Biology subjects

Brubaker, D. K.

Publications and source records attributed to Brubaker, D. K..

17 recordsLinked to original sources

bioRxiv2026

Cross-Species Translation Enhances the Use of Mouse Models for Translatability and Drug Discovery in Late-Onset Alzheimer's Disease

Alzheimers disease (AD) is a brain disease characterized by deposition of insoluble amyloid-{beta} plaque, intraneuronal neurofibrillary tangles, and cognitive dysfunction. AD can be characterized as early-onset or late-onset based on age and genetic factors. For early-onset, these genetic factors can include amyloid precursor protein (APP), presenilin-1 (PSEN1), and presenilin-2 (PSEN2). For late-onset, these can include apolipoprotein E e4 (APOE4), and the R47H variant of triggering receptor expressed on myeloid cells 2 (TREM2). Mouse models incorporating these risk factors provide critical knowledge for studying AD pathology and preclinical studies for drug development. However, these transgenic mice depend on early-onset genetic mutations and are deficient in certain AD features that are present in late-onset. Here, we developed innovative non-linear and feature selection procedures for our cross-species translation framework, Translatable Components Regression (TransComp-R), to identify transcriptomic features in mouse models predictive of human late-onset AD pathobiology. We used the cross-species computational translatability links of TransComp-R to perform computational high-throughput drug screening and identified multiple repurposable drugs for AD treatment that targeted the sleep-wake cycle. We tested these predictions in an orthogonal, prospective cohort of human subjects treated with an orexin receptor antagonist, suvorexant. We correlated conserved protein-level biomarkers from our cross-species transcriptomics model with significant reductions in phosphorylated tau in cerebrospinal fluid collected from humans treated with suvorexant. This study demonstrates the power of computational methods like TransComp-R to enhance the utility of murine disease models for discovering new therapeutic approaches for AD. One Sentence SummaryCross-species translation modeling across different mouse models reveals sleep-relevant drug mechanisms as potentially therapeutic for Alzheimers disease.

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

Personalized Gut-Liver Microphysiological System Maps Donor-Specific Tissue Resident Immunity and Reveals a Conserved Metabolic Crosstalk

Tissue-resident immune (TRI) niches are unique to tissues and greatly vary between individuals. We built a personalized gut-liver microphysiological system (MPS) to recapitulate these profiles, combining primary colon epithelium, hepatocytes, and autologous CD45 TRI cells of two donors. Single-cell RNA-seq of colon and liver revealed distinct TRI profiles and predicted responses distinct between donors. Co-culture established organ and donor-specific immune programs: colonic epithelium induced Th1/Th17 polarization in Donor 1 but B cell differentiation in Donor 2. Gut-liver crosstalk in all donors converged on a retinoid-bile acid metabolic axis with a muted inflammatory set-point, indicating that circulating metabolites can override baseline immune differences. Microbial agonist challenges of gut compartments revealed distinct liver responses: Poly(I:C) induced a uniform type-I/III interferon burst, LPS triggered a stronger response in Donor 1, and 5-OP-RU selectively activated Donor 2. Our personalized, immune-competent gut-liver MPS demonstrates that a conserved metabolic dialogue coexists with and is modulated by TRI profiles. This work provides a blueprint for exploring immunometabolic diseases and precision therapeutics in multi-organ models reflecting human immune diversity.

bioengineering↗

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↗

bioRxiv2024

Computational Microbiome Pharmacology Analysis Elucidates the Anti-Cancer Potential of Vaginal Microbes and Metabolites

The vaginal microbiomes role in risk, progression, and treatment of female cancers has been widely explored. Yet, there remains a need to develop methods to understand the interaction of microbiome factors with host cells and to characterize their potential therapeutic functions. To address this challenge, we developed a systems biology framework we term the Pharmacobiome for microbiome pharmacology analysis. The Pharmacobiome framework evaluates similarities between microbes and microbial byproducts and known drugs based on their impact on host transcriptomic cellular signatures. Here, we apply our framework to characterization of the Anti-Gynecologic Cancer Vaginal Pharmacobiome. Using published vaginal microbiome multi-omics data from the Partners PrEP clinical trial, we constructed vaginal epithelial gene signatures associated with each profiled vaginal microbe and metabolite. We compared these microbiome-associated host gene signatures to post-drug perturbation host gene signatures associated with 35 FDA-approved anti-cancer drugs from the Library of Integrated Network-based Cellular Signatures database to identify vaginal microbes and metabolites with high statistical and functional similarity to these drugs. We found that Lactobacilli and their metabolites can regulate host gene expression in ways similar to many anti-cancer drugs. Additionally, we experimentally tested our model prediction that taurine, a metabolite produced by L. crispatus, kills cancerous breast and endometrial cancer cells. Our study shows that the Pharmacobiome is a powerful framework for characterizing the anti-cancer therapeutic potential of vaginal microbiome factors with generalizability to other cancers, microbiomes, and diseases.

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↗

bioRxiv2023

Enhanced Annotation of CD45RA to Distinguish T cell Subsets in Single Cell RNA-seq via Machine Learning

T cell heterogeneity presents a challenge for accurate cell identification, understanding their inherent plasticity, and characterizing their critical role in adaptive immunity. Immunologists have traditionally employed techniques such as flow cytometry to identify T cell subtypes based on a well-established set of surface protein markers. With the advent of single-cell RNA sequencing (scRNA-seq), researchers can now investigate the gene expression profiles of these surface proteins at the single-cell level. The insights gleaned from these profiles offer valuable clues and a deeper understanding of cell identity. However, CD45RA, the isoform of CD45 which distinguish between naive/central memory T cells and effector memory/effector memory cells re-expressing CD45RA T cells, cannot be well profiled by scRNA-seq due to the difficulty in mapping short reads to genes. In order to facilitate cell type annotation in T cell scRNA-seq analysis, we employed machine learning and trained a CD45RA+/- classifier on single-cell mRNA count data annotated with known CD45RA antibody levels provided by cellular indexing of transcriptomes and epitopes sequencing (CITE-seq) data. Among all algorithms we tested, the trained support vector machine (SVM) with a radial basis function (RBF) kernel with optimized hyperparameters achieved a 99.96% accuracy on an unseen dataset. The multilayer Perceptron (MLP) classifier, the second most predictive method overall, also achieved a decent accuracy of 99.74%. Our simple yet robust machine learning approach provides a valid inference on the CD45RA level, assisting the cell identity annotation and further exploring the heterogeneity within human T cells.

bioinformatics↗

bioRxiv2022

Latent Interacting Variable-Effects Modeling of Gut Microbiome Multi-Omics in Inflammatory Bowel Disease

Latent Interacting Variable Effects (LIVE) modeling is a framework to integrate different types of microbiome multi-omics data by combining latent variables from single-omic models into a structured meta-model to determine discriminative, interacting multi-omics features driving disease status. We implemented and tested LIVE modeling in publicly available metagenomics and metabolomics datasets from Crohns Disease and Ulcerative Colitis patients. Here, LIVE modeling reduced the number of feature correlations from the original data set for CD and UC to tractable numbers and facilitated prioritization of biological associations between microbes, metabolites, enzymes and IBD status through the application of stringent thresholds on generated inferential statistics. We determined LIVE modeling confirmed previously reported IBD biomarkers and uncovered potentially novel disease mechanisms in IBD. LIVE modeling makes a distinct and complementary contribution to the current methods to integrate microbiome data to predict IBD status because of its flexibility to adapt to different types of microbiome multi-omics data, scalability for large and small cohort studies via reliance on latent variables and dimensionality reduction, and the intuitive interpretability of the linear meta-model integrating -omic data types. The results of LIVE modeling and the biological relationships can be represented in networks that connect local correlation structure of single omic data types with global community and omic structure in the latent variable VIP scores. This model arises as novel tool that allows researchers to be more selective about omic feature interaction without disrupting the structural correlation framework provided by sPLS-DA interaction effects modeling. It will lead to form testable hypothesis by identifying potential and unique interactions between metabolome and microbiome that must be considered for future studies. AUTHOR SUMMARYLatent Interacting Variable Effects (LIVE) modeling integrates microbiome multiomics features by encoding them in a set of latent variables (LVs) from single-omic sparse Partial Lease Squares models, and then combine these LVs into structured metamodel to determine the most discriminative features driving IBD. We used publicly available metagenomic and metabolomics data from Crohns Disease and Ulcerative Colitis patients to develop LIVE modeling. LIVE modeling reduced data dimensionality efficiently and identified statistical interactions among microbiome multi-omics data, which can be visualized as a mineable network data structure. LIVE modeling confirmed features previously reported and revealed novel microbiome interactions in IBD. LIVE offers a flexible framework for multi-omic modeling that may aid in interpretation of complex microbiome datasets.

systems biology↗

bioRxiv2022

Endogenous production of hyaluronan, PRG4, and cytokines is sensitive to cyclic loading in synoviocytes

Synovial fluid is composed of hyaluronan and proteoglycan-4 (PRG4 or lubricin), which work synergistically to maintain joint lubrication. In diseases like osteoarthritis, hyaluronan and PRG4 concentrations can be altered, resulting in lowered synovial fluid viscosity, and pro-inflammatory cytokine concentrations within the synovial fluid increase. Synovial fibroblasts within the synovium are responsible for contributing to synovial fluid and can be targeted to improve endogenous production of hyaluronan and PRG4 and to alter the cytokine profile. We cyclically loaded SW982 synoviocytes to 0%, 5%, 10%, or 20% strain for three hours at 1 Hz. To assess the impact of substrate stiffness, we compared the 0% strain group to cells grown on tissue culture plastic. We measured the expression of hyaluronan turnover genes, hyaluronan localization within the cell layer, hyaluronan concentration, PRG4 concentration, and the cytokine profile within the media. Our results show that the addition of cyclic loading increased HAS3 expression, but not in a magnitude-dependent response. Hyaluronidase expression was impacted by strain magnitude, which is exemplified by the decrease in hyaluronan concentration due to cyclic loading. We also show that PRG4 concentration is increased at 5% strain, while higher strain magnitude decreases overall PRG4 concentration. Finally, 10% and 20% strain show a distinct, more pro-inflammatory cytokine profile when compared to the unloaded group. Multivariate analysis showed distinct separation between certain strain groups in being able to predict strain group, hyaluronan concentration, and PRG4 concentration from gene expression or cytokine concentration data, highlighting the complexity of the system. Overall, this study shows that cyclic loading can be used tool to modulate the endogenous production of hyaluronan, PRG4, and cytokines from synovial fibroblasts.

bioengineering↗

bioRxiv2019

An Inter-Species Translation Model Implicates Integrin Signaling in Infliximab-Resistant Colonic Crohn’s Disease

Anti-TNF therapy resistance is a major clinical challenge in Crohns Disease (CD), partly due to insufficient understanding of disease-site, protein-level mechanisms of CD and anti-TNF treatment resistance. Although some proteomics data from CD mouse models exists, data type and phenotype discrepancies contribute to confounding attempts to translate between preclinical animal models of disease and human clinical cohorts. To meet this important challenge, we develop and demonstrate here an approach called Translatable Components Regression (TransComp-R) to overcome inter-species and trans-omic discrepancies between CD mouse models and human subjects. TransComp-R combines CD mouse model proteomic data with patient pre-treatment transcriptomic data to identify molecular features discernable in the mouse data predictive of patient response to anti-TNF therapy. Interrogating the TransComp-R models predominantly revealed upregulated integrin pathway signaling via collagen-binding integrin ITGA1 in anti-TNF resistant colonic CD (cCD) patients. Toward validation, we performed single-cell RNA sequencing on biopsies from a cCD patient and analyzed publicly available immune cell proteomics data to characterize the immune and intestinal cell types contributing to anti-TNF resistance. We found that ITGA1 is indeed expressed in colonic T-cell populations and that interactions between collagen-binding integrins on T-cells and colonic cell types expressing secreted collagens are associated with anti-TNF therapy resistance. Biologically, TransComp-R linked previously disparate observations about collagen and ITGA1 signaling to a potential therapeutic avenue for overcoming anti-TNF therapy resistance in cCD. Methodologically, TransComp-R provides a flexible, generalizable framework for addressing inter-species, inter-omic, and inter-phenotypic discrepancies between animal models and patients to deliver translationally relevant biological insights.\n\nOne Sentence SummaryBrubaker et al. implicate dysregulated collagen-binding integrin signaling in resistance to anti-TNF therapy in Crohns Disease by developing a mouse-proteomic to human-transcriptomic translation model and confirm the associated inter-cellular signaling network using single-cell RNA sequencing.

systems biology↗

bioRxiv2019

Coagulopathy signature precedes and predicts severity of end-organ heat stroke pathology in a mouse model

Heat stroke is a life-threatening condition characterized by loss of thermoregulation and severe elevation of core body temperature, which can cause organ failure and damage to the central nervous system. While no definitive test exists to measure heat stroke severity, immune challenge is known to increase heat stroke risk, although the mechanism of this increased risk is unclear. In this study, we used a mouse model of classic heat stroke to test the effect of immune challenge on pathology. Employing multivariate supervised machine learning to identify patterns of molecular and cellular markers associated with heat stroke, we found that prior viral infection simulated with poly I:C injection resulted in heat stroke presenting with high levels of factors indicating coagulopathy. Despite a decreased number of platelets in the blood, platelets are large and non-uniform in size, suggesting younger, more active platelets. Levels of D-dimer and soluble thrombomodulin were increased in more severe heat stroke, and in cases presenting with the highest level of organ damage markers D-dimer levels dropped, indicating potential fibrinolysis-resistant thrombosis. Genes corresponding to immune response, coagulation, hypoxia, and vessel repair were up-regulated in kidneys of heat-challenged animals, and these increases correlated with both viral treatment and distal organ damage while appearing before discernible tissue damage to the kidney itself. We conclude that heat stroke-induced coagulopathy may be a driving mechanistic force in heat stroke pathology, especially when exacerbated by prior infection, and that coagulation markers may serve as an accessible biomarker for heat stroke severity and therapeutic strategies.\n\nKey pointsO_LIA signature of pro-coagulation markers predicts circadian core body temperature and levels of organ damage in heat stroke\nC_LIO_LIChanges in coagulopathy-related gene expression are evidenced before histopathological organ damage\nC_LI

physiology↗

bioRxiv2019

C. difficile-associated antibiotics prime the host for infection by a microbiome-independent mechanism.

The most clinically relevant risk factor for Clostridioides difficile-associated disease (CDAD) is recent antibiotic treatment. Though most broad-spectrum antibiotics significantly disrupt the structure of the gut microbiota, only particular ones increase CDAD risk, suggesting additional factors might increase the risk from certain antibiotics. Here we show that commensal-independent effects of antibiotics collectively prime an in vitro germ-free human gut for CDAD. We found a marked loss of mucosal barrier and immune function with CDAD-associated antibiotic pretreatment distinct from pretreatment with an antibiotic unassociated with CDAD, which did not reduce innate immune or mucosal barrier functions. Importantly, pretreatment with CDAD-associated antibiotics sensitized mucosal barriers to C. difficile toxin activity in primary cell-derived enteroid monolayers. These data implicate commensal-independent host changes in the increased risk of CDAD with specific antibiotics. Our findings are contrary to the previously held belief that antibiotics allow for CDAD solely through disruption of the microbiome. We anticipate this work to suggest potential avenues of research for host-directed treatment and preventive therapies for CDAD, and to impact human tissue culturing protocols.

molecular biology↗

bioRxiv2018

A framework for translation of genomic responses from mouse models to human inflammatory disease contexts

The high failure rate of therapeutics showing promise in mouse disease models to translate to patients is a pressing challenge in biomedical science. However, mouse models are a useful tool for evaluating mechanisms of disease and prioritizing novel therapeutic agents for clinical trials. Though retrospective studies have examined the fidelity of mouse models of inflammatory disease to their respective human in vivo conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for prospective inference of disease-associated human in vivo differentially expressed genes and pathways from mouse model experiments. We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human in vivo biology from mouse model datasets. We found that a semi-supervised artificial neural network identified significantly more true human in vivo associations than interpreting mouse experiments directly (95% CI on F-score for mouse experiments [0.090, 0.175], neural network [0.278, 0.375], p = 0.00013). Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches combining mouse and human data for biological inference provides the most accurate assessment of human in vivo disease and therapeutic mechanisms. The task of translating insights from model systems to human disease contexts may therefore be better accomplished by the use of systems modeling driven approaches.\n\nAuthor SummaryComparison of genomic responses in mouse models and human disease contexts is not sufficient for addressing the challenge of prospective translation from mouse models to human disease contexts. Here, we address this challenge by developing a semi-supervised machine learning approach that combines supervised modeling of mouse experiment datasets with unsupervised modeling of human disease-context datasets to predict human in vivo differentially expressed genes and pathways as if the model system experiment had been run in the human cohort. A semi-supervised version of a feed forward artificial neural network was the most efficacious model for translating experimentally derived mouse molecule-phenotype associations to the human in vivo disease context. We find that computational generalization of signaling insights from mouse to human contexts substantially improves upon direct generalization of mouse experimental insights and argue that such approaches can facilitate more clinically impactful translation of insights from preclinical studies in model systems to patients.

systems biology↗