Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe

N1-Methyladenosine modification of mRNA regulates neuronal gene expression and oxygen glucose deprivation/reoxygenation induction

N¹-Methyladenosine (m1A) is an abundant modification of transcripts, plays important roles in regulating mRNA structure and translation efficiency, and is dynamically regulated under stress. However, the characteristics and functions of mRNA m1A modification in primary neurons and oxygen glucose deprivation/reoxygenation (OGD/R) induced remain unclear. We first constructed a mouse cortical neuron OGD/R model and then used methylated RNA immunoprecipitation (MeRIP) and sequencing technology to demonstrate that m1A modification is abundant in neuron mRNAs and dynamically regulated during OGD/R induction. Our study suggests that Trmt10c, Alkbh3, and Ythdf3 may be m1A-regulating enzymes in neurons during OGD/R induction. The level and pattern of m1A modification change significantly during OGD/R induction, and differential methylation is closely associated with the nervous system. Our findings show that m1A peaks in cortical neurons aggregate at both the 5' and 3' untranslated regions. m1A modification can regulate gene expression, and peaks in different regions have different effects on gene expression. By analysing m1A-seq and RNA-seq data, we show a positive correlation between differentially methylated m1A peaks and gene expression. The correlation was verified by using qRT-PCR and MeRIP-RT-PCR. Moreover, we selected human tissue samples from Parkinson's disease (PD) and Alzheimer's disease (AD) patients from the Gene Expression Comprehensive (GEO) database to analyse the selected differentially expressed genes (DEGs) and differential methylation modification regulatory enzymes, respectively, and found similar differential expression results. We highlight the potential relationship between m1A modification and neuronal apoptosis following OGD/R induction. Furthermore, by mapping mouse cortical neurons and OGD/R-induced modification characteristics, we reveal the important role of m1A modification in OGD/R and gene expression regulation, providing new ideas for research on neurological damage.

Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits

Genomic variations are associated with gene expression levels, which are called expression quantitative trait loci (eQTL). Most eQTL may affect the total gene expression levels by regulating transcriptional activities of a specific promoter. However, the direct exploration of genomic loci associated with promoter activities using RNA-seq data has been challenging because eQTL analyses treat the total expression levels estimated by summing those of all isoforms transcribed from distinct promoters. Here we propose a new method for identifying genomic loci associated with promoter activities, called promoter usage quantitative trait loci (puQTL), using conventional RNA-seq data. By leveraging public RNA-seq datasets from the lymphoblastoid cell lines of 438 individuals from the GEUVADIS project, we obtained promoter activity estimates and mapped 2,592 puQTL at the 10% FDR level. The results of puQTL mapping enabled us to interpret the manner in which genomic variations regulate gene expression. We found that 310 puQTL genes (16.1%) were not detected by eQTL analysis, suggesting that our pipeline can identify novel variant–gene associations. Furthermore, we identified genomic loci associated with the activity of “hidden” promoters, which the standard eQTL studies have ignored. We found that most puQTL signals were concordant with at least one genome-wide association study (GWAS) signal, enabling novel interpretations of the molecular mechanisms of complex traits. Our results emphasize the importance of the re-analysis of public RNA-seq datasets to obtain novel insights into gene regulation by genomic variations and their contributions to complex traits.

Many variations exist in the human genome, creating phenotypic diversity among individuals. It is well known that they are associated with the risk of disease development by affecting the expression levels of genes. Genes are transcribed from regulatory elements called promoters. Although some genes are transcribed from multiple promoters and translated into proteins with different functions, the relationship between genomic variations and promoter activities has not been investigated in depth compared to the relationship between genomic variations and gene expression levels. In this study, we proposed a new method to detect the association between genomic variations and promoter activities. Our method identified the associations between many variations and promoters using genomic and promoter activity data from blood cells of 438 individuals. This study allowed us to identify new functional associations between genomic variations and genes. Furthermore, we identified previously undiscovered variation-gene-disease associations. Our results will help to elucidate the molecular mechanisms of diseases in which genetic factors are involved.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

Medin co-aggregates with vascular amyloid-β in Alzheimer's disease

Aggregates of medin amyloid (a fragment of the protein MFG-E8, also known as lactadherin) are found in the vasculature of almost all humans over 50 years of age^1,2, making it the most common amyloid currently known. We recently reported that medin also aggregates in blood vessels of ageing wild-type mice, causing cerebrovascular dysfunction³. Here we demonstrate in amyloid-β precursor protein (APP) transgenic mice and in patients with Alzheimer's disease that medin co-localizes with vascular amyloid-β deposits, and that in mice, medin deficiency reduces vascular amyloid-β deposition by half. Moreover, in both the mouse and human brain, MFG-E8 is highly enriched in the vasculature and both MFG-E8 and medin levels increase with the severity of vascular amyloid-β burden. Additionally, analysing data from 566 individuals in the ROSMAP cohort, we find that patients with Alzheimer's disease have higher MFGE8 expression levels, which are attributable to vascular cells and are associated with increased measures of cognitive decline, independent of plaque and tau pathology. Mechanistically, we demonstrate that medin interacts directly with amyloid-β to promote its aggregation, as medin forms heterologous fibrils with amyloid-β, affects amyloid-β fibril structure, and cross-seeds amyloid-β aggregation both in vitro and in vivo. Thus, medin could be a therapeutic target for prevention of vascular damage and cognitive decline resulting from amyloid-β deposition in the blood vessels of the brain.

MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies

Traditional predictive models for transcriptome-wide association studies (TWAS) consider only single nucleotide polymorphisms (SNPs) local to genes of interest and perform parameter shrinkage with a regularization process. These approaches ignore the effect of distal-SNPs or other molecular effects underlying the SNP-gene association. Here, we outline multi-omics strategies for transcriptome imputation from germline genetics to allow more powerful testing of gene-trait associations by prioritizing distal-SNPs to the gene of interest. In one extension, we identify mediating biomarkers (CpG sites, microRNAs, and transcription factors) highly associated with gene expression and train predictive models for these mediators using their local SNPs. Imputed values for mediators are then incorporated into the final predictive model of gene expression, along with local SNPs. In the second extension, we assess distal-eQTLs (SNPs associated with genes not in a local window around it) for their mediation effect through mediating biomarkers local to these distal-eSNPs. Distal-eSNPs with large indirect mediation effects are then included in the transcriptomic prediction model with the local SNPs around the gene of interest. Using simulations and real data from ROS/MAP brain tissue and TCGA breast tumors, we show considerable gains of percent variance explained (1-2% additive increase) of gene expression and TWAS power to detect gene-trait associations. This integrative approach to transcriptome-wide imputation and association studies aids in identifying the complex interactions underlying genetic regulation within a tissue and important risk genes for various traits and disorders.

rs1990622 variant associates with Alzheimer's disease and regulates TMEM106B expression in human brain tissues

BACKGROUND

It has been well established that the TMEM106B gene rs1990622 variant was a frontotemporal dementia (FTD) risk factor. Until recently, growing evidence highlights the role of TMEM106B in Alzheimer's disease (AD). However, it remains largely unclear about the role of rs1990622 variant in AD.

METHODS

Here, we conducted comprehensive analyses including genetic association study, gene expression analysis, eQTLs analysis, and colocalization analysis. In stage 1, we conducted a genetic association analysis of rs1990622 using large-scale genome-wide association study (GWAS) datasets from International Genomics of Alzheimer's Project (21,982 AD and 41,944 cognitively normal controls) and UK Biobank (314,278 participants). In stage 2, we performed a gene expression analysis of TMEM106B in 49 different human tissues using the gene expression data in GTEx. In stage 3, we performed an expression quantitative trait loci (eQTLs) analysis using multiple datasets from UKBEC, GTEx, and Mayo RNAseq Study. In stage 4, we performed a colocalization analysis to provide evidence of the AD GWAS and eQTLs pair influencing both AD and the TMEM106B expression at a particular region.

RESULTS

We found (1) rs1990622 variant T allele contributed to AD risk. A sex-specific analysis in UK Biobank further indicated that rs1990622 T allele only contributed to increased AD risk in females, but not in males; (2) TMEM106B showed different expression in different human brain tissues especially high expression in cerebellum; (3) rs1990622 variant could regulate the expression of TMEM106B in human brain tissues, which vary considerably in different disease statuses, the mean ages at death, the percents of females, and the different descents of the selected donors; (4) colocalization analysis provided suggestive evidence that the same variant contributed to AD risk and TMEM106B expression in cerebellum.

CONCLUSION

Our comprehensive analyses highlighted the role of FTD rs1990622 variant in AD risk. This cross-disease approach may delineate disease-specific and common features, which will be important for both diagnostic and therapeutic development purposes. Meanwhile, these findings highlight the importance to better understand TMEM106B function and dysfunction in the context of normal aging and neurodegenerative diseases.

Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis

Expression quantitative trait loci (eQTL) analyses detect genetic variants (SNPs) associated with RNA expression levels of genes. The conventional eQTL analysis is to perform individual tests for each gene-SNP pair using simple linear regression and to perform the test on each tissue separately ignoring the extensive information known about RNA expression in other tissue(s). Although Bayesian models have been recently developed to improve eQTL prediction on multiple tissues, they are often based on uninformative priors or treat all tissues equally. In this study, we develop a novel tissue augmented Bayesian model for eQTL analysis (TA-eQTL), which takes prior eQTL information from a different tissue into account to better predict eQTL for another tissue. We demonstrate that our modified Bayesian model has comparable performance to several existing methods in terms of sensitivity and specificity using allele-specific expression (ASE) as the gold standard. Furthermore, the tissue augmented Bayesian model improves the power and accuracy for local-eQTL prediction especially when the sample size is small. In summary, TA-eQTL's performance is comparable to existing methods but has additional flexibility to evaluate data from different platforms, can focus prediction on one tissue using only summary statistics from the secondary tissue(s), and provides a closed form solution for estimation.

Association between NRGN gene polymorphism and resting-state hippocampal functional connectivity in schizophrenia

BACKGROUND

Based on genome-wide association studies, a single-nucleotide polymorphism in the NRGN gene (rs12807809) is considered associated with schizophrenia (SZ). Moreover, hippocampal dysfunction is associated with rs12807809. In addition, converging evidence suggests that hippocampal dysfunction is involved in SZ pathophysiology. However, the association among rs12807809, hippocampal dysfunction and SZ pathophysiology is unknown. Therefore, this study investigated the association between rs12807809 and hippocampal functional connectivity at rest in SZ.

METHODS

In total, 158 participants were studied, including a C-carrier group carrying the non-risk C allele (29 SZ patients and 46 healthy controls) and a TT homozygous group carrying the risk T allele (30 SZ patients and 53 healthy controls). All participants were scanned using resting-state functional magnetic resonance imaging. Hippocampal functional connectivity was computed and compared among the 4 groups.

RESULTS

Significant main effects of diagnosis were observed in the functional connectivity between the hippocampus and bilateral fusiform gyrus, bilateral lingual gyrus, left inferior temporal gyrus, left caudate nucleus, bilateral thalamus and bilateral anterior cingulate gyri. In contrast, no significant main effect of genotype was found. In addition, a significant genotype by diagnosis interaction in the functional connectivity between the hippocampus and left anterior cingulate gyrus, as well as bilateral middle cingulate gyri, was observed, with TT homozygotes with SZ showing less functional connectivity than C-carriers with SZ and healthy control TT homozygotes.

CONCLUSIONS

These findings are the first to suggest an association between rs12807809 and abnormal Papez circuit function in patients with SZ. This study also implicates NRGN variation and abnormal Papez circuit function in SZ pathophysiology.

Antisense Transcription in Loci Associated to Hereditary Neurodegenerative Diseases

Natural antisense transcripts are common features of mammalian genes providing additional regulatory layers of gene expression. A comprehensive description of antisense transcription in loci associated to familial neurodegenerative diseases may identify key players in gene regulation and provide tools for manipulating gene expression. We take advantage of the FANTOM5 sequencing datasets that represent the largest collection to date of genome-wide promoter usage in almost 2000 human samples. Transcription start sites (TSSs) are mapped at high resolution by the use of a modified protocol of cap analysis of gene expression (CAGE) for high-throughput single molecule next-generation sequencing with Helicos (hCAGE). Here we present the analysis of antisense transcription at 17 loci associated to hereditary Alzheimer’s disease, Frontotemporal Dementia, Parkinson’s disease, Amyotrophic Lateral Sclerosis, and Huntington’s disease. We focused our analysis on libraries derived from brain tissues and primary cells. We also screened libraries from total blood and blood cell populations in the quest for peripheral biomarkers of neurodegenerative diseases. We identified 63 robust promoters in antisense orientation to genes associated to familial neurodegeneration. When applying a less stringent cutoff, this number increases to over 400. A subset of these promoters represents alternative TSSs for 24 FANTOM5 annotated long noncoding RNA (lncRNA) genes, in antisense orientation to 13 of the loci analyzed here, while the remaining contribute to the expression of additional transcript variants. Intersection with GWAS studies, sample ontology, and dynamic expression reveals association to specific genetic traits as well as cell and tissue types, not limited to neurodegenerative diseases. Antisense transcription was validated for a subset of genes, including those encoding for Microtubule-Associated Protein Tau, α-synuclein, Parkinsonism-associated deglycase DJ-1, and Leucin-Rich Repeat Kinase 2. This work provides evidence for the existence of additional regulatory mechanisms of the expression of neurodegenerative disease-causing genes by previously not-annotated and/or not-validated antisense long noncoding RNAs.

The role of monogenic genes in idiopathic Parkinson's disease

In the past two decades, mutations in multiple genes have been linked to autosomal dominant or recessive forms of monogenic Parkinson's disease (PD). Collectively, these monogenic (often familial) cases account for less than 5% of all PD, the majority being apparently sporadic cases. More recently, large-scale genome-wide association studies have identified over 40 loci that increase risk of PD. Importantly, there is overlap between monogenic and sporadic PD genes, particularly for the loci that contain the genes SNCA and LRRK2, which are mutated in monogenic dominant PD. There have also been reports of idiopathic PD cases with heterozygous variants in autosomal recessive genes suggesting that these mutations may increase risk of PD. These observations suggest that monogenic and idiopathic PD may have shared pathogenic mechanisms. Here, we focus mainly on the role of monogenic PD genes that represent pleomorphic risk loci for idiopathic PD. We also discuss the functional mechanisms that may play a role in increasing risk of disease in both monogenic and idiopathic forms.

SLIC-CAGE: high-resolution transcription start site mapping using nanogram-levels of total RNA

Cap analysis of gene expression (CAGE) is a methodology for genome-wide quantitative mapping of mRNA 5′ ends to precisely capture transcription start sites at a single nucleotide resolution. In combination with high-throughput sequencing, CAGE has revolutionized our understanding of the rules of transcription initiation, led to discovery of new core promoter sequence features, and discovered transcription initiation at enhancers genome-wide. The biggest limitation of CAGE is that even the most recently improved version (nAnT-iCAGE) still requires large amounts of total cellular RNA (5 µg), preventing its application to scarce biological samples such as those from early embryonic development or rare cell types. Here, we present SLIC-CAGE, a Super-Low Input Carrier-CAGE approach to capture 5′ ends of RNA polymerase II transcripts from as little as 5–10 ng of total RNA. This dramatic increase in sensitivity is achieved by specially designed, selectively degradable carrier RNA. We demonstrate the ability of SLIC-CAGE to generate data for genome-wide promoterome with 1000-fold less material than required by existing CAGE methods, by generating a complex, high-quality library from mouse embryonic day 11.5 primordial germ cells.

A comprehensive analysis of SNCA-related genetic risk in sporadic parkinson disease

OBJECTIVE

The goal of this study was to refine our understanding of disease risk attributable to common genetic variation in SNCA, a major locus in Parkinson disease, with potential implications for clinical trials targeting α-synuclein. We aimed to dissect the multiple independent association signals, stratify individuals by SNCA-specific risk profiles, and explore expression quantitative trait loci.

METHODS

We analyzed participant-level data from 12,503 patients and 12,502 controls, optimizing a risk model and assessing SNCA-specific risk scores and haplotypes as predictors of individual risk. We also explored hypotheses about functional mechanisms and correlated risk variants to gene expression in human brain and protein levels in cerebrospinal fluid.

RESULTS

We report and replicate a novel, third independent association signal at genome-wide significance level downstream of SNCA (rs2870004, p = 3.0*10^-8 , odds ratio [OR] = 0.88, 95% confidence interval [CI] = 0.84-0.92). SNCA risk score stratification showed a 2-fold difference in disease susceptibility between top and bottom quintiles (OR = 1.99, 95% CI = 1.78-2.23). Contrary to previous reports, we provide evidence supporting top variant rs356182 as functional in itself and associated with a specific SNCA 5' untranslated region transcript isoform in frontal cortex.

INTERPRETATION

The SNCA locus harbors a minimum of 3 independent association signals for Parkinson disease. We demonstrate a fine-grained stratification of α-synuclein-related genetic burden in individual patients of potential future clinical relevance. Further efforts to pinpoint the functional mechanisms are warranted, including studies of the likely causal top variant rs356182 and its role in regulating levels of specific SNCA mRNA transcript variants. Ann Neurol 2018;83:117-129.

The effect of genetic variation on promoter usage and enhancer activity

The identification of genetic variants affecting gene expression, namely expression quantitative trait loci (eQTLs), has contributed to the understanding of mechanisms underlying human traits and diseases. The majority of these variants map in non-coding regulatory regions of the genome and their identification remains challenging. Here, we use natural genetic variation and CAGE transcriptomes from 154 EBV-transformed lymphoblastoid cell lines, derived from unrelated individuals, to map 5376 and 110 regulatory variants associated with promoter usage (puQTLs) and enhancer activity (eaQTLs), respectively. We characterize five categories of genes associated with puQTLs, distinguishing single from multi-promoter genes. Among multi-promoter genes, we find puQTL effects either specific to a single promoter or to multiple promoters with variable effect orientations. Regulatory variants associated with opposite effects on different mRNA isoforms suggest compensatory mechanisms occurring between alternative promoters. Our analyses identify differential promoter usage and modulation of enhancer activity as molecular mechanisms underlying eQTLs related to regulatory elements.

Expression quantitative trait loci (eQTL) are widely studied, yet the mechanisms by which they exert their effects are largely unknown. Here, performing CAGE-seq on 154 lymphoblastoid cell lines, the authors map regulatory variants associated with promoter usage (puQTLs) and enhancer activity (eaQTLs).

Genome-wide Pleiotropy Between Parkinson Disease and Autoimmune Diseases

Importance

Recent genome-wide association studies (GWAS) and pathway analyses supported long-standing observations of an association between immune-mediated diseases and Parkinson disease (PD). The post-GWAS era provides an opportunity for cross-phenotype analyses between different complex phenotypes.

Objectives

To test the hypothesis that there are common genetic risk variants conveying risk of both PD and autoimmune diseases (ie, pleiotropy) and to identify new shared genetic variants and their pathways by applying a novel statistical framework in a genome-wide approach.

Design, Setting, and Participants

Using the conjunction false discovery rate method, this study analyzed GWAS data from a selection of archetypal autoimmune diseases among 138 511 individuals of European ancestry and systemically investigated pleiotropy between PD and type 1 diabetes, Crohn disease, ulcerative colitis, rheumatoid arthritis, celiac disease, psoriasis, and multiple sclerosis. NeuroX data (6927 PD cases and 6108 controls) were used for replication. The study investigated the biological correlation between the top loci through protein-protein interaction and changes in the gene expression and methylation levels. The dates of the analysis were June 10, 2015, to March 4, 2017.

Main Outcomes and Measures

The primary outcome was a list of novel loci and their pathways involved in PD and autoimmune diseases.

Results

Genome-wide conjunctional analysis identified 17 novel loci at false discovery rate less than 0.05 with overlap between PD and autoimmune diseases, including known PD loci adjacent to GAK, HLA-DRB5, LRRK2, and MAPT for rheumatoid arthritis, ulcerative colitis and Crohn disease. Replication confirmed the involvement of HLA, LRRK2, MAPT, TRIM10, and SETD1A in PD. Among the novel genes discovered, WNT3, KANSL1, CRHR1, BOLA2, and GUCY1A3 are within a protein-protein interaction network with known PD genes. A subset of novel loci was significantly associated with changes in methylation or expression levels of adjacent genes.

Conclusions and Relevance

The study findings provide novel mechanistic insights into PD and autoimmune diseases and identify a common genetic pathway between these phenotypes. The results may have implications for future therapeutic trials involving anti-inflammatory agents.

Increased brain expression of GPNMB is associated with genome wide significant risk for Parkinson's disease on chromosome 7p15.3

Genome wide association studies (GWAS) for Parkinson’s disease (PD) have previously revealed a significant association with a locus on chromosome 7p15.3, initially designated as the glycoprotein non-metastatic melanoma protein B (GPNMB) locus. In this study, the functional consequences of this association on expression were explored in depth by integrating different expression quantitative trait locus (eQTL) datasets (Braineac, CAGEseq, GTEx, and Phenotype-Genotype Integrator (PheGenI)). Top risk SNP rs199347 eQTLs demonstrated increased expressions of GPNMB, KLHL7, and NUPL2 with the major allele (AA) in brain, with most significant eQTLs in cortical regions, followed by putamen. In addition, decreased expression of the antisense RNA KLHL7-AS1 was observed in GTEx. Furthermore, rs199347 is an eQTL with long non-coding RNA (AC005082.12) in human tissues other than brain. Interestingly, transcript-specific eQTLs in immune-related tissues (spleen and lymphoblastoid cells) for NUPL2 and KLHL7-AS1 were observed, which suggests a complex functional role of this eQTL in specific tissues, cell types at specific time points. Significantly increased expression of GPNMB linked to rs199347 was consistent across all datasets, and taken in combination with the risk SNP being located within the GPNMB gene, these results suggest that increased expression of GPNMB is the causative link explaining the association of this locus with PD. However, other transcript eQTLs and subsequent functional roles cannot be excluded. This highlights the importance of further investigations to understand the functional interactions between the coding genes, antisense, and non-coding RNA species considering the tissue and cell-type specificity to understand the underlying biological mechanisms in PD.