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The majority of mammalian genes encode multiple transcript isoforms that result from differential promoter use, changes in exonic splicing, and alternative 3’ end choice. Detecting and quantifying transcript isoforms across tissues, cell types, and species has been extremely challenging because transcripts are much longer than the short reads normally used for RNA-seq. By contrast, long-read RNA-seq (LR-RNA-seq) gives the complete structure of most transcripts. We sequenced 264 LR-RNA-seq PacBio libraries totaling over 1 billion circular consensus reads (CCS) for 81 unique human and mouse samples. We detect at least one full-length transcript from 87.7% of annotated human protein coding genes and a total of 200,000 full-length transcripts, 40% of which have novel exon junction chains.To capture and compute on the three sources of transcript structure diversity, we introduce a gene and transcript annotation framework that uses triplets representing the transcript start site, exon junction chain, and transcript end site of each transcript. Using triplets in a simplex representation demonstrates how promoter selection, splice pattern, and 3’ processing are deployed across human tissues, with nearly half of multitranscript protein coding genes showing a clear bias toward one of the three diversity mechanisms. Evaluated across samples, the predominantly expressed transcript changes for 74% of protein coding genes. In evolution, the human and mouse transcriptomes are globally similar in types of transcript structure diversity, yet among individual orthologous gene pairs, more than half (57.8%) show substantial differences in mechanism of diversification in matching tissues. This initial large-scale survey of human and mouse long-read transcriptomes provides a foundation for further analyses of alternative transcript usage, and is complemented by short-read and microRNA data on the same samples and by epigenome data elsewhere in the ENCODE4 collection.

MotivationWeighted gene co-expression network analysis (WGCNA) is frequently used to identify modules of genes that are co-expressed across many RNA-seq samples. However, the current R implementation is slow, not designed to compare modules between multiple WGCNA networks, and results are hard to interpret and visualize. We introduce the PyWGCNA Python package designed to identify and compare co-expression modules from RNA-seq data.ResultsWe apply PyWGCNA to two distinct datasets of brain bulk RNA-seq from MODEL-AD to identify modules associated with the genotypes. We compare the resulting modules to each other to find modules with significant overlap across the datasets.AvailabilityThe PyWGCNA library for Python 3 is available on PyPi at https://pypi.org/project/PyWGCNA and on GitHub at https://github.com/mortazavilab/PyWGCNA.

Genome-Wide Association Studies revealed the TREM2 R47H variant as one of the strongest genetic risk factors for late-onset Alzheimer’s Disease (AD). Unfortunately, many current TREM2*R47H mouse models are associated with cryptic mRNA splicing of the mutant allele that produces a confounding reduction in protein product. We have developed the Trem2R47H NSS (Normal Splice Site) mouse model where the Trem2 allele is expressed at a similar level to the wild-type Trem2 allele, without evidence of cryptic splicing products, and appropriate inflammatory responses to cuprizone challenge. Utilizing the 5xFAD mouse model, we report age- and disease-dependent changes in response to pathology. At an early disease stage (4 mo), homozygous Trem2R47H NSS; hemizygous 5xFAD (Trem2R47H NSS ; 5xFAD) mice have reduced size and number of microglia plus impaired interaction with plaques, that is associated with increased dystrophic neurites and axonal damage detected through plasma neurofilament light chain (NfL) level and suppressed inflammation. However, homozygosity for Trem2R47H NSS suppressed LTP deficits and presynaptic puncta loss caused by the 5xFAD transgene array. At a more advanced disease stage (12 mo,) Trem2R47H NSS ; 5xFAD mice no longer display impaired plaque-microglia interaction or suppressed inflammatory gene expression, although NfL levels remain elevated, and a unique interferon-related gene expression signature is seen. Furthermore, Trem2R47H NSS ; 5xFAD mice also display robust LTP deficits and exacerbated presynaptic loss. Collectively, we provide a Trem2R47H variant mouse without cryptic splicing, and demonstrate it has disease stage dependent effects when combined with a plaque bearing model, with an initial loss of function that ultimately resolves, giving rise to a unique interferon signature and associated tissue damage.

Animal models of disease are valuable resources for investigating pathogenic mechanisms and potential therapeutic interventions. However, for complex disorders such as Alzheimer’s disease (AD), the generation and availability of innumerous distinct animal models present unique challenges to AD researchers and hinder the success of useful therapies. Here, we conducted an in-depth analysis of the 3xTg-AD mouse model of AD across its lifespan to better inform the field of the various pathologies that appear at specific ages, and comment on drift that has occurred in the development of pathology in this line since its development 20 years ago. This modern characterization of the 3xTg-AD model includes an assessment of impairments in behavior, cognition, and long-term potentiation followed by quantification of amyloid beta (Aβ) plaque burden and neurofibrillary tau tangles, biochemical levels of Aβ and tau protein, and neuropathological markers such as gliosis and accumulation of dystrophic neurites. We also present a novel comparison of the 3xTg-AD model with the 5xFAD model using the same deep-phenotyping characterization pipeline. The results from these analyses are freely available via the AD Knowledge Portal (https://admodelexplorer.synapse.org). Our work demonstrates the utility of a characterization pipeline that generates robust and standardized information relevant to investigating and comparing disease etiologies of current and future models of AD.Contribution to the Field StatementAlzheimer’s Disease (AD) is an age-related neurodegenerative disorder characterized by progressive memory impairments and affects more than 30 million individuals worldwide. Using animal models of AD, researchers have elucidated disease progression and hallmark pathologies that may underpin the memory impairments seen in patients. However, therapeutic targets have failed to translate successfully from animal studies to human clinical trials, prompting a reassessment of the development, use, and interpretation of data acquired using the innumerous AD animal models available to researchers. To address these shortcomings, we have developed a robust and reproducible modern characterization of pathologies within current and future animal models of AD to better assess distinct pathologies that arise at specific brain regions and ages of different models. Using the popular 3xTg-AD mouse, we demonstrate the utility of these deep-phenotyping analyses and highlight the drift that affected development of pathologies in this line over the past two decades. Utilizing this same systematic characterization, we also perform a direct comparison with 5xFAD mice, another popular animal model of AD. The robust and standardized data generated from these systematic deep-phenotyping analyses are available for broad use by the AD research community to assess, compare, and determine appropriate animal models of AD.

Mouse models of human diseases are invaluable tools for studying pathogenic mechanisms and testing interventions and therapeutics. For disorders such as Alzheimer’s disease in which numerous models are being generated, a challenging first step is to identify the most appropriate model and age to effectively evaluate new therapeutic approaches. Here we conducted a detailed phenotypic characterization of the 5xFAD model on a congenic C57BL/6 J strain background, across its lifespan – including a seldomly analyzed 18-month old time point to provide temporally correlated phenotyping of this model and a template for characterization of new models of LOAD as they are generated. This comprehensive analysis included quantification of plaque burden, Aβ biochemical levels, and neuropathology, neurophysiological measurements and behavioral and cognitive assessments, and evaluation of microglia, astrocytes, and neurons. Analysis of transcriptional changes was conducted using bulk-tissue generated RNA-seq data from microdissected cortices and hippocampi as a function of aging, which can be explored at the MODEL-AD Explorer and AD Knowledge Portal. This deep-phenotyping pipeline identified novel aspects of age-related pathology in the 5xFAD model., Measurement(s)Protein Expression • gene expression • electrophysiology data • protein measurement • behaviorTechnology Type(s)immunofluorescence microscopy assay • RNA sequencing • electrophysiology assay • Electrochemiluminescence Immunoassay • animal activity monitoring systemFactor Type(s)genotype • age • sexSample Characteristic - OrganismMus musculus Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.15176109

Multiple mouse models have been generated that strive to recapitulate human Alzheimer’s disease (AD) pathological features to investigate disease mechanisms and potential treatments. The 3xTg-AD mouse presents the two major hallmarks of AD, which are plaques and tangles that increase during aging. While behavioral changes and the accumulation of plaques and tangles have been well described in the 3xTg-AD mice, the subpopulations of neurons and glial cells present throughout disease progression have not been characterized. Here, we used single-cell RNA-seq to investigate changes in subpopulations of microglia, and single-nucleus RNA-seq to explore subpopulations of neurons, astrocytes, and oligodendrocytes in the hippocampus and cortex of aging 3xTg-AD as well as 5xFAD mice for comparison. We recovered a common path of age-associated astrocyte activation between the 3xTg-AD and the 5xFAD models and found that 3xTg-AD-derived astrocytes seem to be less activated. We identified multiple subtypes of microglia, including a subpopulation with a distinct transcription factor expression profile that showed an early increase in Csf1 expression before the switch to disease associated microglia (DAM). We used bulk RNA-seq in the hippocampus of 3xTg-AD mice across their lifespan to identify distinct modules of genes whose expression increases with aging and worsening pathology. Finally, scATAC-seq revealed multiple subpopulations of cells with accessible chromatin in regions around genes associated with glial activation. Overall, differences between the main glial groups point to a slower activation process in the 3xTg-AD model when compared to the 5xFAD. Our study contributes to the identification of progressive transcriptional changes of glial cells in a mouse model that has plaques and tangles, thus providing information to aid in targeted AD therapeutics that could translate into positive clinical outcomes.

De novo somatic point mutations identified in breast cancer are predominantly non-coding and typically attributed to altered regulatory elements such as enhancers and promoters. However, while the non-coding RNAs (ncRNAs) form a large portion of the mammalian genome, their biological functions are mostly poorly characterized in cancers. In this study, using a newly developed tool, SomaGene, we reanalyze de novo somatic point mutations from the International Cancer Genome Consortium (ICGC) whole-genome sequencing data of 1,855 breast cancers. We identify 929 candidates of ncRNAs that are significantly and explicitly mutated in breast cancer samples. By integrating data from the ENCODE regulatory features and FANTOM5 expression atlas, we show that the candidate ncRNAs in breast cancer samples significantly enrich for active chromatin histone marks (1.9 times), CTCF binding sites (2.45 times), DNase accessibility (1.76 times), HMM predicted enhancers (2.26 times) and eQTL polymorphisms (1.77 times). Importantly, we show that the 929 ncRNAs contain a much higher level (3.64 times) of breast cancer-associated genome-wide association (GWAS) single nucleotide polymorphisms (SNPs) than genome-wide expectation. Such enrichment has not been seen with GWAS SNPs from other diseases. Using breast tissue related Hi-C data we then show that 82% of our candidate ncRNAs (1.9 times) significantly interact with the promoter of protein-coding genes, including previously known cancer-associated genes, suggesting the critical role for candidate ncRNA genes in activation of essential regulators of development and differentiation in breast cancer. We provide an extensive web-based resource (http://ncrna.ictic.sharif.edu), to communicate our results with the research community. Our list of breast cancer-specific ncRNA genes has the potential to provide a better understanding of the underlying genetic causes of breast cancer. Lastly, the tool developed in this study can be used in the analysis of somatic mutations in all cancers.

Non-coding RNAs (ncRNAs) form a large portion of the mammalian genome. However, their biological functions are poorly characterized in cancers. In this study, using a newly developed tool, SomaGene, we analyze de novo somatic point mutations from the International Cancer Genome Consortium (ICGC) whole-genome sequencing data of 1,855 breast cancer samples. We identify 1030 candidates of ncRNAs that are significantly and explicitly mutated in breast cancer samples. By integrating data from the ENCODE regulatory features and FANTOM5 expression atlas, we show that the candidate ncRNAs significantly enrich active chromatin histone marks (1.9 times), CTCF binding sites (2.45 times), DNase accessibility (1.76 times), HMM predicted enhancers (2.26 times) and eQTL polymorphisms (1.77 times). Importantly, we show that the 1030 ncRNAs contain a much higher level (3.64 times) of breast cancer-associated genome-wide association (GWAS) single nucleotide polymorphisms (SNPs) than genome-wide expectation. Such enrichment has not been seen with GWAS SNPs from other cancers. Using breast cell line related Hi-C data, we then show that 82% of our candidate ncRNAs (1.9 times) significantly interact with the promoter of protein-coding genes, including previously known cancer-associated genes, suggesting the critical role of candidate ncRNA genes in the activation of essential regulators of development and differentiation in breast cancer. We provide an extensive web-based resource (https://www.ihealthe.unsw.edu.au/research) to communicate our results with the research community. Our list of breast cancer-specific ncRNA genes has the potential to provide a better understanding of the underlying genetic causes of breast cancer. Lastly, the tool developed in this study can be used to analyze somatic mutations in all cancers.

Hi-C is a genome-wide chromosome conformation capture technology that detects interactions between pairs of genomic regions and exploits higher order chromatin structures. Conceptually Hi-C data counts interaction frequencies between every position in the genome and every other position. Biologically functional interactions are expected to occur more frequently than transient background and artefactual interactions. To identify biologically relevant interactions, several background models that take biases such as distance, GC content and mappability into account have been proposed. Here we introduce MaxHiC, a background correction tool that deals with these complex biases and robustly identifies statistically significant interactions in both Hi-C and capture Hi-C experiments. MaxHiC uses a negative binomial distribution model and a maximum likelihood technique to correct biases in both Hi-C and capture Hi-C libraries. We systematically benchmark MaxHiC against major Hi-C background correction tools including Hi-C significant interaction callers (SIC) and Hi-C loop callers using published Hi-C, capture Hi-C, and Micro-C datasets. Our results demonstrate that 1) Interacting regions identified by MaxHiC have significantly greater levels of overlap with known regulatory features (e.g. active chromatin histone marks, CTCF binding sites, DNase sensitivity) and also disease-associated genome-wide association SNPs than those identified by currently existing models, 2) the pairs of interacting regions are more likely to be linked by eQTL pairs and 3) more likely to link known regulatory features including known functional enhancer-promoter pairs validated by CRISPRi than any of the existing methods. We also demonstrate that interactions between different genomic region types have distinct distance distributions only revealed by MaxHiC. MaxHiC is publicly available as a python package for the analysis of Hi-C, capture Hi-C and Micro-C data.

Background: With the growing use of bottled water, the continuous research and monitoring of the quality of these products are crucial. The present study aimed to assess the chemical quality of the bottled water distributed in Gorgan, Iran. Methods: This cross-sectional, descriptive-analytical study was conducted on the samples of bottled water distributed in Gorgan city. Sampling was performed during four months (one sample of each brand obtained every month; total: 36). The chemical quality of the samples was measured using standard methods. Results: The mean concentrations of nitrate, fluoride, and iron and mean pH of the samples were 12.92 ± 11.05, 0.33 ± 0.12, and 0.64 ± 2.9 mg/l and 6.89 ± 0.39, respectively. Only the physicochemical parameters of pH and iron were significantly higher than the standard values, and the mean levels of the chemical factors were significantly lower than the standard values (P < 0.05). Conclusion: Consumers expect bottled water to have higher quality as they perceive the product to be a healthier choice than the water supplied by urban distribution networks. High levels of some chemical parameters could adversely affect the health of consumers, especially vulnerable populations, which should be taken into consideration by custodians and authorities.

Animal models of disease are valuable resources for investigating pathogenic mechanisms and potential therapeutic interventions. However, for complex disorders such as Alzheimer’s disease (AD), the generation and availability of innumerous distinct animal models present unique challenges to AD researchers and hinder the success of useful therapies. Here, we conducted an in-depth analysis of the 3xTg-AD mouse model of AD across its lifespan to better inform the field of the various pathologies that appear at specific ages, and comment on drift that has occurred in the development of pathology in this line since its development 20 years ago. This modern characterization of the 3xTg-AD model includes an assessment of impairments in behavior, cognition, and long-term potentiation followed by quantification of amyloid beta (Aβ) plaque burden and neurofibrillary tau tangles, biochemical levels of Aβ and tau protein, and neuropathological markers such as gliosis and accumulation of dystrophic neurites. We also present a novel comparison of the 3xTg-AD model with the 5xFAD model using the same deep-phenotyping characterization pipeline. The results from these analyses are freely available via the AD Knowledge Portal (https://admodelexplorer.synapse.org). Our work demonstrates the utility of a characterization pipeline that generates robust and standardized information relevant to investigating and comparing disease etiologies of current and future models of AD. Contribution to the Field Statement Alzheimer’s Disease (AD) is an age-related neurodegenerative disorder characterized by progressive memory impairments and affects more than 30 million individuals worldwide. Using animal models of AD, researchers have elucidated disease progression and hallmark pathologies that may underpin the memory impairments seen in patients. However, therapeutic targets have failed to translate successfully from animal studies to human clinical trials, prompting a reassessment of the development, use, and interpretation of data acquired using the innumerous AD animal models available to researchers. To address these shortcomings, we have developed a robust and reproducible modern characterization of pathologies within current and future animal models of AD to better assess distinct pathologies that arise at specific brain regions and ages of different models. Using the popular 3xTg-AD mouse, we demonstrate the utility of these deep-phenotyping analyses and highlight the drift that affected development of pathologies in this line over the past two decades. Utilizing this same systematic characterization, we also perform a direct comparison with 5xFAD mice, another popular animal model of AD. The robust and standardized data generated from these systematic deep-phenotyping analyses are available for broad use by the AD research community to assess, compare, and determine appropriate animal models of AD.