Biology subjects

Ball, R. L.

Publications and source records attributed to Ball, R. L..

9 recordsLinked to original sources

bioRxiv2026

CellMAGE: cell-type deconvolution for multi-parent population analysis of gene expression

Single-cell RNA-sequencing remains prohibitively expensive for multiparental population (MPP) studies. Existing deconvolution methods treat bulk RNA-seq as genetically anonymous mixtures, but in MPPs, the proportional contribution of each parental strain to each progeny's transcriptome is already known. CellMAGE (Cell-type deconvolution for Multi-parent Analysis of Gene Expression) weights parental cell-type profiles by each progeny's known genetic composition, requiring no model training and no minimum sample size. Validated in 16 Diversity Outbred mice across 12 prefrontal cortex cell types and 23,116 genes, predicted and measured gene expression were statistically equivalent (0.05) in all cell types (pooled Spearman = 0.923, 95% CI: 0.910, 0.934). CIBERSORTx required 96 additional samples to resolve at most 12.4% of genes and only 3 cell types; CellMAGE outperformed it even within this restricted comparison (per-cell-type median : 0.908-0.974 vs. 0.353-0.641). CellMAGE is applicable to any MPP with parental single-cell data, including diploid crop MAGIC populations.

genomics↗

bioRxiv2026

Data-driven prioritization of mouse strains for improved preclinical modeling of rare and common disease

Choosing an appropriate mouse genetic background is a persistent challenge for successful translation of preclinical disease modeling. We present Strain Recommender, a genomic framework that prioritizes inbred mouse strains as relatively vulnerable or resilient to a disease state using disease-associated gene signatures and strain-specific transcriptome predictions. The method represents disease states as weighted gene scores, ranks 657 strains based on resemblance to the disease state, and estimates uncertainty via a permutation-derived false positive rate (FPR). In a prospective validation of connective tissue disorder predictions, vulnerable and resilient Collaborative Cross strains showed significantly different cardiovascular abnormalities. In a global retrospective validation predicting previously reported strain background effects, Strain Recommender achieved [≥] 90% sensitivity for 86.6% of diseases with 94.4% mean sensitivity (95% CI: 94.0-94.8%) across 5,890 diseases, including 92.3% (95% CI: 91.6-93.0%) for 2,598 rare diseases, demonstrating its potential to improve the validity of mouse models of human disease.

bioinformatics↗

bioRxiv2025

A shared genetic regulator of metabolism and addiction-related behavior in mice and humans

Substance use disorders and other mental health conditions often co-occur with metabolic disorders, suggesting shared biological underpinnings1. These heightened behavioral and physiological responses may have evolved to promote survival during resource scarcity but can become maladaptive in modern environments2. The genetic mechanisms linking these traits have remained elusive. Here, we show that a variable gene enhancer in mice jointly regulates genes encoding an epigenetic factor (Eed) and a mitochondrial enzyme (Me3) thereby influencing propensity to addiction-related behaviors and mitochondrial function. We further identify variation in a corresponding enhancer in humans regulating EED and ME3 associated with substance use, psychiatric and metabolic disorders. These findings reveal a convergent genetic regulatory network linking mitochondrial biology to behavioral and metabolic risk, offering insight into how genetic variation in beneficial regulatory pathways can predispose individuals to substance use disorders and related conditions.

genetics↗

bioRxiv2025

Behavioral variation across multiple phases of intravenous cocaine self-administration among genetically diverse mouse populations

Genetic and other predisposing factors can influence the progression from initiation of drug intake to compulsive substance use through distinct biobehavioral processes. Operant cocaine self-administration studies in laboratory mice offer a powerful method to dissect the biology of this progression from initiation, dose-response, extinction, and cued reinstatement in a controlled, tractable system. However, many such studies encompass limited genetic diversity and rarely examine self-administration behaviors beyond the acquisition stage. Here, we study three high-diversity mouse populations - 50 strains from the Collaborative Cross (CC) reference panel, a large sample of Diversity Outbred (J:DO) population and their eight founder strains - to characterize the varied phenotypic manifestation of behaviors across multiple phases of cocaine intravenous self-administration (IVSA) in both sexes. We observed distinct strain differences among the founders and CC strains in all phases of self-administration, with heritability estimates ranging from 0 to 0.585 and many CC and J:DO phenotypic values exceeding the range of founders including the C57BL/6J strain. Sex differences were common across behaviors, some manifesting as main effects, others as strain interactions. Finally, by adopting a multi-stage design, we identified extreme strains for various cocaine intake and response traits and evaluated whether these strains exhibited differences in behavioral assays that model compulsive drug seeking. Together, these findings demonstrate the utility of extended self-administration protocols in advanced mouse populations for discovery and characterization of biological mechanisms of substance use traits and for preclinical studies in relevant, complex mouse models.

genetics↗

bioRxiv2023

GenomeMUSter mouse genetic variation service enables multi-trait, multi-population data integration and analyses

Hundreds of inbred laboratory mouse strains and intercross populations have been used to functionalize genetic variants that contribute to disease. Thousands of disease relevant traits have been characterized in mice and made publicly available. New strains and populations including the Collaborative Cross, expanded BXD and inbred wild-derived strains add to set of complex disease mouse models, genetic mapping resources and sensitized backgrounds against which to evaluate engineered mutations. The genome sequences of many inbred strains, along with dense genotypes from others could allow integrated analysis of trait - variant associations across populations, but these analyses are not feasible due to the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense data resource by harmonizing multiple variant datasets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extensible to other model organism species. The result is a web- and programmatically-accessible data service called GenomeMUSter (https://muster.jax.org), comprising allelic data covering 657 strains at 106.8M segregating sites. Interoperation with phenotype databases, analytic tools and other resources enable a wealth of applications including multi-trait, multi-population meta-analysis. We demonstrate this in a cross-species comparison of the meta-analysis of Type 2 Diabetes and of substance use disorders, resulting in the more specific characterization of the role of human variant effects in light of mouse phenotype data. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease.

genetics↗

bioRxiv2023

The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

bioinformatics↗

bioRxiv2022

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

It is well established that epigenetic features, such as histone modifications and DNA methylation, are associated with variation in gene expression across cell types. Less well known is the extent to which epigenetic states vary across genetically diverse individuals, and whether such variation corresponds to inter-individual variation in gene expression. To investigate genetically driven variation in epigenetics, we conducted a survey of epigenetic modifications and gene expression in hepatocytes of nine inbred mouse strains. We surveyed four histone modifications (H3K4me1, H3K4me3, H3K27me3, and H3K27ac), and DNA methylation. We used ChromHMM to identify 14 chromatin states, each of which represented a distinct combination of the four histone modifications. We found that chromatin states varied widely across the nine strains and that epigenetic state was strongly correlated with local gene expression. We replicated this correspondence between chromatin state and gene expression in an independent population of Diversity Outbred mice in which we imputed local chromatin state. In contrast, we found that DNA methylation did not vary across the inbred strains and was not correlated with variation in gene expression in DO mice. This work suggests that chromatin state is highly influenced by local genotype and may be a primary mode through which expression quantitative trait loci (eQTLs) are mediated. Through examples, we demonstrate that naturally occurring chromatin state variation, in conjunction with gene expression, can aid in functional annotation of the mouse genome. Finally, we provide a data resource that documents variation in chromatin state in hepatocytes across genetically distinct mice.

genomics↗

bioRxiv2021

Genetic Analysis of Bphse: a Novel Gene Complementing Resistance to Bordetella pertussis-Induced Histamine Sensitization

AO_SCPLOWBSTRACTC_SCPLOWHistamine is a bioactive amine associated with a plethora of normal and pathophysiological processes, with the latter being dependent on both genetic and environmental factors including infectious agents. Previously, we showed in mice that susceptibility to Bordetella pertussis and pertussis toxin (PTX) induced histamine sensitization (Bphs) is controlled by histamine receptor H1 (Hrh1/HRH1) alleles. Bphs susceptible and resistant alleles (Bphss/Bphsr) encode for two-conserved protein haplotypes. Given the importance of HRH1 signaling in health and disease, we sequenced Hrh1 across an extended panel of laboratory and wild-derived inbred strains and phenotyped them for Bphs. Unexpectedly, eight strains homozygous for the Bphsr allele phenotyped as Bphss, suggesting the existence of a modifying locus segregating among the strains capable of complementing Bphsr. Genetic analyses mapped this modifier locus to mouse chromosome 6; designated Bphs-enhancer (Bphse), within a functional linkage disequilibrium domain encoding multiple loci controlling responsiveness to histamine (Bphs/Hrh1 and Histh1-4). Interval-specific single-nucleotide polymorphism (SNP) based association testing across 50 laboratory and wild-derived inbred mouse strains and functional prioritization analyses resulted in the identification of candidate genes for Bphse within a [~]5.5 Mb interval (Chr6:111.0-116.4 Mb), including Atg7, Plxnd1, Tmcc1, Mkrn2, Il17re, Pparg, Lhfpl4, Vgll4, Rho and Syn2. Taken together, these results demonstrate the power of combining network-based computational methods with the evolutionarily significant diversity of wild-derived inbred mice to identify novel genetic mechanisms controlling susceptibility and resistance to histamine shock.

genetics↗

bioRxiv2020

From Farm to Pharmacy: Strawberry-Enabled Oral Delivery of Protein Drugs

Although oral drug delivery is preferred by patients, it is not possible for proteins because the gastrointestinal tract is not sufficiently permeable. To enable the non-toxic oral uptake of protein drugs, we investigated plant-based foods as intestinal permeation enhancers, hypothesizing that compounds found in food would be well-tolerated by the gastrointestinal tract. Following a screen of over 100 fruits, vegetables, herbs, and fungi, we identified strawberry as a potent enhancer of macromolecular permeability in vitro and in mice. Natural product chemistry techniques identified pelargonidin, an anthocyanidin, as the active compound. In mice, insulin was orally administered with pelargonidin to induce sustained pharmacodynamic effects with doses as low as 1 U/kg and bioactivity of over 100% relative to the current gold standard of subcutaneous injection. Pelargonidin-induced permeability was reversible within two hours of treatment, and one month of daily dosing did not adversely affect mice as determined by weight tracking, serum concentrations of inflammatory markers, and tight junction gene expression. Results underscore the utility of plant-based foods in biomedical applications and demonstrate pelargonidin as an especially potent enhancer for the oral delivery of biologics.

bioengineering↗