Classification of Patients With Sepsis According to Immune Cell Characteristics: A Bioinformatic Analysis of Two Cohort Studies

Immune dysregulation in sepsis: experiences, lessons and perspectives

Sepsis is a life-threatening organ dysfunction syndrome caused by dysregulated host responses to infection. Not only does sepsis pose a serious hazard to human health, but it also imposes a substantial economic burden on the healthcare system. The cornerstones of current treatment for sepsis remain source control, fluid resuscitation, and rapid administration of antibiotics, etc. To date, no drugs have been approved for treating sepsis, and most clinical trials of potential therapies have failed to reduce mortality. The immune response caused by the pathogen is complex, resulting in a dysregulated innate and adaptive immune response that, if not promptly controlled, can lead to excessive inflammation, immunosuppression, and failure to re-establish immune homeostasis. The impaired immune response in patients with sepsis and the potential immunotherapy to modulate the immune response causing excessive inflammation or enhancing immunity suggest the importance of demonstrating individualized therapy. Here, we review the immune dysfunction caused by sepsis, where immune cell production, effector cell function, and survival are directly affected during sepsis. In addition, we discuss potential immunotherapy in septic patients and highlight the need for precise treatment according to clinical and immune stratification.

Exploring the Role of Different Cell-Death-Related Genes in Sepsis Diagnosis Using a Machine Learning Algorithm

Sepsis, a disease caused by severe infection, has a high mortality rate. At present, there is a lack of reliable algorithmic models for biomarker mining and diagnostic model construction for sepsis. Programmed cell death (PCD) has been shown to play a vital role in disease occurrence and progression, and different PCD-related genes have the potential to be targeted for the treatment of sepsis. In this paper, we analyzed PCD-related genes in sepsis. Implicated PCD processes include apoptosis, necroptosis, ferroptosis, pyroptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, and alkaliptosis. We screened for diagnostic-related genes and constructed models for diagnosing sepsis using multiple machine-learning models. In addition, the immune landscape of sepsis was analyzed based on the diagnosis-related genes that were obtained. In this paper, 10 diagnosis-related genes were screened for using machine learning algorithms, and diagnostic models were constructed. The diagnostic model was validated in the internal and external test sets, and the Area Under Curve (AUC) reached 0.7951 in the internal test set and 0.9627 in the external test set. Furthermore, we verified the diagnostic gene via a qPCR experiment. The diagnostic-related genes and diagnostic genes obtained in this paper can be utilized as a reference for clinical sepsis diagnosis. The results of this study can act as a reference for the clinical diagnosis of sepsis and for target discovery for potential therapeutic drugs.

IFIH1/IRF1/STAT1 promotes sepsis associated inflammatory lung injury via activating macrophage M1 polarization

BACKGROUND

A growing body of research has shown that the phenotypic change in macrophages from M0 to M1 is essential for the start of the inflammatory process in septic acute respiratory distress syndrome (ARDS). Potential treatment targets might be identified with more knowledge of the molecular regulation of M1 macrophages in septic ARDS.

METHODS

A multi-microarray interrelated analysis of high-throughput experiments from ARDS patients and macrophage polarization was conducted to identify the hub genes associated with macrophage M1 polarization and septic ARDS. Lipopolysaccharide (LPS) and Poly (I:C) were utilized to stimulate bone marrow-derived macrophages (BMDMs) for M1-polarized macrophage model construction. Knock down of the hub genes on BMDMs via shRNAs was used to screen the genes regulating macrophage M1 polarization in vitro. The cecal ligation and puncture (CLP) mouse model was constructed in knockout (KO) mice and wild-type (WT) mice to explore whether the screened genes regulate macrophage M1 polarization in septic ARDS in vivo. ChIP-seq and further experiments on BMDMs were performed to investigate the molecular mechanism.

RESULTS

The bioinformatics analysis of gene expression profiles from a clinical cohort of 26 ARDS patients and macrophage polarization found that the 5 hub genes (IFIH1, IRF1, STAT1, IFIT3, GBP1) may have a synergistic effect on macrophage M1 polarization in septic ARDS. Further in vivo investigations indicated that IFIH1, STAT1 and IRF1 contribute to macrophage M1 polarization. The histological evaluation and immunohistochemistry of the lungs from the IRF1^-/- and WT mice indicated that knockout of IRF1 markedly alleviated CLP-induced lung injury and M1-polarized infiltration. Moreover, the molecular mechanism investigations indicated that knockdown of IFIH1 markedly promoted IRF1 translocation into the nucleus. Knockout of IRF1 significantly decreases the expression of STAT1. ChIP-seq and PCR further confirmed that IRF1, as a transcription factor of STAT1, binds to the promoter region of STAT1.

CONCLUSION

IRF1 was identified as the key molecule that regulates macrophage M1polarization and septic ARDS development in vivo and in vitro. Moreover, as the adaptor in response to infection mimics irritants, IFIH1 promotes IRF1 (transcription factor) translocation into the nucleus to initiate STAT1 transcription.

Pathway Analysis of Genome Wide Association Studies (GWAS) Data Associated with Male Infertility

Background: Infertility is a common condition affecting approximately 10–20% of the reproductive age population. Idiopathic infertility cases are thought to have a genetic basis, but the underlying causes are largely unknown. However, the genetic basis underlying male infertility in humans is only partially understood. The Purpose of the study is to understand the current state of research on the genetics of male infertility and its association with significant biological mechanisms. Results: We performed an Identify Candidate Causal SNPs and Pathway (ICSN Pathway) analysis using a genome-wide association study (GWAS) dataset, and NCBI-PubMed search which included 632 SNPs in GWAS and 451 SNPs from the PubMed server, respectively. The ICSN Pathway analysis produced three hypothetical biological mechanisms associated with male infertility: (1) rs8084 and rs7192→HLA-DRA→inflammatory pathways and cell adhesion; rs7550231 and rs2234167→TNFRSF14→TNF Receptor Superfamily Member 14→T lymphocyte proliferation and activation; rs1105879 and rs2070959→UGT1A6→UDP glucuronosyltransferase family 1 member A6→Metabolism of Xenobiotics, androgen, estrogen, retinol, and carbohydrates. Conclusions: We believe that our results may be helpful to study the genetic mechanisms of male infertility. Pathway-based methods have been applied to male infertility GWAS datasets to investigate the biological mechanisms and reported some novel male infertility risk pathways. This pathway analysis using GWAS dataset suggests that the biological process related to inflammation and metabolism might contribute to male infertility susceptibility. Our analysis suggests that genetic contribution to male infertility operates through multiple genes affecting common inflammatory diseases interacting in functional pathways.

A combined risk model for the multi-encompassing identification of heterogeneities of prognoses, biological pathway variations and immune states for sepsis patients

Background

Sepsis is a highly heterogeneous syndrome with stratified severity levels and immune states. Even in patients with similar clinical appearances, the underlying signal transduction pathways are significantly different. To identify the heterogeneities of sepsis from multiple angles, we aimed to establish a combined risk model including the molecular risk score for rapid mortality prediction, pathway risk score for the identification of biological pathway variations, and immunity risk score for guidance with immune-modulation therapy.

Methods

We systematically searched and screened the mRNA expression profiles of patients with sepsis in the Gene Expression Omnibus public database. The screened datasets were divided into a training cohort and a validation cohort. In the training cohort, authentic prognostic predictor characteristics (differentially expressed mRNAs, pathway activity variations and immune cells) were screened for model construction through bioinformatics analysis and univariate Cox regression, and a P value less than 0.05 of univariate Cox regression on 28-day mortality was set as the cut-off value. The combined risk model was finally established by the decision tree algorithm. In the validation cohort, the model performance was assessed and validated by C statistics and the area under the receiver operating characteristic curve (AUC). Additionally, the current models were further compared in clinical value with traditional indicators, including procalcitonin (PCT) and interleukin-8 (IL-8).

Results

Datasets from two sepsis cohort studies with a total of 585 consecutive sepsis patients admitted to two intensive care units were downloaded as the training cohort (n = 479) and external validation cohort (n = 106). In the training cohort, 15 molecules, 20 pathways and 4 immune cells were eventually enrolled in model construction. These prognostic factors mainly reflected hypoxia, cellular injury, metabolic disorders and immune dysregulation in sepsis patients. In the validation cohort, the AUCs of the molecular model, pathway model, immune model, and combined model were 0.81, 0.82, 0.62 and 0.873, respectively. The AUCs of the traditional biomarkers (PCT and IL-8) were 0.565 and 0.585, respectively. The survival analysis indicated that patients in the high-risk group identified by models in the current study had a poor prognosis (P < 0.05). The above results indicated that the models in this study are all superior to the traditional biomarkers for the predicting the prognosis of sepsis patients. Furthermore, the current study provides some therapeutic recommendations for patients with high risk scores identified by the three submodels.

Conclusions

In summary, the present study provides opportunities for bedside tests that could quantitatively and rapidly measure heterogeneous prognosis, underlying biological pathway variations and immune dysfunction in sepsis patients. Further therapeutic recommendations for patients with high risk scores could improve the therapeutic system for sepsis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01552-x.

Immunomonitoring of Monocyte and Neutrophil Function in Critically Ill Patients: From Sepsis and/or Trauma to COVID-19

Immune cells and mediators play a crucial role in the critical care setting but are understudied. This review explores the concept of sepsis and/or injury-induced immunosuppression and immuno-inflammatory response in COVID-19 and reiterates the need for more accurate functional immunomonitoring of monocyte and neutrophil function in these critically ill patients. in addition, the feasibility of circulating and cell-surface immune biomarkers as predictors of infection and/or outcome in critically ill patients is explored. It is clear that, for critically ill, one size does not fit all and that immune phenotyping of critically ill patients may allow the development of a more personalized approach with tailored immunotherapy for the specific patient. In addition, at this point in time, caution is advised regarding the quality of evidence of some COVID-19 studies in the literature.