NPJ Systems Biology and Applications最新文献

Multicellularity is one of the major evolutionary transitions, and its rise provided the ingredients for the emergence of a biosphere inhabited by complex organisms. Over the last decades, the potential for bioengineering multicellular systems has been instrumental in interrogating nature and exploring novel paths to regeneration, disease, cognition, and behaviour. Here, we provide a list of open problems that encapsulate many of the ongoing and future challenges in the field and suggest conceptual approaches that may facilitate progress.

Dysregulated pH is now recognised as a hallmark of cancer. Recent evidence has revealed that the endosomal pH regulator Na⁺/H⁺ exchanger NHE9 is upregulated in colorectal cancer to impose a pseudo-starvation state associated with invasion, highlighting an underexplored mechanistic link between adaptive endosomal reprogramming and malignant transformation. In this study, we use a model that quantitatively captures the dynamics of the core regulatory network governing epithelial mesenchymal plasticity. The model recapitulated NHE9-induced calcium signalling and the emergence of migratory phenotypes in colorectal cancer cells. Model predictions were compared with patient data and experimental results from RNA sequencing analysis of colorectal cancer cells with stable NHE9 expression. Mathematical analyses identified that tumours leverage elevated NHE9 levels to delay the transition of cells to a mesenchymal state and allow for metastatic progression. Ectopic expression of NHE9 is sufficient to induce loss of epithelial nature but does not fully couple with gain of mesenchymal state, resulting in a hybrid epithelial-mesenchymal population with increased aggressiveness and metastatic competence. Higher NHE9 expression is associated with cancer cell migration, and the effect appears to be independent of hypoxia status. Our data suggests that alterations in endosomal pH, an evolutionarily conserved starvation response, may be hijacked by colorectal cancer cells to drive phenotypic plasticity and invasion. We propose that cancer cells rewire their endosomal pH not only to meet the demands of rapid cell proliferation, but also to enable invasion, metastasis, and cell survival. Endosomal pH may be an attractive therapeutic target for halting tumour progression.

This perspective discusses the convergence of digital twin (DT) technology and on-the-chip systems as pivotal innovations in precision medicine, substantially advancing drug discovery. DT leverages extensive health data to create dynamic virtual patient models, enabling predictive insights and optimized treatment strategies. Concurrently, on-the-chip systems from the Carbon world replicate human biological processes on microfluidic platforms, providing detailed insights into disease mechanisms and pharmacological interactions. The convergence of these technologies promises to revolutionize drug development by enhancing therapeutic precision, accelerating discovery timelines, and reducing costs. Specifically, it assesses their role in drug development, from refining therapeutic precision to expediting discovery timelines and reducing the final price. Nevertheless, integrating these technologies faces challenges, including data collection and privacy concerns, technical intricacies, and clinical adoption barriers. This manuscript argues for interdisciplinary cooperation to navigate these challenges, positing DTs and on-the-chip technologies as foundational elements in personalized healthcare and drug discovery.

The KEAP1-NRF2 system plays a crucial role in responding to oxidative and electrophilic stress. Its dysregulation can cause the overexpression of downstream genes, a known cancer hallmark. Understanding and detecting abnormal KEAP1-NRF2 activity is essential for understanding disease mechanisms and identifying therapeutic targets. This study presents an approach that analyzes splicing patterns by a naive Bayes-based classifier to identify constitutive activation of the KEAP1-NRF2 system, focusing on the higher presence of abnormal splicing junctions as a subproduct of overexpression of downstream genes. Our splicing-based classifier demonstrated robust performance, reliably identifying activation of the KEAP1-NRF2 pathway across extensive datasets, including The Cancer Genome Atlas and the Sequence Read Archive. This shows the classifier's potential to analyze hundreds of thousands of transcriptomes, highlighting its utility in broad-scale genomic studies and provides a new perspective on utilizing splicing aberrations caused by overexpression as diagnostic markers, offering potential improvements in diagnosis and treatment strategies.

One in five deaths worldwide is associated with sepsis, which is defined as organ dysfunction caused by a dysregulated host response to infection. An increased understanding of the pathophysiology of sepsis could provide improved approaches for early detection and treatment. Here we describe the development and validation of a mechanistic mathematical model of the inflammatory response, making use of a combination of in vitro and human in vivo data obtained from experiments where bacterial lipopolysaccharide (LPS) was used to induce an inflammatory response. The new model can simulate the responses to both acute and prolonged inflammatory stimuli in an experimental setting, as well as the response to infection in the clinical setting. This model serves as a foundation for a sepsis simulation model with a potentially wide range of applications in different disciplines involved with sepsis research.

HSCs differentiation has been difficult to study experimentally due to the high number of components and interactions involved, as well as the impact of diverse physiological conditions. From a 200-node network, that was grounded on experimental data, we derived a 21-node regulatory network by collapsing linear pathways and retaining the functional feedback loops. This regulatory network core integrates key nodes and interactions underlying HSCs differentiation, including transcription factors, metabolic, and redox signaling pathways. We used Boolean, continuous, and stochastic dynamic models to simulate the hypoxic conditions of the HSCs niche, as well as the patterns and temporal sequences of HSCs transitions and differentiation. Our findings indicate that HSCs differentiation is a plastic process in which cell fates can transdifferentiate among themselves. Additionally, we found that cell heterogeneity is fundamental for HSCs differentiation. Lastly, we found that oxygen activates ROS production, inhibiting quiescence and promoting growth and differentiation pathways of HSCs.

Metastasis is the leading cause of death in patients with cancer, driving considerable scientific and clinical interest in immunosurveillance of micrometastases. We investigated this process by creating a multiscale mathematical model to study the interactions between the immune system and the progression of micrometastases in general epithelial tissue. We analyzed the parameter space of the model using high-throughput computing resources to generate over 100,000 virtual patient trajectories. We demonstrated that the model could recapitulate a wide variety of virtual patient trajectories, including uncontrolled growth, partial response, and complete immune response to tumor growth. We classified the virtual patients and identified key patient parameters with the greatest effect on the simulated immunosurveillance. We highlight the lessons derived from this analysis and their impact on the nascent field of cancer patient digital twins (CPDTs). While CPDTs could enable clinicians to systematically dissect the complexity of cancer in each individual patient and inform treatment choices, our work shows that key challenges remain before we can reach this vision. In particular, we show that there remain considerable uncertainties in immune responses, unreliable patient stratification, and unpredictable personalized treatment. Nonetheless, we also show that in spite of these challenges, patient-specific models suggest strategies to increase control of clinically undetectable micrometastases even without complete parameter certainty.

Cutaneous T-cell lymphomas (CTCLs) are non-Hodgkin lymphomas caused by malignant T cells which migrate to the skin and lead to rash-like lesions which can be difficult to distinguish from inflammatory skin conditions like atopic dermatitis (AD) and psoriasis (PSO). To characterize CTCL in comparison to these differential diagnoses, we carried out multi-antigen imaging on 69 skin tissue samples (21 CTCL, 23 AD, 25 PSO). The resulting protein abundance maps were then analyzed via scoring functions to quantify the heterogeneity of the individual cells' neighborhoods within spatial graphs inferred from the cells' positions in the tissue samples. Our analyses reveal characteristic patterns of skin tissue organization in CTCL as compared to AD and PSO, including a combination of increased local entropy and egophily in T-cell neighborhoods. These results could not only pave the way for high-precision diagnosis of CTCL, but may also facilitate further insights into cellular disease mechanisms.

10X Visium, a popular Spatial transcriptomics (ST) method, faces limited adoption due to its high cost and restricted sample usage per slide. To address these issues, we propose Microarray Integrated Spatial Transcriptomics (MIST), combining conventional tissue microarray (TMA) with Visium, using laser-cutting and 3D printing to enhance slide throughput. Our design facilitates independent replication and customization in individual labs to suit specific experimental needs. We provide a step-by-step guide from designing TMAs to the library preparation step. We demonstrate MIST's cost-effectiveness and technical benefits over Visium and GeoMx Nanostring. We also introduce 'AnnotateMap', a novel computational tool for efficient analysis of multiple ROIs processed through MIST.