Clostridioides difficile-Associated Antibiotics Alter Human Mucosal Barrier Functions by Microbiome-Independent Mechanisms.

A clinically relevant risk factor for Clostridioides difficile-associated disease (CDAD) is recent antibiotic treatment. Although broad-spectrum antibiotics have been shown to disrupt the structure of the gut microbiota, some antibiotics appear to increase CDAD risk without being highly active against intestinal anaerobes, suggesting direct nonantimicrobial effects. We examined cell biological effects of antibiotic exposure that may be involved in bacterial pathogenesis using an in vitro germfree human colon epithelial culture model. We found a marked loss of mucosal barrier and immune function with exposure to the CDAD-associated antibiotics clindamycin and ciprofloxacin, distinct from the results of pretreatment with an antibiotic unassociated with CDAD, tigecycline, 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 gut mucosal barrier changes in the increased risk of CDAD with specific antibiotics and warrant further studies in in vivo systems. We anticipate this work to suggest potential avenues of research for host-directed treatment and preventive therapies for CDAD.

Computational translation of genomic responses from experimental model systems to humans.

The high failure rate of therapeutics showing promise in mouse models to translate to patients is a pressing challenge in biomedical science. Though retrospective studies have examined the fidelity of mouse models to their respective human conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for inference of disease-associated human 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 biology from mouse datasets. We found that semi-supervised training of a neural network identified significantly more true human biological associations than interpreting mouse experiments directly. Evaluating the experimental design of mouse experiments where our model was most successful revealed principles of experimental design that may improve translational performance. Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches, combining mouse and human data for biological inference, provide the most accurate assessment of human in vivo disease processes. Finally, we proffer a delineation of four categories of model system-to-human "Translation Problems" defined by the resolution and coverage of the datasets available for molecular insight translation and suggest that the task of translating insights from model systems to human disease contexts may be better accomplished by a combination of translation-minded experimental design and computational approaches.

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 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.

Detailed survey of an in vitro intestinal epithelium model by single-cell transcriptomics.

The co-culture of two adult human colorectal cancer cell lines, Caco-2 and HT29, on Transwell is commonly used as an in vitro gut mimic, yet the translatability of insights from such a system to adult human physiological contexts is not fully characterized. Here, we used single-cell RNA sequencing on the co-culture to obtain a detailed survey of cell type heterogeneity in the system and conducted a holistic comparison with human physiology. We identified the intestinal stem cell-, transit amplifying-, enterocyte-, goblet cell-, and enteroendocrine-like cells in the system. In general, the co-culture was fetal intestine-like, with less variety of gene expression compared to the adult human gut. Transporters for major types of nutrients were found in the majority of the enterocytes-like cells in the system. TLR 4 was not expressed in the sample, indicating that the co-culture model is incapable of mimicking the innate immune aspect of the human epithelium.

Enhanced annotation of CD45RA to distinguish T cell subsets in single-cell RNA-seq via machine learning.

Motivation: 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 distinguishes 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. Results: 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 data. Among all the algorithms we tested, the trained support vector machine with a radial basis function kernel with optimized hyperparameters achieved a 99.96% accuracy on an unseen dataset. The multilayer perceptron 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. Based on the overall performance, we chose the support vector machine with a radial basis function kernel as the model implemented in our Python package scCD45RA. Availability and implementation: The resultant package scCD45RA can be found at https://github.com/BrubakerLab/ScCD45RA and can be installed from the Python Package Index (PyPI) using the command "pip install sccd45ra."

TRANS-OMIC KNOWLEDGE TRANSFER MODELING INFERS GUT MICROBIOME BIOMARKERS OF ANTI-TNF RESISTANCE IN ULCERATIVE COLITIS.

A critical challenge in analyzing multi-omics data from clinical cohorts is the re-use of these valuable datasets to answer biological questions beyond the scope of the original study. Transfer Learning and Knowledge Transfer approaches are machine learning methods that leverage knowledge gained in one domain to solve a problem in another. Here, we address the challenge of developing Knowledge Transfer approaches to map trans-omic information from a multi-omic clinical cohort to another cohort in which a novel phenotype is measured. Our test case is that of predicting gut microbiome and gut metabolite biomarkers of resistance to anti-TNF therapy in Ulcerative Colitis patients. Three approaches are proposed for Trans-omic Knowledge Transfer, and the resulting performance and downstream inferred biomarkers are compared to identify efficacious methods. We find that multiple approaches reveal similar metabolite and microbial biomarkers of anti-TNF resistance and that these commonly implicated biomarkers can be validated in literature analysis. Overall, we demonstrate a promising approach to maximize the value of the investment in large clinical multi-omics studies by re-using these data to answer biological and clinical questions not posed in the original study.

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 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.

Vaginal epithelial dysfunction is mediated by the microbiome, metabolome, and mTOR signaling.

Bacterial vaginosis (BV) is characterized by depletion of Lactobacillus and overgrowth of anaerobic and facultative bacteria, leading to increased mucosal inflammation, epithelial disruption, and poor reproductive health outcomes. However, the molecular mediators contributing to vaginal epithelial dysfunction are poorly understood. Here we utilize proteomic, transcriptomic, and metabolomic analyses to characterize biological features underlying BV in 405 African women and explore functional mechanisms in vitro. We identify five major vaginal microbiome groups: L. crispatus (21%), L. iners (18%), Lactobacillus (9%), Gardnerella (30%), and polymicrobial (22%). Using multi-omics we show that BV-associated epithelial disruption and mucosal inflammation link to the mammalian target of rapamycin (mTOR) pathway and associate with Gardnerella, M. mulieris, and specific metabolites including imidazole propionate. Experiments in vitro confirm that type strain G. vaginalis and M. mulieris supernatants and imidazole propionate directly affect epithelial barrier function and activation of mTOR pathways. These results find that the microbiome-mTOR axis is a central feature of epithelial dysfunction in BV.

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.

Computational Interspecies Translation Between Alzheimer's Disease Mouse Models and Human Subjects Identifies Innate Immune Complement, TYROBP, and TAM Receptor Agonist Signatures, Distinct From Influences of Aging.

Mouse models are vital for preclinical research on Alzheimer's disease (AD) pathobiology. Many traditional models are driven by autosomal dominant mutations identified from early onset AD genetics whereas late onset and sporadic forms of the disease are predominant among human patients. Alongside ongoing experimental efforts to improve fidelity of mouse model representation of late onset AD, a computational framework termed Translatable Components Regression (TransComp-R) offers a complementary approach to leverage human and mouse datasets concurrently to enhance translation capabilities. We employ TransComp-R to integratively analyze transcriptomic data from human postmortem and traditional amyloid mouse model hippocampi to identify pathway-level signatures present in human patient samples yet predictive of mouse model disease status. This method allows concomitant evaluation of datasets across different species beyond observational seeking of direct commonalities between the species. Additional linear modeling focuses on decoupling disease signatures from effects of aging. Our results elucidated mouse-to-human translatable signatures associated with disease: excitatory synapses, inflammatory cytokine signaling, and complement cascade- and TYROBP-based innate immune activity; these signatures all find validation in previous literature. Additionally, we identified agonists of the Tyro3 / Axl / MerTK (TAM) receptor family as significant contributors to the cross-species innate immune signature; the mechanistic roles of the TAM receptor family in AD merit further dedicated study. We have demonstrated that TransComp-R can enhance translational understanding of relationships between AD mouse model data and human data, thus aiding generation of biological hypotheses concerning AD progression and holding promise for improved preclinical evaluation of therapies.

Cross-species transcriptomic signatures predict response to MK2 inhibition in mouse models of chronic inflammation.

Inflammatory bowel diseases (IBDs) are genetically complex and exhibit significant inter-patient heterogeneity in disease presentation and therapeutic response. Here, we show that mouse models of IBD exhibit variable responses to inhibition of MK2, a pro-inflammatory serine/threonine kinase, and that MK2 inhibition suppresses inflammation by targeting inflammatory monocytes and neutrophils in murine models. Using a computational approach (TransComp-R) that allows for cross-species comparison of transcriptomic features, we identified an IBD patient subgroup that is predicted to respond to MK2 inhibition, and an independent preclinical model of chronic intestinal inflammation predicted to be non-responsive, which we validated experimentally. Thus, cross-species mouse-human translation approaches can help to identify patient subpopulations in which to deploy new therapies.

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

BACKGROUND: Immune challenge is known to increase heat stroke risk, although the mechanism of this increased risk is unclear. OBJECTIVES: We sought to understand the effect of immune challenge on heat stroke pathology. PATIENTS/METHODS: Using a mouse model of classic heat stroke, we examined the impact of prior viral or bacterial infection on hematological aspects of recovery. Mice were exposed to heat either 48 or 72 hours following polyinosinic:polycytidylic acid (poly I:C) or lipopolysaccharide injection, time points when symptoms of illness (fever, lethargy, anorexia) were minimized or completely absent. RESULTS: 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 of 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; these correlated with both viral treatment and distal organ damage while appearing before discernible tissue damage to the kidney itself. CONCLUSIONS: Heat stroke-induced coagulopathy may be a driving mechanistic force in heat stroke pathology, especially when exacerbated by prior infection. Coagulation markers may serve as accessible biomarkers for heat stroke severity and therapeutic strategies.

An interspecies translation model implicates integrin signaling in infliximab-resistant inflammatory bowel disease.

Anti-tumor necrosis factor (anti-TNF) therapy resistance is a major clinical challenge in inflammatory bowel disease (IBD), due, in part, to insufficient understanding of disease-site, protein-level mechanisms. Although proteomics data from IBD mouse models exist, data and phenotype discrepancies contribute to confounding translation from preclinical animal models of disease to clinical cohorts. We developed an approach called translatable components regression (TransComp-R) to overcome interspecies and trans-omic discrepancies between mouse models and human subjects. TransComp-R combines mouse proteomic data with patient pretreatment transcriptomic data to identify molecular features discernable in the mouse data that are predictive of patient response to therapy. Interrogating the TransComp-R models revealed activated integrin pathway signaling in patients with anti-TNF-resistant colonic Crohn's disease (cCD) and ulcerative colitis (UC). As a step toward validation, we performed single-cell RNA sequencing (scRNA-seq) on biopsies from a patient with cCD 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 was expressed in T cells and that interactions between these cells and intestinal cell types were associated with resistance to anti-TNF therapy. We experimentally showed that the α1 integrin subunit mediated the effectiveness of anti-TNF therapy in human immune cells. Thus, TransComp-R identified an integrin signaling mechanism with potential therapeutic implications for overcoming anti-TNF therapy resistance. We suggest that TransComp-R is a generalizable framework for addressing species, molecular, and phenotypic discrepancies between model systems and patients to translationally deliver relevant biological insights.

Proteogenomic Network Analysis of Context-Specific KRAS Signaling in Mouse-to-Human Cross-Species Translation.

The highest frequencies of KRAS mutations occur in colorectal carcinoma (CRC) and pancreatic ductal adenocarcinoma (PDAC). The ability to target downstream pathways mediating KRAS oncogenicity is limited by an incomplete understanding of the contextual cues modulating the signaling output of activated K-RAS. We performed mass spectrometry on mouse tissues expressing wild-type or mutant Kras to determine how tissue context and genetic background modulate oncogenic signaling. Mutant Kras dramatically altered the proteomes and phosphoproteomes of preneoplastic and neoplastic colons and pancreases in a context-specific manner. We developed an approach to statistically humanize the mouse networks with data from human cancer and identified genes within the humanized CRC and PDAC networks synthetically lethal with mutant KRAS. Our studies demonstrate the context-dependent plasticity of oncogenic signaling, identify non-canonical mediators of KRAS oncogenicity within the KRAS-regulated signaling network, and demonstrate how statistical integration of mouse and human datasets can reveal cross-species therapeutic insights.

Substrate-based kinase activity inference identifies MK2 as driver of colitis.

Inflammatory bowel disease (IBD) is a chronic and debilitating disorder that has few treatment options due to a lack of comprehensive understanding of its molecular pathogenesis. We used multiplexed mass spectrometry to collect high-content information on protein phosphorylation in two different mouse models of IBD. Because the biological function of the vast majority of phosphorylation sites remains unknown, we developed Substrate-based Kinase Activity Inference (SKAI), a methodology to infer kinase activity from phosphoproteomic data. This approach draws upon prior knowledge of kinase-substrate interactions to construct custom lists of kinases and their respective substrate sites, termed kinase-substrate sets that employ prior knowledge across organisms. This expansion as much as triples the amount of prior knowledge available. We then used these sets within the Gene Set Enrichment Analysis framework to infer kinase activity based on increased or decreased phosphorylation of its substrates in a dataset. When applied to the phosphoproteomic datasets from the two mouse models, SKAI predicted largely non-overlapping kinase activation profiles. These results suggest that chronic inflammation may arise through activation of largely divergent signaling networks. However, the one kinase inferred to be activated in both mouse models was mitogen-activated protein kinase-activated protein kinase 2 (MAPKAPK2 or MK2), a serine/threonine kinase that functions downstream of p38 stress-activated mitogen-activated protein kinase. Treatment of mice with active colitis with ATI450, an orally bioavailable small molecule inhibitor of the MK2 pathway, reduced inflammatory signaling in the colon and alleviated the clinical and histological features of inflammation. These studies establish MK2 as a therapeutic target in IBD and identify ATI450 as a potential therapy for the disease.

Tissue-Specific Oncogenic Activity of KRASA146T.

KRAS is the most frequently mutated oncogene. The incidence of specific KRAS alleles varies between cancers from different sites, but it is unclear whether allelic selection results from biological selection for specific mutant KRAS proteins. We used a cross-disciplinary approach to compare KRASG12D, a common mutant form, and KRASA146T, a mutant that occurs only in selected cancers. Biochemical and structural studies demonstrated that KRASA146T exhibits a marked extension of switch 1 away from the protein body and nucleotide binding site, which activates KRAS by promoting a high rate of intrinsic and guanine nucleotide exchange factor-induced nucleotide exchange. Using mice genetically engineered to express either allele, we found that KRASG12D and KRASA146T exhibit distinct tissue-specific effects on homeostasis that mirror mutational frequencies in human cancers. These tissue-specific phenotypes result from allele-specific signaling properties, demonstrating that context-dependent variations in signaling downstream of different KRAS mutants drive the KRAS mutational pattern seen in cancer. SIGNIFICANCE: Although epidemiologic and clinical studies have suggested allele-specific behaviors for KRAS, experimental evidence for allele-specific biological properties is limited. We combined structural biology, mass spectrometry, and mouse modeling to demonstrate that the selection for specific KRAS mutants in human cancers from different tissues is due to their distinct signaling properties.See related commentary by Hobbs and Der, p. 696.This article is highlighted in the In This Issue feature, p. 681.

Integrated in vivo multiomics analysis identifies p21-activated kinase signaling as a driver of colitis.

Inflammatory bowel disease (IBD) is a chronic disorder of the gastrointestinal tract that has limited treatment options. To gain insight into the pathogenesis of chronic colonic inflammation (colitis), we performed a multiomics analysis that integrated RNA microarray, total protein mass spectrometry (MS), and phosphoprotein MS measurements from a mouse model of the disease. Because we collected all three types of data from individual samples, we tracked information flow from RNA to protein to phosphoprotein and identified signaling molecules that were coordinately or discordantly regulated and pathways that had complex regulation in vivo. For example, the genes encoding acute-phase proteins were expressed in the liver, but the proteins were detected by MS in the colon during inflammation. We also ascertained the types of data that best described particular facets of chronic inflammation. Using gene set enrichment analysis and trans-omics coexpression network analysis, we found that each data set provided a distinct viewpoint on the molecular pathogenesis of colitis. Combining human transcriptomic data with the mouse multiomics data implicated increased p21-activated kinase (Pak) signaling as a driver of colitis. Chemical inhibition of Pak1 and Pak2 with FRAX597 suppressed active colitis in mice. These studies provide translational insights into the mechanisms contributing to colitis and identify Pak as a potential therapeutic target in IBD.