ACUTE AND SUSTAINED ALTERATIONS TO THE BONE MARROW IMMUNE MICROENVIRONMENT FOLLOWING POLYMICROBIAL INFECTION

Comprehensive Sepsis Risk Prediction in Leukemia Using a Random Forest Model and Restricted Cubic Spline Analysis

Background

Sepsis is a severe complication in leukemia patients, contributing to high mortality rates. Identifying early predictors of sepsis is crucial for timely intervention. This study aimed to develop and validate a predictive model for sepsis risk in leukemia patients using machine learning techniques.

Methods

This retrospective study included 4310 leukemia patients admitted to the Affiliated Hospital of Guangdong Medical University from 2005 to 2024, using 70% for training and 30% for validation. Feature selection was performed using univariate logistic regression, LASSO, and the Boruta algorithm, followed by multivariate logistic regression analysis. Seven machine learning models were constructed and evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Shapley additive explanations (SHAP) were applied to interpret the results, and restricted cubic spline (RCS) regression explored the nonlinear relationships between variables and sepsis risk. Furthermore, we examined the interactions among predictors to better understand their potential interrelationships.

Results

The random forest (RF) model outperformed all others, achieving an AUC of 0.765 in the training cohort and 0.700 in the validation cohort. Key predictors of sepsis identified by SHAP analysis included C-reactive protein (CRP), procalcitonin (PCT), neutrophil count (Neut), lymphocyte count (Lymph), thrombin time (TT), red blood cell count (RBC), total bile acid (TBA), and systolic blood pressure (SBP). RCS analysis revealed significant non-linear associations between CPR, PCT, Neut, Lymph, TT, RBC and SBP with sepsis risk. Pairwise correlation analysis further revealed interactions among these variables.

Conclusion

The RF model exhibited robust predictive power for sepsis in leukemia patients, providing clinicians with a valuable tool for early risk assessment and the optimization of treatment strategies.

Compartmentalization of the inflammatory response during bacterial sepsis and severe COVID-19

Acute infections cause local and systemic disorders which can lead in the most severe forms to multi-organ failure and eventually to death. The host response to infection encompasses a large spectrum of reactions with a concomitant activation of the so-called inflammatory response aimed at fighting the infectious agent and removing damaged tissues or cells, and the anti-inflammatory response aimed at controlling inflammation and initiating the healing process. Fine-tuning at the local and systemic levels is key to preventing local and remote injury due to immune system activation. Thus, during bacterial sepsis and Coronavirus disease 2019 (COVID-19), concomitant systemic and compartmentalized pro-inflammatory and compensatory anti-inflammatory responses are occurring. Immune cells (e.g., macrophages, neutrophils, natural killer cells, and T-lymphocytes), as well as endothelial cells, differ from one compartment to another and contribute to specific organ responses to sterile and microbial insult. Furthermore, tissue-specific microbiota influences the local and systemic response. A better understanding of the tissue-specific immune status, the organ immunity crosstalk, and the role of specific mediators during sepsis and COVID-19 can foster the development of more accurate biomarkers for better diagnosis and prognosis and help to define appropriate host-targeted treatments and vaccines in the context of precision medicine.

Survivors of polymicrobial sepsis are refractory to G-CSF-induced emergency myelopoiesis and hematopoietic stem and progenitor cell mobilization

Sepsis survivors exhibit immune dysfunction, hematological changes, and increased risk of infection. The long-term impacts of sepsis on hematopoiesis were analyzed using a surgical model of murine sepsis, resulting in 50% survival. During acute disease, phenotypic hematopoietic stem and progenitor cells (HSPCs) were reduced in the bone marrow (BM), concomitant with increased myeloid colony-forming units and extramedullary hematopoiesis. Upon recovery, BM HSPCs were increased and exhibited normal function in the context of transplantation. To evaluate hematopoietic responses in sepsis survivors, we treated recovered sham and cecal ligation and puncture mice with a mobilizing regimen of granulocyte colony-stimulating factor (G-CSF) at day 20 post-surgery. Sepsis survivors failed to undergo emergency myelopoiesis and HSPC mobilization in response to G-CSF administration. G-CSF is produced in response to acute infection and injury to expedite the production of innate immune cells; therefore, our findings contribute to a new understanding of how sepsis predisposes to subsequent infection.

When inflammatory stressors dramatically change, disease phenotypes may transform between autoimmune hematopoietic failure and myeloid neoplasms

Aplastic anemia (AA) and hypoplastic myelodysplastic syndrome are paradigms of autoimmune hematopoietic failure (AHF). Myelodysplastic syndrome and acute myeloid leukemia are unequivocal myeloid neoplasms (MNs). Currently, AA is also known to be a clonal hematological disease. Genetic aberrations typically observed in MNs are detected in approximately one-third of AA patients. In AA patients harboring MN-related genetic aberrations, a poor response to immunosuppressive therapy (IST) and an increased risk of transformation to MNs occurring either naturally or after IST are predicted. Approximately 10%-15% of patients with severe AA transform the disease phenotype to MNs following IST, and in some patients, leukemic transformation emerges during or shortly after IST. Phenotypic transformations between AHF and MNs can occur reciprocally. A fraction of advanced MN patients experience an aplastic crisis during which leukemic blasts are repressed. The switch that shapes the disease phenotype is a change in the strength of extramedullary inflammation. Both AHF and MNs have an immune-active bone marrow (BM) environment (BME). In AHF patients, an inflamed BME can be evoked by infiltrated immune cells targeting neoplastic molecules, which contributes to the BM-specific autoimmune impairment. Autoimmune responses in AHF may represent an antileukemic mechanism, and inflammatory stressors strengthen antileukemic immunity, at least in a significant proportion of patients who have MN-related genetic aberrations. During active inflammatory episodes, normal and leukemic hematopoieses are suppressed, which leads to the occurrence of aplastic cytopenia and leukemic cell regression. The successful treatment of underlying infections mitigates inflammatory stress-related antileukemic activities and promotes the penetration of leukemic hematopoiesis. The effect of IST is similar to that of treating underlying infections. Investigating inflammatory stress-powered antileukemic immunity is highly important in theoretical studies and clinical practice, especially given the wide application of immune-activating agents and immune checkpoint inhibitors in the treatment of hematological neoplasms.

SINGLE-CELL TRANSCRIPTOME ANALYSIS IN HEALTH AND DISEASE

ABSTRACT

The analysis of the single-cell transcriptome has emerged as a powerful tool to gain insights on the basic mechanisms of health and disease. It is widely used to reveal the cellular diversity and complexity of tissues at cellular resolution by RNA sequencing of the whole transcriptome from a single cell. Equally, it is applied to discover an unknown, rare population of cells in the tissue. The prime advantage of single-cell transcriptome analysis is the detection of stochastic nature of gene expression of the cell in tissue. Moreover, the availability of multiple platforms for the single-cell transcriptome has broadened its approaches to using cells of different sizes and shapes, including the capture of short or full-length transcripts, which is helpful in the analysis of challenging biological samples. And with the development of numerous packages in R and Python, new directions in the computational analysis of single-cell transcriptomes can be taken to characterize healthy versus diseased tissues to obtain novel pathological insights. Downstream analysis such as differential gene expression analysis, gene ontology term analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, cell-cell interaction analysis, and trajectory analysis has become standard practice in the workflow of single-cell transcriptome analysis to further examine the biology of different cell types. Here, we provide a broad overview of single-cell transcriptome analysis in health and disease conditions currently applied in various studies.

Bone Marrow: The Central Immune System

Bone marrow is known as the site of hematopoiesis. What is not being described in textbooks of immunology is the fact that bone marrow is not only a generative, but also an antigen-responsive, immune organ. It is also a major storage site for antigen-specific memory B and T cells. That bone marrow is a priming site for T cell responses to blood borne antigens was discovered exactly 20 years ago. This review celebrates this important discovery. The review provides a number of examples of medical relevance of bone marrow as a central immune system, including cancer, microbial infections, autoimmune reactions, and bone marrow transplantation. Bone marrow mesenchymal stem cell-derived stromal cells provide distinct bone marrow niches for stem cells and immune cells. By transmitting anti-inflammatory dampening effects, facilitating wound healing and tissue regeneration mesenchymal stem cells contribute to homeostasis of bone and other tissues. Based on the evidence presented, the review proposes that bone marrow is a multifunctional and protective immune system. In an analogy to the central nervous system, it is suggested that bone marrow be designated as the central immune system.