Mapping the genetic landscape establishing a tumor immune microenvironment favorable for anti-PD-1 response in mice and humans.

Identifying host genetic factors modulating immune checkpoint inhibitor (ICI) efficacy has been experimentally challenging because of variations in both host and tumor genomes, differences in the microbiome, and patient life exposures. Utilizing the Collaborative Cross (CC) multi-parent mouse genetic resource population, we developed an approach that fixes the tumor genomic configuration while varying host genetics. With this approach, we discovered that response to anti-PD-1 (aPD1) immunotherapy was significantly heritable in four distinct murine tumor models ( H 2 between 0.18-0.40). For the MC38 colorectal carcinoma system ( H 2 = 0.40), we mapped four significant ICI response quantitative trait loci (QTL) localized to mouse chromosomes (mChr) 5, 9, 15 and 17, and identified significant epistatic interactions between specific QTL pairs. Differentially expressed genes within these QTL were highly enriched for immune genes and pathways mediating allograft rejection and graft vs host disease. Using a cross species analytical approach, we found a core network of 48 genes within the four QTLs that showed significant prognostic value for overall survival in aPD1 treated human cohorts that outperformed all other existing validated immunotherapy biomarkers, especially in human tumors of the previously defined immune subtype 4. Functional blockade of two top candidate immune targets within the 48 gene network, GM-CSF and high affinity IL-2/IL-15 signaling, completely abrogated the MC38 tumor transcriptional response to aPD1 therapy in vivo . Thus, we have established a powerful cross species in vivo platform capable of uncovering host genetic factors that establish the tumor immune microenvironment configuration propitious for ICI response.

Genetic context modulates aging and degeneration in the murine retina.

Background: Age is the principal risk factor for neurodegeneration in both the retina and brain. The retina and brain share many biological properties; thus, insights into retinal aging and degeneration may shed light onto similar processes in the brain. Genetic makeup strongly influences susceptibility to age-related retinal disease. However, studies investigating retinal aging have not sufficiently accounted for genetic diversity. Therefore, examining molecular aging in the retina across different genetic backgrounds will enhance our understanding of human-relevant aging and degeneration in both the retina and brain-potentially improving therapeutic approaches to these debilitating conditions. Methods: Transcriptomics and proteomics were employed to elucidate retinal aging signatures in nine genetically diverse mouse strains (C57BL/6J, 129S1/SvlmJ, NZO/HlLtJ, WSB/EiJ, CAST/EiJ, PWK/PhK, NOD/ShiLtJ, A/J, and BALB/cJ) across lifespan. These data predicted human disease-relevant changes in WSB and NZO strains. Accordingly, B6, WSB and NZO mice were subjected to human-relevant in vivo examinations at 4, 8, 12, and/or 18M, including: slit lamp, fundus imaging, optical coherence tomography, fluorescein angiography, and pattern/full-field electroretinography. Retinal morphology, vascular structure, and cell counts were assessed ex vivo. Results: We identified common molecular aging signatures across the nine mouse strains, which included genes associated with photoreceptor function and immune activation. Genetic background strongly modulated these aging signatures. Analysis of cell type-specific marker genes predicted age-related loss of photoreceptors and retinal ganglion cells (RGCs) in WSB and NZO, respectively. Fundus exams revealed retinitis pigmentosa-relevant pigmentary abnormalities in WSB retinas and diabetic retinopathy (DR)-relevant cotton wool spots and exudates in NZO retinas. Profound photoreceptor dysfunction and loss were confirmed in WSB. Molecular analyses indicated changes in photoreceptor-specific proteins prior to loss, suggesting photoreceptor-intrinsic dysfunction in WSB. In addition, age-associated RGC dysfunction, loss, and concomitant microvascular dysfunction was observed in NZO mice. Proteomic analyses revealed an early reduction in protective antioxidant processes, which may underlie increased susceptibility to DR-relevant pathology in NZO. Conclusions: Genetic context is a strong determinant of retinal aging, and our multi-omics resource can aid in understanding age-related diseases of the eye and brain. Our investigations identified and validated WSB and NZO mice as improved preclinical models relevant to common retinal neurodegenerative diseases.

Imputation of 3D genome structure by genetic-epigenetic interaction modeling in mice.

Gene expression is known to be affected by interactions between local genetic variation and DNA accessibility, with the latter organized into three-dimensional chromatin structures. Analyses of these interactions have previously been limited, obscuring their regulatory context, and the extent to which they occur throughout the genome. Here, we undertake a genome-scale analysis of these interactions in a genetically diverse population to systematically identify global genetic-epigenetic interaction, and reveal constraints imposed by chromatin structure. We establish the extent and structure of genotype-by-epigenotype interaction using embryonic stem cells derived from Diversity Outbred mice. This mouse population segregates millions of variants from eight inbred founders, enabling precision genetic mapping with extensive genotypic and phenotypic diversity. With 176 samples profiled for genotype, gene expression, and open chromatin, we used regression modeling to infer genetic-epigenetic interactions on a genome-wide scale. Our results demonstrate that statistical interactions between genetic variants and chromatin accessibility are common throughout the genome. We found that these interactions occur within the local area of the affected gene, and that this locality corresponds to topologically associated domains (TADs). The likelihood of interaction was most strongly defined by the three-dimensional (3D) domain structure rather than linear DNA sequence. We show that stable 3D genome structure is an effective tool to guide searches for regulatory elements and, conversely, that regulatory elements in genetically diverse populations provide a means to infer 3D genome structure. We confirmed this finding with CTCF ChIP-seq that revealed strain-specific binding in the inbred founder mice. In stem cells, open chromatin participating in the most significant regression models demonstrated an enrichment for developmental genes and the TAD-forming CTCF-binding complex, providing an opportunity for statistical inference of shifting TAD boundaries operating during early development. These findings provide evidence that genetic and epigenetic factors operate within the context of 3D chromatin structure.

RNA viruses, M satellites, chromosomal killer genes, and killer/nonkiller phenotypes in the 100-genomes S. cerevisiae strains.

We characterized previously identified RNA viruses (L-A, L-BC, 20S, and 23S), L-A-dependent M satellites (M1, M2, M28, and Mlus), and M satellite-dependent killer phenotypes in the Saccharomyces cerevisiae 100-genomes genetic resource population. L-BC was present in all strains, albeit in 2 distinct levels, L-BChi and L-BClo; the L-BC level is associated with the L-BC genotype. L-BChi, L-A, 20S, 23S, M1, M2, and Mlus (M28 was absent) were in fewer strains than the similarly inherited 2µ plasmid. Novel L-A-dependent phenotypes were identified. Ten M+ strains exhibited M satellite-dependent killing (K+) of at least 1 of the naturally M0 and cured M0 derivatives of the 100-genomes strains; in these M0 strains, sensitivities to K1+, K2+, and K28+ strains varied. Finally, to complement our M satellite-encoded killer toxin analysis, we assembled the chromosomal KHS1 and KHR1 killer genes and used naturally M0 and cured M0 derivatives of the 100-genomes strains to assess and characterize the chromosomal killer phenotypes.

Enhlink infers distal and context-specific enhancer-promoter linkages.

Enhancers play a crucial role in regulating gene expression and their functional status can be queried with cell type precision using using single-cell (sc)ATAC-seq. To facilitate analysis of such data, we developed Enhlink, a novel computational approach that leverages single-cell signals to infer linkages between regulatory DNA sequences, such as enhancers and promoters. Enhlink uses an ensemble strategy that integrates cell-level technical covariates to control for batch effects and biological covariates to infer robust condition-specific links and their associated p-values. It can integrate simultaneous gene expression and chromatin accessibility measurements of individual cells profiled by multi-omic experiments for increased specificity. We evaluated Enhlink using simulated and real scATAC-seq data, including those paired with physical enhancer-promoter links enumerated by promoter capture Hi-C and with multi-omic scATAC-/RNA-seq data we generated from the mouse striatum. These examples demonstrated that our method outperforms popular alternative strategies. In conjunction with eQTL analysis, Enhlink revealed a putative super-enhancer regulating key cell type-specific markers of striatal neurons. Taken together, our analyses demonstrate that Enhlink is accurate, powerful, and provides features that can lead to novel biological insights.

Genetic dissection of the pluripotent proteome through multi-omics data integration.

Genetic background drives phenotypic variability in pluripotent stem cells (PSCs). Most studies to date have used transcript abundance as the primary molecular readout of cell state in PSCs. We performed a comprehensive proteogenomics analysis of 190 genetically diverse mouse embryonic stem cell (mESC) lines. The quantitative proteome is highly variable across lines, and we identified pluripotency-associated pathways that were differentially activated in the proteomics data that were not evident in transcriptome data from the same lines. Integration of protein abundance to transcript levels and chromatin accessibility revealed broad co-variation across molecular layers as well as shared and unique drivers of quantitative variation in pluripotency-associated pathways. Quantitative trait locus (QTL) mapping localized the drivers of these multi-omic signatures to genomic hotspots. This study reveals post-transcriptional mechanisms and genetic interactions that underlie quantitative variability in the pluripotent proteome and provides a regulatory map for mESCs that can provide a basis for future mechanistic studies.

Genetic control of the pluripotency epigenome determines differentiation bias in mouse embryonic stem cells.

Genetically diverse pluripotent stem cells display varied, heritable responses to differentiation cues. Here, we harnessed these disparities through derivation of mouse embryonic stem cells from the BXD genetic reference panel, along with C57BL/6J (B6) and DBA/2J (D2) parental strains, to identify loci regulating cell state transitions. Upon transition to formative pluripotency, B6 stem cells quickly dissolved naïve networks adopting gene expression modules indicative of neuroectoderm lineages, whereas D2 retained aspects of naïve pluripotency. Spontaneous formation of embryoid bodies identified divergent differentiation where B6 showed a propensity toward neuroectoderm and D2 toward definitive endoderm. Genetic mapping identified major trans-acting loci co-regulating chromatin accessibility and gene expression in both naïve and formative pluripotency. These loci distally modulated occupancy of pluripotency factors at hundreds of regulatory elements. One trans-acting locus on Chr 12 primarily impacted chromatin accessibility in embryonic stem cells, while in epiblast-like cells, the same locus subsequently influenced expression of genes enriched for neurogenesis, suggesting early chromatin priming. These results demonstrate genetically determined biases in lineage commitment and identify major regulators of the pluripotency epigenome.

Proteomic and transcriptomic profiling reveal different aspects of aging in the kidney.

Little is known about the molecular changes that take place in the kidney during the aging process. In order to better understand these changes, we measured mRNA and protein levels in genetically diverse mice at different ages. We observed distinctive change in mRNA and protein levels as a function of age. Changes in both mRNA and protein are associated with increased immune infiltration and decreases in mitochondrial function. Proteins show a greater extent of change and reveal changes in a wide array of biological processes including unique, organ-specific features of aging in kidney. Most importantly, we observed functionally important age-related changes in protein that occur in the absence of corresponding changes in mRNA. Our findings suggest that mRNA profiling alone provides an incomplete picture of molecular aging in the kidney and that examination of changes in proteins is essential to understand aging processes that are not transcriptionally regulated.

Ex uno, plures-From One Tissue to Many Cells: A Review of Single-Cell Transcriptomics in Cardiovascular Biology.

Recent technological advances have revolutionized the study of tissue biology and garnered a greater appreciation for tissue complexity. In order to understand cardiac development, heart tissue homeostasis, and the effects of stress and injury on the cardiovascular system, it is essential to characterize the heart at high cellular resolution. Single-cell profiling provides a more precise definition of tissue composition, cell differentiation trajectories, and intercellular communication, compared to classical bulk approaches. Here, we aim to review how recent single-cell multi-omic studies have changed our understanding of cell dynamics during cardiac development, and in the healthy and diseased adult myocardium.

Natural genetic variation determines microglia heterogeneity in wild-derived mouse models of Alzheimer's disease.

Genetic and genome-wide association studies suggest a central role for microglia in Alzheimer's disease (AD). However, single-cell RNA sequencing (scRNA-seq) of microglia in mice, a key preclinical model, has shown mixed results regarding translatability to human studies. To address this, scRNA-seq of microglia from C57BL/6J (B6) and wild-derived strains (WSB/EiJ, CAST/EiJ, and PWK/PhJ) with and without APP/PS1 demonstrates that genetic diversity significantly alters features and dynamics of microglia in baseline neuroimmune functions and in response to amyloidosis. Results show significant variation in the abundance of microglial subtypes or states, including numbers of previously identified disease-associated and interferon-responding microglia, across the strains. For each subtype, significant differences in the expression of many genes are observed in wild-derived strains relative to B6, including 19 genes previously associated with human AD including Apoe, Trem2, and Sorl1. This resource is critical in the development of appropriately targeted therapeutics for AD and other neurological diseases.

Cross-Priming Dendritic Cells Exacerbate Immunopathology After Ischemic Tissue Damage in the Heart.

BACKGROUND: Ischemic heart disease is a leading cause of heart failure and despite advanced therapeutic options, morbidity and mortality rates remain high. Although acute inflammation in response to myocardial cell death has been extensively studied, subsequent adaptive immune activity and anti-heart autoimmunity may also contribute to the development of heart failure. After ischemic injury to the myocardium, dendritic cells (DC) respond to cardiomyocyte necrosis, present cardiac antigen to T cells, and potentially initiate a persistent autoimmune response against the heart. Cross-priming DC have the ability to activate both CD4+ helper and CD8+ cytotoxic T cells in response to necrotic cells and may thus be crucial players in exacerbating autoimmunity targeting the heart. This study investigates a role for cross-priming DC in post-myocardial infarction immunopathology through presentation of self-antigen from necrotic cardiac cells to cytotoxic CD8+ T cells. METHODS: We induced type 2 myocardial infarction-like ischemic injury in the heart by treatment with a single high dose of the ß-adrenergic agonist isoproterenol. We characterized the DC population in the heart and mediastinal lymph nodes and analyzed long-term cardiac immunopathology and functional decline in wild type and Clec9a-depleted mice lacking DC cross-priming function. RESULTS: A diverse DC population, including cross-priming DC, is present in the heart and activated after ischemic injury. Clec9a-/- mice deficient in DC cross-priming are protected from persistent immune-mediated myocardial damage and decline of cardiac function, likely because of dampened activation of cytotoxic CD8+ T cells. CONCLUSION: Activation of cytotoxic CD8+ T cells by cross-priming DC contributes to exacerbation of postischemic inflammatory damage of the myocardium and corresponding decline in cardiac function. Importantly, this provides novel therapeutic targets to prevent postischemic immunopathology and heart failure.

Publisher Correction: Bayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics.

An amendment to this paper has been published and can be accessed via the original article.

Naive Pluripotent Stem Cells Exhibit Phenotypic Variability that Is Driven by Genetic Variation.

Variability among pluripotent stem cell (PSC) lines is a prevailing issue that hampers not only experimental reproducibility but also large-scale applications and personalized cell-based therapy. This variability could result from epigenetic and genetic factors that influence stem cell behavior. Naive culture conditions minimize epigenetic fluctuation, potentially overcoming differences in PSC line differentiation potential. Here we derived PSCs from distinct mouse strains under naive conditions and show that lines from distinct genetic backgrounds have divergent differentiation capacity, confirming a major role for genetics in PSC phenotypic variability. This is explained in part through inconsistent activity of extra-cellular signaling, including the Wnt pathway, which is modulated by specific genetic variants. Overall, this study shows that genetic background plays a dominant role in driving phenotypic variability of PSCs.

Mapping the Effects of Genetic Variation on Chromatin State and Gene Expression Reveals Loci That Control Ground State Pluripotency.

Mouse embryonic stem cells (mESCs) cultured in the presence of LIF occupy a ground state with highly active pluripotency-associated transcriptional and epigenetic circuitry. However, ground state pluripotency in some inbred strain backgrounds is unstable in the absence of ERK1/2 and GSK3 inhibition. Using an unbiased genetic approach, we dissect the basis of this divergent response to extracellular cues by profiling gene expression and chromatin accessibility in 170 genetically heterogeneous mESCs. We map thousands of loci affecting chromatin accessibility and/or transcript abundance, including 10 QTL hotspots where genetic variation at a single locus coordinates the regulation of genes throughout the genome. For one hotspot, we identify a single enhancer variant ∼10 kb upstream of Lifr associated with chromatin accessibility and mediating a cascade of molecular events affecting pluripotency. We validate causation through reciprocal allele swaps, demonstrating the functional consequences of noncoding variation in gene regulatory networks that stabilize pluripotent states in vitro.

High-Resolution Transcriptomic Profiling of the Heart During Chronic Stress Reveals Cellular Drivers of Cardiac Fibrosis and Hypertrophy.

BACKGROUND: Cardiac fibrosis is a key antecedent to many types of cardiac dysfunction including heart failure. Physiological factors leading to cardiac fibrosis have been recognized for decades. However, the specific cellular and molecular mediators that drive cardiac fibrosis, and the relative effect of disparate cell populations on cardiac fibrosis, remain unclear. METHODS: We developed a novel cardiac single-cell transcriptomic strategy to characterize the cardiac cellulome, the network of cells that forms the heart. This method was used to profile the cardiac cellular ecosystem in response to 2 weeks of continuous administration of angiotensin II, a profibrotic stimulus that drives pathological cardiac remodeling. RESULTS: Our analysis provides a comprehensive map of the cardiac cellular landscape uncovering multiple cell populations that contribute to pathological remodeling of the extracellular matrix of the heart. Two phenotypically distinct fibroblast populations, Fibroblast-Cilp and Fibroblast-Thbs4, emerged after induction of tissue stress to promote fibrosis in the absence of smooth muscle actin-expressing myofibroblasts, a key profibrotic cell population. After angiotensin II treatment, Fibroblast-Cilp develops as the most abundant fibroblast subpopulation and the predominant fibrogenic cell type. Mapping intercellular communication networks within the heart, we identified key intercellular trophic relationships and shifts in cellular communication after angiotensin II treatment that promote the development of a profibrotic cellular microenvironment. Furthermore, the cellular responses to angiotensin II and the relative abundance of fibrogenic cells were sexually dimorphic. CONCLUSIONS: These results offer a valuable resource for exploring the cardiac cellular landscape in health and after chronic cardiovascular stress. These data provide insights into the cellular and molecular mechanisms that promote pathological remodeling of the mammalian heart, highlighting early transcriptional changes that precede chronic cardiac fibrosis.

Bayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics.

BACKGROUND: Single-cell RNA sequencing is a powerful tool for characterizing cellular heterogeneity in gene expression. However, high variability and a large number of zero counts present challenges for analysis and interpretation. There is substantial controversy over the origins and proper treatment of zeros and no consensus on whether zero-inflated count distributions are necessary or even useful. While some studies assume the existence of zero inflation due to technical artifacts and attempt to impute the missing information, other recent studies argue that there is no zero inflation in scRNA-seq data. RESULTS: We apply a Bayesian model selection approach to unambiguously demonstrate zero inflation in multiple biologically realistic scRNA-seq datasets. We show that the primary causes of zero inflation are not technical but rather biological in nature. We also demonstrate that parameter estimates from the zero-inflated negative binomial distribution are an unreliable indicator of zero inflation. CONCLUSIONS: Despite the existence of zero inflation in scRNA-seq counts, we recommend the generalized linear model with negative binomial count distribution, not zero-inflated, as a suitable reference model for scRNA-seq analysis.

Improved mouse models and advanced genetic and genomic technologies for the study of neutrophils.

Mice have been excellent surrogates for studying neutrophil biology and, furthermore, murine models of human disease have provided fundamental insights into the roles of human neutrophils in innate immunity. The emergence of novel humanized mice and high-diversity mouse populations offers the research community innovative and powerful platforms for better understanding, respectively, the mechanisms by which human neutrophils drive pathogenicity, and how genetic differences underpin the variation in neutrophil biology observed among humans. Here, we review key examples of these new resources. Additionally, we provide an overview of advanced genetic engineering tools available to further improve such murine model systems, of sophisticated neutrophil-profiling technologies, and of multifunctional nanoparticle (NP)-based neutrophil-targeting strategies.

Dynamic Interstitial Cell Response during Myocardial Infarction Predicts Resilience to Rupture in Genetically Diverse Mice.

Cardiac ischemia leads to the loss of myocardial tissue and the activation of a repair process that culminates in the formation of a scar whose structural characteristics dictate propensity to favorable healing or detrimental cardiac wall rupture. To elucidate the cellular processes underlying scar formation, here we perform unbiased single-cell mRNA sequencing of interstitial cells isolated from infarcted mouse hearts carrying a genetic tracer that labels epicardial-derived cells. Sixteen interstitial cell clusters are revealed, five of which were of epicardial origin. Focusing on stromal cells, we define 11 sub-clusters, including diverse cell states of epicardial- and endocardial-derived fibroblasts. Comparing transcript profiles from post-infarction hearts in C57BL/6J and 129S1/SvImJ inbred mice, which displays a marked divergence in the frequency of cardiac rupture, uncovers an early increase in activated myofibroblasts, enhanced collagen deposition, and persistent acute phase response in 129S1/SvImJ mouse hearts, defining a crucial time window of pathological remodeling that predicts disease outcome.

Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples.

Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript-trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative "reference" traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint subsamples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. After splitting the data set and hiding one set of anxiety-related traits in half the samples, we identified transcripts correlated with the hidden traits using the other set of anxiety-related traits and exploiting the highest canonical correlation (R = 0.69) between the trait data sets. We demonstrate that this approach outperforms TWAS in identifying associated transcripts. Together, these results demonstrate the validity, reliability, and power of reference trait analysis for identifying relations between complex traits and their molecular substrates.

Mitochondrial Genome Variation Affects Multiple Respiration and Nonrespiration Phenotypes in Saccharomyces cerevisiae.

Mitochondrial genome variation and its effects on phenotypes have been widely analyzed in higher eukaryotes but less so in the model eukaryote Saccharomyces cerevisiae Here, we describe mitochondrial genome variation in 96 diverse S. cerevisiae strains and assess associations between mitochondrial genotype and phenotypes as well as nuclear-mitochondrial epistasis. We associate sensitivity to the ATP synthase inhibitor oligomycin with SNPs in the mitochondrially encoded ATP6 gene. We describe the use of iso-nuclear F1 pairs, the mitochondrial genome equivalent of reciprocal hemizygosity analysis, to identify and analyze mitochondrial genotype-dependent phenotypes. Using iso-nuclear F1 pairs, we analyze the oligomycin phenotype-ATP6 association and find extensive nuclear-mitochondrial epistasis. Similarly, in iso-nuclear F1 pairs, we identify many additional mitochondrial genotype-dependent respiration phenotypes, for which there was no association in the 96 strains, and again find extensive nuclear-mitochondrial epistasis that likely contributes to the lack of association in the 96 strains. Finally, in iso-nuclear F1 pairs, we identify novel mitochondrial genotype-dependent nonrespiration phenotypes: resistance to cycloheximide, ketoconazole, and copper. We discuss potential mechanisms and the implications of mitochondrial genotype and of nuclear-mitochondrial epistasis effects on respiratory and nonrespiratory quantitative traits.