Biology subjects

Ball, B. K.

Publications and source records attributed to Ball, B. K..

10 recordsLinked to original sources

bioRxiv2026

Mouse lymph node colonization models predict human tumor metastasis in melanoma

Aggressive tumor progression and distant metastases of cancers such as cutaneous melanoma are thought to be preceded by regional lymph node (LN) infiltration. Previous mouse studies have selectively enriched for tumor cells with enhanced LN migratory capacity but the mechanistic biology underpinning this proclivity has not been directly linked to human outcomes. Likewise, the exact role LN colonization plays in distant metastasis in humans remains unclear. To translate and conserve findings from mouse models to humans, we used a computational framework termed Translatable Components Regression (TransComp-R) to integrate mouse LN tumor samples with human primary and metastatic tumors. We identified transcriptional programs of early- and late-stage mouse tumor generations that stratified primary and metastatic tumors from patients with melanoma. These transcriptional programs associated with immune function, cell cycle checkpoints, and DNA-to-protein biosynthesis pathways. We then employed EcoTyper and identified six distinct melanoma-specific cellular communities (ecotypes) comprising co-occurring cell states. Of these six melanoma ecotypes, we identified a community strongly associated with primary tumor cells, while the other five represented a spectrum of metastatic tumor microenvironments with increased adverse outcomes. These ecotypes and their significantly expressed genes may contribute to the tumor-immune microenvironment that facilitates LN colonization in melanoma. We further validated the physical co-localization of these melanoma ecotypes using spatial transcriptomics. Our integration of pre-clinical models with human data reveals potential biological pathways and transcriptomic signatures for future investigation and supports the utility of mouse models in studying cancer metastasis.

systems biology↗

bioRxiv2026

Proteomic analysis reveals shared biological pathways linking acrolein to biomolecular changes in the acute phase of rat spinal cord injury

Spinal cord injury (SCI) pathology is highly difficult to treat due to substantial heterogeneity in injury presentation and spread, along with unclear mechanisms linking damage to pathology. Damages from injury forces (primary injury) are exacerbated by a series of biochemical events that follow the initial damage and injure additional tissue, known as secondary injury. Reactive aldehydes, such as acrolein, play a key role in propagating secondary injury cascades following SCI. Targeting acrolein after SCI has demonstrated therapeutic potential in limiting injury spread and pathology. However, injury mechanisms linking reactive aldehydes to SCI outcome have not been fully characterized. To gain a more comprehensive understanding of the cellular and molecular mechanisms underlying SCI, we generated proteomic profiles of rat spinal cords 24 h (acute phase) after subjection to SCI, sham injury, saline injection, or acrolein injection. We performed gene set enrichment analysis (GSEA) to characterize proteins and pathways significantly enriched after SCI and acrolein-injection. We then used Translatable Components Regression (TransComp-R), a framework for translating biological signatures across systems, to assess whether acrolein-associated spinal cord signatures can stratify SCI from sham outcomes. Our proteomics analysis revealed 467 differentially expressed proteins (DEPs) between the sham and SCI groups and 7 DEPs between saline and acrolein injection groups. Notably, the complement and coagulation cascades were upregulated in spinal cords subjected to SCI and acrolein injection. Our TransComp-R analysis further demonstrated that acrolein-associated signatures could distinguish SCI from sham conditions. Taken together, our findings suggest that acrolein induces proteomic alterations during the acute phase of SCI and is associated with complement and coagulation cascade activation, among other pathways. Therefore, this study reinforces the notion that understanding the role of acrolein in the acute phase of secondary SCI may be beneficial.

neuroscience↗

bioRxiv2025

An integrative framework linking molecular signatures and locomotory phenotypes in space-induced sarcopenia

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 microgravity-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.

systems biology↗

bioRxiv2025

Metabolites associated with type 2 diabetes and Alzheimer's disease trigger differential intracellular signaling responses in mouse primary neurons

Alzheimers 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 associated with 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. Using univariate and multivariate analysis approaches, we identified potential intracellular signaling pathways linked to AD and T2D pathology. With partial least squares discriminant analysis, we identified the separation between the disease and protective-associated metabolites. 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 performed a canonical correlation analysis to link 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 that correlated with IL-9 and MCP-1, respectively. Our experimental and computational approach identified potential associations 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.

systems biology↗

bioRxiv2025

Computational Translation of Mouse Models of Osteoarthritis Predicts Human Disease

ObjectiveTranslation 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 that has previously been used to synthesize preclinical and human OA data 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 OA outcomes. DesignTranscriptomics cartilage data of DMM and ACLR mouse and human data was acquired from Gene Expression Omnibus. TransComp-R was used to project human OA data into a mouse model (DMM or ACLR) principal component analysis space. The principal components (PCs) were regressed against human OA conditions using increasing complexity of linear regression models incorporating human demographic covariates of OA, sex, and age. Biological pathways of the mouse PCs that significantly stratified human OA and control groups were then interpreted using Gene Set Enrichment Analysis. ResultsFrom the TransComp-R model, we identified different enriched biological pathways across DMM and ACLR models. While PCs among the DMM models revealed pathways associated with cell signaling and metabolism, ACLR PCs represented immune function and cellular pathways associated with OA condition. The immune pathways presented in the ACLR further highlighted the potential relevance of the OA pathways observed in human conditions. ConclusionsThe ACLR mouse model more successfully predicted human OA conditions, particularly with the human control groups without a history of joint injury or disease. Cross-species translational approaches support the selection of preclinical models intended for therapeutic discovery and pathway analysis in humans.

systems biology↗

bioRxiv2025

Rumenomics: Evaluation of rumen metabolites from healthy sheep identifies differentially produced metabolites across sex, age, and weight

BackgroundThe 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. ResultsWe 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. ConclusionOur 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.

systems biology↗

bioRxiv2025

Mouse-to-human modeling of microglia single-nuclei transcriptomics identifies immune signaling pathways and potential therapeutic candidates associated with Alzheimer's disease

Alzheimers 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.

systems biology↗

bioRxiv2024

Cross-disease modeling of peripheral blood identifies biomarkers of type 2 diabetes predictive of Alzheimer's disease

Type 2 diabetes (T2D) is a significant risk factor for Alzheimers 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. Finally, we 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. 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.

systems biology↗

bioRxiv2023

Differential responses of primary neuron-secreted MCP-1 and IL-9 to type 2 diabetes and Alzheimer's disease-associated metabolites

Type 2 diabetes (T2D) is implicated as a risk factor for Alzheimers 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.

systems biology↗

bioRxiv2020

Multiple particle tracking detects changes in brain extracellular matrix structure and predicts neurodevelopmental age

Abstract SummaryBrain extracellular matrix (ECM) structure mediates many aspects of neuronal function. Probing changes in ECM structure could provide insights into aging and neurological disease. Herein, we demonstrate the ability to characterize changes in brain ECM structure using multiple particle tracking (MPT). MPT was carried out in organotypic rat brain slices to detect induced and naturally occurring changes in ECM structure. Induced degradation of neural ECM led to a significant increase in nanoparticle diffusive ability in the brain extracellular space. For structural changes that occur naturally during development, an inverse relationship existed between age and nanoparticle diffusion. Using the age-dependent dataset, we applied extreme gradient boosting (XGBoost) to generate models capable of classifying nanoparticle trajectories. Collectively, this work demonstrates the utility of MPT combined with machine learning for measuring changes in brain ECM structure and predicting associated complex features such as developmental age.

developmental biology↗