Variation in histone configurations correlates with gene expression across nine inbred strains of mice.

The Diversity Outbred (DO) mice and their inbred founders are widely used models of human disease. However, although the genetic diversity of these mice has been well documented, their epigenetic diversity has not. Epigenetic modifications, such as histone modifications and DNA methylation, are important regulators of gene expression and, as such, are a critical mechanistic link between genotype and phenotype. Therefore, creating a map of epigenetic modifications in the DO mice and their founders is an important step toward understanding mechanisms of gene regulation and the link to disease in this widely used resource. To this end, we performed a strain survey of epigenetic modifications in hepatocytes of the DO founders. We surveyed four histone modifications (H3K4me1, H3K4me3, H3K27me3, and H3K27ac), as well as DNA methylation. We used ChromHMM to identify 14 chromatin states, each of which represents a distinct combination of the four histone modifications. We found that the epigenetic landscape is highly variable across the DO founders and is associated with variation in gene expression across strains. We found that epigenetic state imputed into a population of DO mice recapitulated the association with gene expression seen in the founders, suggesting that both histone modifications and DNA methylation are highly heritable mechanisms of gene expression regulation. We illustrate how DO gene expression can be aligned with inbred epigenetic states to identify putative cis-regulatory regions. Finally, we provide a data resource that documents strain-specific variation in the chromatin state and DNA methylation in hepatocytes across nine widely used strains of laboratory mice.

Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice.

Mycobacterium tuberculosis infects two billion people across the globe, and results in 8-9 million new tuberculosis (TB) cases and 1-1.5 million deaths each year. Most patients have no known genetic basis that predisposes them to disease. Here, we investigate the complex genetic basis of pulmonary TB by modelling human genetic diversity with the Diversity Outbred mouse population. When infected with M. tuberculosis, one-third develop early onset, rapidly progressive, necrotizing granulomas and succumb within 60 days. The remaining develop non-necrotizing granulomas and survive longer than 60 days. Genetic mapping using immune and inflammatory mediators; and clinical, microbiological, and granuloma correlates of disease identified five new loci on mouse chromosomes 1, 2, 4, 16; and three known loci on chromosomes 3 and 17. Further, multiple positively correlated traits shared loci on chromosomes 1, 16, and 17 and had similar patterns of allele effects, suggesting these loci contain critical genetic regulators of inflammatory responses to M. tuberculosis. To narrow the list of candidate genes, we used a machine learning strategy that integrated gene expression signatures from lungs of M. tuberculosis-infected Diversity Outbred mice with gene interaction networks to generate scores representing functional relationships. The scores were used to rank candidates for each mapped trait, resulting in 11 candidate genes: Ncf2, Fam20b, S100a8, S100a9, Itgb5, Fstl1, Zbtb20, Ddr1, Ier3, Vegfa, and Zfp318. Although all candidates have roles in infection, inflammation, cell migration, extracellular matrix remodeling, or intracellular signaling, and all contain single nucleotide polymorphisms (SNPs), SNPs in only four genes (S100a8, Itgb5, Fstl1, Zfp318) are predicted to have deleterious effects on protein functions. We performed methodological and candidate validations to (i) assess biological relevance of predicted allele effects by showing that Diversity Outbred mice carrying PWK/PhJ alleles at the H-2 locus on chromosome 17 QTL have shorter survival; (ii) confirm accuracy of predicted allele effects by quantifying S100A8 protein in inbred founder strains; and (iii) infection of C57BL/6 mice deficient for the S100a8 gene. Overall, this body of work demonstrates that systems genetics using Diversity Outbred mice can identify new (and known) QTLs and functionally relevant gene candidates that may be major regulators of complex host-pathogens interactions contributing to granuloma necrosis and acute inflammation in pulmonary TB.

Differential regulation of gene expression pathways with dexamethasone and ACTH after early life seizures.

Early-life seizures (ELS) are associated with persistent cognitive deficits such as ADHD and memory impairment. These co-morbidities have a dramatic negative impact on the quality of life of patients. Therapies that improve cognitive outcomes have enormous potential to improve patients' quality of life. Our previous work in a rat flurothyl-induction model showed that administration of adrenocorticotropic hormone (ACTH) at time of seizure induction led to improved learning and memory in the animals despite no effect on seizure latency or duration. Administration of dexamethasone (Dex), a corticosteroid, did not have the same positive effect on learning and memory and has even been shown to exacerbate injury in a rat model of temporal lobe epilepsy. We hypothesized that ACTH exerted positive effects on cognitive outcomes through beneficial changes to gene expression and proposed that administration of ACTH at seizure induction would return gene-expression in the brain towards the normal pattern of expression in the Control animals whereas Dex would not. Twenty-six Sprague-Dawley rats were randomized into vehicle- Control, and ACTH-, Dex-, and vehicle- ELS. Rat pups were subjected to 60 flurothyl seizures from P5 to P14. After seizure induction, brains were removed and the hippocampus and PFC were dissected, RNA was extracted and sequenced, and differential expression analysis was performed using generalized estimating equations. Differential expression analysis showed that ACTH pushes gene expression in the brain back to a more normal state of expression through enrichment of pathways involved in supporting homeostatic balance and down-regulating pathways that might contribute to excitotoxic cell-damage post-ELS.

Fine Spike Timing in Hippocampal-Prefrontal Ensembles Predicts Poor Encoding and Underlies Behavioral Performance in Healthy and Malformed Brains.

Spatial working memory (SWM) is a central cognitive process during which the hippocampus and prefrontal cortex (PFC) encode and maintain spatial information for subsequent decision-making. This occurs in the context of ongoing computations relating to spatial position, recall of long-term memory, attention, among many others. To establish how intermittently presented information is integrated with ongoing computations we recorded single units, simultaneously in hippocampus and PFC, in control rats and those with a brain malformation during performance of an SWM task. Neurons that encode intermittent task parameters are also well modulated in time and incorporated into a functional network across regions. Neurons from animals with cortical malformation are poorly modulated in time, less likely to encode task parameters, and less likely to be integrated into a functional network. Our results implicate a model in which ongoing oscillatory coordination among neurons in the hippocampal-PFC network describes a functional network that is poised to receive sensory inputs that are then integrated and multiplexed as working memory. The background temporal modulation is systematically altered in disease, but the relationship between these dynamics and behaviorally relevant firing is maintained, thereby providing potential targets for stimulation-based therapies.

Identification of novel loci controlling inflammatory bowel disease susceptibility utilizing the genetic diversity of wild-derived mice.

Inflammatory bowel disease (IBD) is a complex disorder that imposes a growing health burden. Multiple genetic associations have been identified in IBD, but the mechanisms underlying many of these associations are poorly understood. Animal models are needed to bridge this gap, but conventional laboratory mouse strains lack the genetic diversity of human populations. To more accurately model human genetic diversity, we utilized a panel of chromosome (Chr) substitution strains, carrying chromosomes from the wild-derived and genetically divergent PWD/PhJ (PWD) strain on the commonly used C57BL/6J (B6) background, as well as their parental B6 and PWD strains. Two models of IBD were used, TNBS- and DSS-induced colitis. Compared with B6 mice, PWD mice were highly susceptible to TNBS-induced colitis, but resistant to DSS-induced colitis. Using consomic mice, we identified several PWD-derived loci that exhibited profound effects on IBD susceptibility. The most pronounced of these were loci on Chr1 and Chr2, which yielded high susceptibility in both IBD models, each acting at distinct phases of the disease. Leveraging transcriptomic data from B6 and PWD immune cells, together with a machine learning approach incorporating human IBD genetic associations, we identified lead candidate genes, including Itga4, Pip4k2a, Lcn10, Lgmn, and Gpr65.

Detection and Classification of Novel Renal Histologic Phenotypes Using Deep Neural Networks.

With the advent and increased accessibility of deep neural networks (DNNs), complex properties of histologic images can be rigorously and reproducibly quantified. We used DNN-based transfer learning to analyze histologic images of periodic acid-Schiff-stained renal sections from a cohort of mice with different genotypes. We demonstrate that DNN-based machine learning has strong generalization performance on multiple histologic image processing tasks. The neural network extracted quantitative image features and used them as classifiers to look for differences between mice of different genotypes. Excellent performance was observed at segmenting glomeruli from non-glomerular structure and subsequently predicting the genotype of the animal on the basis of glomerular quantitative image features. The DNN-based genotype classifications highly correlate with mesangial matrix expansion scored by a pathologist (R.E.C.), which differed in these animals. In addition, by analyzing non-glomeruli images, the neural network identified novel histologic features that differed by genotype, including the presence of vacuoles, nuclear count, and proximal tubule brush border integrity, which was validated with immunohistologic staining. These features were not identified in systematic pathologic examination. Our study demonstrates the power of DNNs to extract biologically relevant phenotypes and serve as a platform for discovering novel phenotypes. These results highlight the synergistic possibilities for pathologists and DNNs to radically scale up our ability to generate novel mechanistic hypotheses in disease.

Global development and adaptive behaviour in children with early-onset epilepsy: a population-based case-control study.

AIM: There are limited population-based data on global development and adaptive behaviour in children with early-onset epilepsy. The aims of this study were: (1) to identify the prevalence of deficits in global development and adaptive behaviour experienced by children with early-onset epilepsy; (2) to identify factors associated with such deficits; and (3) to compare the relationship between measures of neurodevelopment in the group with epilepsy to a group without epilepsy who had other neurological or neurodevelopmental difficulties. METHOD: The Sussex Early Epilepsy and Neurobehaviour study is a prospective, community-based study involving children (1-7y) with epilepsy. We undertook comprehensive psychological assessment with participants, including measures of global development and adaptive behaviour. We compared the children with epilepsy with a sex, age, and developmentally-matched group of children without epilepsy who had neurodevelopmental or neurological difficulties using correlation matrices. RESULTS: Forty-eight children (91% of the eligible population) with epilepsy underwent assessment. Seventy-one per cent of children displayed delayed global development (<2SD) and 56% showed significant deficits (<2SD) in adaptive behaviour. Our analysis revealed that non-white ethnicity and use of polytherapy were independently associated with decreased scores on measures of global development and adaptive behaviour. The correlations between measures of developmental functioning were higher in children with epilepsy than in those without. INTERPRETATION: Children with early-onset epilepsy frequently have difficulties with global development and adaptive behaviour. The higher correlations between neurodevelopmental measures in children with epilepsy suggest that the profile in children with epilepsy is different. This may have significant implications for both neuropathology and interventions. WHAT THIS PAPER ADDS: Children with early-onset epilepsy are at significant risk of intellectual disability. Developmental impairment is associated with use of polytherapy but not with any seizure parameters. Developmental profiles in young children with epilepsy differ from other conditions.

A multiprotein occupancy map of the mRNP on the 3' end of histone mRNAs.

The animal replication-dependent (RD) histone mRNAs are coordinately regulated with chromosome replication. The RD-histone mRNAs are the only known cellular mRNAs that are not polyadenylated. Instead, the mature transcripts end in a conserved stem-loop (SL) structure. This SL structure interacts with the stem-loop binding protein (SLBP), which is involved in all aspects of RD-histone mRNA metabolism. We used several genomic methods, including high-throughput sequencing of cross-linked immunoprecipitate (HITS-CLIP) to analyze the RNA-binding landscape of SLBP. SLBP was not bound to any RNAs other than histone mRNAs. We performed bioinformatic analyses of the HITS-CLIP data that included (i) clustering genes by sequencing read coverage using CVCA, (ii) mapping the bound RNA fragment termini, and (iii) mapping cross-linking induced mutation sites (CIMS) using CLIP-PyL software. These analyses allowed us to identify specific sites of molecular contact between SLBP and its RD-histone mRNA ligands. We performed in vitro crosslinking assays to refine the CIMS mapping and found that uracils one and three in the loop of the histone mRNA SL preferentially crosslink to SLBP, whereas uracil two in the loop preferentially crosslinks to a separate component, likely the 3'hExo. We also performed a secondary analysis of an iCLIP data set to map UPF1 occupancy across the RD-histone mRNAs and found that UPF1 is bound adjacent to the SLBP-binding site. Multiple proteins likely bind the 3' end of RD-histone mRNAs together with SLBP.

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning.

The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species-humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/.

Systems level analysis of systemic sclerosis shows a network of immune and profibrotic pathways connected with genetic polymorphisms.

Systemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited) are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient's subset assignment is stable over 6-12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-gene interaction network, and place the genetic risk loci in the context of the intrinsic subsets. To identify gene expression modules common to three independent datasets from three different clinical centers, we developed a consensus clustering procedure based on mutual information of partitions, an information theory concept, and performed a meta-analysis of these genome-wide gene expression datasets. We created a gene-gene interaction network of the conserved molecular features across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms. The network is composed of distinct, but interconnected, components related to interferon activation, M2 macrophages, adaptive immunity, extracellular matrix remodeling, and cell proliferation. The network shows extensive connections between the inflammatory- and fibroproliferative-specific genes. The network also shows connections between these subset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, and several human leukocyte antigen (HLA) genes. Our analyses suggest that the gene expression changes underlying the SSc subsets may be long-lived, but mechanistically interconnected and related to a patients underlying genetic risk.

Disrupted Hippocampal-Prefrontal Networks in a Rat Model of Fragile X Syndrome: A Study Linking Neural Dynamics to Autism-Like Behavioral Impairments.

Fragile X Syndrome (FXS) is associated with autism spectrum disorder (ASD) symptoms that are associated with cognitive, learning, and behavioral challenges. We investigated how known molecular disruptions in the Fmr1 knockout (FMR-KO) rat model of FXS negatively impact hippocampal-prefrontal cortex (H-PFC) neural network activity and consequent behavior. Methods: FMR-KO and control rats underwent a battery of behavioral tests assessing sociability, memory, and anxiety. Single-unit electrophysiology recordings were then conducted to measure patterns of neural activity in H-PFC circuit. Advanced mathematical models were used to characterize the patterns that were then compared between groups using generalized linear mixed models. Results: FMR-KO rats demonstrated significant behavioral deficits in sociability, spatial learning, and anxiety, aligning with symptoms of ASD. At the neural level, these rats exhibited abnormal firing patterns in the H-PFC circuit that is critical for learning, memory, and social behavior. The neural networks in FMR-KO rats were also less densely connected and more fragmented, particularly in hippocampal-PFC correlated firing. These findings suggest that disruptions in neural network dynamics underlie the observed behavioral impairments in FMR-KO rats. Conclusion: FMR-KO significantly disrupts several characteristics of action potential firing in the H-PFC network, leading to deficits in social behavior, memory, and anxiety, as seen in FXS. This disruption is characterized by less organized and less resilient hippocampal-PFC networks. These findings suggest that therapeutic strategies aimed at normalizing neural dynamics, such as with brain stimulation, could potentially improve behavior and cognitive functions in autistic individuals. HIGHLIGHTS: Fragile X Syndrome is associated with autism, cognitive challenges and anxietyThe loss of Fmr1 protein disrupts processes involved in building neural networksThe consequence is abnormal neural dynamics in hippocampal-prefrontal cortex networksNormalization of dynamics could improve outcomes in FXS and ASD.

Causal network perturbation analysis identifies known and novel type-2 diabetes driver genes.

The molecular pathogenesis of diabetes is multifactorial, involving genetic predisposition and environmental factors that are not yet fully understood. However, pancreatic ß-cell failure remains among the primary reasons underlying the progression of type-2 diabetes (T2D) making targeting ß-cell dysfunction an attractive pathway for diabetes treatment. To identify genetic contributors to ß-cell dysfunction, we investigated single-cell gene expression changes in ß-cells from healthy (C57BL/6J) and diabetic (NZO/HlLtJ) mice fed with normal or high-fat, high-sugar diet (HFHS). Our study presents an innovative integration of the causal network perturbation assessment (ssNPA) framework with meta-cell transcriptome analysis to explore the genetic underpinnings of type-2 diabetes (T2D). By generating a reference causal network and in silico perturbation, we identified novel genes implicated in T2D and validated our candidates using the Knockout Mouse Phenotyping (KOMP) Project database.

Visual detection of seizures in mice using supervised machine learning.

Seizures are caused by abnormally synchronous brain activity that can result in changes in muscle tone, such as twitching, stiffness, limpness, or rhythmic jerking. These behavioral manifestations are clear on visual inspection and the most widely used seizure scoring systems in preclinical models, such as the Racine scale in rodents, use these behavioral patterns in semiquantitative seizure intensity scores. However, visual inspection is time-consuming, low-throughput, and partially subjective, and there is a need for rigorously quantitative approaches that are scalable. In this study, we used supervised machine learning approaches to develop automated classifiers to predict seizure severity directly from noninvasive video data. Using the PTZ-induced seizure model in mice, we trained video-only classifiers to predict ictal events, combined these events to predict an univariate seizure intensity for a recording session, as well as time-varying seizure intensity scores. Our results show, for the first time, that seizure events and overall intensity can be rigorously quantified directly from overhead video of mice in a standard open field using supervised approaches. These results enable high-throughput, noninvasive, and standardized seizure scoring for downstream applications such as neurogenetics and therapeutic discovery.

Inferring single-cell spatial gene expression with tissue morphology via explainable deep learning.

The spatial arrangement of cells is vital in developmental processes and organogenesis in multicellular life forms. Deep learning models trained with spatial omics data uncover complex patterns and relationships among cells, genes, and proteins in a high-dimensional space, providing new insights into biological processes and diseases. State-of-the-art in silico spatial multi-cell gene expression methods using histological images of tissue stained with hematoxylin and eosin (H&E) to characterize cellular heterogeneity. These computational techniques offer the advantage of analyzing vast amounts of spatial data in a scalable and automated manner, thereby accelerating scientific discovery and enabling more precise medical diagnostics and treatments. In this work, we developed a vision transformer (ViT) framework to map histological signatures to spatial single-cell transcriptomic signatures, named SPiRiT ( S patial Omics P rediction and R eproducibility integrated T ransformer). Our framework was enhanced by integrating cross validation with model interpretation during hyper-parameter tuning. SPiRiT predicts single-cell spatial gene expression using the matched histopathological image tiles of human breast cancer and whole mouse pup, evaluated by Xenium (10x Genomics) datasets. Furthermore, ViT model interpretation reveals the high-resolution, high attention area (HAR) that the ViT model uses to predict the gene expression, including marker genes for invasive cancer cells ( FASN ), stromal cells ( POSTN ), and lymphocytes ( IL7R ). In an apple-to-apple comparison with the ST-Net Convolutional Neural Network algorithm, SPiRiT improved predictive accuracy by 40% using human breast cancer Visium (10x Genomics) dataset. Cancer biomarker gene prediction and expression level are highly consistent with the tumor region annotation. In summary, our work highlights the feasibility to infer spatial single-cell gene expression using tissue morphology in multiple-species, i.e., human and mouse, and multi-organs, i.e., mouse whole body morphology. Importantly, incorporating model interpretation and vision transformer is expected to serve as a general-purpose framework for spatial transcriptomics.

A machine learning approach for quantifying age-related histological changes in the mouse kidney.

The ability to quantify aging-related changes in histological samples is important, as it allows for evaluation of interventions intended to effect health span. We used a machine learning architecture that can be trained to detect and quantify these changes in the mouse kidney. Using additional held out data, we show validation of our model, correlation with scores given by pathologists using the Geropathology Research Network aging grading scheme, and its application in providing reproducible and quantifiable age scores for histological samples. Aging quantification also provides the insights into possible changes in image appearance that are independent of specific geropathology-specified lesions. Furthermore, we provide trained classifiers for H&E-stained slides, as well as tutorials on how to use these and how to create additional classifiers for other histological stains and tissues using our architecture. This architecture and combined resources allow for the high throughput quantification of mouse aging studies in general and specifically applicable to kidney tissues.

Network-based analysis predicts interacting genetic modifiers from a meta-mapping study of spike-wave discharge in mice.

Absence seizures are characterized by brief lapses in awareness accompanied by a hallmark spike-and-wave discharge (SWD) electroencephalographic pattern and are common to genetic generalized epilepsies (GGEs). While numerous genes have been associated with increased risk, including some Mendelian forms with a single causal allele, most cases of GGE are idiopathic and there are many unknown genetic modifiers of GGE influencing risk and severity. In a previous meta-mapping study, crosses between transgenic C57BL/6 and C3HeB/FeJ strains, each carrying one of three SWD-causing mutations (Gabrg2tm1Spet(R43Q) , Scn8a8j or Gria4spkw1 ), demonstrated an antagonistic epistatic interaction between loci on mouse chromosomes 2 and 7 influencing SWD. These results implicate universal modifiers in the B6 background that mitigate SWD severity through a common pathway, independent of the causal mutation. In this study, we prioritized candidate modifiers in these interacting loci. Our approach integrated human genome-wide association results with gene interaction networks and mouse brain gene expression to prioritize candidate genes and pathways driving variation in SWD outcomes. We considered candidate genes that are functionally associated with human GGE risk genes and genes with evidence for coding or non-coding allele effects between the B6 and C3H backgrounds. Our analyses output a summary ranking of gene pairs, one gene from each locus, as candidates for explaining the epistatic interaction. Our top-ranking gene pairs implicate microtubule function, cytoskeletal stability and cell cycle regulation as novel hypotheses about the source of SWD variation across strain backgrounds, which could clarify underlying mechanisms driving differences in GGE severity in humans.

Analysis of CNS autoimmunity in genetically diverse mice reveals unique phenotypes and mechanisms.

Multiple sclerosis (MS) is a complex disease with significant heterogeneity in disease course and progression. Genetic studies have identified numerous loci associated with MS risk, but the genetic basis of disease progression remains elusive. To address this, we leveraged the Collaborative Cross (CC), a genetically diverse mouse strain panel, and experimental autoimmune encephalomyelitis (EAE). The thirty-two CC strains studied captured a wide spectrum of EAE severity, trajectory, and presentation, including severe-progressive, monophasic, relapsing remitting, and axial rotary (AR)-EAE, accompanied by distinct immunopathology. Sex differences in EAE severity were observed in six strains. Quantitative trait locus analysis revealed distinct genetic linkage patterns for different EAE phenotypes, including EAE severity and incidence of AR-EAE. Machine learning-based approaches prioritized candidate genes for loci underlying EAE severity (Abcc4 and Gpc6) and AR-EAE (Yap1 and Dync2h1). This work expands the EAE phenotypic repertoire and identifies novel loci controlling unique EAE phenotypes, supporting the hypothesis that heterogeneity in MS disease course is driven by genetic variation.

Probing the basis of disease heterogeneity in multiple sclerosis using genetically diverse mice.

Multiple sclerosis (MS) is a complex disease with significant heterogeneity in disease course and progression. Genetic studies have identified numerous loci associated with MS risk, but the genetic basis of disease progression remains elusive. To address this, we leveraged the Collaborative Cross (CC), a genetically diverse mouse strain panel, and experimental autoimmune encephalomyelitis (EAE). The thirty-two CC strains studied captured a wide spectrum of EAE severity, trajectory, and presentation, including severe-progressive, monophasic, relapsing remitting, and axial rotary (AR)-EAE, accompanied by distinct immunopathology. Sex differences in EAE severity were observed in six strains. Quantitative trait locus analysis revealed distinct genetic linkage patterns for different EAE phenotypes, including EAE severity and incidence of AR-EAE. Machine learning-based approaches prioritized candidate genes for loci underlying EAE severity ( Abcc4 and Gpc6 ) and AR-EAE ( Yap1 and Dync2h1 ). This work expands the EAE phenotypic repertoire and identifies novel loci controlling unique EAE phenotypes, supporting the hypothesis that heterogeneity in MS disease course is driven by genetic variation. Summary: The genetic basis of disease heterogeneity in multiple sclerosis (MS) remains elusive. We leveraged the Collaborative Cross to expand the phenotypic repertoire of the experimental autoimmune encephalomyelitis (EAE) model of MS and identify loci controlling EAE severity, trajectory, and presentation.

Transcripts with high distal heritability mediate genetic effects on complex metabolic traits.

Although many genes are subject to local regulation, recent evidence suggests that complex distal regulation may be more important in mediating phenotypic variability. To assess the role of distal gene regulation in complex traits, we combined multi-tissue transcriptomes with physiological outcomes to model diet-induced obesity and metabolic disease in a population of Diversity Outbred mice. Using a novel high-dimensional mediation analysis, we identified a composite transcriptome signature that summarized genetic effects on gene expression and explained 30% of the variation across all metabolic traits. The signature was heritable, interpretable in biological terms, and predicted obesity status from gene expression in an independently derived mouse cohort and multiple human studies. Transcripts contributing most strongly to this composite mediator frequently had complex, distal regulation distributed throughout the genome. These results suggest that trait-relevant variation in transcription is largely distally regulated, but is nonetheless identifiable, interpretable, and translatable across species.

Functional network changes in hippocampal CA1 after status epilepticus predict spatial memory deficits in rats.

Status epilepticus (SE) is a common neurological emergency, which has been associated with subsequent cognitive impairments. Neuronal death in hippocampal CA1 is thought to be an important mechanism of these impairments. However, it is also possible that functional interactions between surviving neurons are important. In this study we recorded in vivo single-unit activity in the CA1 hippocampal region of rats while they performed a spatial memory task. From these data we constructed functional networks describing pyramidal cell interactions. To build the networks, we used maximum entropy algorithms previously applied only to in vitro data. We show that several months following SE pyramidal neurons display excessive neuronal synchrony and less neuronal reactivation during rest compared with those in healthy controls. Both effects predict rat performance in a spatial memory task. These results provide a physiological mechanism for SE-induced cognitive impairment and highlight the importance of the systems-level perspective in investigating spatial cognition.