Diet Diurnally Regulates Small Intestinal Microbiome-Epithelial-Immune Homeostasis and Enteritis.

Throughout a 24-h period, the small intestine (SI) is exposed to diurnally varying food- and microbiome-derived antigenic burdens but maintains a strict immune homeostasis, which when perturbed in genetically susceptible individuals, may lead to Crohn disease. Herein, we demonstrate that dietary content and rhythmicity regulate the diurnally shifting SI epithelial cell (SIEC) transcriptional landscape through modulation of the SI microbiome. We exemplify this concept with SIEC major histocompatibility complex (MHC) class II, which is diurnally modulated by distinct mucosal-adherent SI commensals, while supporting downstream diurnal activity of intra-epithelial IL-10+ lymphocytes regulating the SI barrier function. Disruption of this diurnally regulated diet-microbiome-MHC class II-IL-10-epithelial barrier axis by circadian clock disarrangement, alterations in feeding time or content, or epithelial-specific MHC class II depletion leads to an extensive microbial product influx, driving Crohn-like enteritis. Collectively, we highlight nutritional features that modulate SI microbiome, immunity, and barrier function and identify dietary, epithelial, and immune checkpoints along this axis to be potentially exploitable in future Crohn disease interventions.

Lipid-Associated Macrophages Control Metabolic Homeostasis in a Trem2-Dependent Manner.

Immune cells residing in white adipose tissue have been highlighted as important factors contributing to the pathogenesis of metabolic diseases, but the molecular regulators that drive adipose tissue immune cell remodeling during obesity remain largely unknown. Using index and transcriptional single-cell sorting, we comprehensively map all adipose tissue immune populations in both mice and humans during obesity. We describe a novel and conserved Trem2+ lipid-associated macrophage (LAM) subset and identify markers, spatial localization, origin, and functional pathways associated with these cells. Genetic ablation of Trem2 in mice globally inhibits the downstream molecular LAM program, leading to adipocyte hypertrophy as well as systemic hypercholesterolemia, body fat accumulation, and glucose intolerance. These findings identify Trem2 signaling as a major pathway by which macrophages respond to loss of tissue-level lipid homeostasis, highlighting Trem2 as a key sensor of metabolic pathologies across multiple tissues and a potential therapeutic target in metabolic diseases.

Nmes1 is a novel regulator of mucosal response influencing intestinal healing potential.

The initiation of tissue remodeling following damage is a critical step in preventing the development of immune-mediated diseases. Several factors contribute to mucosal healing, leading to innovative therapeutic approaches for managing intestinal disorders. However, uncovering alternative targets and gaining mechanistic insights are imperative to enhance therapy efficacy and broaden its applicability across different intestinal diseases. Here we demonstrate that Nmes1, encoding for Normal Mucosa of Esophagus-Specific gene 1, also known as Aa467197, is a novel regulator of mucosal healing. Nmes1 influences the macrophage response to the tissue remodeling cytokine IL-4 in vitro. In addition, using two murine models of intestinal damage, each characterized by a type 2-dominated environment with contrasting functions, the ablation of Nmes1 results in decreased intestinal regeneration during the recovery phase of colitis, while enhancing parasitic egg clearance and reducing fibrosis during the advanced stages of Schistosoma mansoni infection. These outcomes are associated with alterations in CX3CR1+ macrophages, cells known for their wound-healing potential in the inflamed colon, hence promising candidates for cell therapies. All in all, our data indicate Nmes1 as a novel contributor to mucosal healing, setting the basis for further investigation into its potential as a new target for the treatment of colon-associated inflammation.

Inference of differential key regulatory networks and mechanistic drug repurposing candidates from scRNA-seq data with SCANet.

MOTIVATION: The reconstruction of small key regulatory networks that explain the differences in the development of cell (sub)types from single-cell RNA sequencing is a yet unresolved computational problem. RESULTS: To this end, we have developed SCANet, an all-in-one package for single-cell profiling that covers the whole differential mechanotyping workflow, from inference of trait/cell-type-specific gene co-expression modules, driver gene detection, and transcriptional gene regulatory network reconstruction to mechanistic drug repurposing candidate prediction. To illustrate the power of SCANet, we examined data from two studies. First, we identify the drivers of the mechanotype of a cytokine storm associated with increased mortality in patients with acute respiratory illness. Secondly, we find 20 drugs for eight potential pharmacological targets in cellular driver mechanisms in the intestinal stem cells of obese mice. AVAILABILITY AND IMPLEMENTATION: SCANet is a free, open-source, and user-friendly Python package that can be seamlessly integrated into single-cell-based systems medicine research and mechanistic drug discovery.

Protein abundance of AKT and ERK pathway components governs cell type-specific regulation of proliferation.

Signaling through the AKT and ERK pathways controls cell proliferation. However, the integrated regulation of this multistep process, involving signal processing, cell growth and cell cycle progression, is poorly understood. Here, we study different hematopoietic cell types, in which AKT and ERK signaling is triggered by erythropoietin (Epo). Although these cell types share the molecular network topology for pro-proliferative Epo signaling, they exhibit distinct proliferative responses. Iterating quantitative experiments and mathematical modeling, we identify two molecular sources for cell type-specific proliferation. First, cell type-specific protein abundance patterns cause differential signal flow along the AKT and ERK pathways. Second, downstream regulators of both pathways have differential effects on proliferation, suggesting that protein synthesis is rate-limiting for faster cycling cells while slower cell cycles are controlled at the G1-S progression. The integrated mathematical model of Epo-driven proliferation explains cell type-specific effects of targeted AKT and ERK inhibitors and faithfully predicts, based on the protein abundance, anti-proliferative effects of inhibitors in primary human erythroid progenitor cells. Our findings suggest that the effectiveness of targeted cancer therapy might become predictable from protein abundance.

The quorum-sensing regulator ComA from Bacillus subtilis activates transcription using topologically distinct DNA motifs.

ComA-like transcription factors regulate the quorum response in numerous Gram-positive bacteria. ComA proteins belong to the tetrahelical helix-turn-helix superfamily of transcriptional activators, which bind as homodimers to inverted sequence repeats in the DNA. Here, we report that ComA from Bacillus subtilis recognizes a topologically distinct motif, in which the binding elements form a direct repeat. We provide in vitro and in vivo evidence that the canonical and non-canonical site play an important role in facilitating type I and type II promoter activation, respectively, by interacting with different subunits of RNA polymerase. We furthermore show that there is a variety of contexts in which the non-canonical site can occur and identify new direct target genes that are located within the integrative and conjugative element ICEBs1. We therefore suggest that ComA acts as a multifunctional transcriptional activator and provides a striking example for complexity in protein-DNA interactions that evolved in the context of quorum sensing.

Identification of isoform-specific dynamics in phosphorylation-dependent STAT5 dimerization by quantitative mass spectrometry and mathematical modeling.

STAT5A and STAT5B are important transcription factors that dimerize and transduce activation signals of cytokine receptors directly to the nucleus. A typical cytokine that mediates STAT5 activation is erythropoietin (Epo). Differential functions of STAT5A and STAT5B have been reported. However, the extent to which phosphorylated STAT5A and STAT5B (pSTAT5A, pSTAT5B) form homo- or heterodimers is not understood, nor is how this might influence the signal transmission to the nucleus. To study this, we designed a concept to investigate the isoform-specific dimerization behavior of pSTAT5A and pSTAT5B that comprises isoform-specific immunoprecipitation (IP), measurement of the degree of phosphorylation, and isoform ratio determination between STAT5A and STAT5B. For the main analytical method, we employed quantitative label-free and -based mass spectrometry. For the cellular model system, we used Epo receptor (EpoR)-expressing BaF3 cells (BaF3-EpoR) stimulated with Epo. Three hypotheses of dimer formation between pSTAT5A and pSTAT5B were used to explain the analytical results by a static mathematical model: formation of (i) homodimers only, (ii) heterodimers only, and (iii) random formation of homo- and heterodimers. The best agreement between experimental data and model simulations was found for the last case. Dynamics of cytoplasmic STAT5 dimerization could be explained by distinct nuclear import rates and individual nuclear retention for homo- and heterodimers of phosphorylated STAT5.

Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease.

Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease.

Erythropoietin-driven dynamic proteome adaptations during erythropoiesis prevent iron overload in the developing embryo.

Erythropoietin (Epo) ensures survival and proliferation of colony-forming unit erythroid (CFU-E) progenitor cells and their differentiation to hemoglobin-containing mature erythrocytes. A lack of Epo-induced responses causes embryonic lethality, but mechanisms regulating the dynamic communication of cellular alterations to the organismal level remain unresolved. By time-resolved transcriptomics and proteomics, we show that Epo induces in CFU-E cells a gradual transition from proliferation signature proteins to proteins indicative for differentiation, including heme-synthesis enzymes. In the absence of the Epo receptor (EpoR) in embryos, we observe a lack of hemoglobin in CFU-E cells and massive iron overload of the fetal liver pointing to a miscommunication between liver and placenta. A reduction of iron-sulfur cluster-containing proteins involved in oxidative phosphorylation in these embryos leads to a metabolic shift toward glycolysis. This link connecting erythropoiesis with the regulation of iron homeostasis and metabolic reprogramming suggests that balancing these interactions is crucial for protection from iron intoxication and for survival.

Machine learning in clinical decision making.

Machine learning is increasingly integrated into clinical practice, with applications ranging from pre-clinical data processing, bedside diagnosis assistance, patient stratification, treatment decision making, and early warning as part of primary and secondary prevention. However, a multitude of technological, medical, and ethical considerations are critical in machine-learning utilization, including the necessity for careful validation of machine-learning-based technologies in real-life contexts, unbiased evaluation of benefits and risks, and avoidance of technological over-dependence and associated loss of clinical, ethical, and social-related decision-making capacities. Other challenges include the need for careful benchmarking and external validations, dissemination of end-user knowledge from computational experts to field users, and responsible code and data sharing, enabling transparent assessment of pipelines. In this review, we highlight key promises and achievements in integration of machine-learning platforms into clinical medicine while highlighting limitations, pitfalls, and challenges toward enhanced integration of learning systems into the medical realm.