The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models.

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × âˆ¼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification.

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

Modulation of C5a-C5aR1 signaling alters the dynamics of AD progression.

BACKGROUND: The complement system is part of the innate immune system that clears pathogens and cellular debris. In the healthy brain, complement influences neurodevelopment and neurogenesis, synaptic pruning, clearance of neuronal blebs, recruitment of phagocytes, and protects from pathogens. However, excessive downstream complement activation that leads to generation of C5a, and C5a engagement with its receptor C5aR1, instigates a feed-forward loop of inflammation, injury, and neuronal death, making C5aR1 a potential therapeutic target for neuroinflammatory disorders. C5aR1 ablation in the Arctic (Arc) model of Alzheimer's disease protects against cognitive decline and neuronal injury without altering amyloid plaque accumulation. METHODS: To elucidate the effects of C5a-C5aR1 signaling on AD pathology, we crossed Arc mice with a C5a-overexpressing mouse (ArcC5a+) and tested hippocampal memory. RNA-seq was performed on hippocampus and cortex from Arc, ArcC5aR1KO, and ArcC5a+ mice at 2.7-10 months and age-matched controls to assess mechanisms involved in each system. Immunohistochemistry was used to probe for protein markers of microglia and astrocytes activation states. RESULTS: ArcC5a+ mice had accelerated cognitive decline compared to Arc. Deletion of C5ar1 delayed or prevented the expression of some, but not all, AD-associated genes in the hippocampus and a subset of pan-reactive and A1 reactive astrocyte genes, indicating a separation between genes induced by amyloid plaques alone and those influenced by C5a-C5aR1 signaling. Biological processes associated with AD and AD mouse models, including inflammatory signaling, microglial cell activation, and astrocyte migration, were delayed in the ArcC5aR1KO hippocampus. Interestingly, C5a overexpression also delayed the increase of some AD-, complement-, and astrocyte-associated genes, suggesting the possible involvement of neuroprotective C5aR2. However, these pathways were enhanced in older ArcC5a+ mice compared to Arc. Immunohistochemistry confirmed that C5a-C5aR1 modulation in Arc mice delayed the increase in CD11c-positive microglia, while not affecting other pan-reactive microglial or astrocyte markers. CONCLUSION: C5a-C5aR1 signaling in AD largely exerts its effects by enhancing microglial activation pathways that accelerate disease progression. While C5a may have neuroprotective effects via C5aR2, engagement of C5a with C5aR1 is detrimental in AD models. These data support specific pharmacological inhibition of C5aR1 as a potential therapeutic strategy to treat AD.

Transcriptome analysis of heterogeneity in mouse model of metastatic breast cancer.

BACKGROUND: Cancer metastasis is a complex process involving the spread of malignant cells from a primary tumor to distal organs. Understanding this cascade at a mechanistic level could provide critical new insights into the disease and potentially reveal new avenues for treatment. Transcriptome profiling of spontaneous cancer models is an attractive method to examine the dynamic changes accompanying tumor cell spread. However, such studies are complicated by the underlying heterogeneity of the cell types involved. The purpose of this study was to examine the transcriptomes of metastatic breast cancer cells using the well-established MMTV-PyMT mouse model. METHODS: Organ-derived metastatic cell lines were harvested from 10 female MMTV-PyMT mice. Cancer cells were isolated and sorted based on the expression of CD44low/EpCAMhigh or CD44high/EpCAMhigh surface markers. RNA from each cell line was extracted and sequenced using the NextSeq 500 Illumina platform. Tissue-specific genes were compared across the different metastatic and primary tumor samples. Reads were mapped to the mouse genome using STAR, and gene expression was quantified using RSEM. Single-cell RNA-seq (scRNA-seq) was performed on select samples using the ddSeq platform by BioRad and analyzed using Seurat v3.2.3. Monocle2 was used to infer pseudo-time progression. RESULTS: Comparison of RNA sequencing data across all cell populations produced distinct gene clusters. Differential gene expression patterns related to CD44 expression, organ tropism, and immunomodulatory signatures were observed. scRNA-seq identified expression profiles based on tissue-dependent niches and clonal heterogeneity. These cohorts of data were narrowed down to identify subsets of genes with high expression and known metastatic propensity. Dot plot analyses further revealed clusters expressing cancer stem cell and cancer dormancy markers. Changes in relevant genes were investigated across pseudo-time and tissue origin using Monocle2. These data revealed transcriptomes that may contribute to sub-clonal evolution and treatment evasion during cancer progression. CONCLUSIONS: We performed a comprehensive transcriptome analysis of tumor heterogeneity and organ tropism during breast cancer metastasis. These data add to our understanding of metastatic progression and highlight targets for breast cancer treatment. These markers could also be used to image the impact of tumor heterogeneity on metastases.

Identification of robust cellular programs using reproducible LDA that impact sex-specific disease progression in different genotypes of a mouse model of AD.

The gene expression profiles of distinct cell types reflect complex genomic interactions among multiple simultaneous biological processes within each cell that can be altered by disease progression as well as genetic background. The identification of these active cellular programs is an open challenge in the analysis of single-cell RNA-seq data. Latent Dirichlet Allocation (LDA) is a generative method used to identify recurring patterns in counts data, commonly referred to as topics that can be used to interpret the state of each cell. However, LDA's interpretability is hindered by several key factors including the hyperparameter selection of the number of topics as well as the variability in topic definitions due to random initialization. We developed Topyfic, a Reproducible LDA (rLDA) package, to accurately infer the identity and activity of cellular programs in single-cell data, providing insights into the relative contributions of each program in individual cells. We apply Topyfic to brain single-cell and single-nucleus datasets of two 5xFAD mouse models of Alzheimer's disease crossed with C57BL6/J or CAST/EiJ mice to identify distinct cell types and states in different cell types such as microglia. We find that 8-month 5xFAD/Cast F1 males show higher level of microglial activation than matching 5xFAD/BL6 F1 males, whereas female mice show similar levels of microglial activation. We show that regulatory genes such as TFs, microRNA host genes, and chromatin regulatory genes alone capture cell types and cell states. Our study highlights how topic modeling with a limited vocabulary of regulatory genes can identify gene expression programs in single-cell data in order to quantify similar and divergent cell states in distinct genotypes.

The ENCODE Uniform Analysis Pipelines.

The Encyclopedia of DNA elements (ENCODE) project is a collaborative effort to create a comprehensive catalog of functional elements in the human genome. The current database comprises more than 19000 functional genomics experiments across more than 1000 cell lines and tissues using a wide array of experimental techniques to study the chromatin structure, regulatory and transcriptional landscape of the Homo sapiens and Mus musculus genomes. All experimental data, metadata, and associated computational analyses created by the ENCODE consortium are submitted to the Data Coordination Center (DCC) for validation, tracking, storage, and distribution to community resources and the scientific community. The ENCODE project has engineered and distributed uniform processing pipelines in order to promote data provenance and reproducibility as well as allow interoperability between genomic resources and other consortia. All data files, reference genome versions, software versions, and parameters used by the pipelines are captured and available via the ENCODE Portal. The pipeline code, developed using Docker and Workflow Description Language (WDL; https://openwdl.org/) is publicly available in GitHub, with images available on Dockerhub (https://hub.docker.com), enabling access to a diverse range of biomedical researchers. ENCODE pipelines maintained and used by the DCC can be installed to run on personal computers, local HPC clusters, or in cloud computing environments via Cromwell. Access to the pipelines and data via the cloud allows small labs the ability to use the data or software without access to institutional compute clusters. Standardization of the computational methodologies for analysis and quality control leads to comparable results from different ENCODE collections - a prerequisite for successful integrative analyses.

The ENCODE Uniform Analysis Pipelines.

The Encyclopedia of DNA elements (ENCODE) project is a collaborative effort to create a comprehensive catalog of functional elements in the human genome. The current database comprises more than 19000 functional genomics experiments across more than 1000 cell lines and tissues using a wide array of experimental techniques to study the chromatin structure, regulatory and transcriptional landscape of the Homo sapiens and Mus musculus genomes. All experimental data, metadata, and associated computational analyses created by the ENCODE consortium are submitted to the Data Coordination Center (DCC) for validation, tracking, storage, and distribution to community resources and the scientific community. The ENCODE project has engineered and distributed uniform processing pipelines in order to promote data provenance and reproducibility as well as allow interoperability between genomic resources and other consortia. All data files, reference genome versions, software versions, and parameters used by the pipelines are captured and available via the ENCODE Portal. The pipeline code, developed using Docker and Workflow Description Language (WDL; https://openwdl.org/) is publicly available in GitHub, with images available on Dockerhub (https://hub.docker.com), enabling access to a diverse range of biomedical researchers. ENCODE pipelines maintained and used by the DCC can be installed to run on personal computers, local HPC clusters, or in cloud computing environments via Cromwell. Access to the pipelines and data via the cloud allows small labs the ability to use the data or software without access to institutional compute clusters. Standardization of the computational methodologies for analysis and quality control leads to comparable results from different ENCODE collections - a prerequisite for successful integrative analyses.

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification.

The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

The ENCODE4 long-read RNA-seq collection reveals distinct classes of transcript structure diversity.

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 multi-transcript 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.

C5aR1 antagonism alters microglial polarization and mitigates disease progression in a mouse model of Alzheimer's disease.

Multiple studies have recognized the involvement of the complement cascade during Alzheimer's disease pathogenesis. However, the specific role of C5a-C5aR1 signaling in the progression of this neurodegenerative disease is still not clear. Furthermore, its potential as a therapeutic target to treat AD still remains to be elucidated. Canonically, generation of the anaphylatoxin C5a as the result of complement activation and interaction with its receptor C5aR1 triggers a potent inflammatory response. Previously, genetic ablation of C5aR1 in a mouse model of Alzheimer's disease exerted a protective effect by preventing cognitive deficits. Here, using PMX205, a potent, specific C5aR1 antagonist, in the Tg2576 mouse model of Alzheimer's disease we show a striking reduction in dystrophic neurites in parallel with the reduced amyloid load, rescue of the excessive pre-synaptic loss associated with AD cognitive impairment and the polarization of microglial gene expression towards a DAM-like phenotype that are consistent with the neuroprotective effects seen. These data support the beneficial effect of a pharmacological inhibition of C5aR1 as a promising therapeutic approach to treat Alzheimer's disease. Supportive of the safety of this treatment is the recent FDA-approval of another other C5a receptor 1 antagonist, Avacopan, as a treatment for autoimmune inflammatory diseases.

Mapping and modeling the genomic basis of differential RNA isoform expression at single-cell resolution with LR-Split-seq.

The rise in throughput and quality of long-read sequencing should allow unambiguous identification of full-length transcript isoforms. However, its application to single-cell RNA-seq has been limited by throughput and expense. Here we develop and characterize long-read Split-seq (LR-Split-seq), which uses combinatorial barcoding to sequence single cells with long reads. Applied to the C2C12 myogenic system, LR-split-seq associates isoforms to cell types with relative economy and design flexibility. We find widespread evidence of changing isoform expression during differentiation including alternative transcription start sites (TSS) and/or alternative internal exon usage. LR-Split-seq provides an affordable method for identifying cluster-specific isoforms in single cells.

An Infection-Tolerant Mammalian Reservoir for Several Zoonotic Agents Broadly Counters the Inflammatory Effects of Endotoxin.

Animals that are competent reservoirs of zoonotic pathogens commonly suffer little morbidity from the infections. To investigate mechanisms of this tolerance of infection, we used single-dose lipopolysaccharide (LPS) as an experimental model of inflammation and compared the responses of two rodents: Peromyscus leucopus, the white-footed deermouse and reservoir for the agents of Lyme disease and other zoonoses, and the house mouse Mus musculus Four hours after injection with LPS or saline, blood, spleen, and liver samples were collected and subjected to transcriptome sequencing (RNA-seq), metabolomics, and specific reverse transcriptase quantitative PCR (RT-qPCR). Differential expression analysis was at the gene, pathway, and network levels. LPS-treated deermice showed signs of sickness similar to those of exposed mice and had similar increases in corticosterone levels and expression of interleukin 6 (IL-6), tumor necrosis factor, IL-1ß, and C-reactive protein. By network analysis, the M. musculus response to LPS was characterized as cytokine associated, while the P. leucopus response was dominated by neutrophil activity terms. In addition, dichotomies in the expression levels of arginase 1 and nitric oxide synthase 2 and of IL-10 and IL-12 were consistent with type M1 macrophage responses in mice and type M2 responses in deermice. Analysis of metabolites in plasma and RNA in organs revealed species differences in tryptophan metabolism. Two genes in particular signified the different phenotypes of deermice and mice: the Slpi and Ibsp genes. Key RNA-seq findings for P. leucopus were replicated in older animals, in a systemic bacterial infection, and with cultivated fibroblasts. The findings indicate that P. leucopus possesses several adaptive traits to moderate inflammation in its balancing of infection resistance and tolerance.IMPORTANCE Animals that are natural carriers of pathogens that cause human diseases commonly manifest little or no sickness as a consequence of infection. Examples include the deermouse, Peromyscus leucopus, which is a reservoir for Lyme disease and several other disease agents in North America, and some types of bats, which are carriers of viruses with pathogenicity for humans. Mechanisms of this phenomenon of infection tolerance and entailed trade-off costs are poorly understood. Using a single injection of lipopolysaccharide (LPS) endotoxin as a proxy for infection, we found that deermice differed from the mouse (Mus musculus) in responses to LPS in several diverse pathways, including innate immunity, oxidative stress, and metabolism. Features distinguishing the deermice cumulatively would moderate downstream ill effects of LPS. Insights gained from the P. leucopus model in the laboratory have implications for studying infection tolerance in other important reservoir species, including bats and other types of wildlife.

Systematic phenotyping and characterization of the 5xFAD mouse model of Alzheimer's disease.

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, Aß 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.

Generation of a humanized Aß expressing mouse demonstrating aspects of Alzheimer's disease-like pathology.

The majority of Alzheimer's disease (AD) cases are late-onset and occur sporadically, however most mouse models of the disease harbor pathogenic mutations, rendering them better representations of familial autosomal-dominant forms of the disease. Here, we generated knock-in mice that express wildtype human Aß under control of the mouse App locus. Remarkably, changing 3 amino acids in the mouse Aß sequence to its wild-type human counterpart leads to age-dependent impairments in cognition and synaptic plasticity, brain volumetric changes, inflammatory alterations, the appearance of Periodic Acid-Schiff (PAS) granules and changes in gene expression. In addition, when exon 14 encoding the Aß sequence was flanked by loxP sites we show that Cre-mediated excision of exon 14 ablates hAß expression, rescues cognition and reduces the formation of PAS granules.

The genome of Peromyscus leucopus, natural host for Lyme disease and other emerging infections.

The rodent Peromyscus leucopus is the natural reservoir of several tick-borne infections, including Lyme disease. To expand the knowledge base for this key species in life cycles of several pathogens, we assembled and scaffolded the P. leucopus genome. The resulting assembly was 2.45 Gb in total length, with 24 chromosome-length scaffolds harboring 97% of predicted genes. RNA sequencing following infection of P. leucopus with Borreliella burgdorferi, a Lyme disease agent, shows that, unlike blood, the skin is actively responding to the infection after several weeks. P. leucopus has a high level of segregating nucleotide variation, suggesting that natural resistance alleles to Crispr gene targeting constructs are likely segregating in wild populations. The reference genome will allow for experiments aimed at elucidating the mechanisms by which this widely distributed rodent serves as natural reservoir for several infectious diseases of public health importance, potentially enabling intervention strategies.