An integrated transcriptome and expressed variant analysis of sepsis survival and death

Molecular endotypes in sepsis: integration of multicohort transcriptomics based on RNA sequencing

BACKGROUND

The heterogeneity of host responses in sepsis has hindered efforts to develop targeted therapies for this large patient population. Although growing evidence has identified sepsis endotypes based on the microarray data, studies using RNA-seq data-which offers higher sensitivity and a broader dynamic range-remain limited. We hypothesized that integrating RNA-seq data from patients with sepsis would reveal molecular endotypes with distinct biological and clinical signatures.

METHODS

In this meta-analysis, we systematically searched for publicly available RNA-seq datasets of sepsis. Using identified datasets, we applied a consensus clustering algorithm to identify distinct endotypes. To characterize the biological differences between these endotypes, we performed gene-set enrichment analysis and immune cell deconvolution. Next, we investigated the association between these endotypes and mortality risks. We finally developed gene classifiers for endotype stratification and validated our endotype classification by applying these classifiers to an external cohort.

RESULTS

A total of 280 adults with sepsis from four datasets were included in this analysis. Using an unsupervised approach, we identified three distinct endotypes: coagulopathic (n = 83, 30%), inflammatory (n = 118, 42%), and adaptive endotype (n = 79, 28%). The coagulopathic endotype exhibited upregulated coagulation signaling, along with an increased monocyte and neutrophil composition, although the adaptive endotype demonstrated enhanced adaptive immune cell responses, marked by elevated T and B cell compositions. The inflammatory endotype was characterized by upregulated TNF-α/NF-κB signaling and IL-6/JAK/STAT3 pathways with an increased neutrophil composition. Patients with the coagulopathic endotype had a significantly higher risk of mortality than those with the adaptive endotype (30% vs. 16%, odds ratio 2.19, 95% confidence interval 1.04-4.78, p = 0.04). To enable the practical application of these findings, we developed endotype classification models and identified 14 gene classifiers. In a validation cohort of 123 patients, we consistently identified these three endotypes. Furthermore, the mortality risk pattern was reproduced, with the coagulopathic endotype showing greater mortality risk than the adaptive endotype (34% vs 18%, p = 0.10).

CONCLUSIONS

This multicohort RNA-seq meta-analysis identified three biologically and clinically distinct sepsis endotypes characterized by coagulopathic, adaptive, and inflammatory responses. This endotype-based approach to patient stratification may facilitate the development of more precise therapeutic strategies for sepsis.

LINC02363: a potential biomarker for early diagnosis and treatment of sepsis

BACKGROUND

Sepsis remains a leading cause of global morbidity and mortality, yet early diagnosis is hindered by the limited specificity and sensitivity of current biomarkers.

AIM

The aim of this study was to identify lncRNAs that play a key role in sepsis and provide potential biomarkers for the diagnosis and treatment of sepsis.

METHODS

Transcriptomic data from sepsis patients were retrieved from the Chinese National Genebank (CNGBdb). Differential expression analysis identified 2,348 LncRNAs and 5,125 mRNAs (|FC|≥2, FDR < 0.05). Weighted gene co-expression network analysis (WGCNA) and meta-analysis were applied to screen core genes. Gene set enrichment analysis (GSEA) explored functional pathways, while single-cell sequencing and qPCR validated cellular localization and expression patterns.

RESULTS

WGCNA identified three key genes: LINC02363 (LncRNA), DYNLT1, and FCGR1B. Survival and meta-analyses revealed strong correlations between these genes and sepsis outcomes. GSEA highlighted LINC02363's involvement in "herpes simplex virus type 1 infection," "tuberculosis," and ribosome pathways. Single-cell sequencing showed FCGR1B's broad distribution across immune cells, while DYNLT1 localized predominantly in macrophages. qPCR confirmed significant upregulation of LINC02363 (p < 0.01), FCGR1B (p < 0.05), and DYNLT1 (p < 0.05) in sepsis patients compared to controls.

CONCLUSION

LINC02363 may serve as a new biomarker for the diagnosis and treatment of sepsis.

Transcriptomics in Human Septic Shock: State of the Art

Background: Septic shock is a complex syndrome characterized by signs of intense systemic inflammation and a profound dysregulation of the immune response. Large-scale gene expression analysis is a valuable tool in this scenario because sepsis affects various cellular components and signaling pathways. Results: In this article, we provide an overview of the transcriptomic studies that investigated human sepsis from 2007 to 2024, highlighting their major contributions. Conclusions: The field, however, still faces substantial limitations and several challenges. To advance further, we believe that standardization of sample collection and data analysis, preservation of cell and tissue architecture, and integration with other omics techniques are crucial for a broader understanding of this lethal disease.

Preliminary screening of new biomarkers for sepsis using bioinformatics and experimental validation

BACKGROUND

The morbidity and mortality of sepsis remain high, and so far specific diagnostic and therapeutic means are lacking.

OBJECTIVE

To screen novel biomarkers for sepsis.

METHODS

Raw sepsis data were downloaded from the Chinese National Genebank (CNGBdb) and screened for differentially expressed RNAs. Key genes with predictive value were identified through weighted correlation network analysis (WGCNA) and meta-analysis and survival analysis using multiple public databases. Core genes were analyzed for functional enrichment using Gene Set Enrichment Analysis(GSEA). The core genes were localized using single-cell sequencing. qPCR was used to validate the core genes.

RESULTS

Differential analysis yielded a total of 5125 mRNA. WGCNA identified 5 modules and screened 3 core genes (S100A11, QPCT, and IFITM2). The prognosis of sepsis patients was strongly linked to S100A11, QPCT, and IFITM2 based on meta-analysis and survival analysis(P < 0.05).GSEA analysis showed that S100A11, QPCT, and IFITM2 were significantly enriched in ribosome-related pathways. S100A11 and QPCT were widely distributed in all immune cells, and QPCT was mainly localized in the macrophage cell lineage. In the sepsis group, the qPCR results showed that S100A11, QPCT, and IFITM2 expression levels were significantly higher in the sepsis group(P < 0.05).

CONCLUSION

In this study, S100A11, QPCT, and IFITM2 were screened as new potential biomarkers for sepsis. Validated by bioinformatics analysis and qPCR, these genes are closely associated with the prognosis of sepsis patients and have potential as diagnostic and therapeutic targets.

Identification of prognostic biomarkers of sepsis and construction of ceRNA regulatory networks

Sepsis is a life-threatening severe organ dysfunction, and the pathogenesis remains uncertain. Increasing evidence suggests that circRNAs, mRNAs, and microRNAs can interact to jointly regulate the development of sepsis. Identifying the interaction between ceRNA regulatory networks and sepsis may contribute to our deeper understanding of the pathogenesis of sepsis, bring new insights into early recognition and treatment of sepsis. Blood samples from sepsis patients in the Affiliated Hospital of Southwest Medical University were collected. RNA sequencing (mRNA/circRNA) was performed on Survivor group (n = 26) and Non-survivor group (n = 6), then quality control and differential expression analysis were performed. Subsequently, GO analysis was performed on the differential expression genes; Meta-analysis was used to screen for prognostic related genes; 10 × Single-cell RNA sequencing was used to annotate the cell distribution of core genes. Finally, combined with base complementary pairing and intergroup correlation analysis, a sepsis-associated circRNA-miRNA-mRNA regulatory network was constructed. Differential expression analysis screened 28 mRNAs and 16 circRNAs. GO results showed that differential expression genes were mainly involved in membrane raft, actin cytoskeleton, regulation of immune response, negative regulation of cAMP-dependent protein kinase activity, etc. Meta-analysis screened 2 core genes, GSPT1 and NPRL3, which are associated with sepsis prognosis. 10 × Single-cell RNA sequencing showed that GSPT1 and NPRL3 were widely localized in immune cells, mainly macrophages and T cells. A ceRNA network consisting of 4 circRNA, 26 miRNA, and 2 mRNA was constructed. GSPT1 and NPRL3 were lowly expressed in the sepsis Survivor group, compared with Non-survivor group, which may become novel prognostic biomarkers for sepsis. A sepsis-related ceRNA networks, which consists of 4 circRNA, 26 miRNA, and 2 core gene, may guide mechanistic studies.

Pathogen class-specific transcriptional responses derived from PBMCs accurately discriminate between fungal, bacterial, and viral infections

Immune responses during acute infection often contain canonical elements which are shared across the responses to an array of agents within a given pathogen class (i.e., respiratory viral infection). Identification of these shared, canonical elements across similar infections offers the potential for impacting development of novel diagnostics and therapeutics. In this way, analysis of host gene expression patterns ('signatures') in white blood cells has been shown to be useful for determining the etiology of some acute viral and bacterial infections. In order to study conserved immune elements shared across the host response to related pathogens, we performed in vitro human PBMC challenges with common fungal pathogens (Candida albicans, Cryptococcus neoformans and gattii); four strains of influenza virus (Influenza A/Puerto Rico/08/34 [H1N1, PR8], A/Brisbane/59/2007 [H1N1], A/Solomon Islands/3/2006 [H1N1], and A/Wisconsin/67/2005 [H3N2]); and gram-negative (Escherichia coli) and gram-positive (Streptococcus pneumoniae) bacteria. Exposed human cells were then analyzed for differential gene expression utilizing Affymetrix microarrays. Analysis of pathogen exposure of PBMCs revealed strong, conserved gene expression patterns representing these canonical immune response elements to each broad pathogen class. A 41-gene multinomial signature was developed which correctly classified fungal, viral, or bacterial exposure with 94-98% accuracy. Furthermore, a 21-gene signature consisting of a subset of the discriminatory PBMC-derived genes was capable of accurately differentiating human patients with invasive candidiasis, acute viral infection, or bacterial infection (AUC 0.94, 0.83, and 0.96 respectively). These data reinforce the conserved nature of the genomic responses in human peripheral blood cells upon exposure to infectious agents and highlight the potential for in vitro models to augment our ability to develop novel diagnostic classifiers for acute infectious diseases, particularly devastating fungal infections.

Upregulation of CRISP3 and its clinical values in adult sepsis: a comprehensive analysis based on microarrays and a two-retrospective-cohort study

Background

Current lines of evidence indicate that cysteine-rich secretory protein 3 (CRISP3) is an immunoregulatory factor. Nevertheless, no study has explored the relationships between the values of CRISP3 and sepsis.

Methods

We conducted a comprehensive literature search and meta-analysis from the Gene Expression Omnibus (GEO) and ArrayExpress to determine the expression of CRISP3 in sepsis patients. Then, we explored whether plasma CRISP3 could serve as a potential biomarker to predict the risk of sepsis via two retrospective trauma cohorts. We evaluated the prediction power using the area under the curve (AUC).

Results

A total of 23 datasets were recruited for the comprehensive meta-analysis, and the combined standardized mean difference (SMD) of CRISP3 was 0.90 (0.50-1.30) (p < 0.001), suggesting that CRISP3 was overexpressed in sepsis patients. Meanwhile, sepsis patients had higher CRISP3 concentrations than non-sepsis patients in 54 trauma patients (p < 0.001). Plasma CRISP3 on admission was significantly associated with the incidence of sepsis [OR = 1.004 (1.002-1.006), p < 0.001]. As a predictive biomarker, CRISP3 obtained a better AUC [0.811 (0.681-0.905)] than C-reactive protein (CRP) [0.605 (0.463-0.735)], procalcitonin (PCT) [0.554 (0.412-0.689)], and Sequential Organ Failure Assessment (SOFA) [0.754 (0.618-0.861)]. Additionally, the clinical relationships between plasma CRISP3 and sepsis were verified in another trauma cohort with 166 patients [OR = 1.002 (1.001-1.003), p < 0.001]. The AUC of CRISP3 was 0.772 (0.701-0.834), which was better than that of CRP [0.521 (0.442-0.599)] and PCT [0.531 (0.452-0.609)], but not SOFA [0.791 (0.717-0.853)].

Conclusion

Our study indicated and validated that CRISP3 was highly expressed in sepsis. More importantly, CRISP3 may serve as a latent biomarker to predict the risk of sepsis.

LncRNA-mRNA co-expression analysis reveals aquaporin-9-promoted neutrophil extracellular trap formation and inflammatory activation in sepsis

Sepsis is a life-threatening condition caused by an excessive inflammatory response to an infection. However, the precise regulatory mechanism of sepsis remains unclear. Using a strand-specific RNA-sequencing, we identified 115 hub differentially expressed long noncoding RNAs (lncRNAs) and 443 mRNAs in septic patients, primarily participated in crucial pathways including neutrophil extracellular trap (NET) formation and toll-like receptor signaling. Notably, NETs related gene aquaporin-9 (AQP9) and its associated lncRNAs exhibited significant upregulation in septic neutrophils. Functional experiments revealed AQP9 interacts with its lncRNAs to augment the formation of neutrophil NETs. In murine sepsis models, AQP9 inhibition with phloretin reduced proinflammatory cytokine production and lung damage. These findings provide crucial insights into the regulatory role of AQP9 in sepsis, unraveling its interaction with associated lncRNAs in transmitting downstream signals, holding promise in informing the development of novel therapeutic strategies aimed at ameliorating the debilitating effects of sepsis.

Exploring the prognostic and diagnostic value of lactylation-related genes in sepsis

The discovery of Lactylation may pave the way for novel approaches to investigating sepsis. This study focused on the prognostic and diagnostic significance of lactylated genes in sepsis. RNA sequencing was performed on blood samples from 20 sepsis patients and 10 healthy individuals at Southwest Medical University in Luzhou, Sichuan, China. Genes associated with sepsis were identified through analysis of RNA sequencing data. Afterward, the genes that were expressed differently were compared with the lactylation genes, resulting in the identification of 55 lactylation genes linked to sepsis. The overlapping genes underwent analysis using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-Protein Network Interactions were used to screen for the core genes. The datasets GSE65682, GSE69528, GSE54514, GSE63042, and GSE95233 were obtained from the GEO database to validate core genes. Survival analysis evaluated the predictive significance of central genes in sepsis, while Receiver Operating Characteristic (ROC) Curve analysis was employed to establish the diagnostic value of genes. Additionally, a meta-analysis was conducted to confirm the precision of RNA-seq data. We obtained five peripheral blood samples, including two from healthy individuals, one from a patient with systemic inflammatory response syndrome (SIRS), and two from patients with sepsis. These samples were used to identify the specific location of core genes using 10×single-cell sequencing. High-throughput sequencing and bioinformatics techniques identified two lactylation-related genes (S100A11 and CCNA2) associated with sepsis. Survival analysis indicated that septic patients with reduced levels of S100A11 had a decreased 28-day survival rate compared to those with elevated levels. Conversely, individuals exhibiting decreased CCNA2 levels demonstrated a greater likelihood of surviving for 28 days than those in the high expression category, indicating a favorable association with survival rates among sepsis patients (P < 0.05). Both genes showed high sensitivity and specificity based on the ROC curve, with AUC values of 0.961 for S100A11 and 0.890 for CCNA2. The meta-analysis revealed that S100A11 exhibited high levels of expression in the sepsis survivors, whereas it displayed low levels of expression in the non-survivors; on the other hand, CCNA2 demonstrated low expression in the sepsis survivors and high expression in the non-survivors (P < 0.05). Single-cell RNA sequencing ultimately showed that monocyte macrophages, T cells, and B cells exhibited high expression levels of the crucial genes associated with sepsis-induced lactylation. In conclusion, the lactylation genes S100A11 and CCNA2 are strongly linked to sepsis and could be valuable markers for diagnosing, predicting outcomes, and providing guidance for sepsis.

Risk assessment with gene expression markers in sepsis development

Infection is a commonplace, usually self-limiting, condition but can lead to sepsis, a severe life-threatening dysregulated host response. We investigate the individual phenotypic predisposition to developing uncomplicated infection or sepsis in a large cohort of non-infected patients undergoing major elective surgery. Whole-blood RNA sequencing analysis was performed on preoperative samples from 267 patients. These patients developed postoperative infection with (n = 77) or without (n = 49) sepsis, developed non-infectious systemic inflammatory response (n = 31), or had an uncomplicated postoperative course (n = 110). Machine learning classification models built on preoperative transcriptomic signatures predict postoperative outcomes including sepsis with an area under the curve of up to 0.910 (mean 0.855) and sensitivity/specificity up to 0.767/0.804 (mean 0.746/0.769). Our models, confirmed by quantitative reverse-transcription PCR (RT-qPCR), potentially offer a risk prediction tool for the development of postoperative sepsis with implications for patient management. They identify an individual predisposition to developing sepsis that warrants further exploration to better understand the underlying pathophysiology.

Analyzing the molecular mechanism of Scutellaria Radix in the treatment of sepsis using RNA sequencing

BACKGROUND

Sepsis is a life-threatening organ dysfunction, which seriously threatens human health. The clinical and experimental results have confirmed that Traditional Chinese medicine (TCM), such as Scutellariae Radix, has anti-inflammatory effects. This provides a new idea for the treatment of sepsis. This study systematically analyzed the mechanism of Scutellariae Radix treatment in sepsis based on network pharmacology, RNA sequencing and molecular docking.

METHODS

Gene expression analysis was performed using Bulk RNA sequencing on sepsis patients and healthy volunteers. After quality control of the results, the differentially expressed genes (DEGs) were analyzed. The active ingredients and targets of Scutellariae Radix were identified using The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Gene Ontology (GO) and Protein-Protein Interaction (PPI) analysis were performed for disease-drug intersection targets. With the help of GEO database, Survival analysis and Meta-analysis was performed on the cross-targets to evaluate the prognostic value and screen the core targets. Subsequently, single-cell RNA sequencing was used to determine where the core targets are located within the cell. Finally, in this study, molecular docking experiments were performed to further clarify the interrelationship between the active components of Scutellariae Radix and the corresponding targets.

RESULTS

There were 72 active ingredients of Scutellariae Radix, and 50 common targets of drug and disease. GO and PPI analysis showed that the intersection targets were mainly involved in response to chemical stress, response to oxygen levels, response to drug, regulation of immune system process. Survival analysis showed that PRKCD, EGLN1 and CFLAR were positively correlated with sepsis prognosis. Meta-analysis found that the three genes were highly expressed in sepsis survivor, while lowly in non-survivor. PRKCD was mostly found in Macrophages, while EGLN1 and CFLAR were widely expressed in immune cells. The active ingredient Apigenin regulates CFLAR expression, Baicalein regulates EGLN1 expression, and Wogonin regulates PRKCD expression. Molecular docking studies confrmed that the three active components of astragalus have good binding activities with their corresponding targets.

CONCLUSIONS

Apigenin, Baicalein and Wogonin, important active components of Scutellaria Radix, produce anti-sepsis effects by regulating the expression of their targets CFLAR, EGLN1 and PRKCD.

Sepsis endotypes identified by host gene expression across global cohorts

BACKGROUND

Sepsis from infection is a global health priority and clinical trials have failed to deliver effective therapeutic interventions. To address complicating heterogeneity in sepsis pathobiology, and improve outcomes, promising precision medicine approaches are helping identify disease endotypes, however, they require a more complete definition of sepsis subgroups.

METHODS

Here, we use RNA sequencing from peripheral blood to interrogate the host response to sepsis from participants in a global observational study carried out in West Africa, Southeast Asia, and North America (N = 494).

RESULTS

We identify four sepsis subtypes differentiated by 28-day mortality. A low mortality immunocompetent group is specified by features that describe the adaptive immune system. In contrast, the three high mortality groups show elevated clinical severity consistent with multiple organ dysfunction. The immunosuppressed group members show signs of a dysfunctional immune response, the acute-inflammation group is set apart by molecular features of the innate immune response, while the immunometabolic group is characterized by metabolic pathways such as heme biosynthesis.

CONCLUSIONS

Our analysis reveals details of molecular endotypes in sepsis that support immunotherapeutic interventions and identifies biomarkers that predict outcomes in these groups.

Machine learning reveals ferroptosis features and a novel ferroptosis classifier in patients with sepsis

OBJECTIVE

Sepsis is an organ malfunction disease that may become fatal and is commonly accompanied by severe complications such as multiorgan dysfunction. Patients who are already hospitalized have a high risk of death due to sepsis. Even though early diagnosis is very important, the technology and clinical approaches that are now available are inadequate. Hence, there is an immediate necessity to investigate biological markers that are sensitive, specific, and reliable for the prompt detection of sepsis to reduce mortality and improve patient prognosis. Mounting research data indicate that ferroptosis contributes to the occurrence, development, and prevention of sepsis. However, the specific regulatory mechanism of ferroptosis remains to be elucidated. This research evaluated the expression profiles of ferroptosis-related genes (FRGs) and the diagnostic significance of the ferroptosis-related classifiers in sepsis.

METHODS AND RESULTS

We collected three peripheral blood data sets from septic patients, integrated the clinical examination data and mRNA expression profile of these patients, and identified 13 FRGs in sepsis through a co-expression network and differential analysis. Then, an optimal classifier tool for sepsis was constructed by integrating a variety of machine learning algorithms. Two key genes, ATG16L1 and SRC, were shown to be shared between the algorithms, and thus were identified as the FRG signature of classifier. The tool exhibited satisfactory diagnostic efficiency in the training data set (AUC = 0.711) and two external verification data sets (AUC = 0.961; AUC = 0.913). In the rat cecal ligation puncture sepsis model, in vivo experiments verified the involvement of ATG16L1 and SRC in the early sepsis process.

CONCLUSION

These findings confirm that FRGs may participate in the development of sepsis, the ferroptosis related classifiers can provide a basis for the development of new strategies for the early diagnosis of sepsis and the discovery of new potential therapeutic targets for life-threatening infections.

Integrative analysis of metabolomics and transcriptomics to uncover biomarkers in sepsis

To utilize metabolomics in conjunction with RNA sequencing to identify biomarkers in the blood of sepsis patients and discover novel targets for diagnosing and treating sepsis. In January 2019 and December 2020, blood samples were collected from a cohort of 16 patients diagnosed with sepsis and 11 patients diagnosed with systemic inflammatory response syndrome (SIRS). Non-targeted metabolomics analysis was conducted using liquid chromatography coupled with mass spectrometry (LC-MS/MS technology), while gene sequencing was performed using RNA sequencing. Afterward, the metabolite data and sequencing data underwent quality control and difference analysis, with a fold change (FC) greater than or equal to 2 and a false discovery rate (FDR) less than 0.05.Co-analysis was then performed to identify differential factors with consistent expression trends based on the metabolic pathway context; KEGG enrichment analysis was performed on the crossover factors, and Meta-analysis of the targets was performed at the transcriptome level using the public dataset. In the end, a total of five samples of single nucleated cells from peripheral blood (two normal controls, one with systemic inflammatory response syndrome, and two with sepsis) were collected and examined to determine the cellular location of the essential genes using 10× single cell RNA sequencing (scRNA-seq). A total of 485 genes and 1083 metabolites were found to be differentially expressed in the sepsis group compared to the SIRS group. Among these, 40 genes were found to be differentially expressed in both the metabolome and transcriptome. Functional enrichment analysis revealed that these genes were primarily involved in biological processes related to inflammatory response, immune regulation, and amino acid metabolism. Furthermore, a meta-analysis identified four genes, namely ITGAM, CD44, C3AR1, and IL2RG, which were highly expressed in the sepsis group compared to the normal group (P < 0.05). Additionally, scRNA-seq analysis revealed that the core genes ITGAM and C3AR1 were predominantly localized within the macrophage lineage. The primary genes ITGAM and C3AR1 exhibit predominant expression in macrophages, which play a significant role in inflammatory and immune responses. Moreover, these genes show elevated expression levels in the plasma of individuals with sepsis, indicating their potential as valuable subjects for further research in sepsis.

Host-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance

Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.

A gene network database for the identification of key genes for diagnosis, prognosis, and treatment in sepsis

Sepsis and sepsis-related diseases cause a high rate of mortality worldwide. The molecular and cellular mechanisms of sepsis are still unclear. We aim to identify key genes in sepsis and reveal potential disease mechanisms. Six sepsis-related blood transcriptome datasets were collected and analyzed by weighted gene co-expression network analysis (WGCNA). Functional annotation was performed in the gProfiler tool. DSigDB was used for drug signature enrichment analysis. The proportion of immune cells was estimated by the CIBERSORT tool. The relationships between modules, immune cells, and survival were identified by correlation analysis and survival analysis. A total of 37 stable co-expressed gene modules were identified. These modules were associated with the critical biology process in sepsis. Four modules can independently separate patients with long and short survival. Three modules can recurrently separate sepsis and normal patients with high accuracy. Some modules can separate bacterial pneumonia, influenza pneumonia, mixed bacterial and influenza A pneumonia, and non-infective systemic inflammatory response syndrome (SIRS). Drug signature analysis identified drugs associated with sepsis, such as testosterone, phytoestrogens, ibuprofen, urea, dichlorvos, potassium persulfate, and vitamin B12. Finally, a gene co-expression network database was constructed ( https://liqs.shinyapps.io/sepsis/ ). The recurrent modules in sepsis may facilitate disease diagnosis, prognosis, and treatment.

Extending the 'host response' paradigm from sepsis to cardiogenic shock: evidence, limitations and opportunities

Recent clinical and research efforts in cardiogenic shock (CS) have largely focussed on the restoration of the low cardiac output state that is the conditio sine qua non of the clinical syndrome. This approach has failed to translate into improved outcomes, and mortality has remained static at 30-50%. There is an unmet need to better delineate the pathobiology of CS to understand the observed heterogeneity of presentation and treatment effect and to identify novel therapeutic targets. Despite data in other critical illness syndromes, specifically sepsis, the role of dysregulated inflammation and immunity is hitherto poorly described in CS. High-dimensional molecular profiling, particularly through leukocyte transcriptomics, may afford opportunity to better characterise subgroups of patients with shared mechanisms of immune dysregulation. In this state-of-the-art review, we outline the rationale for considering molecular subtypes of CS. We describe how high-dimensional molecular technologies can be used to identify these subtypes, and whether they share biological features with sepsis and other critical illness states. Finally, we propose how the identification of molecular subtypes of patients may enrich future clinical trial design and identification of novel therapies for CS.

Genome-wide identification and functional analysis of dysregulated alternative splicing profiles in sepsis

BACKGROUND

An increasing body of evidence now shows that the long-term mortality of patients with sepsis are associated with various sepsis-related immune cell defects. Alternative splicing (AS), as a sepsis-related immune cell defect, is considered as a potential immunomodulatory therapy target to improve patient outcomes. However, our understanding of the role AS plays in sepsis is currently insufficient.

AIM

This study investigated possible associations between AS and the gene regulatory networks affecting immune cells. We also investigated apoptosis and AS functionality in sepsis pathophysiology.

METHODS

In this study, we assessed publicly available mRNA-seq data that was obtained from the NCBI GEO dataset (GSE154918), which included a healthy group (HLTY), a mild infection group (INF1), asepsis group (Seps), and a septic shock group (Shock). A total of 79 samples (excluding significant outliers) were identified by a poly-A capture method to generate RNA-seq data. The variable splicing events and highly correlated RNA binding protein (RBP) genes in each group were then systematically analyzed.

RESULTS

For the first time, we used systematic RNA-seq analysis of sepsis-related AS and identified 1505 variable AS events that differed significantly (p <= 0.01) across the four groups. In the sepsis group, the genes related to significant AS events, such as, SHISA5 and IFI27, were mostly enriched in the cell apoptosis pathway. Furthermore, we identified differential splicing patterns within each of the four groups. Significant differences in the expression of RNA Binding Protein(RBP) genes were observed between the control group and the sepsis group. RBP gene expression was highly correlated with variant splicing events in sepsis, as determined by co-expression analysis; The expression of DDX24, CBFA2T2, NOP, ILF3, DNMT1, FTO, PPRC1, NOLC1 RBPs were significant reduced in sepsis compared to the healthy group. Finally, we constructed an RBP-AS functional network.

CONCLUSION

Analysis indicated that the RBP-AS functional network serves as a critical post-transcriptional mechanism that regulates the development of sepsis. AS dysregulation is associated with alterations in the regulatory gene expression network that is involved in sepsis. Therefore, the RBP-AS expression network could be useful in refining biomarker predictions in the development of new therapeutic targets for the pathogenesis of sepsis.

Long-term immunosuppressive treatment is not associated with worse outcome in patients hospitalized in the intensive care unit for septic shock: the PACIFIC study

BACKGROUND

Except in a few retrospective studies mainly including patients under chemotherapy, information regarding the impact of immunosuppressive therapy on the prognosis of patients admitted to the intensive care unit (ICU) for septic shock is scarce. Accordingly, the PACIFIC study aimed to asses if immunosuppressive therapy is associated with an increased mortality in patients admitted to the ICU for septic shock.

METHODS

This was a retrospective epidemiological multicentre study. Eight high enroller centres in septic shock randomised controlled trials (RCTs) participated in the study. Patients in the "exposed" group were selected from the screen failure logs of seven recent RCTs and excluded because of immunosuppressive treatment. The "non-exposed" patients were those included in the placebo arm of the same RCTs. A multivariate logistic regression model was used to estimate the risk of death.

RESULTS

Among the 433 patients enrolled, 103 were included in the "exposed" group and 330 in the "non-exposed" group. Reason for immunosuppressive therapy included organ transplantation (n = 45 [44%]) or systemic disease (n = 58 [56%]). ICU mortality rate was 24% in the "exposed" group and 25% in the "non-exposed" group (p = 0.9). Neither in univariate nor in multivariate analysis immunosuppressive therapy was associated with a higher ICU mortality (OR: 0.95; [95% CI 0.56-1.58]: p =  0.86 and 1.13 [95% CI 0.61-2.05]: p =  0.69, respectively) or 3-month mortality (OR: 1.13; [95% CI 0.69-1.82]: p =  0.62 and OR: 1.36 [95% CI 0.78-2.37]: p =  0.28, respectively).

CONCLUSIONS

In this study, long-term immunosuppressive therapy excluding chemotherapy was not associated with significantly higher or lower ICU and 3-month mortality in patients admitted to the ICU for septic shock.

Lipocalin 10 is essential for protection against inflammation-triggered vascular leakage by activating LDL receptor-related protein 2-slingshot homologue 1 signalling pathway

AIMS

Systemic inflammation occurs commonly during many human disease settings and increases vascular permeability, leading to organ failure, and lethal outcomes. Lipocalin 10 (Lcn10), a poorly characterized member of the lipocalin family, is remarkably altered in the cardiovascular system of human patients with inflammatory conditions. Nonetheless, whether Lcn10 regulates inflammation-induced endothelial permeability remains unknown.

METHODS AND RESULTS

Systemic inflammation models were induced using mice by injection of endotoxin lipopolysaccharide (LPS) or caecal ligation and puncture (CLP) surgery. We observed that the expression of Lcn10 was dynamically altered only in endothelial cells (ECs), but not in either fibroblasts or cardiomyocytes isolated from mouse hearts following the LPS challenge or CLP surgery. Using in vitro gain- and loss-of-function approaches and an in vivo global knockout mouse model, we discovered that Lcn10 negatively regulated endothelial permeability upon inflammatory stimuli. Loss of Lcn10 augmented vascular leakage, leading to severe organ damage and higher mortality following LPS challenge, compared to wild-type controls. By contrast, overexpression of Lcn10 in ECs displayed opposite effects. A mechanistic analysis revealed that both endogenous and exogenous elevation of Lcn10 in ECs could activate slingshot homologue 1 (Ssh1)-Cofilin signalling cascade, a key axis known to control actin filament dynamics. Accordingly, a reduced formation of stress fibre and increased generation of cortical actin band were exhibited in Lcn10-ECs, when compared to controls upon endotoxin insults. Furthermore, we identified that Lcn10 interacted with LDL receptor-related protein 2 (LRP2) in ECs, which acted as an upstream factor of the Ssh1-Confilin signalling. Finally, injection of recombinant Lcn10 protein into endotoxic mice showed therapeutic effects against inflammation-induced vascular leakage.

CONCLUSION

This study identifies Lcn10 as a novel regulator of EC function and illustrates a new link in the Lcn10-LRP2-Ssh1 axis to controlling endothelial barrier integrity. Our findings may provide novel strategies for the treatment of inflammation-related diseases.

Identification of Potential Prognosticators for Sepsis through Expression Analysis of Transcriptomic Data from Sepsis Survivors and Nonsurvivors

Background

Infection can be severely complicated by a dysregulated, whole-body inflammatory response known as sepsis. While previous research showed that genetic predisposition is linked to outcome differences, current patient characterization fails to determine which septic patients have greater tendencies to develop into severe sepsis or go into septic shock. As such, the identification of prognostic biomarkers may assist in identifying these high-risk patients and help improve the clinical management of the disease.

Objective

In this study, we aimed to identify molecular patterns involved in sepsis. We also aimed to identify essential genes associated with the disease's survival which could serve as potential prognosticators for the disease.

Methods

We used weighted gene co-expression analysis (WGCNA) to analyze GSE63042, an RNA expression dataset from 129 patients with systemic inflammatory response syndrome or sepsis, including 78 sepsis survivors and 28 sepsis nonsurvivors. This analysis included identifying gene modules that differentiate sepsis survivors from nonsurvivors and qualitatively assessing differentially expressed genes. We then used STRING's protein-protein interaction and gene ontology analysis to determine the functional and pathway relationships of the genes in the top modules. Lastly, we assessed the prognosticator abilities of the hub genes using ROC analysis.

Results

We found four diverse co-expression gene modules significantly associated with sepsis survival. Our differential gene expression analysis, combined with protein-protein interaction and gene ontology analysis, revealed that the hub genes of these modules - TAF10, SNAPIN, PSME2, PSMB9, JUNB, and CEBPD - may serve as candidate markers for sepsis prognosis. These markers were significantly downregulated in sepsis nonsurvivors compared with sepsis survivors.

Conclusion

Weighted gene co-expression analysis, gene ontology enrichment analysis, and proteinprotein network interaction analysis of transcriptomic data from sepsis survivors and nonsurvivors revealed TAF10, SNAPIN, PSME2, PSMB9, JUNB, and CEBPD as potential biomarkers for sepsis prognosis. These genes are associated with functions related to proper immune response, and their downregulation in sepsis nonsurvivors suggests eventual immune exhaustion in late sepsis. Further analyses, however, are necessary to validate their roles in sepsis progression and patient survival.

Systemic alterations in neutrophils and their precursors in early-stage chronic obstructive pulmonary disease

Systemic inflammation is established as part of late-stage severe lung disease, but molecular, functional, and phenotypic changes in peripheral immune cells in early disease stages remain ill defined. Chronic obstructive pulmonary disease (COPD) is a major respiratory disease characterized by small-airway inflammation, emphysema, and severe breathing difficulties. Using single-cell analyses we demonstrate that blood neutrophils are already increased in early-stage COPD, and changes in molecular and functional neutrophil states correlate with lung function decline. Assessing neutrophils and their bone marrow precursors in a murine cigarette smoke exposure model identified similar molecular changes in blood neutrophils and precursor populations that also occur in the blood and lung. Our study shows that systemic molecular alterations in neutrophils and their precursors are part of early-stage COPD, a finding to be further explored for potential therapeutic targets and biomarkers for early diagnosis and patient stratification.

Screening of core genes prognostic for sepsis and construction of a ceRNA regulatory network

OBJECTIVE

To screen out core genes potentially prognostic for sepsis and construct a competing endogenous RNA (ceRNA) regulatory network.

METHODS

Subjects included in this project were 23 sepsis patients and 10 healthy people. RNA-seq for lncRNA, miRNA and mRNA was performed in the peripheral blood samples. Differentially expressed RNAs (DER) were screened out for further analysis. GO annotation and GSEA functional clustering were performed to view the functional enrichment of DEmRNAs. Core genes of prognostic significance were screened out with the weighted correlation network analysis (WGCNA). Meta-analysis and Survival analysis was devised in different microarray datasets. RT-qPCR was conducted to validate these core genes. A ceRNA network was accordingly constructed according to the correlation analysis and molecular interaction prediction.

RESULTS

RNA-seq and differential analysis screened out 1,044 DEmRNAs, 66 DEmiRNAs and 155 DElncRNAs. The GO and GSEA analysis revealed that DEmRNAs are mainly involved in inflammatory response, immune regulation, neutrophil activation. WGCNA revealed 4 potential core genes, including CD247, IL-2Rβ, TGF-βR3 and IL-1R2. In vitro cellular experiment showed up-regulated expression of IL-1R2 while down-regulated of CD247, IL-2Rβ, TGF-βR3 in sepsis patients. Correspondingly, a ceRNA regulatory network was build based on the core genes, and multiple lncRNAs and miRNAs were identified to have a potential regulatory role in sepsis.

CONCLUSION

This study identified four core genes, including CD247, IL-1R2, IL-2Rβ and TGF-βR3, with potential to be novel biomarkers for the prognosis of sepsis. In the meantime, a ceRNA network was constructed aiming to guide further study on prognostic mechanism in sepsis.

Mechanisms of Panax Ginseng on Treating Sepsis by RNA-Seq Technology

Purpose

To explore the potential active targets and mechanisms of Panax Ginseng in the treatment of sepsis using network pharmacology and RNA-seq technology.

Patients and Methods

Patients with sepsis and healthy volunteers were collected according to SEPSIS 3.0, and their peripheral blood was used for RNA-seq analysis. The active ingredients and targets of Panax Ginseng were obtained using the TCMSP database, PPI and GO analysis were performed for disease-drug intersection targets. Then, we used Meta-analysis to screen core genes. Finally, single-cell RNA-seq was used to perform cell localization analysis on core genes.

Results

RNA-seq analysis collected 4521 sepsis-related genes, TCMSP database obtained 86 Panax Ginseng active ingredients and their 294 active targets. PPI and GO analysis showed intersection targets were closely linked, and mainly involved in cellular response to chemical stress, response to drug and molecule of bacterial origin, etc. Then, core targets, IL1B, ALOX5, BCL2 and IL4R, were sorted by Meta-analysis, and all four genes have high expression in the sepsis survivor group compared to the sepsis non-survivor group; single-cell RNA-seq revealed that IL1B was mainly localized in macrophages, ALOX5 was mainly localized in macrophages and B cells, BCL2 was mainly localized in natural killer cells, T cells and B cells, IL4R was widely distributed in immune cells. Finally, according to the correspondence between the active ingredients and targets of Panax Ginseng in TCMSP database, we found that Ginsenoside rh2 regulates the expression of IL1B, Ginsenoside rf regulates the expression of IL1B and IL4R, Kaempferol regulates the expression of ALOX5 and BCL2, and β-sitosterol regulates the expression of BCL2.

Conclusion

Ginsenoside rh2, Ginsenoside rf, Kaempferol and β-sitosterol may produce anti-sepsis effects by regulating the expression of IL1B, ALOX5, BCL2 and IL4R, thus improving the survival rate of sepsis patients.

Host Gene Expression to Predict Sepsis Progression

OBJECTIVES

Sepsis causes significant mortality. However, most patients who die of sepsis do not present with severe infection, hampering efforts to deliver early, aggressive therapy. It is also known that the host gene expression response to infection precedes clinical illness. This study seeks to develop transcriptomic models to predict progression to sepsis or shock within 72 hours of hospitalization and to validate previously identified transcriptomic signatures in the prediction of 28-day mortality.

DESIGN

Retrospective differential gene expression analysis and predictive modeling using RNA sequencing data.

PATIENTS

Two hundred seventy-seven patients enrolled at four large academic medical centers; all with clinically adjudicated infection were considered for inclusion in this study.

MEASUREMENTS AND MAIN RESULTS

Sepsis progression was defined as an increase in Sepsis 3 category within 72 hours. Transcriptomic data were generated using RNAseq of whole blood. Least absolute shrinkage and selection operator modeling was used to identify predictive signatures for various measures of disease progression. Four previously identified gene signatures were tested for their ability to predict 28-day mortality. There were no significant differentially expressed genes in 136 subjects with worsened Sepsis 3 category compared with 141 nonprogressor controls. There were 1,178 differentially expressed genes identified when sepsis progression was defined as ICU admission or 28-day mortality. A model based on these genes predicted progression with an area under the curve of 0.71. Validation of previously identified gene signatures to predict sepsis mortality revealed area under the receiver operating characteristic values of 0.70-0.75 and no significant difference between signatures.

CONCLUSIONS

Host gene expression was unable to predict sepsis progression when defined by an increase in Sepsis-3 category, suggesting this definition is not a useful framework for transcriptomic prediction methods. However, there was a differential response when progression was defined as ICU admission or death. Validation of previously described signatures predicted 28-day mortality with insufficient accuracy to offer meaningful clinical utility.

Prospective validation of a transcriptomic severity classifier among patients with suspected acute infection and sepsis in the emergency department

BACKGROUND AND IMPORTANCE

mRNA-based host response signatures have been reported to improve sepsis diagnostics. Meanwhile, prognostic markers for the rapid and accurate prediction of severity in patients with suspected acute infections and sepsis remain an unmet need. IMX-SEV-2 is a 29-host-mRNA classifier designed to predict disease severity in patients with acute infection or sepsis.

OBJECTIVE

Validation of the host-mRNA infection severity classifier IMX-SEV-2.

DESIGN, SETTINGS AND PARTICIPANTS

Prospective, observational, convenience cohort of emergency department (ED) patients with suspected acute infections.

OUTCOME MEASURES AND ANALYSIS

Whole blood RNA tubes were analyzed using independently trained and validated composite target genes (IMX-SEV-2). IMX-SEV-2-generated risk scores for severity were compared to the patient outcomes in-hospital mortality and 72-h multiorgan failure.

MAIN RESULTS

Of the 312 eligible patients, 22 (7.1%) died in hospital and 58 (18.6%) experienced multiorgan failure within 72 h of presentation. For predicting in-hospital mortality, IMX-SEV-2 had a significantly higher area under the receiver operating characteristic (AUROC) of 0.84 [95% confidence intervals (CI), 0.76-0.93] compared to 0.76 (0.64-0.87) for lactate, 0.68 (0.57-0.79) for quick Sequential Organ Failure Assessment (qSOFA) and 0.75 (0.65-0.85) for National Early Warning Score 2 (NEWS2), ( P  = 0.015, 0.001 and 0.013, respectively). For identifying and predicting 72-h multiorgan failure, the AUROC of IMX-SEV-2 was 0.76 (0.68-0.83), not significantly different from lactate (0.73, 0.65-0.81), qSOFA (0.77, 0.70-0.83) or NEWS2 (0.81, 0.75-0.86).

CONCLUSION

The IMX-SEV-2 classifier showed a superior prediction of in-hospital mortality compared to biomarkers and clinical scores among ED patients with suspected infections. No improvement for predicting multiorgan failure was found compared to established scores or biomarkers. Identifying patients with a high risk of mortality or multiorgan failure may improve patient outcomes, resource utilization and guide therapy decision-making.

Diagnostic model based on bioinformatics and machine learning to distinguish Kawasaki disease using multiple datasets

Background

Kawasaki disease (KD), characterized by systemic vasculitis, is the leading cause of acquired heart disease in children. Herein, we developed a diagnostic model, with some prognosis ability, to help distinguish children with KD.

Methods

Gene expression datasets were downloaded from Gene Expression Omnibus (GEO), and gene sets with a potential pathogenic mechanism in KD were identified using differential expressed gene (DEG) screening, pathway enrichment analysis, random forest (RF) screening, and artificial neural network (ANN) construction.

Results

We extracted 2,017 DEGs (1,130 with upregulated and 887 with downregulated expression) from GEO. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that the DEGs were significantly enriched in innate/adaptive immune response-related processes. Subsequently, the results of weighted gene co-expression network analysis and DEG screening were combined and, using RF and ANN, a model with eight genes (VPS9D1, CACNA1E, SH3GLB1, RAB32, ADM, GYG1, PGS1, and HIST2H2AC) was constructed. Classification results of the new model for KD diagnosis showed excellent performance for different datasets, including those of patients with KD, convalescents, and healthy individuals, with area under the curve values of 1, 0.945, and 0.95, respectively.

Conclusions

We used machine learning methods to construct and validate a diagnostic model using multiple bioinformatic datasets, and identified molecules expected to serve as new biomarkers for or therapeutic targets in KD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-022-03557-y.

Mortality Prediction in Sepsis With an Immune-Related Transcriptomics Signature: A Multi-Cohort Analysis

Background

Novel biomarkers are needed to progress toward individualized patient care in sepsis. The immune profiling panel (IPP) prototype has been designed as a fully-automated multiplex tool measuring expression levels of 26 genes in sepsis patients to explore immune functions, determine sepsis endotypes and guide personalized clinical management. The performance of the IPP gene set to predict 30-day mortality has not been extensively characterized in heterogeneous cohorts of sepsis patients.

Methods

Publicly available microarray data of sepsis patients with widely variable demographics, clinical characteristics and ethnical background were co-normalized, and the performance of the IPP gene set to predict 30-day mortality was assessed using a combination of machine learning algorithms.

Results

We collected data from 1,801 arrays sampled on sepsis patients and 598 sampled on controls in 17 studies. When gene expression was assayed at day 1 following admission (1,437 arrays sampled on sepsis patients, of whom 1,161 were alive and 276 (19.2%) were dead at day 30), the IPP gene set showed good performance to predict 30-day mortality, with an area under the receiving operating characteristics curve (AUROC) of 0.710 (CI 0.652-0.768). Importantly, there was no statistically significant improvement in predictive performance when training the same models with all genes common to the 17 microarray studies (n = 7,122 genes), with an AUROC = 0.755 (CI 0.697-0.813, p = 0.286). In patients with gene expression data sampled at day 3 following admission or later, the IPP gene set had higher performance, with an AUROC = 0.804 (CI 0.643-0.964), while the total gene pool had an AUROC = 0.787 (CI 0.610-0.965, p = 0.811).

Conclusion

Using pooled publicly-available gene expression data from multiple cohorts, we showed that the IPP gene set, an immune-related transcriptomics signature conveys relevant information to predict 30-day mortality when sampled at day 1 following admission. Our data also suggests that higher predictive performance could be obtained when assaying gene expression at later time points during the course of sepsis. Prospective studies are needed to confirm these findings using the IPP gene set on its dedicated measurement platform.

Loss of Lipocalin 10 Exacerbates Diabetes-Induced Cardiomyopathy via Disruption of Nr4a1-Mediated Anti-Inflammatory Response in Macrophages

Metabolic disorders (i.e., hyperglycemia, hyperlipidemia, and hyperinsulinemia) cause increased secretion of inflammatory cytokines/chemokines, leading to gradual loss of cardiac resident macrophage population and increased accumulation of inflammatory monocytes/macrophages in the heart. Such self-perpetuating effect may contribute to the development of cardiomyopathy during diabetes. Recent meta-analysis data reveal that lipocalin 10 (Lcn10) is significantly downregulated in cardiac tissue of patients with heart failure but is increased in the blood of septic patients. However, the functional role of Lcn10 in cardiac inflammation triggered by metabolic disorders has never been investigated. In this study, we demonstrate that the expression of Lcn10 in macrophages was significantly decreased under multiple metabolic stress conditions. Furthermore, Lcn10-null macrophages exhibited pro-inflammatory phenotype in response to inflammation stimuli. Next, using a global Lcn10-knockout (KO) mouse model to induce type-2 diabetes (T2D), we observed that loss of Lcn10 promoted more pro-inflammatory macrophage infiltration into the heart, compared to controls, leading to aggravated insulin resistance and impaired cardiac function. Similarly, adoptive transfer of Lcn10-KO bone marrow cells into X-ray irradiated mice displayed higher ratio of pro-/anti-inflammatory macrophages in the heart and worsened cardiac function than those mice received wild-type (WT) bone marrows upon T2D conditions. Mechanistically, RNA-sequencing analysis showed that Nr4a1, a nuclear receptor known to have potent anti-inflammatory effects, is involved in Lcn10-mediated macrophage activation. Indeed, we found that nuclear translocation of Nr4a1 was disrupted in Lcn10-KO macrophages upon stimulation with LPS + IFNγ. Accordingly, treatment with Cytosporone B (CsnB), an agonist of Nr4a1, attenuated the pro-inflammatory response in Lcn10-null macrophages and partially improved cardiac function in Lcn10-KO diabetic mice. Together, these findings indicate that loss of Lcn10 skews macrophage polarization to pro-inflammatory phenotype and aggravates cardiac dysfunction during type-2 diabetes through the disruption of Nr4a1-mediated anti-inflammatory signaling pathway in macrophages. Therefore, reduction of Lcn10 expression observed in diabetic macrophages may be responsible for the pathogenesis of diabetes-induced cardiac dysfunction. It suggests that Lcn10 might be a potential therapeutic factor for diabetic heart failure.

Prospective Validation of a Rapid Host Gene Expression Test to Discriminate Bacterial From Viral Respiratory Infection

Importance

Bacterial and viral causes of acute respiratory illness (ARI) are difficult to clinically distinguish, resulting in the inappropriate use of antibacterial therapy. The use of a host gene expression-based test that is able to discriminate bacterial from viral infection in less than 1 hour may improve care and antimicrobial stewardship.

Objective

To validate the host response bacterial/viral (HR-B/V) test and assess its ability to accurately differentiate bacterial from viral infection among patients with ARI.

Design, Setting, and Participants

This prospective multicenter diagnostic study enrolled 755 children and adults with febrile ARI of 7 or fewer days' duration from 10 US emergency departments. Participants were enrolled from October 3, 2014, to September 1, 2019, followed by additional enrollment of patients with COVID-19 from March 20 to December 3, 2020. Clinical adjudication of enrolled participants identified 616 individuals as having bacterial or viral infection. The primary analysis cohort included 334 participants with high-confidence reference adjudications (based on adjudicator concordance and the presence of an identified pathogen confirmed by microbiological testing). A secondary analysis of the entire cohort of 616 participants included cases with low-confidence reference adjudications (based on adjudicator discordance or the absence of an identified pathogen in microbiological testing). Thirty-three participants with COVID-19 were included post hoc.

Interventions

The HR-B/V test quantified the expression of 45 host messenger RNAs in approximately 45 minutes to derive a probability of bacterial infection.

Main Outcomes and Measures

Performance characteristics for the HR-B/V test compared with clinical adjudication were reported as either bacterial or viral infection or categorized into 4 likelihood groups (viral very likely [probability score <0.19], viral likely [probability score of 0.19-0.40], bacterial likely [probability score of 0.41-0.73], and bacterial very likely [probability score >0.73]) and compared with procalcitonin measurement.

Results

Among 755 enrolled participants, the median age was 26 years (IQR, 16-52 years); 360 participants (47.7%) were female, and 395 (52.3%) were male. A total of 13 participants (1.7%) were American Indian, 13 (1.7%) were Asian, 368 (48.7%) were Black, 131 (17.4%) were Hispanic, 3 (0.4%) were Native Hawaiian or Pacific Islander, 297 (39.3%) were White, and 60 (7.9%) were of unspecified race and/or ethnicity. In the primary analysis involving 334 participants, the HR-B/V test had sensitivity of 89.8% (95% CI, 77.8%-96.2%), specificity of 82.1% (95% CI, 77.4%-86.6%), and a negative predictive value (NPV) of 97.9% (95% CI, 95.3%-99.1%) for bacterial infection. In comparison, the sensitivity of procalcitonin measurement was 28.6% (95% CI, 16.2%-40.9%; P < .001), the specificity was 87.0% (95% CI, 82.7%-90.7%; P = .006), and the NPV was 87.6% (95% CI, 85.5%-89.5%; P < .001). When stratified into likelihood groups, the HR-B/V test had an NPV of 98.9% (95% CI, 96.1%-100%) for bacterial infection in the viral very likely group and a positive predictive value of 63.4% (95% CI, 47.2%-77.9%) for bacterial infection in the bacterial very likely group. The HR-B/V test correctly identified 30 of 33 participants (90.9%) with acute COVID-19 as having a viral infection.

Conclusions and Relevance

In this study, the HR-B/V test accurately discriminated bacterial from viral infection among patients with febrile ARI and was superior to procalcitonin measurement. The findings suggest that an accurate point-of-need host response test with high NPV may offer an opportunity to improve antibiotic stewardship and patient outcomes.

Gene Expression Scoring of Immune Activity Levels for Precision Use of Hydrocortisone in Vasodilatory Shock

PURPOSE

Among patients with vasodilatory shock, gene expression scores may identify different immune states. We aimed to test whether such scores are robust in identifying patients' immune state and predicting response to hydrocortisone treatment in vasodilatory shock.

MATERIALS AND METHODS

We selected genes to generate continuous scores to define previously established subclasses of sepsis. We used these scores to identify a patient's immune state. We evaluated the potential for these states to assess the differential effect of hydrocortisone in two randomized clinical trials of hydrocortisone versus placebo in vasodilatory shock.

RESULTS

We initially identified genes associated with immune-adaptive, immune-innate, immune-coagulant functions. From these genes, 15 were most relevant to generate expression scores related to each of the functions. These scores were used to identify patients as immune-adaptive prevalent (IA-P) and immune-innate prevalent (IN-P). In IA-P patients, hydrocortisone therapy increased 28-day mortality in both trials (43.3% vs 14.7%, P = 0.028) and (57.1% vs 0.0%, P = 0.99). In IN-P patients, this effect was numerically reversed.

CONCLUSIONS

Gene expression scores identified the immune state of vasodilatory shock patients, one of which (IA-P) identified those who may be harmed by hydrocortisone. Gene expression scores may help advance the field of personalized medicine.

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.

FYN and CD247: key Genes for Septic Shock Based on Bioinformatics and Meta-Analysis

BACKGROUND

Septic shock is sepsis accompanied by hemodynamic instability and high clinical mortality.

MATERIAL AND METHODS

GSE95233, GSE57065, GSE131761 gene-expression profiles of healthy control subjects and septic shock patients were downloaded from the Gene-Expression Omnibus (GEO) database, and differences of expression profiles and their intersection were analysed using GEO2R. Function and pathway enrichment analysis was performed on common differentially expressed genes (DEG), and key genes for septic shock were screened using a protein-protein interaction network created with STRING. Also, data from the GEO database were used for survival analysis for key genes, and a meta-analysis was used to explore expression trends of core genes. Finally, high-throughput sequencing using the blood of a murine sepsis model was performed to analyse the expression of CD247 and FYN in mice.

RESULTS

A total of 539 DEGs were obtained (p < 0.05). Gene ontology analysis showed that key genes were enriched in functions, such as immune response and T cell activity, and DEGs were enriched in signal pathways, such as T cell receptors. FYN and CD247 are in the centre of the protein-protein interaction network, and survival analysis found that they are positively correlated with survival from sepsis. Further, meta-analysis results showed that FYN could be useful for the prognosis of patients, and CD247 might distinguish between sepsis and systemic inflammatory response syndrome patients. Finally, RNA sequencing using a mouse septic shock model showed low expression of CD247 and FYN in this model.

CONCLUSION

FYN and CD247 are expected to become new biomarkers of septic shock.

SDF4 Is a Prognostic Factor for 28-Days Mortality in Patients With Sepsis via Negatively Regulating ER Stress

Sepsis is a heterogeneous syndrome induced by infection and results in high mortality. Even though more than 100 biomarkers for sepsis prognosis were evaluated, prediction of patient outcomes in sepsis continues to be driven by clinical signs because of unsatisfactory specificity and sensitivity of these biomarkers. This study aimed to elucidate the key candidate genes involved in sepsis response and explore their downstream effects based on weighted gene co-expression network analysis (WGCNA). The dataset GSE63042 with sepsis outcome information was obtained from the Gene Expression Omnibus (GEO) database and then consensus WGCNA was conducted. We identified the hub gene SDF4 (stromal cell derived factor 4) from the M6 module, which was significantly associated with mortality. Subsequently, two datasets (GSE54514 and E-MTAB-4421) and cohort validation (n=89) were performed. Logistic regression analysis was used to build a prediction model and the combined score resulting in a satisfactory prognosis value (area under the ROC curve=0.908). The model was subsequently tested by another sepsis cohort (n=70, ROC= 0.925). We next demonstrated that endoplasmic reticulum (ER) stress tended to be more severe in patients PBMCs with negative outcomes compared to those with positive outcomes and SDF4 was related to this phenomenon. In addition, our results indicated that adenovirus-mediated Sdf4 overexpression attenuated ER stress in cecal ligation and puncture (CLP) mice lung. In summary, our study indicates that incorporation of SDF4 can improve clinical parameters predictive value for the prognosis of sepsis, and decreased expression levels of SDF4 contributes to excessive ER stress, which is associated with worsened outcomes, whereas overexpression of SDF4 attenuated such activation.

A single transcript for the prognosis of disease severity in COVID-19 patients

With many countries strapped for medical resources due to the COVID-19 pandemic, it is highly desirable to allocate the precious resources to those who need them the most. Several markers have been found to be associated with the disease severity in COVID-19 patients. However, the established markers only display modest prognostic power individually and better markers are urgently needed. The aim of this study is to investigate the potential of S100A12, a prominent marker gene for bacterial infection, in the prognosis of disease severity in COVID-19 patients. To ensure the robustness of the association, a total of 1695 samples from 14 independent transcriptome datasets on sepsis, influenza infection and COVID-19 infection were examined. First, it was demonstrated that S100A12 was a marker for sepsis and severity of sepsis. Then, S100A12 was found to be a marker for severe influenza infection, and there was an upward trend of S100A12 expression as the severity level of influenza infection increased. As for COVID-19 infection, it was found that S100A12 expression was elevated in patients with severe and critical COVID-19 infection. More importantly, S100A12 expression at hospital admission was robustly correlated with future quantitative indexes of disease severity and outcome in COVID-19 patients, superior to established prognostic markers including CRP, PCT, d-dimer, ferritin, LDH and fibrinogen. Thus, S100A12 is a valuable novel prognostic marker for COVID-19 severity and deserves more attention.

A long noncoding RNA antisense to ICAM-1 is involved in allergic asthma associated hyperreactive response of airway epithelial cells

Epithelial cells of the conducting airways are a pivotal first line of defense against airborne pathogens and allergens that orchestrate inflammatory responses and mucociliary clearance. Nonetheless, the molecular mechanisms responsible for epithelial hyperreactivity associated with allergic asthma are not completely understood. Transcriptomic analysis of human airway epithelial cells (HAECs), differentiated in-vitro at air-liquid interface (ALI), showed 725 differentially expressed immediate-early transcripts, including putative long noncoding RNAs (lncRNAs). A novel lncRNA on the antisense strand of ICAM-1 or LASI was identified, which was induced in LPS-primed HAECs along with mucin MUC5AC and its transcriptional regulator SPDEF. LPS-primed expression of LASI, MUC5AC, and SPDEF transcripts were higher in ex-vivo cultured asthmatic HAECs that were further augmented by LPS treatment. Airway sections from asthmatics with increased mucus load showed higher LASI expression in MUC5AC+ goblet cells following multi-fluorescent in-situ hybridization and immunostaining. LPS- or IL-13-induced LASI transcripts were mostly enriched in the nuclear/perinuclear region and were associated with increased ICAM-1, IL-6, and CXCL-8 expression. Blocking LASI expression reduced the LPS or IL-13-induced epithelial inflammatory factors and MUC5AC expression, suggesting that the novel lncRNA LASI could play a key role in LPS-primed trained airway epithelial responses that are dysregulated in allergic asthma.

Sectm1a deficiency aggravates inflammation-triggered cardiac dysfunction through disruption of LXRα signalling in macrophages

AIMS

Cardiac dysfunction is a prevalent comorbidity of disrupted inflammatory homeostasis observed in conditions such as sepsis (acute) or obesity (chronic). Secreted and transmembrane protein 1a (Sectm1a) has previously been implicated to regulate inflammatory responses, yet its role in inflammation-associated cardiac dysfunction is virtually unknown.

METHODS AND RESULTS

Using the CRISPR/Cas9 system, we generated a global Sectm1a-knockout (KO) mouse model and observed significantly increased mortality and cardiac injury after lipopolysaccharide (LPS) injection, when compared with wild-type (WT) control. Further analysis revealed significantly increased accumulation of inflammatory macrophages in hearts of LPS-treated KO mice. Accordingly, ablation of Sectm1a remarkably increased inflammatory cytokines levels both in vitro [from bone marrow-derived macrophages (BMDMs)] and in vivo (in serum and myocardium) after LPS challenge. RNA-sequencing results and bioinformatics analyses showed that the most significantly down-regulated genes in KO-BMDMs were modulated by LXRα, a nuclear receptor with robust anti-inflammatory activity in macrophages. Indeed, we identified that the nuclear translocation of LXRα was disrupted in KO-BMDMs when treated with GW3965 (LXR agonist), resulting in higher levels of inflammatory cytokines, compared to GW3965-treated WT-cells. Furthermore, using chronic inflammation model of high-fat diet (HFD) feeding, we observed that infiltration of inflammatory monocytes/macrophages into KO-hearts were greatly increased and accordingly, worsened cardiac function, compared to WT-HFD controls.

CONCLUSION

This study defines Sectm1a as a new regulator of inflammatory-induced cardiac dysfunction through modulation of LXRα signalling in macrophages. Our data suggest that augmenting Sectm1a activity may be a potential therapeutic approach to resolve inflammation and associated cardiac dysfunction.

Rethinking animal models of sepsis - working towards improved clinical translation whilst integrating the 3Rs

Sepsis is a major worldwide healthcare issue with unmet clinical need. Despite extensive animal research in this area, successful clinical translation has been largely unsuccessful.

We propose one reason for this is that, sometimes, the experimental question is misdirected or unrealistic expectations are being made of the animal model.

As sepsis models can lead to a rapid and substantial suffering – it is essential that we continually review experimental approaches and undertake a full harm:benefit impact assessment for each study. In some instances, this may require refinement of existing sepsis models. In other cases, it may be replacement to a different experimental system altogether, answering a mechanistic question whilst aligning with the principles of reduction, refinement and replacement (3Rs).

We discuss making better use of patient data to identify potentially useful therapeutic targets which can subsequently be validated in preclinical systems. This may be achieved through greater use of construct validity models, from which mechanistic conclusions are drawn. We argue that such models could provide equally useful scientific data as face validity models, but with an improved 3Rs impact. Indeed, construct validity models may not require sepsis to be modelled, per se. We propose that approaches that could support and refine clinical translation of research findings, whilst reducing the overall welfare burden on research animals.

Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

BACKGROUND

The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system.

METHODS

In order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings.

RESULTS

Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host.

CONCLUSIONS

Our study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity.

Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

BACKGROUND

The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system.

METHODS

In order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings.

RESULTS

Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host.

CONCLUSIONS

Our study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity.

Sectm1a Facilitates Protection against Inflammation-Induced Organ Damage through Promoting TRM Self-Renewal

Tissue-resident macrophages (TRMs) are sentinel cells for maintaining tissue homeostasis and organ function. In this study, we discovered that lipopolysaccharide (LPS) administration dramatically reduced TRM populations and suppressed their self-renewal capacities in multiple organs. Using loss- and gain-of-function approaches, we define Sectm1a as a novel regulator of TRM self-renewal. Specifically, at the earlier stage of endotoxemia, Sectm1a deficiency exaggerated acute inflammation-induced reduction of TRM numbers in multiple organs by suppressing their proliferation, which was associated with more infiltrations of inflammatory monocytes/neutrophils and more serious organ damage. By contrast, administration of recombinant Sectm1a enhanced TRM populations and improved animal survival upon endotoxin challenge. Mechanistically, we identified that Sectm1a-induced upregulation in the self-renewal capacity of TRM is dependent on GITR-activated T helper cell expansion and cytokine production. Meanwhile, we found that TRMs may play an important role in protecting local vascular integrity during endotoxemia. Our study demonstrates that Sectm1a contributes to stabling TRM populations through maintaining their self-renewal capacities, which benefits the host immune response to acute inflammation. Therefore, Sectm1a may serve as a new therapeutic agent for the treatment of inflammatory diseases.

Identification of a Novel Antisepsis Pathway: Sectm1a Enhances Macrophage Phagocytosis of Bacteria through Activating GITR

The inability to effectively control invading bacteria or other pathogens is a major cause of multiple organ dysfunction and death in sepsis. As the first-line defense of the immune system, macrophages play a crucial role in the removal of pathogens during sepsis. In this study, we define secreted and transmembrane 1A (Sectm1a) as a novel ligand of glucocorticoid-induced TNFR (GITR) that greatly boosts macrophage phagocytosis and bactericidal capacity. Using a global Sectm1a knockout (KO) mouse model, we observed that Sectm1a deficiency significantly suppressed phagocytosis and bactericidal activity in both recruited macrophages and tissue-resident macrophages, which consequently aggravated bacterial burden in the blood and multiple organs and further increased systemic inflammation, leading to multiple organ injury and increased mortality during polymicrobial sepsis. By contrast, treatment of septic mice with recombinant Sectm1a protein (rSectm1a) not only promoted macrophage phagocytosis and bactericidal activity but also significantly improved survival outcome. Mechanistically, we identified that Sectm1a could bind to GITR in the surface of macrophages and thereby activate its downstream PI3K-Akt pathway. Accordingly, rSectm1a-mediated phagocytosis and bacterial killing were abolished in macrophages by either KO of GITR or pharmacological inhibition of the PI3K-Akt pathway. In addition, rSectm1a-induced therapeutic effects on sepsis injury were negated in GITR KO mice. Taken together, these results uncover that Sectm1a may represent a novel target for drug development to control bacterial dissemination during sepsis or other infectious diseases.

A Guide to Understanding "State-of-the-Art" Basic Research Techniques in Anesthesiology

Perioperative medicine is changing from a "protocol-based" approach to a progressively personalized care model. New molecular techniques and comprehensive perioperative medical records allow for detection of patient-specific phenotypes that may better explain, or even predict, a patient's response to perioperative stress and anesthetic care. Basic science technology has significantly evolved in recent years with the advent of powerful approaches that have translational relevance. It is incumbent on us as a primarily clinical specialty to have an in-depth understanding of rapidly evolving underlying basic science techniques to incorporate such approaches into our own research, critically interpret the literature, and improve future anesthesia patient care. This review focuses on 3 important and most likely practice-changing basic science techniques: next-generation sequencing (NGS), clustered regularly interspaced short palindromic repeat (CRISPR) modulations, and inducible pluripotent stem cells (iPSCs). Each technique will be described, potential advantages and limitations discussed, open questions and challenges addressed, and future developments outlined. We hope to provide insight for practicing physicians when confronted with basic science articles and encourage investigators to apply "state-of-the-art" technology to their future experiments.

Two Approaches to Classifying and Quantifying Physical Resilience in Longitudinal Data

BACKGROUND

Approaches for quantifying physical resilience in older adults have not been described.

METHODS

We apply two conceptual approaches to defining physical resilience to existing longitudinal data sets in which outcomes are measured after an acute physical stressor. A "recovery phenotype" approach uses statistical methods to describe how quickly and completely a patient recovers. Statistical methods using a recovery phenotype approach can consider multiple outcomes simultaneously in a composite score (eg, factor analysis and principal components analysis) or identify groups of patients with similar recovery trajectories across multiple outcomes (eg, latent class profile analysis). An "expected recovery differential" approach quantifies how patients' actual outcomes are compared to their predicted outcome based on a population-derived model and their individual clinical characteristics at the time of the stressor.

RESULTS

Application of the approaches identified different participants as being the most or least physically resilient. In the viral respiratory cohort (n = 186) weighted kappa for agreement across resilience quartiles was 0.37 (0.27-0.47). The expected recovery differential approach identified a group with more comorbidities and lower baseline function as highly resilient. In the hip fracture cohort (n = 541), comparison of the expected recovery differentials across 10 outcome measures within individuals provided preliminary support for the hypothesis that there is a latent resilience trait at the whole-person level.

CONCLUSIONS

We posit that recovery phenotypes may be useful in clinical applications such as prediction models because they summarize the observed outcomes across multiple measures. Expected recovery differentials offer insight into mechanisms behind physical resilience not captured by age and other comorbidities.

Companion and complementary diagnostics for infectious diseases

INTRODUCTION

Companion diagnostics (CDx) are important in oncology therapeutic decision-making, but specific regulatory-approved CDx for infectious disease treatment are officially lacking. While not approved as CDx, several ID diagnostics are used as CDx. The diagnostics community, manufacturers, and regulatory agencies have made major efforts to ensure that diagnostics for new antimicrobials are available at or near release of new agents.

AREAS COVERED

This review highlights the status of Complementary and companion diagnostic (c/CDx) in the infectious disease literature, with a focus on genotypic antimicrobial resistance testing against pathogens as a class of diagnostic tests.

EXPERT OPINION

CRISPR, sepsis markers, and narrow spectrum antimicrobials, in addition to current and emerging technologies, present opportunities for infectious disease c/CDx. Challenges include slow guideline revision, high costs for regulatory approval, lengthy buy in by agencies, discordant pharmaceutical/diagnostic partnerships, and higher treatment costs. The number of patients and available medications used to treat different infectious diseases is well suited to support competing diagnostic tests. However, newer approaches to treatment (for example, narrow spectrum antibiotics), may be well suited for a small number of patients, i.e. a niche market in support of a CDx. The current emphasis is rapid and point-of-care (POC) diagnostic platforms as well as changes in treatment.

Transcriptome Meta-Analysis Deciphers a Dysregulation in Immune Response-Associated Gene Signatures during Sepsis

Sepsis is a life-threatening disease induced by a systemic inflammatory response, which leads to organ dysfunction and mortality. In sepsis, the host immune response is depressed and unable to cope with infection; no drug is currently available to treat this. The lungs are frequently the starting point for sepsis. This study aimed to identify potential genes for diagnostics and therapeutic purposes in sepsis by a comprehensive bioinformatics analysis. Our criteria are to unravel sepsis-associated signature genes from gene expression datasets. Differentially expressed genes (DEGs) were identified from samples of sepsis patients using a meta-analysis and then further subjected to functional enrichment and protein‒protein interaction (PPI) network analysis for examining their potential functions. Finally, the expression of the topmost upregulated genes (ARG1, IL1R2, ELANE, MMP9) was quantified by reverse transcriptase-PCR (RT-PCR), and myeloperoxidase (MPO) expression was confirmed by immunohistochemistry (IHC) staining in the lungs of a well-established sepsis mouse model. We found that all the four genes were upregulated in semiquantitative RT-PCR studies; however, MMP9 showed a nonsignificant increase in expression. MPO staining showed strong immunoreactivity in sepsis as compared to the control. This study demonstrates the role of significant and widespread immune activation (IL1R2, MMP9), along with oxidative stress (ARG1) and the recruitment of neutrophils, in sepsis (ELANE, MPO).

Biomarkers of Infection and Sepsis

The role of biomarkers for detection of sepsis has come a long way. Molecular biomarkers are taking front stage at present, but machine learning and other computational measures using bigdata sets are promising. Clinical research in sepsis is hampered by lack of specificity of the diagnosis; sepsis is a syndrome with no uniformly agreed definition. This lack of diagnostic precision means there is no gold standard for this diagnosis. The final conclusion is expert opinion, which is not bad but not perfect. Perhaps machine learning will displace expert opinion as the final and most accurate definition for sepsis.

Validation of a Host Response Assay, SeptiCyte LAB, for Discriminating Sepsis from Systemic Inflammatory Response Syndrome in the ICU

RATIONALE

A molecular test to distinguish between sepsis and systemic inflammation of noninfectious etiology could potentially have clinical utility.

OBJECTIVES

This study evaluated the diagnostic performance of a molecular host response assay (SeptiCyte LAB) designed to distinguish between sepsis and noninfectious systemic inflammation in critically ill adults.

METHODS

The study employed a prospective, observational, noninterventional design and recruited a heterogeneous cohort of adult critical care patients from seven sites in the United States (n = 249). An additional group of 198 patients, recruited in the large MARS (Molecular Diagnosis and Risk Stratification of Sepsis) consortium trial in the Netherlands ( www.clinicaltrials.gov identifier NCT01905033), was also tested and analyzed, making a grand total of 447 patients in our study. The performance of SeptiCyte LAB was compared with retrospective physician diagnosis by a panel of three experts.

MEASUREMENTS AND MAIN RESULTS

In receiver operating characteristic curve analysis, SeptiCyte LAB had an estimated area under the curve of 0.82-0.89 for discriminating sepsis from noninfectious systemic inflammation. The relative likelihood of sepsis versus noninfectious systemic inflammation was found to increase with increasing test score (range, 0-10). In a forward logistic regression analysis, the diagnostic performance of the assay was improved only marginally when used in combination with other clinical and laboratory variables, including procalcitonin. The performance of the assay was not significantly affected by demographic variables, including age, sex, or race/ethnicity.

CONCLUSIONS

SeptiCyte LAB appears to be a promising diagnostic tool to complement physician assessment of infection likelihood in critically ill adult patients with systemic inflammation. Clinical trial registered with www.clinicaltrials.gov (NCT01905033 and NCT02127502).

Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients

Septic shock is a devastating health condition caused by uncontrolled sepsis. Advancements in high-throughput sequencing techniques have increased the number of potential genetic biomarkers under review. Multiple genetic markers and functional pathways play a part in development and progression of pediatric septic shock. We identified 53 differentially expressed pediatric septic shock biomarkers using gene expression data sampled from 181 patients admitted to the pediatric intensive care unit within the first 24 hours of their admission. The gene expression signatures showed discriminatory power between pediatric septic shock survivors and nonsurvivor types. Using functional enrichment analysis of differentially expressed genes, we validated the known genes and pathways in septic shock and identified the unexplored septic shock-related genes and functional groups. Differential gene expression analysis revealed the genes involved in the immune response, chemokine-mediated signaling, neutrophil chemotaxis, and chemokine activity and distinguished the septic shock survivor from non-survivor. The identification of the septic shock gene biomarkers may facilitate in septic shock diagnosis, treatment, and prognosis.