APOE4 is the strongest genetic risk factor associated with late-onset Alzheimer's disease (AD). To address the underlying mechanism, we develop cerebral organoid models using induced pluripotent stem ...cells (iPSCs) with APOE ε3/ε3 or ε4/ε4 genotype from individuals with either normal cognition or AD dementia. Cerebral organoids from AD patients carrying APOE ε4/ε4 show greater apoptosis and decreased synaptic integrity. While AD patient-derived cerebral organoids have increased levels of Aβ and phosphorylated tau compared to healthy subject-derived cerebral organoids, APOE4 exacerbates tau pathology in both healthy subject-derived and AD patient-derived organoids. Transcriptomics analysis by RNA-sequencing reveals that cerebral organoids from AD patients are associated with an enhancement of stress granules and disrupted RNA metabolism. Importantly, isogenic conversion of APOE4 to APOE3 attenuates the APOE4-related phenotypes in cerebral organoids from AD patients. Together, our study using human iPSC-organoids recapitulates APOE4-related phenotypes and suggests APOE4-related degenerative pathways contributing to AD pathogenesis.
Evidence suggests interplay among the three major risk factors for Alzheimer’s disease (AD): age, APOE genotype, and sex. Here, we present comprehensive datasets and analyses of brain transcriptomes ...and blood metabolomes from human apoE2-, apoE3-, and apoE4-targeted replacement mice across young, middle, and old ages with both sexes. We found that age had the greatest impact on brain transcriptomes highlighted by an immune module led by Trem2 and Tyrobp, whereas APOE4 was associated with upregulation of multiple Serpina3 genes. Importantly, these networks and gene expression changes were mostly conserved in human brains. Finally, we observed a significant interaction between age, APOE genotype, and sex on unfolded protein response pathway. In the periphery, APOE2 drove distinct blood metabolome profile highlighted by the upregulation of lipid metabolites. Our work identifies unique and interactive molecular pathways underlying AD risk factors providing valuable resources for discovery and validation research in model systems and humans.
Display omitted
•Aging leads to the most profound changes in brain gene expression networks•Immune module led by Alzheimer’s risk genes Trem2/Tyrobp is upregulated with aging•Alzheimer’s risk allele APOE4 increases the expression of Serpina3 family genes•Alzheimer’s protective allele APOE2 drives unique serum metabolome profiles
Zhao et al. present comprehensive datasets and analyses of brain transcriptomes and blood metabolomes from human apoE2-, apoE3-, and apoE4-targeted replacement mice across young, middle, and old ages with both sexes. The study provides critical insight on the molecular pathways underlying three major Alzheimer’s risk factors age, APOE, and sex.
OBJECTIVE:The ε4 allele of the APOE gene (APOE4) is the strongest genetic risk factor for Alzheimer disease when compared with the common ε3 allele. Although there has been significant progress in ...understanding how apoE4 (apolipoprotein E4) drives amyloid pathology, its effects on amyloid-independent pathways, in particular cerebrovascular integrity and function, are less clear.
APPROACH AND RESULTS:Here, we show that brain pericytes, the mural cells of the capillary walls, differentially modulate endothelial cell phenotype in an apoE isoform-dependent manner. Extracellular matrix protein induction, tube-like structure formation, and barrier formation were lower with endothelial cells cocultured with pericytes isolated from apoE4-targeted replacement (TR) mice compared with those from apoE3-TR mice. Importantly, aged apoE4-targeted replacement mice had decreased extracellular matrix protein expression and increased plasma protein leakages compared with apoE3-TR mice.
CONCLUSIONS:ApoE4 impairs pericyte-mediated basement membrane formation, potentially contributing to the cerebrovascular effects of apoE4.
The recent methodological advances in multi-omics approaches, including genomic, transcriptomic, metabolomic, lipidomic, and proteomic, have revolutionized the research field by generating “big data” ...which greatly enhanced our understanding of the molecular complexity of the brain and disease states. Network approaches have been routinely applied to single-omics data to provide critical insight into disease biology. Furthermore, multi-omics integration has emerged as both a vital need and a new direction to connect the different layers of information underlying disease mechanisms. In this review article, we summarize popular network analytic approaches for single-omics data and multi-omics integration and discuss how these approaches have been utilized in studying neurodegenerative diseases.
Caloric restriction (CR) mitigates many detrimental effects of aging and prolongs life span. CR has been suggested to increase mitochondrial biogenesis, thereby attenuating age-related declines in ...mitochondrial function, a concept that is challenged by recent studies. Here we show that lifelong CR in mice prevents age-related loss of mitochondrial oxidative capacity and efficiency, measured in isolated mitochondria and permeabilized muscle fibers. We find that these beneficial effects of CR occur without increasing mitochondrial abundance. Whole-genome expression profiling and large-scale proteomic surveys revealed expression patterns inconsistent with increased mitochondrial biogenesis, which is further supported by lower mitochondrial protein synthesis with CR. We find that CR decreases oxidant emission, increases antioxidant scavenging, and minimizes oxidative damage to DNA and protein. These results demonstrate that CR preserves mitochondrial function by protecting the integrity and function of existing cellular components rather than by increasing mitochondrial biogenesis.
► Caloric restriction preserves mitochondrial function in aging skeletal muscle ► Mitochondrial biogenesis is not increased by caloric restriction ► Caloric restriction maintains integrity of DNA and the proteome
The human genome contains "dark" gene regions that cannot be adequately assembled or aligned using standard short-read sequencing technologies, preventing researchers from identifying mutations ...within these gene regions that may be relevant to human disease. Here, we identify regions with few mappable reads that we call dark by depth, and others that have ambiguous alignment, called camouflaged. We assess how well long-read or linked-read technologies resolve these regions.
Based on standard whole-genome Illumina sequencing data, we identify 36,794 dark regions in 6054 gene bodies from pathways important to human health, development, and reproduction. Of these gene bodies, 8.7% are completely dark and 35.2% are ≥ 5% dark. We identify dark regions that are present in protein-coding exons across 748 genes. Linked-read or long-read sequencing technologies from 10x Genomics, PacBio, and Oxford Nanopore Technologies reduce dark protein-coding regions to approximately 50.5%, 35.6%, and 9.6%, respectively. We present an algorithm to resolve most camouflaged regions and apply it to the Alzheimer's Disease Sequencing Project. We rescue a rare ten-nucleotide frameshift deletion in CR1, a top Alzheimer's disease gene, found in disease cases but not in controls.
While we could not formally assess the association of the CR1 frameshift mutation with Alzheimer's disease due to insufficient sample-size, we believe it merits investigating in a larger cohort. There remain thousands of potentially important genomic regions overlooked by short-read sequencing that are largely resolved by long-read technologies.
To gain insight into the genomic basis of diffuse large B-cell lymphoma (DLBCL), we performed massively parallel whole-exome sequencing of 55 primary tumor samples from patients with DLBCL and ...matched normal tissue. We identified recurrent mutations in genes that are well known to be functionally relevant in DLBCL, including MYD88, CARD11, EZH2, and CREBBP. We also identified somatic mutations in genes for which a functional role in DLBCL has not been previously suspected. These genes include MEF2B, MLL2, BTG1, GNA13, ACTB, P2RY8, PCLO, and TNFRSF14. Further, we show that BCL2 mutations commonly occur in patients with BCL2/IgH rearrangements as a result of somatic hypermutation normally occurring at the IgH locus. The BCL2 point mutations are primarily synonymous, and likely caused by activation-induced cytidine deaminase–mediated somatic hypermutation, as shown by comprehensive analysis of enrichment of mutations in WRCY target motifs. Those nonsynonymous mutations that are observed tend to be found outside of the functionally important BH domains of the protein, suggesting that strong negative selection against BCL2 loss-of-function mutations is at play. Last, by using an algorithm designed to identify likely functionally relevant but infrequent mutations, we identify KRAS, BRAF, and NOTCH1 as likely drivers of DLBCL pathogenesis in some patients. Our data provide an unbiased view of the landscape of mutations in DLBCL, and this in turn may point toward new therapeutic strategies for the disease.
Primary central nervous system lymphoma (PCNSL) is an aggressive non-Hodgkin lymphoma confined to the central nervous system. Whether there is a PCNSL-specific genomic signature and, if so, how it ...differs from systemic diffuse large B-cell lymphoma (DLBCL) is uncertain.
We performed a comprehensive genomic study of tumor samples from 19 immunocompetent PCNSL patients. Testing comprised array-comparative genomic hybridization and whole exome sequencing.
Biallelic inactivation of TOX and PRKCD was recurrently found in PCNSL but not in systemic DLBCL, suggesting a specific role in PCNSL pathogenesis. In addition, we found a high prevalence of MYD88 mutations (79%) and CDKN2A biallelic loss (60%). Several genes recurrently affected in PCNSL were common with systemic DLBCL, including loss of TNFAIP3, PRDM1, GNA13, TMEM30A, TBL1XR1, B2M, CD58, activating mutations of CD79B, CARD11, and translocations IgH-BCL6. Overall, B-cell receptor/Toll-like receptor/NF-κB pathways were altered in >90% of PNCSL, highlighting its value for targeted therapeutic approaches. Furthermore, integrated analysis showed enrichment of pathways associated with immune response, proliferation, apoptosis, and lymphocyte differentiation.
In summary, genome-wide analysis uncovered novel recurrent alterations, including TOX and PRKCD, helping to differentiate PCNSL from systemic DLBCL and related lymphomas.
Explainable artificial intelligence aims to interpret how machine learning models make decisions, and many model explainers have been developed in the computer vision field. However, understanding of ...the applicability of these model explainers to biological data is still lacking. In this study, we comprehensively evaluated multiple explainers by interpreting pre-trained models for predicting tissue types from transcriptomic data and by identifying the top contributing genes from each sample with the greatest impacts on model prediction. To improve the reproducibility and interpretability of results generated by model explainers, we proposed a series of optimization strategies for each explainer on two different model architectures of multilayer perceptron (MLP) and convolutional neural network (CNN). We observed three groups of explainer and model architecture combinations with high reproducibility. Group II, which contains three model explainers on aggregated MLP models, identified top contributing genes in different tissues that exhibited tissue-specific manifestation and were potential cancer biomarkers. In summary, our work provides novel insights and guidance for exploring biological mechanisms using explainable machine learning models.
Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing ...data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational workflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any genome.
For optimization of tools and parameters, MAP-RSeq was validated using both simulated and real datasets. MAP-RSeq workflow consists of six major modules such as alignment of reads, quality assessment of reads, gene expression assessment and exon read counting, identification of expressed single nucleotide variants (SNVs), detection of fusion transcripts, summarization of transcriptomics data and final report. This workflow is available for Human transcriptome analysis and can be easily adapted and used for other genomes. Several clinical and research projects at the Mayo Clinic have applied the MAP-RSeq workflow for RNA-Seq studies. The results from MAP-RSeq have thus far enabled clinicians and researchers to understand the transcriptomic landscape of diseases for better diagnosis and treatment of patients.
Our software provides gene counts, exon counts, fusion candidates, expressed single nucleotide variants, mapping statistics, visualizations, and a detailed research data report for RNA-Seq. The workflow can be executed on a standalone virtual machine or on a parallel Sun Grid Engine cluster. The software can be downloaded from http://bioinformaticstools.mayo.edu/research/maprseq/.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
PDF
