Transcriptomic meta-analysis reveals up-regulation of gene expression functional in osteoclast differentiation in human septic shock

The molecular landscape of sepsis severity in infants: enhanced coagulation, innate immunity, and T cell repression

Introduction

Sepsis remains a major cause of mortality and morbidity in infants. In recent years, several gene marker strategies for the early identification of sepsis have been proposed but only a few have been independently validated for adult cohorts and applicability to infant sepsis remains unclear. Biomarkers to assess disease severity and risks of shock also represent an important unmet need.

Methods

To elucidate characteristics driving sepsis in infants, we assembled a multi-transcriptomic dataset from public microarray datasets originating from five independent studies pertaining to bacterial sepsis in infant < 6-months of age (total n=335). We utilized a COmbat co-normalization strategy to enable comparative evaluation across multiple studies while preserving the relationship between cases and controls.

Results

We found good concordance with only two out of seven of the published adult sepsis gene signatures (accuracy > 80%), highlighting the narrow utility of adult-derived signatures for infant diagnosis. Pseudotime analysis of individual subjects' gene expression profiles showed a continuum of molecular changes forming tight clusters concurrent with disease progression between healthy controls and septic shock cases. In depth gene expression analyses between bacteremia, septic shock, and healthy controls characterized lymphocyte activity, hemostatic processes, and heightened innate immunity during the molecular transition toward a state of shock.

Discussion

Our analysis revealed the presence of multiple significant transcriptomic perturbations that occur during the progression to septic shock in infants that are characterized by late-stage induction of clotting factors, in parallel with a heightened innate immune response and a suppression of adaptive cell functionality.

Bacterial heat shock protein: A new crosstalk between T lymphocyte and macrophage via JAK2/STAT1 pathway in bloodstream infection

Bloodstream infection (BSI) refers to the infection of blood by pathogens. Severe immune response to BSI can lead to sepsis, a systemic infection leading to multiple organ dysfunction, coupled with drug resistance, mortality, and limited clinical treatment options. This work aims to further investigate the new interplay between bacterial exocrine regulatory protein and host immune cells in the context of highly drug-resistant malignant BSI. Whether interfering with related regulatory signaling pathways can reverse the inflammatory disorder of immune cells. In-depth analysis of single-cell sequencing results in Septic patients for potential immunodeficiency factors. Analysis of key proteins enriched by host cells and key pathways using proteomics. Cell models and animal models validate the pathological effects of DnaK on T cells, MAITs, macrophages, and osteoclasts. The blood of patients was analyzed for the immunosuppression of T cells and MAITs. We identified that S. maltophilia-DnaK was enriched in immunodeficient T cells. The activation of the JAK2/STAT1 axis initiated the exhaustion of T cells. Septic patients with Gram-negative bacterial infections exhibited deficiencies in MAITs, which correspond to IFN-γ. Cellular and animal experiments confirmed that DnaK could facilitate MAIT depletion and M1 polarization of macrophages. Additionally, Fludarabine mitigated M1 polarization of blood, liver, and spleen in mice. Interestingly, DnaK also repressed osteoclastogenesis of macrophages stimulated by RANKL. S.maltophilia-DnaK prompts the activation of the JAK2/STAT1 axis in T cells and the M1 polarization of macrophages. Targeting the DnaK's crosstalk can be a potentially effective approach for treating the inflammatory disorder in the broad-spectrum drug-resistant BSI.

Identification of module genes and functional pathway analysis in septic shock subtypes by integrated bioinformatics analysis

BACKGROUND

The present study aimed to identify the module genes and key gene functions and biological pathways of septic shock (SS) through integrated bioinformatics analysis.

METHODS

In the study, we performed batch correction and principal component analysis on 282 SS samples and 79 normal control samples in three datasets, GSE26440, GSE95233 and GSE57065, to obtain a combined corrected gene expression matrix containing 21,654 transcripts. Patients with SS were then divided into three molecular subtypes according to sample subtyping analysis.

RESULTS

By analyzing the demographic characteristics of the different subtypes, we found no statistically significant differences in gender ratio and age composition among the three groups. Then, three subtypes of differentially expressed genes (DEGs) and specific upregulated DEGs (SDEGs) were identified by differential gene expression analysis. We found 7361 DEGs in the type I group, 5594 DEGs in the type II group, and 7159 DEGs in the type III group. There were 1698 SDEGs in the type I group, 2443 in the type II group, and 1831 in the type III group. In addition, we analyzed the correlation between the expression data of 5972 SDEGs in the three subtypes and the gender and age of 227 patients, constructed a weighted gene co-expression network, and identified 11 gene modules, among which the module with the highest correlation with gender ratio was MEgrey. The modules with the highest correlation with age composition were MEgrey60 and MElightyellow. Then, by analyzing the differences in module genes among different subgroups of SS, we obtained the differential expression of 11 module genes in four groups: type I, type II, type III and the control group. Finally, we analyzed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of all module DEGs, and the GO function and KEGG pathway enrichment of different module genes were different.

CONCLUSIONS

Our findings aim to identify the specific genes and intrinsic molecular functional pathways of SS subtypes, as well as further explore the genetic and molecular pathophysiological mechanisms of SS.

Bone homeostasis disorders increased the mortality of sepsis patients: A preliminary retrospective cohort study

Introduction

Sepsis is a common clinical syndrome and nearly 20% of all deaths are related to sepsis. As an important part of the body, bone homeostasis disorders are closely related to inflammatory response, but the correlation between bone homeostasis and sepsis, sepsis shock was unknown. The objective of this study was to explore the relation of bone homeostasis on sepsis and sepsis shock.

Methods

In this retrospective cohort study, patients were enrolled between April 2018 and May 2022 from Beijing Chaoyang hospital. Primary outcomes were serum indicators reflected bone homeostasis, such as cross-linked carboxy-terminal telopeptide of type I collagen (CTX-I), tartrate-resistant acid phosphatase 5b (TRACP-5b) and piezo-type mechanosensitive ion channel component 1 (PIEZO1).

Results

The data were analyzed retrospectively. among 88 evaluable patients, 45 were sepsis (19 were sepsis shock) and 43 were non-sepsis. There was no significant difference in age, gender, BMI, combination diseases, operation time, intraoperative blood loss, and hospital stay. Patients with sepsis or sepsis shock had higher serum CTX-I, TRACP-5b, PIEZO1 (p &lt; 0.05). Spearman’s rank correlation test showed that CTX-I, TRACP-5b, PIEZO1 and the three together (CTX-I + TRACP-5b + PIEZO1) had strong correlation with sepsis or sepsis shock (p &lt; 0.05). The receiver operating characteristic curve (ROC) and precision-recall curve (PRC) showed that these indicators could predict the occurrence of sepsis or sepsis shock (p &lt; 0.05). Besides, decision curve analysis (DCA) and interventions avoided curve (IAC) displayed a high net benefit of bone homeostasis disorders indicators on sepsis or sepsis shock. Kaplan–Meier survival curves revealed that sepsis or shock patients with high value indicators (&gt;0.47227) had a higher mortality (p &lt; 0.05).

Conclusion

Bone homeostasis disorders could increase the mortality of sepsis and sepsis shock patients.

Estimates of gene ensemble noise highlight critical pathways and predict disease severity in H1N1, COVID-19 and mortality in sepsis patients

Finding novel biomarkers for human pathologies and predicting clinical outcomes for patients is challenging. This stems from the heterogeneous response of individuals to disease and is reflected in the inter-individual variability of gene expression responses that obscures differential gene expression analysis. Here, we developed an alternative approach that could be applied to dissect the disease-associated molecular changes. We define gene ensemble noise as a measure that represents a variance for a collection of genes encoding for either members of known biological pathways or subunits of annotated protein complexes and calculated within an individual. The gene ensemble noise allows for the holistic identification and interpretation of gene expression disbalance on the level of gene networks and systems. By comparing gene expression data from COVID-19, H1N1, and sepsis patients we identified common disturbances in a number of pathways and protein complexes relevant to the sepsis pathology. Among others, these include the mitochondrial respiratory chain complex I and peroxisomes. This suggests a Warburg effect and oxidative stress as common hallmarks of the immune host-pathogen response. Finally, we showed that gene ensemble noise could successfully be applied for the prediction of clinical outcome namely, the mortality of patients. Thus, we conclude that gene ensemble noise represents a promising approach for the investigation of molecular mechanisms of pathology through a prism of alterations in the coherent expression of gene circuits.

Experimental Acute Sepsis Reduced Number of Osteocalcin Immunolabeled Cells in Periodontal Ligament

The aim of this study was to evaluate the effect of acute sepsis in the periodontal ligament, alveolar and furcation bone in absence of periodontitis induction through histological and immunohistochemical analyses. A septic rat model was established by cecal ligation and puncture (CLP). Twelve rats were randomly divided into CLP (n=6) and Sham (n=6) groups. The animals were euthanized at 24 h and hemimandibles were submitted to histomorfometric (bone matrix, collagenous fibers, fibroblasts, osteocytes, inflammatory cells, and blood vessels) and immunohistochemical (BMP-2/4, RANKL and osteocalcin) evaluation in alveolar bone, furcation bone and periodontal ligament. Our results demonstrated that histomorphometric parameters were similar in alveolar bone, furcation bone and periodontal ligament of Sham and CLP rats. Regarding to immunohistochemical analyses, the number of BMP-2/4 and RANKL immunolabeled cells was also similar in both groups. Furthermore, it was detected a reduction in the osteocalcin immunolabeled cells in periodontal ligaments of CLP compared to Sham rats (p=0.0014). In conclusion, the acute sepsis induction resulted in reduced number of osteocalcin labelled cells in periodontal ligament region. Moreover, no significant histological differences were observed in the periodontium of rats under acute sepsis. Considering the role of osteocalcin in bone remodeling, the study contributes to revealing the importance of careful periodontal evaluation in the presence of sepsis.

Sepsis-associated pathways segregate cancer groups

BACKGROUND

Sepsis and cancer are both leading causes of death, and occurrence of any one, increases the likelihood of the other. While cancer patients are susceptible to sepsis, survivors of sepsis are also susceptible to develop certain cancers. This mutual dependence for susceptibility suggests shared biology between the two disease categories. Earlier analysis had revealed a cancer-related pathway to be up-regulated in Septic Shock (SS), an advanced stage of sepsis. This has motivated a more comprehensive comparison of the transcriptomes of SS and cancer.

METHODS

Gene Set Enrichment Analysis was performed to detect the pathways enriched in SS and cancer. Thereafter, hierarchical clustering was applied to identify relative segregation of 17 cancer types into two groups vis-a-vis SS. Biological significance of the selected pathways was explored by network analysis. Clinical significance of the pathways was tested by survival analysis. A robust classifier of cancer groups was developed based on machine learning.

RESULTS

A total of 66 pathways were observed to be enriched in both SS and cancer. However, clustering segregated cancer types into two categories based on the direction of transcriptomic change. In general, there was up-regulation in SS and one group of cancer (termed Sepsis-Like Cancer, or SLC), but not in other cancers (termed Cancer Alone, or CA). The SLC group mainly consisted of malignancies of the gastrointestinal tract (head and neck, oesophagus, stomach, liver and biliary system) often associated with infection. Machine learning classifier successfully segregated the two cancer groups with high accuracy (> 98%). Additionally, pathway up-regulation was observed to be associated with survival in the SLC group of cancers.

CONCLUSION

Transcriptome-based systems biology approach segregates cancer into two groups (SLC and CA) based on similarity with SS. Host response to infection plays a key role in pathogenesis of SS and SLC. However, we hypothesize that some component of the host response is protective in both SS and SLC.