Molecular Systems Biology最新文献

Adult stem cells are important for tissue turnover and regeneration. However, in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here, we dissected the diversity of neural stem cells in the adult zebrafish brain, an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single-cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze the regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells, with some subtypes being restricted to a single brain region, while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation, with different states being involved in proliferative and non-proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for the functional manipulation of neural stem cell subtypes.

Sex-based differences in obesity-related hepatic malignancies suggest the protective roles of estrogen. Using a preclinical model, we dissected estrogen receptor (ER) isoform-driven molecular responses in high-fat diet (HFD)-induced liver diseases of male and female mice treated with or without an estrogen agonist by integrating liver multi-omics data. We found that selective ER activation recovers HFD-induced molecular and physiological liver phenotypes. HFD and systemic ER activation altered core liver pathways, beyond lipid metabolism, that are consistent between mice and primates. By including patient cohort data, we uncovered that ER-regulated enhancers govern central regulatory and metabolic genes with clinical significance in metabolic dysfunction-associated steatotic liver disease (MASLD) patients, including the transcription factor TEAD1. TEAD1 expression increased in MASLD patients, and its downregulation by short interfering RNA reduced intracellular lipid content. Subsequent TEAD small molecule inhibition improved steatosis in primary human hepatocyte spheroids by suppressing lipogenic pathways. Thus, TEAD1 emerged as a new therapeutic candidate whose inhibition ameliorates hepatic steatosis.

Chronic liver diseases are worldwide on the rise. Due to the rapidly increasing incidence, in particular in Western countries, metabolic dysfunction-associated steatotic liver disease (MASLD) is gaining importance as the disease can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in MASLD development, but molecular mechanisms responsible for disease progression remained unresolved. Here, we uncover in primary hepatocytes from a preclinical model fed with a Western diet (WD) an increased basal MET phosphorylation and a strong downregulation of the PI3K-AKT pathway. Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveal patient-specific variability in basal MET phosphorylation, which correlates with patient outcome after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery.

The road from transcription to protein synthesis is paved with many obstacles, allowing for several modes of post-transcriptional regulation of gene expression. A fundamental player in mRNA biology is DDX3X, an RNA binding protein that canonically regulates mRNA translation. By monitoring dynamics of mRNA abundance and translation following DDX3X depletion, we observe stabilization of translationally suppressed mRNAs. We use interpretable statistical learning models to uncover GC content in the coding sequence as the major feature underlying RNA stabilization. This result corroborates GC content-related mRNA regulation detectable in other studies, including hundreds of ENCODE datasets and recent work focusing on mRNA dynamics in the cell cycle. We provide further evidence for mRNA stabilization by detailed analysis of RNA-seq profiles in hundreds of samples, including a Ddx3x conditional knockout mouse model exhibiting cell cycle and neurogenesis defects. Our study identifies a ubiquitous feature underlying mRNA regulation and highlights the importance of quantifying multiple steps of the gene expression cascade, where RNA abundance and protein production are often uncoupled.

Precision in the establishment and maintenance of cellular identities is crucial for the development of multicellular organisms and requires tight regulation of gene expression. While extensive research has focused on understanding cell type-specific gene activation, the complex mechanisms underlying the transcriptional repression of alternative fates are not fully understood. Here, we provide an overview of the repressive mechanisms involved in cell fate regulation. We discuss the molecular machinery responsible for suppressing alternative fates and highlight the crucial role of sequence-specific transcription factors (TFs) in this process. Depletion of these TFs can result in unwanted gene expression and increased cellular plasticity. We suggest that these TFs recruit cell type-specific repressive complexes to their cis-regulatory elements, enabling them to modulate chromatin accessibility in a context-dependent manner. This modulation effectively suppresses master regulators of alternative fate programs and their downstream targets. The modularity and dynamic behavior of these repressive complexes enables a limited number of repressors to canalize and maintain major and minor cell fate decisions at different stages of development.

Cells modify their internal organization during continuous state transitions, supporting functions from cell division to differentiation. However, tools to measure dynamic physiological states of individual transitioning cells are lacking. We combined live-cell imaging and machine learning to monitor ERK1/2-inhibited primary murine skeletal muscle precursor cells, that transition rapidly and robustly from proliferating myoblasts to post-mitotic myocytes and then fuse, forming multinucleated myotubes. Our models, trained using motility or actin intensity features from single-cell tracking data, effectively tracked real-time continuous differentiation, revealing that differentiation occurs 7.5-14.5 h post induction, followed by fusion ~3 h later. Co-inhibition of ERK1/2 and p38 led to differentiation without fusion. Our model inferred co-inhibition leads to terminal differentiation, indicating that p38 is specifically required for transitioning from terminal differentiation to fusion. Our model also predicted that co-inhibition leads to changes in actin dynamics. Mass spectrometry supported these in silico predictions and suggested novel fusion and maturation regulators downstream of differentiation. Collectively, this approach can be adapted to various biological processes to uncover novel links between dynamic single-cell states and their functional outcomes.

Carbon source-dependent control of bacterial growth is fundamental to bacterial physiology and survival. However, pinpointing the metabolic steps important for cell growth is challenging due to the complexity of cellular networks. Here, the elastic net model and multilayer perception model that integrated genome-wide gene-deletion data and simulated flux distributions were constructed to identify metabolic reactions beneficial or detrimental to Escherichia coli grown on 30 different carbon sources. Both models outperformed traditional in silico methods by identifying not just essential reactions but also nonessential ones that promote growth. They successfully predicted metabolic reactions beneficial to cell growth, with high convergence between the models. The models revealed that biosynthetic pathways generally promote growth across various carbon sources, whereas the impact of energy-generating pathways varies with the carbon source. Intriguing predictions were experimentally validated for findings beyond experimental training data and the impact of various carbon sources on the glyoxylate shunt, pyruvate dehydrogenase reaction, and redundant purine biosynthesis reactions. These highlight the practical significance and predictive power of the models for understanding and engineering microbial metabolism.

Isogenic cells respond in a heterogeneous manner to interferon. Using a micropatterning approach combined with high-content imaging and spatial analyses, we characterized how the population context (position of a cell with respect to neighboring cells) of epithelial cells affects their response to interferons. We identified that cells at the edge of cellular colonies are more responsive than cells embedded within colonies. We determined that this spatial heterogeneity in interferon response resulted from the polarized basolateral interferon receptor distribution, making cells located in the center of cellular colonies less responsive to ectopic interferon stimulation. This was conserved across cell lines and primary cells originating from epithelial tissues. Importantly, cells embedded within cellular colonies were not protected from viral infection by apical interferon treatment, demonstrating that the population context-driven heterogeneous response to interferon influences the outcome of viral infection. Our data highlights that the behavior of isolated cells does not directly translate to their behavior in a population, placing the population context as one important factor influencing heterogeneity during interferon response in epithelial cells.