased effective training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real application studies identified 6 independent significant risk genes for Alzheimer's disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson's disease (PD) for substantia nigra tissue. Relevant biological interpretations were found for these significant risk genes.

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. We detect diverse cell types, including unique meningeal endothelial, mural, and fibroblast subtypes. For immune cells, we show that most T cells express CD8 and bear characteristics of tissue-resident memory T cells. We also identify distinct subtypes of border-associated macrophages (BAMs) that display differential gene expressions from microglia and express risk genes for Alzheimer's Disease (AD), as nominated by genome-wide association studies (GWAS). We discover cell-type-specific differentially expressed genes in individuals with Alzheimer's dementia, particularly in fibroblasts and BAMs. Indeed, when cultured, leptomeningeal cells display the signature of ex vivo AD fibroblasts upon amyloid-ß treatment. We further explore ligand-receptor interactions within the leptomeningeal niche and computationally infer intercellular communications in AD. Thus, our study establishes a molecular map of human leptomeningeal cell types, providing significant insight into the border immune and fibrotic responses in AD.

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. To address this gap, we employed a linear mixed model (LMM) that has been widely used in genetic association studies for DGE analysis of quantitative traits. We first applied the LMM method to the discovery RNA-Seq data of dorsolateral prefrontal cortex (DLPFC) tissue (n = 632) with four continuous measures of Alzheimer's Disease (AD) cognitive and neuropathologic traits. The quantile-quantile plots of p-values showed that false positive rates were well calibrated by LMM, whereas other methods not accounting for sample-specific mixed effects led to serious inflation. LMM identified 37 potentially significant genes with differential expression in DLPFC for at least one of the AD traits, 17 of which were replicated in the additional RNA-Seq data of DLPFC, supplemental motor area, spinal cord, and muscle tissues. This application study showed not only well calibrated DGE results by LMM, but also possibly shared gene regulatory mechanisms of AD traits across different relevant tissues.

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. We used affinity proteomics in neuronally differentiated neuroblastoma cells (SH-SY5Y) to identify the protein complex associated with KIF1C in neuronal cells; candidate interactions were then validated by immunoprecipitation and mislocalization of putative KIF1C cargoes was studied by immunostainings. We found KIF1C to interact with all core components of the exon junction complex (EJC); expression of mutant KIF1C in neuronal cells leads to loss of the typical localization distally in neurites. Instead, EJC core components accumulate in the pericentrosomal region, here co-localizing with mutant KIF1C. These findings suggest KIF1C as a neuronal transporter of the EJC. Interestingly, the binding of KIF1C to the EJC is RNA-mediated, as treatment with RNAse prior to immunoprecipitation almost completely abolishes the interaction. Silica-based solid-phase extraction of UV-crosslinked RNA-protein complexes furthermore supports direct interaction of KIF1C with RNA, as recently also demonstrated for kinesin heavy chain. Taken together, our findings are consistent with a model where KIF1C transports mRNA in an EJC-bound and therefore transcriptionally silenced state along neurites, thus providing the missing link between the EJC and mRNA localization in neurons.

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. Specifically, by applying the clei2block deep learning model to 1192 brain RNA-seq samples, we identify protein modules and disease-associated expression changes that were not directly observed at the mRNA level. Moreover, pseudo-temporal trajectory inference based on the predicted proteome became more closely correlated with cognitive decline and hippocampal atrophy compared to RNA-based trajectories. This suggests that the predicted changes in protein expression could provide a better molecular representation of ADRD progression. Furthermore, overlaying clinical traits on protein pseudotime trajectory identifies protein modules altered before cognitive impairment. These results demonstrate how our method can be used to identify potential early protein drivers and possible drug targets for treating and/or preventing 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. We expand the list of known brain editing events by identifying 58,761 previously unreported events. We note that only a small proportion of these editing events are found at the protein level in our proteome-wide validation effort. We also identified the occurrence of editing events associated with AD dementia, neuropathological measures and longitudinal cognitive decline in: SYT11, MCUR1, SOD2, ORAI2, HSDL2, PFKP, and GPRC5B. Thus, we present an extended reference set of brain RNA editing events, identify a subset that are found to be expressed at the protein level, and extend the narrative of transcriptomic perturbation in AD to RNA editing.

Enrichment of SARM1 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 SARM1 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. This rise in constitutive activity alone is enough to promote neuronal degeneration in response to otherwise non-harmful, mild stress. Importantly, these strong gain-of-function alleles are completely patient-specific in the cohorts studied and show a highly significant association with disease at the single gene level. These findings of disease-associated coding variants that alter SARM1 function build on previously reported genome-wide significant association with ALS for a neighbouring, more common SARM1 intragenic single nucleotide polymorphism (SNP) to support a contributory role of SARM1 in these disorders. A broad phenotypic heterogeneity and variable age-of-onset of disease among patients with these alleles also raises intriguing questions about the pathogenic mechanism of hyperactive SARM1 variants.

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 Aß and tau species and the levels of plaque and tangle deposition in the brain and, more importantly, with the trajectory of cognitive decline. Proteins and networks are identified that are associated with AD phenotypes in iPSC neurons, and relevant associations are validated in brain. The data presented establish this iPSC collection as a resource for investigating person-specific processes in the brain that can aid in identifying and validating molecular pathways underlying AD.

Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease.

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

A cortical immune network map identifies distinct microglial transcriptional programs associated with ß-amyloid and Tau pathologies.

Microglial dysfunction has been proposed as one of the many cellular mechanisms that can contribute to the development of Alzheimer's disease (AD). Here, using a transcriptional network map of the human frontal cortex, we identify five modules of co-expressed genes related to microglia and assess their role in the neuropathologic features of AD in 540 subjects from two cohort studies of brain aging. Two of these transcriptional programs-modules 113 and 114-relate to the accumulation of ß-amyloid, while module 5 relates to tau pathology. We replicate these associations in brain epigenomic data and in two independent datasets. In terms of tau, we propose that module 5, a marker of activated microglia, may lead to tau accumulation and subsequent cognitive decline. We validate our model further by showing that three representative module 5 genes (ACADVL, TRABD, and VASP) encode proteins that are upregulated in activated microglia in AD.

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. Our results show that Imaging-AMARETTO recapitulates known key drivers of tumor-associated microglia and macrophage mechanisms, mediated by STAT3, AHR, and CCR2, and neurodevelopmental and stemness mechanisms, mediated by OLIG2. Imaging-AMARETTO provides interpretation of their underlying molecular mechanisms in light of imaging biomarkers of clinical outcomes and uncovers novel master drivers, THBS1 and MAP2, that establish relationships across these distinct mechanisms. CONCLUSION: Our network-based imaging genomics tools serve as hypothesis generators that facilitate the interrogation of known and uncovering of novel hypotheses for follow-up with experimental validation studies. We anticipate that our Imaging-AMARETTO imaging genomics tools will be useful to the community of biomedical researchers for applications to similar studies of cancer and other complex diseases with available multiomics, imaging, and clinical data.

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. To avoid bias from population admixture, analyses were limited to participants of European descent with longitudinal neuroimaging data (n = 174). Linear mixed effect models were used to examine the effect of rs3846455G on longitudinal change of the Preclinical Alzheimer Cognitive Composite (PACC) and MRI-measured bilateral hippocampal volume, adjusting for baseline amyloid-ß (Aß) measured by the cortical Pittsburgh Compound B PET distributed volume ratio. We also tested whether hippocampal atrophy mediates the association between rs3846455G and greater PACC decline through a mediation analysis. RESULTS: rs3846455G was associated with greater PACC decline (ß= -0.087/year, 95% CI -0.169 to -0.005, p = 0.039) after controlling for baseline Aß. Further, rs3846455G predicted accelerated hippocampal atrophy after controlling for baseline Aß (ß= -57.3 mm3/year, 95% CI -102.8 to -11.9, p = 0.014). The association between rs3846455G and greater PACC decline was partially mediated by accelerated hippocampal atrophy (mediated effect (relative scale) = -0.014, 95% CI -0.032 to -6.0×10-4, p = 0.039). CONCLUSION: UNC5C rs3846455G predicts greater cognitive decline and accelerated hippocampal atrophy in clinically normal older adults.

Candidate-based screening via gene modulation in human neurons and astrocytes implicates FERMT2 in Aß 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. Five genes (CNN2, GBA, GSTP1, MINT2 and FERMT2) in neurons and nine genes (CNN2, ITGB1, MINT2, SORL1, VLDLR, NPC1, NPC2, PSAP and SCARB2) in astrocytes significantly altered extracellular amyloid-ß (Aß) levels. Knockdown of AP3M2, CNN2, GSTP1, NPC1, NPC2, PSAP and SORL1 reduced interleukin-6 levels in astrocytes. Only knockdown of FERMT2 led to a reduction in the proportion of TAU that is phosphorylated. Further, CRISPR-Cas9 targeting of FERMT2 in both familial AD (fAD) and fAD-corrected human neurons validated the findings of reduced extracellular Aß. Interestingly, FERMT2 reduction had no effect on the Aß42:40 ratio in corrected neurons and a reduction of phospho-tau, but resulted in an elevation in Aß42:40 ratio and no reduction in phospho-tau in fAD neurons. Taken together, this study has prioritized 15 genes as being involved in contributing to Aß accumulation, phosphorylation of tau and/or cytokine secretion, and, as illustrated with FERMT2, it sets the stage for further cell-type-specific dissection of the role of these genes in AD.

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-ß 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-ß, 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. Functional relevance of these chromatin changes was demonstrated by (1) consistent transcriptional changes in three independent datasets and (2) similar findings in two mouse models of Alzheimer's disease. Finally, we found that tau overexpression in induced pluripotent stem cell-derived neurons altered chromatin structure and that these effects could be blocked by a small molecule predicted to reverse the tau effect. Thus, we report broad tau-driven chromatin rearrangements in the aging human brain that may be reversible with heat-shock protein 90 (Hsp90) inhibitors.

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). Generation of other omic data including ATACseq, proteomic and metabolomics profiles is ongoing. Thanks to its prospective design and recruitment of older, non-demented individuals, these data can be repurposed to investigate a large number of syndromic and quantitative neuroscience phenotypes. The many subjects that are cognitively non-impaired at death also offer insights into the biology of the human brain in older non-impaired individuals.

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. We confirm these associations in two independent AD datasets. We also illustrate the use of the network in prioritizing amyloid- and cognition-associated genes for in vitro validation in human neurons and astrocytes. These analyses based on unique cohorts enable us to resolve the role of distinct cortical modules that have a direct effect on the accumulation of AD pathology from those that have a direct effect on cognitive decline, exemplifying a network approach to complex diseases.

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. This HuMi_Aged gene set captures a unique phenotype, which we confirm at the protein level. Furthermore, we find this gene set to be enriched in susceptibility genes for Alzheimer's disease and multiple sclerosis, to be increased with advancing age, and to be reduced by the protective APOEε2 haplotype. APOEε4 has no effect. These findings confirm the existence of an aging-related microglial phenotype in the aged human brain and its involvement in the pathological processes associated with brain aging.