Predicting sepsis severity at first clinical presentation: The role of endotypes and mechanistic signatures

Severe COVID-19 and non-COVID-19 severe sepsis converge transcriptionally after a week in the intensive care unit, indicating common disease mechanisms

Introduction

Severe COVID-19 and non-COVID-19 pulmonary sepsis share pathophysiological, immunological, and clinical features. To what extent they share mechanistically-based gene expression trajectories throughout hospitalization was unknown. Our objective was to compare gene expression trajectories between severe COVID-19 patients and contemporaneous non-COVID-19 severe sepsis patients in the intensive care unit (ICU).

Methods

In this prospective single-center observational cohort study, whole blood was drawn from 20 COVID-19 patients and 22 non-COVID-19 adult sepsis patients at two timepoints: ICU admission and approximately a week later. RNA-Seq was performed on whole blood to identify differentially expressed genes and significantly enriched pathways.

Results

At ICU admission, despite COVID-19 patients being almost clinically indistinguishable from non-COVID-19 sepsis patients, COVID-19 patients had 1,215 differentially expressed genes compared to non-COVID-19 sepsis patients. After one week in the ICU, the number of differentially expressed genes dropped to just 9 genes. This drop coincided with decreased expression of antiviral genes and relatively increased expression of heme metabolism genes over time in COVID-19 patients, eventually reaching expression levels seen in non-COVID-19 sepsis patients. Both groups also had similar underlying immune dysfunction, with upregulation of immune processes such as "Interleukin-1 signaling" and "Interleukin-6/JAK/STAT3 signaling" throughout disease compared to healthy controls.

Discussion

Early on, COVID-19 patients had elevated antiviral responses and suppressed heme metabolism processes compared to non-COVID-19 severe sepsis patients, although both had similar underlying immune dysfunction. However, after one week in the ICU, these diseases became indistinguishable on a gene expression level. These findings highlight the importance of early antiviral treatment for COVID-19, the potential for heme-related therapeutics, and consideration of immunomodulatory therapies for both diseases to treat shared immune dysfunction.

Transcriptomic clustering of critically ill COVID-19 patients

BACKGROUND

Infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may cause a severe disease, termed coronavirus disease 2019 (COVID-19), with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms and their modulation has shown a mortality benefit.

METHODS

In a cohort of 56 critically ill COVID-19 patients, peripheral blood transcriptomes were obtained at admission to an intensive care unit (ICU) and clustered using an unsupervised algorithm. Differences in gene expression, circulating microRNAs (c-miRNAs) and clinical data between clusters were assessed, and circulating cell populations estimated from sequencing data. A transcriptomic signature was defined and applied to an external cohort to validate the findings.

RESULTS

We identified two transcriptomic clusters characterised by expression of either interferon-related or immune checkpoint genes, respectively. Steroids have cluster-specific effects, decreasing lymphocyte activation in the former but promoting B-cell activation in the latter. These profiles have different ICU outcomes, despite no major clinical differences at ICU admission. A transcriptomic signature was used to identify these clusters in two external validation cohorts (with 50 and 60 patients), yielding similar results.

CONCLUSIONS

These results reveal different underlying pathogenetic mechanisms and illustrate the potential of transcriptomics to identify patient endotypes in severe COVID-19 with the aim to ultimately personalise their therapies.

Co-expression and interaction network analysis reveals dysregulated neutrophil and T-cell activation as the core mechanism associated with septic shock

Septic shock as a subset of sepsis, has a much higher mortality, while the mechanism is still elusive. This study was aimed at identifying core mechanisms associated with septic shock and its high mortality by investigating transcriptome data. We screened 72 septic-shock-associated genes (SSAGs) with differential expression between septic shock and sepsis in the discovery dataset. Further gene set enrichment analysis identified upregulated neutrophil activation and impaired T-cell activation in septic shock. Co-expression analysis revealed nine co-expressed gene modules. In addition, we determined twenty-one prognostic SSAGs using cox regression analysis in an independent dataset. Moreover, protein-protein interaction (PPI) network revealed two clusters. Among these neutrophil activation was enriched in the most positively-related modules and the cluster2 PPI network, while T-cell activation was enriched in both the most negatively-related module and one of the most positively-related modules as well as the cluster1 PPI network. ELANE, LCN2 and IFI44 were identified as hub genes with CytoHubba methods and semantic similarity analysis. Notably, ELANE was the only prognostic gene and was further validated in an external dataset. Blood neutrophil count was demonstrated to increase in septic shock and be a risky factor of prognosis based on clinical data. In conclusions, septic shock is associated with upregulated neutrophil activation and dysregulated T-cell activation. Three hub genes might have potentials as sensitive markers for the further translational research and ELANE could be a robust prognostic biomarker and effective therapeutic target.

Immunopathophysiology of human sepsis

Sepsis is an ill-defined syndrome yet is a leading cause of morbidity and mortality worldwide. The most recent consensus defines sepsis as life-threatening organ dysfunction caused by a dysregulated host response to infection. However, this definition belies the complexity and breadth of immune mechanisms involved in sepsis, which are characterized by simultaneous hyperinflammation and immune suppression. In this review, we describe the immunopathogenesis of sepsis and highlight some recent pathophysiological findings that have expanded our understanding of sepsis. Sepsis endotypes can be used to divide sepsis patients in different groups with distinct immune profiles and outcomes. We also summarize evidence on the role of the gut microbiome in sepsis immunity. The challenge of the coming years will be to translate our increasing knowledge about the molecular mechanisms underlying sepsis into therapies that improve relevant patient outcomes.

Association of multiple sclerosis with mortality in sepsis: a population-level analysis

Background

Multiple sclerosis (MS) is associated with increased risk of sepsis and higher sepsis-related mortality, compared to the general population. However, the evidence on the prognostic impact of MS in sepsis has been scarce. We aimed to evaluate the population-level association of MS with short-term mortality in sepsis.

Methods

We performed a retrospective population-based cohort study using a statewide data set to identify hospitalizations aged ≥ 18 years in Texas with sepsis, with and without MS during 2010–2017. Multilevel logistic models were fit to estimate the association of MS with short-term mortality among all sepsis hospitalizations, and for sensitivity analyses among hospitalizations with septic shock and those admitted to ICU.

Results

Among 283,025 sepsis hospitalizations, 1687 (0.6%) had MS. Compared to sepsis hospitalizations without MS, those with MS were younger (aged ≥ 65 years 35.0% vs 56.8%), less commonly racial/ethnic minority (36.2% vs 48.1%), and had lower mean Deyo comorbidity index (1.6 vs 2.7). The rates of septic shock and ICU admission were similar for sepsis hospitalizations with and without MS (58.7% vs 59.6% and 46.7% vs 46.0%, respectively). The unadjusted short-term mortality among sepsis hospitalizations with and without MS for the whole cohort, among those with septic shock, and among ICU admissions were 20.2% vs 31.3%, 25.6% vs 40.0%, and 24.0% vs 34.8%, respectively. On adjusted analyses, MS was associated with 17% lower odds of short-term mortality (adjusted odds ratio [aOR] 0.828 [95% CI 0.723–0.947]). Similar findings were observed on sensitivity analyses of patients with septic shock (aOR 0.764 [95% CI 0.651–0.896]), but MS was not associated with mortality among sepsis hospitalizations admitted to ICU (aOR 0.914 [95% CI 0.759–1.101]).

Conclusions

MS was associated with lower short-term mortality among septic patients, with findings consistent among the subset with septic shock. Among septic patients admitted to ICU, MS was not associated with mortality.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40560-022-00628-1.

Role of miRNA dysregulation in sepsis

Background

Sepsis is defined as a state of multisystem organ dysfunction secondary to a dysregulated host response to infection and causes millions of deaths worldwide annually. Novel ways to counteract this disease are needed and such tools may be heralded by a detailed understanding of its molecular pathogenesis. MiRNAs are small RNA molecules that target mRNAs to inhibit or degrade their translation and have important roles in several disease processes including sepsis.

Main body

The current review adopted a strategic approach to analyzing the widespread literature on the topic of miRNAs and sepsis. A pubmed search of “miRNA or microRNA or small RNA and sepsis not review” up to and including January 2021 led to 1140 manuscripts which were reviewed. Two hundred and thirty-three relevant papers were scrutinized for their content and important themes on the topic were identified and subsequently discussed, including an in-depth look at deregulated miRNAs in sepsis in peripheral blood, myeloid derived suppressor cells and extracellular vesicles.

Conclusion

Our analysis yielded important observations. Certain miRNAs, namely miR-150 and miR-146a, have consistent directional changes in peripheral blood of septic patients across numerous studies with strong data supporting a role in sepsis pathogenesis. Furthermore, a large body of literature show miRNA signatures of clinical relevance, and lastly, many miRNAs deregulated in sepsis are associated with the process of endothelial dysfunction. This review offers a widespread, up-to-date and detailed discussion of the role of miRNAs in sepsis and is meant to stimulate further work in the field due to the potential of these small miRNAs in prompt diagnostics, prognostication and therapeutic agency.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-022-00527-z.

Identification of two robust subclasses of sepsis with both prognostic and therapeutic values based on machine learning analysis

Background

Sepsis is a heterogeneous syndrome with high morbidity and mortality. Optimal and effective classifications are in urgent need and to be developed.

Methods and results

A total of 1,936 patients (sepsis samples, n=1,692; normal samples, n=244) in 7 discovery datasets were included to conduct weighted gene co-expression network analysis (WGCNA) to filter out candidate genes related to sepsis. Then, two subtypes of sepsis were classified in the training sepsis set (n=1,692), the Adaptive and Inflammatory, using K-means clustering analysis on 90 sepsis-related features. We validated these subtypes using 617 samples in 5 independent datasets and the merged 5 sets. Cibersort method revealed the Adaptive subtype was related to high infiltration levels of T cells and natural killer (NK) cells and a better clinical outcome. Immune features were validated by single-cell RNA sequencing (scRNA-seq) analysis. The Inflammatory subtype was associated with high infiltration of macrophages and a disadvantageous prognosis. Based on functional analysis, upregulation of the Toll-like receptor signaling pathway was obtained in Inflammatory subtype and NK cell-mediated cytotoxicity and T cell receptor signaling pathway were upregulated in Adaptive group. To quantify the cluster findings, a scoring system, called, risk score, was established using four datasets (n=980) in the discovery cohorts based on least absolute shrinkage and selection operator (LASSO) and logistic regression and validated in external sets (n=760). Multivariate logistic regression analysis revealed the risk score was an independent predictor of outcomes of sepsis patients (OR [odds ratio], 2.752, 95% confidence interval [CI], 2.234-3.389, P<0.001), when adjusted by age and gender. In addition, the validation sets confirmed the performance (OR, 1.638, 95% CI, 1.309-2.048, P<0.001). Finally, nomograms demonstrated great discriminatory potential than that of risk score, age and gender (training set: AUC=0.682, 95% CI, 0.643-0.719; validation set: AUC=0.624, 95% CI, 0.576-0.664). Decision curve analysis (DCA) demonstrated that the nomograms were clinically useful and had better discriminative performance to recognize patients at high risk than the age, gender and risk score, respectively.

Conclusions

In-depth analysis of a comprehensive landscape of the transcriptome characteristics of sepsis might contribute to personalized treatments and prediction of clinical outcomes.

Adverse outcomes with extracorporeal adsorbent blood treatments in toxic systemic inflammation: a perspective on possible mechanisms

Background

Extracorporeal blood purification (EBP) treatments may be used in patients with sepsis and related conditions to mitigate toxic systemic inflammation, prevent or reverse vital organ injury, and improve outcome. These treatments lack demonstrable efficacy, but are generally considered safe. However, since late 2020, four clinical studies of EBP treatment using adsorbent devices in inflammatory disease reported significantly increased patient mortality associated with the adsorbent treatments. Criticisms of study design and execution were published, but revealed no decisive flaws. None of these critiques considered possible toxic effects of the adsorbent treatments per se.

Perspective and conclusion

In adsorbent EBP treatment of systemic inflammatory disease the adsorbent media are deployed in patient blood or plasma flow for the purpose of broad spectrum, non-specific adsorptive removal of inflammatory mediators. Adsorption and sequestration of inflammatory mediators by adsorbent media is intended to reduce mediator concentrations in circulating blood and neutralize their activity. However, in the past two decades developments in both biomedical engineering and the science of cytokine molecular dynamics suggest that immobilization of inflammatory proteins on solid scaffolds or molecular carriers may stabilize protein structure and preserve or amplify protein function. It is unknown if these mechanisms are operative in EBP adsorbent treatments. If these mechanisms are operative, then the adsorbent medium could become reactive, promoting inflammatory activity which could result in negative outcomes. Considering the recent reports of harm with adsorbent treatments in diverse inflammatory conditions, caution urges investigation of these potentially harmful mechanisms in these devices. Candidate mechanisms for possible inquiry are discussed.

An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.

Omaveloxolone attenuates the sepsis-induced cardiomyopathy via activating the nuclear factor erythroid 2-related factor 2

Sepsis-induced cardiomyopathy (SIC) is a common complication of sepsis and is the main reason for the high mortality in sepsis patients. More recent studies have indicated that activating nuclear factor erythroid 2-related factor 2 (Nrf2) signaling plays a protective role in SIC. As a potent activator of Nrf2, Omaveloxolone plays a pivotal role in defending against oxidative stress and the inflammatory response. Thus, we examined the efficacy of omaveloxolone in SIC. In the present study, the mice were injected intraperitoneally with a single dose of LPS (10 mg/kg) for 12 h to induce SIC. The data in our study indicated that omaveloxolone administration significantly improved cardiac injury and dysfunction in LPS-induced SIC. In addition, omaveloxolone administration reduced SIC-related cardiac oxidative stress, the inflammatory response and cardiomyocyte apoptosis in mice. In addition, omaveloxolone administration also improved LPS-induced cardiomyocyte injury in an in vitro model using H9C2 cells. Moreover, knockdown of Nrf2 by si-Nrf2 abolished the omaveloxolone-mediated cardioprotective effects. In conclusion, omaveloxolone has potent cardioprotective potential in treating sepsis and SIC via activation of the Nrf2 signaling pathway.

Significant difference of differential expression pyroptosis-related genes and their correlations with infiltrated immune cells in sepsis

Background

Sepsis is regarded as a life-threatening organ dysfunction syndrome that responds to infection. Pyroptosis, a unique form of programmed cell death, is characterized by inflammatory cytokine secretion. Recently, an increasing number of studies have investigated the relationship between sepsis and pyroptosis. Appropriate pyroptosis can help to control infection during sepsis, but an immoderate one may cause immune disorders. The present study aimed to identify pyroptosis-related gene biomarkers and their relationship with the immune microenvironment using the genome-wide technique.

Methods

The training dataset GSE154918 and the validation dataset GSE185263 were downloaded for bioinformatics analysis. Differentially expressed pyroptosis-related genes (DEPRGs) were identified between sepsis (including septic shock) and healthy samples. Gene Set Enrichment Analysis (GSEA) was performed to explore gene function. CIBERSORT tools were applied to quantify infiltrating immune cells, and the correlation between differentially infiltrating immune cells and DEPRG expression was investigated. Furthermore, based on multivariable Cox regression, the study also utilized a random forest (RF) model to screen biomarkers.

Results

In total, 12 DEPRGs were identified. The expression level of PLCG1 was continuously significantly decreased, while the expression level of NLRC4 was elevated from control to sepsis and then to septic shock. GSEA found that one DEPRG (PLCG1) was involved in the T-cell receptor signaling pathway and that many T cell-related immunologic signature gene sets were enriched. The proportions of plasma cells, T cells CD4 memory activated, and some innate cells in the sepsis group were significantly higher than those in the healthy group, while the proportions of T cells CD8, T cells CD4 memory resting, T cells regulatory (Tregs), and NK cells were lower. Additionally, CASP4 was positively correlated with Neutrophils and negatively correlated with T cells CD4 memory resting and Tregs. Lastly, two biomarkers (CASP4 and PLCG1) were identified, and a nomogram model was constructed for diagnosis with area under the curve (AUC) values of 0.998.

Conclusion

This study identified two potential pyroptosis-related diagnostic genes, CASP4 and PLCG1, and explored the correlation between DEPRGs and the immune microenvironment. Also, our study indicated that some DEPRGs were satisfactorily correlated with several representative immune cells that can regulate pyroptosis.

An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data

Background: Linking genotypic changes to phenotypic traits based on machine learning methods has various challenges. In this study, we developed a workflow based on bioinformatics and machine learning methods using transcriptomic data for sepsis obtained at the first clinical presentation for predicting the risk of sepsis. By combining bioinformatics with machine learning methods, we have attempted to overcome current challenges in predicting disease risk using transcriptomic data.

Methods: High-throughput sequencing transcriptomic data processing and gene annotation were performed using R software. Machine learning models were constructed, and model performance was evaluated by machine learning methods in Python. The models were visualized and interpreted using the Shapley Additive explanation (SHAP) method.

Results: Based on the preset parameters and using recursive feature elimination implemented via machine learning, the top 10 optimal genes were screened for the establishment of the machine learning models. In a comparison of model performance, CatBoost was selected as the optimal model. We explored the significance of each gene in the model and the interaction between each gene through SHAP analysis.

Conclusion: The combination of CatBoost and SHAP may serve as the best-performing machine learning model for predicting transcriptomic and sepsis risks. The workflow outlined may provide a new approach and direction in exploring the mechanisms associated with genes and sepsis risk.