Brendan Ball

All Publications

• 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

• 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

• 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