Isoforms of RNA-Editing Enzyme ADAR1 Independently Control Nucleic Acid Sensor MDA5-Driven Autoimmunity and Multi-organ Development.

Mutations in ADAR, which encodes the ADAR1 RNA-editing enzyme, cause Aicardi-Goutières syndrome (AGS), a severe autoimmune disease associated with an aberrant type I interferon response. How ADAR1 prevents autoimmunity remains incompletely defined. Here, we demonstrate that ADAR1 is a specific and essential negative regulator of the MDA5-MAVS RNA sensing pathway. Moreover, we uncovered a MDA5-MAVS-independent function for ADAR1 in the development of multiple organs. We showed that the p150 isoform of ADAR1 uniquely regulated the MDA5 pathway, whereas both the p150 and p110 isoforms contributed to development. Abrupt deletion of ADAR1 in adult mice revealed that both of these functions were required throughout life. Our findings delineate genetically separable roles for both ADAR1 isoforms in vivo, with implications for the human diseases caused by ADAR mutations.

The viral hypothesis: how herpesviruses may contribute to Alzheimer's disease.

The hypothesis that infectious agents, particularly herpesviruses, contribute to Alzheimer's disease (AD) pathogenesis has been investigated for decades but has long engendered controversy. In the past 3 years, several studies in mouse models, human tissue models, and population cohorts have reignited interest in this hypothesis. Collectively, these studies suggest that many of the hallmarks of AD, like amyloid beta production and neuroinflammation, can arise as a protective response to acute infection that becomes maladaptive in the case of chronic infection. We place this work in its historical context and explore its etiological implications.

Integrative functional genomic analysis of intron retention in human and mouse brain with Alzheimer's disease.

Intron retention (IR) has been implicated in the pathogenesis of complex diseases such as cancers; its association with Alzheimer's disease (AD) remains unexplored. We performed genome-wide analysis of IR through integrating genetic, transcriptomic, and proteomic data of AD subjects and mouse models from the Accelerating Medicines Partnership-Alzheimer's Disease project. We identified 4535 and 4086 IR events in 2173 human and 1736 mouse genes, respectively. Quantitation of IR enabled the identification of differentially expressed genes that conventional exon-level approaches did not reveal. There were significant correlations of intron expression within innate immune genes, like HMBOX1, with AD in humans. Peptides with a high probability of translation from intron-retained mRNAs were identified using mass spectrometry. Further, we established AD-specific intron expression Quantitative Trait Loci, and identified splicing-related genes that may regulate IR. Our analysis provides a novel resource for the search for new AD biomarkers and pathological mechanisms.

iREAD: a tool for intron retention detection from RNA-seq data.

BACKGROUND: Intron retention (IR) has been traditionally overlooked as 'noise' and received negligible attention in the field of gene expression analysis. In recent years, IR has become an emerging field for interrogating transcriptomes because it has been recognized to carry out important biological functions such as gene expression regulation and it has been found to be associated with complex diseases such as cancers. However, methods for detecting IR today are limited. Thus, there is a need to develop novel methods to improve IR detection. RESULTS: Here we present iREAD (intron REtention Analysis and Detector), a tool to detect IR events genome-wide from high-throughput RNA-seq data. The command line interface for iREAD is implemented in Python. iREAD takes as input a BAM file, representing the transcriptome, and a text file containing the intron coordinates of a genome. It then 1) counts all reads that overlap intron regions, 2) detects IR events by analyzing the features of reads such as depth and distribution patterns, and 3) outputs a list of retained introns into a tab-delimited text file. iREAD provides significant added value in detecting IR compared with output from IRFinder with a higher AUC on all datasets tested. Both methods showed low false positive rates and high false negative rates in different regimes, indicating that use together is generally beneficial. The output from iREAD can be directly used for further exploratory analysis such as differential intron expression and functional enrichment. The software is freely available at https://github.com/genemine/iread. CONCLUSION: Being complementary to existing tools, iREAD provides a new and generic tool to interrogate poly-A enriched transcriptomic data of intron regions. Intron retention analysis provides a complementary approach for understanding transcriptome.

Motor neuron loss and neuroinflammation in a model of α-synuclein-induced neurodegeneration.

Mechanisms underlying α-synuclein (αSyn) mediated neurodegeneration are poorly understood. Intramuscular (IM) injection of αSyn fibrils in human A53T transgenic M83+/- mice produce a rapid model of α-synucleinopathy with highly predictable onset of motor impairment. Using varying doses of αSyn seeds, we show that αSyn-induced phenotype is largely dose-independent. We utilized the synchrony of this IM model to explore the temporal sequence of αSyn pathology, neurodegeneration and neuroinflammation. Longitudinal tracking showed that while motor neuron death and αSyn pathology occur within 2 months post IM, astrogliosis appears at a later timepoint, implying neuroinflammation is a consequence, rather than a trigger, in this prionoid model of synucleinopathy. Initiating at 3 months post IM, immune activation dominates the pathologic landscape in terminal IM-seeded M83+/- mice, as revealed by unbiased transcriptomic analyses. Our findings provide insights into the role of neuroinflammation in αSyn mediated proteostasis and neurodegeneration, which will be key in designing potential therapies.

Divergent brain gene expression patterns associate with distinct cell-specific tau neuropathology traits in progressive supranuclear palsy.

Progressive supranuclear palsy (PSP) is a neurodegenerative parkinsonian disorder characterized by tau pathology in neurons and glial cells. Transcriptional regulation has been implicated as a potential mechanism in conferring disease risk and neuropathology for some PSP genetic risk variants. However, the role of transcriptional changes as potential drivers of distinct cell-specific tau lesions has not been explored. In this study, we integrated brain gene expression measurements, quantitative neuropathology traits and genome-wide genotypes from 268 autopsy-confirmed PSP patients to identify transcriptional associations with unique cell-specific tau pathologies. We provide individual transcript and transcriptional network associations for quantitative oligodendroglial (coiled bodies = CB), neuronal (neurofibrillary tangles = NFT), astrocytic (tufted astrocytes = TA) tau pathology, and tau threads and genomic annotations of these findings. We identified divergent patterns of transcriptional associations for the distinct tau lesions, with the neuronal and astrocytic neuropathologies being the most different. We determined that NFT are positively associated with a brain co-expression network enriched for synaptic and PSP candidate risk genes, whereas TA are positively associated with a microglial gene-enriched immune network. In contrast, TA is negatively associated with synaptic and NFT with immune system transcripts. Our findings have implications for the diverse molecular mechanisms that underlie cell-specific vulnerability and disease risk in PSP.

Conserved brain myelination networks are altered in Alzheimer's and other neurodegenerative diseases.

INTRODUCTION: Comparative transcriptome analyses in Alzheimer's disease (AD) and other neurodegenerative proteinopathies can uncover both shared and distinct disease pathways. METHODS: We analyzed 940 brain transcriptomes including patients with AD, progressive supranuclear palsy (PSP; a primary tauopathy), and control subjects. RESULTS: We identified transcriptional coexpression networks implicated in myelination, which were lower in PSP temporal cortex (TCX) compared with AD. Some of these associations were retained even after adjustments for brain cell population changes. These TCX myelination network structures were preserved in cerebellum but they were not differentially expressed in cerebellum between AD and PSP. Myelination networks were downregulated in both AD and PSP, when compared with control TCX samples. DISCUSSION: Downregulation of myelination networks may underlie both PSP and AD pathophysiology, but may be more pronounced in PSP. These data also highlight conservation of transcriptional networks across brain regions and the influence of cell type changes on these networks.

Quantitative proteomic analysis reveals effects of epidermal growth factor receptor (EGFR) on invasion-promoting proteins secreted by glioblastoma cells.

Glioblastoma multiforme is a highly invasive and aggressive brain tumor with an invariably poor prognosis. The overexpression of epidermal growth factor receptor (EGFR) is a primary influencer of invasion and proliferation in tumor cells and the constitutively active EGFRvIII mutant, found in 30-65% of Glioblastoma multiforme, confers more aggressive invasion. To better understand how EGFR contributes to tumor aggressiveness, we investigated the effect of EGFR on the secreted levels of 65 rationally selected proteins involved in invasion. We employed selected reaction monitoring targeted mass spectrometry using stable isotope labeled internal peptide standards to quantity proteins in the secretome from five GBM (U87) isogenic cell lines in which EGFR, EGFRvIII, and/or PTEN were expressed. Our results show that cell lines with EGFR overexpression and constitutive EGFRvIII expression differ remarkably in the expression profiles for both secreted and intracellular signaling proteins, and alterations in EGFR signaling result in reproducible changes in concentrations of secreted proteins. Furthermore, the EGFRvIII-expressing mutant cell line secretes the majority of the selected invasion-promoting proteins at higher levels than other cell lines tested. Additionally, the intracellular and extracellular protein measurements indicate elevated oxidative stress in the EGFRvIII-expressing cell line. In conclusion, the results of our study demonstrate that EGFR signaling has a significant effect on the levels of secreted invasion-promoting proteins, likely contributing to the aggressiveness of Glioblastoma multiforme. Further characterization of these proteins may provide candidates for new therapeutic strategies and targets as well as biomarkers for this aggressive disease.

Multi-study integration of brain cancer transcriptomes reveals organ-level molecular signatures.

We utilized abundant transcriptomic data for the primary classes of brain cancers to study the feasibility of separating all of these diseases simultaneously based on molecular data alone. These signatures were based on a new method reported herein--Identification of Structured Signatures and Classifiers (ISSAC)--that resulted in a brain cancer marker panel of 44 unique genes. Many of these genes have established relevance to the brain cancers examined herein, with others having known roles in cancer biology. Analyses on large-scale data from multiple sources must deal with significant challenges associated with heterogeneity between different published studies, for it was observed that the variation among individual studies often had a larger effect on the transcriptome than did phenotype differences, as is typical. For this reason, we restricted ourselves to studying only cases where we had at least two independent studies performed for each phenotype, and also reprocessed all the raw data from the studies using a unified pre-processing pipeline. We found that learning signatures across multiple datasets greatly enhanced reproducibility and accuracy in predictive performance on truly independent validation sets, even when keeping the size of the training set the same. This was most likely due to the meta-signature encompassing more of the heterogeneity across different sources and conditions, while amplifying signal from the repeated global characteristics of the phenotype. When molecular signatures of brain cancers were constructed from all currently available microarray data, 90% phenotype prediction accuracy, or the accuracy of identifying a particular brain cancer from the background of all phenotypes, was found. Looking forward, we discuss our approach in the context of the eventual development of organ-specific molecular signatures from peripheral fluids such as the blood.

Novel TCF4:TCF12 heterodimer inhibits glioblastoma growth.

TWIST1 (TW) is a pro-oncogenic basic helix-loop-helix (bHLH) transcription factor and promotes the hallmark features of malignancy (e.g., cell invasion, cancer cell stemness, and treatment resistance), which contribute to poor prognoses of glioblastoma (GBM). We previously reported that specific TW dimerization motifs regulate unique cellular phenotypes in GBM. For example, the TW:E12 heterodimer increases periostin (POSTN) expression and promotes cell invasion. TW dimer-specific transcriptional regulation requires binding to the regulatory E-box consensus sequences, but alternative bHLH dimers that balance TW dimer activity in regulating pro-oncogenic TW target genes are unknown. We leveraged the ENCODE DNase I hypersensitivity data to identify E-box sites and tethered TW:E12 and TW:TW proteins to validate dimer binding to E-boxes in vitro. Subsequently, TW knockdown revealed a novel TCF4:TCF12 bHLH dimer occupying the same TW E-box site that, when expressed as a tethered TCF4:TCF12 dimer, markedly repressed POSTN expression and extended animal survival. These observations support TCF4:TCF12 as a novel dimer with tumor-suppressor activity in GBM that functions in part through displacement of and/or competitive inhibition of pro-oncogenic TW dimers at E-box sites.

Fokker-Planck diffusion maps of multiple single cell microglial transcriptomes reveals radial differentiation into substates associated with Alzheimer's pathology.

The identification of microglia subtypes is important for understanding the role of innate immunity in neurodegenerative diseases. Current methods of unsupervised cell type identification assume a small noise-to-signal ratio of transcriptome measurements that would produce well-separated cell clusters. However, identification of subtypes is obscured by gene expression noise, diminishing the distances in transcriptome space between distinct cell types and blurring boundaries. Here we use Fokker-Planck (FP) diffusion maps to model cellular differentiation as a stochastic process whereby cells settle into local minima, corresponding to cell subtypes, in a potential landscape constructed from transcriptome data using a nearest neighbor graph approach. By applying critical transition fields, we identify individual cells on the verge of transitioning between subtypes, revealing microglial cells in inactivated, homeostatic state before radially transitioning into various specialized subtypes. Specifically, we show that cells from Alzheimer's disease patients are enriched in a microglia subtype associated to antigen presentation and T-cell recruitment.

Single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease.

The emergence of single nucleus RNA sequencing (snRNA-seq) offers to revolutionize the study of Alzheimer's disease (AD). Integration with complementary multiomics data such as genetics, proteomics and clinical data provides powerful opportunities to link cell subpopulations and molecular networks with a broader disease-relevant context. We report snRNA-seq profiles from superior frontal gyrus samples from 101 well characterized subjects from the Banner Brain and Body Donation Program in combination with whole genome sequences. We report findings that link common AD risk variants with CR1 expression in oligodendrocytes as well as alterations in hematological parameters. We observed an AD-associated CD83(+) microglial subtype with unique molecular networks and which is associated with immunoglobulin IgG4 production in the transverse colon. Our major observations were replicated in two additional, independent snRNA-seq data sets. These findings illustrate the power of multi-tissue molecular profiling to contextualize snRNA-seq brain transcriptomics and reveal disease biology.

A genome scale transcriptional regulatory model of the human placenta.

Gene regulation is essential to placental function and fetal development. We built a genome-scale transcriptional regulatory network (TRN) of the human placenta using digital genomic footprinting and transcriptomic data. We integrated 475 transcriptomes and 12 DNase hypersensitivity datasets from placental samples to globally and quantitatively map transcription factor (TF)-target gene interactions. In an independent dataset, the TRN model predicted target gene expression with an out-of-sample R2 greater than 0.25 for 73% of target genes. We performed siRNA knockdowns of four TFs and achieved concordance between the predicted gene targets in our TRN and differences in expression of knockdowns with an accuracy of >0.7 for three of the four TFs. Our final model contained 113,158 interactions across 391 TFs and 7712 target genes and is publicly available. We identified 29 TFs which were significantly enriched as regulators for genes previously associated with preterm birth, and eight of these TFs were decreased in preterm placentas.

AUREA: an open-source software system for accurate and user-friendly identification of relative expression molecular signatures.

BACKGROUND: Public databases such as the NCBI Gene Expression Omnibus contain extensive and exponentially increasing amounts of high-throughput data that can be applied to molecular phenotype characterization. Collectively, these data can be analyzed for such purposes as disease diagnosis or phenotype classification. One family of algorithms that has proven useful for disease classification is based on relative expression analysis and includes the Top-Scoring Pair (TSP), k-Top-Scoring Pairs (k-TSP), Top-Scoring Triplet (TST) and Differential Rank Conservation (DIRAC) algorithms. These relative expression analysis algorithms hold significant advantages for identifying interpretable molecular signatures for disease classification, and have been implemented previously on a variety of computational platforms with varying degrees of usability. To increase the user-base and maximize the utility of these methods, we developed the program AUREA (Adaptive Unified Relative Expression Analyzer)-a cross-platform tool that has a consistent application programming interface (API), an easy-to-use graphical user interface (GUI), fast running times and automated parameter discovery. RESULTS: Herein, we describe AUREA, an efficient, cohesive, and user-friendly open-source software system that comprises a suite of methods for relative expression analysis. AUREA incorporates existing methods, while extending their capabilities and bringing uniformity to their interfaces. We demonstrate that combining these algorithms and adaptively tuning parameters on the training sets makes these algorithms more consistent in their performance and demonstrate the effectiveness of our adaptive parameter tuner by comparing accuracy across diverse datasets. CONCLUSIONS: We have integrated several relative expression analysis algorithms and provided a unified interface for their implementation while making data acquisition, parameter fixing, data merging, and results analysis 'point-and-click' simple. The unified interface and the adaptive parameter tuning of AUREA provide an effective framework in which to investigate the massive amounts of publically available data by both 'in silico' and 'bench' scientists. AUREA can be found at http://price.systemsbiology.net/AUREA/.

Correction: Reproducible big data science: A case study in continuous FAIRness.

[This corrects the article DOI: 10.1371/journal.pone.0213013.].

A public resource of single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease.

The emergence of technologies that can support high-throughput profiling of single cell transcriptomes offers to revolutionize the study of brain tissue from persons with and without Alzheimer's disease (AD). Integration of these data with additional complementary multiomics data such as genetics, proteomics and clinical data provides powerful opportunities to link observed cell subpopulations and molecular network features within a broader disease-relevant context. We report here single nucleus RNA sequencing (snRNA-seq) profiles generated from superior frontal gyrus cortical tissue samples from 101 exceptionally well characterized, aged subjects from the Banner Brain and Body Donation Program in combination with whole genome sequences. We report findings that link common AD risk variants with CR1 expression in oligodendrocytes as well as alterations in peripheral hematological lab parameters, with these observations replicated in an independent, prospective cohort study of ageing and dementia. We also observed an AD-associated CD83(+) microglial subtype with unique molecular networks that encompass many known regulators of AD-relevant microglial biology, and which are associated with immunoglobulin IgG4 production in the transverse colon. These findings illustrate the power of multi-tissue molecular profiling to contextualize snRNA-seq brain transcriptomics and reveal novel disease biology. The transcriptomic, genetic, phenotypic, and network data resources described within this study are available for access and utilization by the scientific community.

The neuronal chromatin landscape in adult schizophrenia brains is linked to early fetal development.

Non-coding variants increase risk of neuropsychiatric disease. However, our understanding of the cell-type specific role of the non-coding genome in disease is incomplete. We performed population scale (N=1,393) chromatin accessibility profiling of neurons and non-neurons from two neocortical brain regions: the anterior cingulate cortex and dorsolateral prefrontal cortex. Across both regions, we observed notable differences in neuronal chromatin accessibility between schizophrenia cases and controls. A per-sample disease pseudotime was positively associated with genetic liability for schizophrenia. Organizing chromatin into cis- and trans-regulatory domains, identified a prominent neuronal trans-regulatory domain (TRD1) active in immature glutamatergic neurons during fetal development. Polygenic risk score analysis using genetic variants within chromatin accessibility of TRD1 successfully predicted susceptibility to schizophrenia in the Million Veteran Program cohort. Overall, we present the most extensive resource to date of chromatin accessibility in the human cortex, yielding insights into the cell-type specific etiology of schizophrenia.

The neuronal chromatin landscape in adult schizophrenia brains is linked to early fetal development.

Non-coding variants increase risk of neuropsychiatric disease. However, our understanding of the cell-type specific role of the non-coding genome in disease is incomplete. We performed population scale (N=1,393) chromatin accessibility profiling of neurons and non-neurons from two neocortical brain regions: the anterior cingulate cortex and dorsolateral prefrontal cortex. Across both regions, we observed notable differences in neuronal chromatin accessibility between schizophrenia cases and controls. A per-sample disease pseudotime was positively associated with genetic liability for schizophrenia. Organizing chromatin into cis- and trans-regulatory domains, identified a prominent neuronal trans-regulatory domain (TRD1) active in immature glutamatergic neurons during fetal development. Polygenic risk score analysis using genetic variants within chromatin accessibility of TRD1 successfully predicted susceptibility to schizophrenia in the Million Veteran Program cohort. Overall, we present the most extensive resource to date of chromatin accessibility in the human cortex, yielding insights into the cell-type specific etiology of schizophrenia.

A metabolomic signature of the APOE2 allele.

With the goal of identifying metabolites that significantly correlate with the protective e2 allele of the apolipoprotein E (APOE) gene, we established a consortium of five studies of healthy aging and extreme human longevity with 3545 participants. This consortium includes the New England Centenarian Study, the Baltimore Longitudinal Study of Aging, the Arivale study, the Longevity Genes Project/LonGenity studies, and the Long Life Family Study. We analyzed the association between APOE genotype groups E2 (e2e2 and e2e3 genotypes, N = 544), E3 (e3e3 genotypes, N = 2299), and E4 (e3e4 and e4e4 genotypes, N = 702) with metabolite profiles in the five studies and used fixed effect meta-analysis to aggregate the results. Our meta-analysis identified a signature of 19 metabolites that are significantly associated with the E2 genotype group at FDR < 10%. The group includes 10 glycerolipids and 4 glycerophospholipids that were all higher in E2 carriers compared to E3, with fold change ranging from 1.08 to 1.25. The organic acid 6-hydroxyindole sulfate, previously linked to changes in gut microbiome that were reflective of healthy aging and longevity, was also higher in E2 carriers compared to E3 carriers. Three sterol lipids and one sphingolipid species were significantly lower in carriers of the E2 genotype group. For some of these metabolites, the effect of the E2 genotype opposed the age effect. No metabolites reached a statistically significant association with the E4 group. This work confirms and expands previous results connecting the APOE gene to lipid regulation and suggests new links between the e2 allele, lipid metabolism, aging, and the gut-brain axis.

Aß Amyloid Scaffolds the Accumulation of Matrisome and Additional Proteins in Alzheimer's Disease.

We report a highly significant correlation in brain proteome changes between Alzheimers disease (AD) and CRND8 APP695NL/F transgenic mice. However, integrating protein changes observed in the CRND8 mice with co-expression networks derived from human AD, reveals both conserved and divergent module changes. For the most highly conserved module (M42, matrisome) we find many proteins accumulate in plaques, cerebrovascular amyloid (CAA), dystrophic processes, or a combination thereof. Overexpression of two M42 proteins, midkine (Mdk) and pleiotrophin (PTN), in CRND8 mice brains leads to increased accumulation of A ß ; in plaques and in CAA; further, recombinant MDK and PTN enhance A ß ; aggregation into amyloid. Multiple M42 proteins, annotated as heparan sulfate binding proteins, bind to fibrillar A ß 42 and a non-human amyloid fibril in vitro. Supporting this binding data, MDK and PTN co-accumulate with transthyretin (TTR) amyloid in the heart and islet amyloid polypeptide (IAPP) amyloid in the pancreas. Our findings establish several critical insights. Proteomic changes in modules observed in human AD brains define an A ß ; amyloid responsome that is well conserved from mouse model to human. Further, distinct amyloid structures may serve as scaffolds, facilitating the co-accumulation of proteins with signaling functions. We hypothesize that this co-accumulation may contribute to downstream pathological sequalae. Overall, this contextualized understanding of proteomic changes and their interplay with amyloid deposition provides valuable insights into the complexity of AD pathogenesis and potential biomarkers and therapeutic targets.