Brendan Ball

All Publications

• Integrated cross-species translation and biophysical multi-scale modeling links molecular signatures and locomotory phenotypes in spaceflight-induced sarcopenia. NPJ microgravity Ball, B. K., Khan, H. F., Park, J. H., Jayant, K., Chan, D. D., Brubaker, D. K. 2026

  Abstract

  Age-related skeletal muscle deterioration, referred to as sarcopenia, poses significant risks to astronaut health and mission success during spaceflight, yet its multisystem drivers remain poorly understood. While terrestrial sarcopenia manifests gradually through aging, spaceflight induces analogous musculoskeletal decline within weeks, providing an accelerated model to study conserved atrophy mechanisms. Here, we introduced an integrative framework combining cross-species genetic analysis with physiological modeling to understand mechanistic pathways in space-induced sarcopenia. By analyzing rodent and human datasets, we identified conserved molecular pathways underlying spaceflight-induced muscle atrophy, revealing shared regulators of neuromuscular signaling including pathways related to neurotransmitter release and regulation, mitochondrial function, and synaptic integration. Building upon these molecular insights, we developed a physiologically grounded central pattern generator model that reproduced spaceflight-induced locomotion deficits in mice. This multi-scale approach established mechanistic connections between transcriptional changes and impaired movement kinetics while identifying potential therapeutic targets applicable to both spaceflight and terrestrial aging-related muscle loss.

  View details for DOI 10.1038/s41526-025-00557-x

  View details for PubMedID 41526375

• Cross-Species Modeling Identifies Gene Signatures in Type 2 Diabetes Mouse Models Predictive of Inflammatory and Estrogen Signaling Pathways Associated with Alzheimer's Disease Outcomes in Humans. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Ball, B. K., Proctor, E. A., Brubaker, D. K. 2025; 30: 426-440

  Abstract

  Alzheimer's disease (AD), the predominant form of dementia, is influenced by several risk factors, including type 2 diabetes (T2D), a metabolic disorder characterized by the dysregulation of blood sugar levels. Despite mouse and human studies reporting this connection between T2D and AD, the mechanism by which T2D contributes to AD pathobiology is not well understood. A challenge in understanding mechanistic links between these conditions is that evidence between mouse and human experimental models must be synthesized, but translating between these systems is difficult due to evolutionary distance, physiological differences, and human heterogeneity. To address this, we employed a computational framework called translatable components regression (TransComp-R) to overcome discrepancies between pre-clinical and clinical studies using omics data. Here, we developed a novel extension of TransComp-R for multi-disease modeling to analyze transcriptomic data from brain samples of mouse models of AD, T2D, and simultaneous occurrence of both disease (ADxT2D) and postmortem human brain data to identify enriched pathways predictive of human AD status. Our TransComp-R model identified inflammatory and estrogen signaling pathways encoded by mouse principal components derived from models of T2D and ADxT2D, but not AD alone, predicted with human AD outcomes. The same mouse PCs predictive of human AD outcomes were able to capture sex-dependent differences in human AD biology, including significant effects unique to female patients, despite the TransComp-R being derived from data from only male mice. We demonstrated that our approach identifies biological pathways of interest at the intersection of the complex etiologies of AD and T2D which may guide future studies into pathogenesis and therapeutic development for patients with T2D-associated AD.

  View details for DOI 10.1142/9789819807024_0031

  View details for PubMedID 39670387

• Computational Translation of Mouse Models of Osteoarthritis Predicts Human Disease. Osteoarthritis and cartilage Frost, M. R., Ball, B. K., Pendyala, M., Douglas, S. R., Brubaker, D. K., Chan, D. D. 2025

  Abstract

  Translation of biological insights from preclinical studies to human disease is a pressing challenge in biomedical research, including in osteoarthritis. Translatable Components Regression (TransComp-R) is a computational framework previously used to identify biological pathways predictive of human disease conditions. We aimed to evaluate the translatability of two common murine models of post-traumatic osteoarthritis - surgical destabilization of the medial meniscus (DMM) and noninvasive anterior cruciate ligament rupture (ACLR) - to transcriptomics cartilage data from human osteoarthritis studies.Publicly available transcriptomics cartilage data from mouse models and human osteoarthritis were analyzed. TransComp-R was used to project human osteoarthritis data into either DMM or ACLR mouse model principal component analysis space. The principal components (PCs) were regressed against human osteoarthritis using increasing complexity of linear regression models incorporating human covariates of sex and age. Biological pathways of the mouse PCs that significantly stratified human osteoarthritis and control groups were then interpreted using Gene Set Enrichment Analysis.Using TransComp-R, we identified different enriched biological pathways across DMM and ACLR models. Both murine models predicted at least one human study with greater than 50% cumulative variance explained. Translatable DMM PCs revealed pathways associated with inflammation, cell signaling, and metabolism, and translatable ACLR PCs represented immune function and other cellular pathways associated with osteoarthritis.Both mouse model more successfully predicted osteoarthritis in human studies with controls without a history of joint pathology. Cross-species, covariate-aware translational approaches support the selection of preclinical models intended for therapeutic discovery and pathway analysis in humans.

  View details for DOI 10.1016/j.joca.2025.09.010

  View details for PubMedID 40976363

• Metabolites associated with type 2 diabetes and Alzheimer's disease trigger differential intracellular signaling responses in mouse primary neurons. Brain research Ball, B. K., Kuhn, M. K., Fleeman Bechtel, R. M., Proctor, E. A., Brubaker, D. K. 2025: 149819

  Abstract

  Alzheimer's disease (AD) is a progressive neurodegenerative disease that is accelerated by the pathological features of type 2 diabetes (T2D). Neuroinflammation is an extensively studied component shared by T2D and AD that remains poorly understood. In this work, we studied nine blood-brain barrier permeable metabolites with hypothesized associations of protective or harmful effects of AD and T2D in literature (aminoadipic acid, arachidonic acid, asparagine, D-sorbitol, fructose-6-phosphate, lauric acid, L-tryptophan, niacinamide, and retinol) and quantified intracellular signaling responses in primary cortical neuron monocultures. After stimulation of neuronal cultures with each metabolite, we quantified signaling analytes with a Luminex assay. Here, we leveraged univariate, multivariate, and canonical correlation analyses to understand neuronal signaling responses to metabolites with associations to AD and T2D and identified potential intracellular proteins linked to AD and T2D pathology. In particular, we identified Akt and STAT5 up-regulation by AD- and T2D-associated metabolites, whereas c-Jun and MEK1 were up-regulated by disease-protective metabolites. Finally, we canonically correlated neuronal cytokine data we previously collected from these cultures to our new intracellular signaling data, to which we found intracellular proteins associated with detrimental and protective properties linked with IL-9 and MCP-1 abundance, respectively. Our experimental and computational approach identified mechanisms for future investigation between intracellular and cytokine signaling molecules in the context of AD and T2D pathology. Nevertheless, primary neuron responses to metabolites associated with T2D and AD may contribute to neuroinflammation and progressive cognitive decline.

  View details for DOI 10.1016/j.brainres.2025.149819

  View details for PubMedID 40618869

• Translational disease modeling of peripheral blood identifies type 2 diabetes biomarkers predictive of Alzheimer's disease. NPJ systems biology and applications Ball, B. K., Park, J. H., Bergendorf, A. M., Proctor, E. A., Brubaker, D. K. 2025; 11 (1): 58

  Abstract

  Type 2 diabetes (T2D) is a significant risk factor for Alzheimer's disease (AD). Despite multiple studies reporting this connection, the mechanism by which T2D exacerbates AD is poorly understood. It is challenging to design studies that address co-occurring and comorbid diseases, limiting the number of existing evidence bases. To address this challenge, we expanded the applications of a computational framework called Translatable Components Regression (TransComp-R), initially designed for cross-species translation modeling, to perform cross-disease modeling to identify biological programs of T2D that may exacerbate AD pathology. Using TransComp-R, we combined peripheral blood-derived T2D and AD human transcriptomic data to identify T2D principal components predictive of AD status. Our model revealed genes enriched for biological pathways associated with inflammation, metabolism, and signaling pathways from T2D principal components predictive of AD. The same T2D PC predictive of AD outcomes unveiled sex-based differences across the AD datasets. We performed a gene expression correlational analysis to identify therapeutic hypotheses tailored to the T2D-AD axis. We identified six T2D and two dementia medications that induced gene expression profiles associated with a non-T2D or non-AD state. We next assessed our blood-based T2DxAD biomarker signature in post-mortem human AD and control brain gene expression data from the hippocampus, entorhinal cortex, superior frontal gyrus, and postcentral gyrus. Using partial least squares discriminant analysis, we identified a subset of genes from our cross-disease blood-based biomarker panel that significantly separated AD and control brain samples. Finally, we validated our findings using single cell RNA-sequencing blood data of AD and healthy individuals and found erythroid cells contained the most gene expression signatures to the T2D PC. Our methodological advance in cross-disease modeling identified biological programs in T2D that may predict the future onset of AD in this population. This, paired with our therapeutic gene expression correlational analysis, also revealed alogliptin, a T2D medication that may help prevent the onset of AD in T2D patients.

  View details for DOI 10.1038/s41540-025-00539-5

  View details for PubMedID 40442087

  View details for PubMedCentralID PMC12122922

• Rumenomics: Evaluation of rumen metabolites from healthy sheep identifies differentially produced metabolites across sex, age, and weight. bioRxiv : the preprint server for biology Briones, J. M., Ball, B. K., Jena, S., Lescun, T. B., Chan, D. D., Brubaker, D. K. 2025

  Abstract

  The rumen harbors a diverse and dynamic microbiome vital in digesting vegetation into metabolic byproducts for energy and general biological function. Although previous studies have reported connections between the rumen and the overall health of the sheep, the exact biological process by which this occurs is not well understood. Therefore, our study aimed to quantify sheep rumen metabolites to determine if enriched biological pathways are differentiable across phenotypic features of sex, age, and weight.We collected and quantified metabolites of rumen samples from sixteen sheep using liquid chromatography-tandem mass spectrometry. We performed a series of univariate and multivariate statistical analyses to interpret the rumen metabolomics data. To identify metabolic pathways associated with the phenotypic features of sex, weight, and age, we used MetaboAnalyst, which identified amino acid metabolism as a distinguishing factor. Among the pathways, phenylalanine metabolism emerged as a key pathway differentiating sheep based on sex and age. Additionally, phenylalanine, tyrosine, and tryptophan biosynthesis were exclusively associated with age. In univariate linear models, we also discovered that these amino acid and protein pathways were associated with weight by age-corrected effect. Finally, we identified arginine and proline biosynthesis as a pathway linked to metabolites with weight.Our study identified differential pathways based on the sex, age, and weight features of sheep. Metabolites produced by the rumen may act as an indicator for sheep health and other ruminants. These findings encourage further investigation of the differentially produced metabolites to assess overall sheep health.

  View details for DOI 10.1101/2025.02.05.636747

  View details for PubMedID 39975146

  View details for PubMedCentralID PMC11839056

• Mouse-to-human modeling of microglia single-nuclei transcriptomics identifies immune signaling pathways and potential therapeutic candidates associated with Alzheimer's disease. bioRxiv : the preprint server for biology Bergendorf, A., Park, J. H., Ball, B. K., Brubaker, D. K. 2025

  Abstract

  Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by memory loss and behavior change. Studies have found that dysregulation of microglial cells is pivotal to AD pathology. These mechanisms have been studied in mouse models to uncover potential therapeutic biomarkers. Despite these findings, there are limitations to the translatable biological information from mice to humans due to differences in physiology, timeline of disease, and the heterogeneity of humans. To address the inter-species discrepancies, we developed a novel implementation of the Translatable Components Regression (TransComp-R) framework, which integrated microglia single-nuclei mouse and human transcriptomics data to identify biological pathways in mice predictive of human AD. We compared model variations with sparse and traditional principal component analysis. We found that both dimensionality reduction techniques encoded similar AD disease biology on mouse principal components with limited differences in technical performance. Several mouse sparse principal components explained high amounts of variance in humans and significantly differentiated human AD from control microglial cells. Additionally, we identified FDA-approved medications that induced gene expression profiles correlated with projections of healthy human microglia on mouse principal components. Such medications included cabergoline, selumetinib, and palbociclib. This computational framework may support uncovering cross-species disease insights and candidate pharmacological solutions from single-cell datasets.

  View details for DOI 10.1101/2025.02.07.637100

  View details for PubMedID 39975195

  View details for PubMedCentralID PMC11839086

• Differential responses of primary neuron-secreted MCP-1 and IL-9 to type 2 diabetes and Alzheimer's disease-associated metabolites. Scientific reports Ball, B. K., Kuhn, M. K., Fleeman Bechtel, R. M., Proctor, E. A., Brubaker, D. K. 2024; 14 (1): 12743

  Abstract

  Type 2 diabetes (T2D) is implicated as a risk factor for Alzheimer's disease (AD), the most common form of dementia. In this work, we investigated neuroinflammatory responses of primary neurons to potentially circulating, blood-brain barrier (BBB) permeable metabolites associated with AD, T2D, or both. We identified nine metabolites associated with protective or detrimental properties of AD and T2D in literature (lauric acid, asparagine, fructose, arachidonic acid, aminoadipic acid, sorbitol, retinol, tryptophan, niacinamide) and stimulated primary mouse neuron cultures with each metabolite before quantifying cytokine secretion via Luminex. We employed unsupervised clustering, inferential statistics, and partial least squares discriminant analysis to identify relationships between cytokine concentration and disease-associations of metabolites. We identified MCP-1, a cytokine associated with monocyte recruitment, as differentially abundant between neurons stimulated by metabolites associated with protective and detrimental properties of AD and T2D. We also identified IL-9, a cytokine that promotes mast cell growth, to be differentially associated with T2D. Indeed, cytokines, such as MCP-1 and IL-9, released from neurons in response to BBB-permeable metabolites associated with T2D may contribute to AD development by downstream effects of neuroinflammation.

  View details for DOI 10.1038/s41598-024-62155-3

  View details for PubMedID 38830911

  View details for PubMedCentralID PMC11148169

• Multiple Particle Tracking Detects Changes in Brain Extracellular Matrix and Predicts Neurodevelopmental Age. ACS nano McKenna, M., Shackelford, D., Pontes, C., Ball, B., Nance, E. 2021; 15 (5): 8559-8573

  Abstract

  Brain extracellular matrix (ECM) structure mediates many aspects of neural development and function. Probing structural changes in brain ECM could thus provide insights into mechanisms of neurodevelopment, the loss of neural function in response to injury, and the detrimental effects of pathological aging and neurological disease. We demonstrate the ability to probe changes in brain ECM microstructure using multiple particle tracking (MPT). We performed MPT of colloidally stable polystyrene nanoparticles in organotypic rat brain slices collected from rats aged 14-70 days old. Our analysis revealed an inverse relationship between nanoparticle diffusive ability in the brain extracellular space and age. Additionally, the distribution of effective ECM pore sizes in the cortex shifted to smaller pores throughout development. We used the raw data and features extracted from nanoparticle trajectories to train a boosted decision tree capable of predicting chronological age with high accuracy. Collectively, this work demonstrates the utility of combining MPT with machine learning for measuring changes in brain ECM structure and predicting associated complex features such as chronological age. This will enable further understanding of the roles brain ECM play in development and aging and the specific mechanisms through which injuries cause aberrant neuronal function. Additionally, this approach has the potential to develop machine learning models capable of detecting the presence of injury or indicating the extent of injury based on changes in the brain microenvironment microstructure.

  View details for DOI 10.1021/acsnano.1c00394

  View details for PubMedID 33969999

  View details for PubMedCentralID PMC8281364