Increased expression of neutrophil-related genes in patients with early sepsis-induced ARDS

FABP4 expression in neutrophils as a predictor of sepsis and SI-ARDS based on BALF transcriptome and peripheral blood validation

BACKGROUND

The objective of this study is to delineate the differential gene expression patterns of neutrophils in bronchoalveolar lavage fluid (BALF) from patients with sepsis and those experiencing progression to sepsis-induced acute respiratory distress syndrome (SI-ARDS). Additionally, we aim to comprehensively profile the transcriptomic landscape of neutrophils in BALF from patients with sepsis and SI-ARDS, particularly focusing on cases caused by specific bacterial pathogens.

METHODS

Patients with confirmed sepsis (n = 14) or SI-ARDS (n = 11) were recruited. Besides, a control group consisting of patients with unrelated diseases (n = 7) who required bronchoscopy was also included (cohort 1). We collected the neutrophils in BALF from participants in cohort 1. To validate the identified differentially expressed genes (DEGs) and evaluate neutrophil apoptosis, an additional cohort (cohort 2) was recruited, consisting of 5 healthy controls, 10 patients with sepsis, and 10 patients with SI-ARDS. Peripheral blood neutrophils were collected from participants in cohort 2 for further analysis. DEGs between SI-ARDS patients and controls, sepsis patients and controls, as well as SI-ARDS patients and sepsis patients were identified. And, publicly available datasets were downloaded to compare with local results. Additionally, the DEGs were also identified between patients infected with drug-resistant Klebsiella pneumoniae and those infected with other bacterial pathogens. Furthermore, a third cohort (cohort 3) consisting of 57 sepsis patients and 46 SI-ARDS patients was recruited for investigating the prognostic significance of neutrophils in SI-ARDS.

RESULTS

In cohort 1, 8/14 of the septic patients and 6/11 of the SI-ARDS patients were affected by drug-resistant Klebsiella pneumonia. There were 9921 DEGs between sepsis patients and controls, 10,252 DEGs between SI-ARDS patients and controls, and 24 DEGs between SI-ARDS and sepsis patients in neutrophils from BALF. Notably, fatty acid-binding pro-tein 4 (FABP4) exhibited significant downregulation in SI-ARDS patients. In cohort 2, peripheral blood analysis confirmed consistent trends, demonstrating that FABP4 expression was decreased, which contributed to the attenuation of neutrophil apoptosis. And FABP4 inhibitor-induced apoptosis resistance was reversed by a phosphatidylinositol 3 kinase (PI3K)/protein kinase B (AKT) inhibitor. Furthermore, survival analysis revealed that SI-ARDS patients with low levels of neutrophil FABP4 expression exhibited poor survival. Additionally, 520 overlapping DEGs were identified between the sepsis and control group comparisons and the SI-ARDS and sepsis group comparisons. Among these overlapping DEGs, 85% were downregulated, predominantly targeting immune-related pathways, whereas a smaller subset was upregulated, mainly associated with metabolism. DEGs in neutrophils in BALF of SI-ARDS and controls notably overlapped with those in neutrophils in peripheral blood. Importantly, DEGs in sepsis/SI-ARDS caused by drug-resistant Klebsiella pneumoniae differed from DEGs in sepsis/SI-ARDS caused by other bacteria. Additionally, FABP4 expression consistently decreased, attenuating neutrophil apoptosis.

CONCLUSIONS

The downregulation of FABP4 in neutrophils was found to inhibit apoptosis through the activation of the PI3K/AKT signaling pathway. Importantly, the expression level of FABP4 in neutrophil emerged as a prognostic indicator for sepsis and SI-ARDS patients, suggesting its potential utility in clinical decision-making to address the challenges posed by this condition.

Protective role of olfactomedin 4 gene polymorphisms in preterm neonates with sepsis

BACKGROUND

Olfactomedin 4 (OLFM4) gene polymorphisms have been associated with variations in inflammatory responses and the severity of infections. This study aimed to investigate the association between OLFM4 single nucleotide polymorphisms (SNPs) rs17552047 and rs1891944 and severe outcomes in preterm neonatal sepsis.

METHODS

A prospective observational cohort study was conducted from April 2023 to April 2024, involving all preterm infants diagnosed with neonatal sepsis. Genotyping was performed using real-time polymerase chain reaction, and the associations with severe outcomes were analyzed using genetic models (dominant, recessive, and additive) through multivariate logistic regression and survival analysis.

RESULTS

Among the 174 preterm newborns included in the study, 39 experienced severe outcomes. The AA/AG genotypes of SNP rs17552047 and TT/TC genotypes of rs1891944 were associated with a reduced risk of severe outcomes (adjusted hazard ratio: 0.271, 95 % confidence interval [CI]: 0.115-0.641, p = 0.003, and adjusted hazard ratio: 0.349, 95 % CI: 0.175-0.698, p = 0.003, respectively). The odds of severe outcomes decreased by 65 % for each additional A allele (95 % CI: 0.15-0.78, p = 0.01). The model incorporating both SNPs and clinical variables demonstrated good predictive capability (area under the receiver operating characteristic curve: 0.826, 95 % CI: 0.748-0.903, p = 0.03).

CONCLUSIONS

The OLFM4 rs17552047 AA/AG and rs1891944 TT/TC genotypes have been linked to favorable outcomes in neonatal sepsis. These SNPs hold promise for predicting severe outcomes in neonatal 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.

Circulating extracellular vesicles as potential biomarkers and mediators of acute respiratory distress syndrome in sepsis

The early sequence of respiratory failure events after the onset of sepsis is still unknown. We hypothesize that the lung should signal through circulating extracellular vesicles (EVs) when it is affected by a systemic inflammatory response. Blood samples were obtained from septic patients with (n = 5) and without acute respiratory distress syndrome (ARDS) (n = 13) at 24 h of intensive care unit admission and 3 days later at Sírio-Libanês Hospital. Pulmonary-originated sepsis was not considered. The characteristics of the plasma-isolated EVs were compatible with exosomes. 48 miRNAs were evaluated by real-time PCR. Comparing all samples from patients with sepsis + ARDS to sepsis only, 9 miRNAs are transported in smaller amounts: miR-766 (-35.7, p = 0.002), miR-127 (-23.8, p = 0.001), miR-340 (-13.5, p = 0.006), miR-29b (-12.8, p = 0.001), miR-744 (-7.1, p = 0.05), miR-618 (-4.0, p = 0.02), miR-598 (-3.8, p = 0.035), miR-1260 (-2.5, p = 0.035); and miR-885-5p is expressed at higher levels (9.5; p = 0.028). In paired samples, the set of altered miRNAs is generally different (p < 0.05) between sepsis + ARDS (miR-1183,-1267,-1290,-17,-192,-199a-3p,-25,-485-3p,-518d,-720) or sepsis only (miR-148a,-193a-5p,-199a-3p,-222,-25,-340,744). Bioinformatic analysis showed that when sepsis is associated with ARDS, those differentially expressed miRNAs potentially target messenger RNAs from the Glycoprotein VI/GP6 signaling pathway. Circulating EV-miRNA cargo could be potential biomarkers of lung inflammation during sepsis in patients requiring mechanical ventilation.

Plasma-derived extracellular vesicles prime alveolar macrophages for autophagy and ferroptosis in sepsis-induced acute lung injury

Sepsis-induced acute respiratory distress syndrome (ARDS) is a severe complication of sepsis and the leading cause of mortality. Although the role of alveolar macrophages (AMs) in stabilizing pulmonary homeostasis is well established, the effects of circulating extracellular vesicles (EVs) on AMs remain largely unknown. In this study, an investigation was conducted to map the miRNA and protein expression profiles of EVs derived from septic plasma. Notably, EV-based panels (miR-122-5p, miR-125b-5p, miR-223-3p, OLFM4, and LCN2) have been found to be associated with the severity or prognosis of sepsis, with promising AUC values. Moreover, the levels of LCN2, miR-122-5p, and miR-223-3p were identified as independent predictors of septic ARDS. The in vitro coculture results revealed that the effects of LPS-EVs from the plasma of sepsis-induced acute lung injury (ALI), which carry pro-inflammatory EVs, were partly mediated by miR-223-3p, as evidenced by the promotion of inflammation, autophagy and ferroptosis in AMs. Mechanistically, the upregulation of miR-223-3p in LPS-EVs triggers autophagy and ferroptosis in AMs by activating Hippo signaling via the targeting of MEF2C. In vivo, the inhibition of miR-223-3p effectively mitigated LPS-EV-induced inflammation and AM death in the lungs, as well as histological lesions. Overall, miR-223-3p in LPS-EVs contributes to sepsis-induced ALI by priming AMs for autophagy and ferroptosis through the MEF2C/Hippo signaling pathway. These findings suggest a novel mechanism of plasma-AM interaction in sepsis-induced ALI, offering a plausible strategy for assessing septic progression and treating lung injury.

Comparative analysis of Lox-1 and CD36 expression in human platelets and on circulating microparticles during ARDS-induced coagulopathy

INTRODUCTION

Acute respiratory distress syndrome (ARDS) patients are at risk of thrombosis through mechanisms implicating oxidized low-density lipoprotein (oxLDL). Endothelial cells, immune cells and platelets were reported to express scavenger receptors for oxLDL: Lox-1 and CD36. We hypothesized that platelets shed a soluble Lox-1 ectodomain (sLox-1) and release CD36-bearing procoagulant microparticles (MPs), that both become elevated in subjects with ARDS-induced coagulopathy.

METHODS

Using anti-extracellular and anti-intracellular Lox-1 antibodies, we first tested by western blot whether platelets express Lox-1 and shed sLox-1 upon activation. Next, we measured sLox-1 in blood plasma of 23 healthy donors and 48 ARDS Omega patients with and without coagulopathy, and assessed the corresponding MP fraction for Lox-1/sLox-1 and CD36. We evaluated mechanisms of sLox-1-MP association. Recombinant proteins were used as controls.

RESULTS

Resting platelets expressed abundant CD36 (7.8 ng/μg protein extract) which was released upon oxLDL stimulation, but undetectable levels of full-length 37 kDa Lox-1 receptor or 24 kDa sLox-1 (below 10 pg/μg). In an RNAseq meta-analysis, platelets expressed negligible OLR1, the mRNA encoding Lox-1, compared to CD36. A subset of ARDS patients showed elevated plasma sLox-1 and MP-associated sLox-1 compared to healthy controls that was positively associated with 90-day survival and low coagulopathy. MP-associated CD36 was reduced in ARDS plasma compared to healthy donors and did not correlate with survival, coagulopathy, or sLox-1. oxLDL promoted sLox-1 binding to CD36-deficient MPs.

CONCLUSION

sLox-1 arising from a non-platelet cell source associates with circulating MPs which could serve a protective role in ARDS.

Essential blood molecular signature for progression of sepsis-induced acute lung injury: Integrated bioinformatic, single-cell RNA Seq and machine learning analysis

In this study, we aimed to identify an essential blood molecular signature for chacterizing the progression of sepsis-induced acute lung injury using integrated bioinformatic and machine learning analysis. The results showed that a total of 88 functionally related ALI-associated hub genes in sepsis were identified by MCODE analysis and they were enriched in infection and inflammtory responses, lung and cardiovascular disease pathways. These hub genes stratified ALI-sepsis and sepsis and further stratified two subtypes of sepsis-ALI with differential ALI scores, hub gene expression patterns, and levels of immune cells. A seven-gene signature including TNFRSF1A, NFKB1, FCGR2A, NFE2L2, ICAM1 and SOCS3 and PDCD1 was derived from the hub genes. These genes were significantly implicated in immune and metabolism pathways. They were expressed in six circulatory immune cells based on analysis of a single cell RNA sequencing dataset. Furthermore, the seven-gene signature was corrobarated using by integrating 12 machine learning algorithms. A premium three-gene signature NFE2L2, FCGR2A and PDCD1 for differentiating ALI-sepsis from sepsis were also derived from the seven-gene signature based on analysis of the seven core hub genes by the machine learning algorithms. Furthermore, the expressions of hub genes were verified in sepsis mice models. Therefore, our study provided an avenue to develop a molecular tool for identify and characterize progression of acute lung injury associated with sepsis.

Transcriptional pathways of terminal differentiation in high- and low-density blood granulocytes in sepsis

BACKGROUND

Trauma and infection induce emergency granulopoiesis. Counts of immature granulocytes and transcriptional pathways of terminal granulocytic differentiation in blood are elevated in sepsis but correlate with disease severity. This limits their performance as sepsis biomarkers in critically ill patients. We hypothesized that activation of these pathways in sepsis is attributable to immature low-density (LD) rather than mature high-density (HD) granulocytes.

METHODS

We included patients with sepsis and systemic inflammatory response syndrome (SIRS) of comparable disease severity, and additionally septic shock, on intensive or intermediate care unit admission. Blood granulocyte isolation by CD15 MicroBeads was followed by density-gradient centrifugation. Flow cytometry was used to determine counts of developmental stages (precursors) and their relative abundancies in total, HD, and LD granulocytes. Five degranulation markers were quantified in plasma by multiplex immunoassays. A set of 135 genes mapping granulocyte differentiation was assayed by QuantiGene™ Plex. CEACAM4, PLAC8, and CD63 were analyzed by qRT-PCR. Nonparametric statistical tests were applied.

RESULTS

Precursor counts appeared higher in sepsis than SIRS but did not correlate with disease severity for early immature and mature granulocytes. Precursor subpopulations were enriched at least ten-fold in LD over HD granulocytes without sepsis-SIRS differences. Degranulation markers in blood were comparable in sepsis and SIRS. Higher expression of early developmental genes in sepsis than SIRS was more pronounced in LD and less in HD than total granulocytes. Only the cell membrane protein encoding genes CXCR2 and CEACAM4 were more highly expressed in SIRS than sepsis. By qRT-PCR, the azurophilic granule genes CD63 and PLAC8 showed higher sepsis than SIRS levels in LD granulocytes and PLAC8 also in total granulocytes where its discriminatory performance resembled C-reactive protein (CRP).

CONCLUSIONS

Transcriptional programs of early terminal granulocytic differentiation distinguish sepsis from SIRS due to both higher counts of immature granulocytes and elevated expression of early developmental genes in sepsis. The sustained expression of PLAC8 in mature granulocytes likely accounts for its selection in the whole blood SeptiCyte™ LAB test. Total granulocyte PLAC8 rivals CRP as sepsis biomarker. However, infection-specific transcriptional pathways, that differentiate sepsis from sterile stress-induced granulocytosis more reliably than CRP, remain to be identified.

Impaired ATP hydrolysis in blood plasma contributes to age-related neutrophil dysfunction

BACKGROUND

The function of polymorphonuclear neutrophils (PMNs) decreases with age, which results in infectious and inflammatory complications in older individuals. The underlying causes are not fully understood. ATP release and autocrine stimulation of purinergic receptors help PMNs combat microbial invaders. Excessive extracellular ATP interferes with these mechanisms and promotes inflammatory PMN responses. Here, we studied whether dysregulated purinergic signaling in PMNs contributes to their dysfunction in older individuals.

RESULTS

Bacterial infection of C57BL/6 mice resulted in exaggerated PMN activation that was significantly greater in old mice (64 weeks) than in young animals (10 weeks). In contrast to young animals, old mice were unable to prevent the systemic spread of bacteria, resulting in lethal sepsis and significantly greater mortality in old mice than in their younger counterparts. We found that the ATP levels in the plasma of mice increased with age and that, along with the extracellular accumulation of ATP, the PMNs of old mice became increasingly primed. Stimulation of the formyl peptide receptors of those primed PMNs triggered inflammatory responses that were significantly more pronounced in old mice than in young animals. However, bacterial phagocytosis and killing by PMNs of old mice were significantly lower than that of young mice. These age-dependent PMN dysfunctions correlated with a decrease in the enzymatic activity of plasma ATPases that convert extracellular ATP to adenosine. ATPases depend on divalent metal ions, including Ca2+, Mg2+, and Zn2+, and we found that depletion of these ions blocked the hydrolysis of ATP and the formation of adenosine in human blood, resulting in ATP accumulation and dysregulation of PMN functions equivalent to those observed in response to aging.

CONCLUSIONS

Our findings suggest that impaired hydrolysis of plasma ATP dysregulates PMN function in older individuals. We conclude that strategies aimed at restoring plasma ATPase activity may offer novel therapeutic opportunities to reduce immune dysfunction, inflammation, and infectious complications in older patients.

Attributable mortality of ARDS among critically ill patients with sepsis: a multicenter, retrospective cohort study

BACKGROUND

Both sepsis and acute respiratory distress syndrome (ARDS) are common severe diseases in the intensive care unit (ICU). There is no large-scale multicenter study to clarify the attributable mortality of ARDS among septic patients. This study aimed to evaluate the excess mortality of ARDS in critically ill patients with sepsis.

METHODS

The data were obtained from a multicenter, prospective cohort study in 18 Chinese ICUs between January 2014 and August 2015. The study population was septic patients after ICU admission. The patients were categorized into two groups: those who developed ARDS (ARDS group) within seven days following a sepsis diagnosis and those who did not develop ARDS (non-ARDS group). Applying propensity score matching (PSM), patients were matched 1:1 as ARDS and non-ARDS groups. Mortality attributed to ARDS was calculated. Subsequently, we conducted a survival analysis to estimate the impact of ARDS on mortality. The primary endpoint was 30-day mortality after sepsis diagnosis.

RESULTS

2323 septic patients were eligible, 67.8% developed ARDS. After PSM, 737 patients with ARDS were matched 1:1 with 737 non-ARDS patients. ARDS's overall 30-day attributable mortality was 11.9% (95% CI 7.5-16.3%, p < 0.001). Subgroup analysis showed that the 30-day attributable mortality of mild, moderate, and severe ARDS was 10.5% (95% CI 4.0-16.8%, p < 0.001), 11.6% (95% CI 4.7-18.4%, p < 0.001) and 18.1% (95% CI 4.5-30.9%, p = 0.006), respectively. ARDS was an independent risk factor for 30-day mortality, with adjusted hazard ratios of 1.30 (95% CI 1.03-1.64, p = 0.027), 1.49 (95% CI 1.20-1.85, p < 0.001), and 1.95 (95% CI 1.51-2.52, p < 0.001) for mild, moderate, and severe ARDS, respectively.

CONCLUSIONS

The overall 30-day attributable mortality of ARDS among critically ill patients with sepsis was 11.9%. Compared with mild and moderate ARDS, severe ARDS contributed more to death. ARDS was significantly associated with an increase in the 30-day mortality.

An IL-10/DEL-1 axis supports granulopoiesis and survival from sepsis in early life

The limited reserves of neutrophils are implicated in the susceptibility to infection in neonates, however the regulation of neutrophil kinetics in infections in early life remains poorly understood. Here we show that the developmental endothelial locus (DEL-1) is elevated in neonates and is critical for survival from neonatal polymicrobial sepsis, by supporting emergency granulopoiesis. Septic DEL-1 deficient neonate mice display low numbers of myeloid-biased multipotent and granulocyte-macrophage progenitors in the bone marrow, resulting in neutropenia, exaggerated bacteremia, and increased mortality; defects that are rescued by DEL-1 administration. A high IL-10/IL-17A ratio, observed in newborn sepsis, sustains tissue DEL-1 expression, as IL-10 upregulates while IL-17 downregulates DEL-1. Consistently, serum DEL-1 and blood neutrophils are elevated in septic adult and neonate patients with high serum IL-10/IL-17A ratio, and mortality is lower in septic patients with high serum DEL-1. Therefore, IL-10/DEL-1 axis supports emergency granulopoiesis, prevents neutropenia and promotes sepsis survival in early life.

In Vivo Near-Infrared Fluorescence Resonance Energy Transfer (NIR-FRET) Imaging of MMP-2 in ALI/ARDS in LPS-Treated Mice

Matrix metalloproteinases (MMPs) are zinc-dependent proteinases that are capable of cleavage of extracellular matrix (ECM) proteins and enzymes and play an important role in lung dysfunction. Specifically, MMP-2 is produced in the lung by alveolar epithelial and endothelial cells and other immune cells, such as macrophages. MMP-2 regulatory pathway is initiated in alveolar macrophages during acute lung injury (ALI), which may increase pulmonary inflammation. Therefore, there is a critical need for fast and reliable techniques to track the acute respiratory distress syndrome (ARDS). Here, we describe near-infrared fluorescence resonance energy transfer (NI-FRET) MMP-2-based probe for the in vivo detection of ALI induced by lipopolysaccharides (LPS). LPS-induced MMP-2 was measured using near-infrared (NIR) imaging after 1, 2, 4, 5, and 24 h of LPS exposure. Our results were compared with the data obtained from ELISA and Western blotting, demonstrating that MMP-2 fluorescence probe provide a promising in vivo diagnostic tool for ALI/ARDS in infected mice.

Increased lipocalin-2 expression in pulmonary inflammation and fibrosis

Introduction

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive interstitial lung disease with dismal prognosis. The underlying pathogenic mechanisms are poorly understood, resulting in a lack of effective treatments. However, recurrent epithelial damage is considered critical for disease initiation and perpetuation, via the secretion of soluble factors that amplify inflammation and lead to fibroblast activation and exuberant deposition of ECM components. Lipocalin-2 (LCN2) is a neutrophil gelatinase-associated lipocalin (NGAL) that has been suggested as a biomarker of kidney damage. LCN2 has been reported to modulate innate immunity, including the recruitment of neutrophils, and to protect against bacterial infections by sequestering iron.

Methods

In silico analysis of publicly available transcriptomic datasets; ELISAs on human IPF patients' bronchoalveolar lavage fluids (BALFs); bleomycin (BLM)-induced pulmonary inflammation and fibrosis and LPS-induced acute lung injury (ALI) in mice: pulmonary function tests, histology, Q-RT-PCR, western blot, and FACS analysis.

Results and discussion

Increased LCN2 mRNA expression was detected in the lung tissue of IPF patients negatively correlating with respiratory functions, as also shown for BALF LCN2 protein levels in a cohort of IPF patients. Increased Lcn2 expression was also detected upon BLM-induced pulmonary inflammation and fibrosis, especially at the acute phase correlating with neutrophilic infiltration, as well as upon LPS-induced ALI, an animal model characterized by neutrophilic infiltration. Surprisingly, and non withstanding the limitations of the study and the observed trends, Lcn2-/- mice were found to still develop BLM- or LPS-induced pulmonary inflammation and fibrosis, thus questioning a major pathogenic role for Lcn2 in mice. However, LCN2 qualifies as a surrogate biomarker of pulmonary inflammation and a possible indicator of compromised pulmonary functions, urging for larger studies.

Single-Cell RNA Sequencing and Transcriptome Analysis Revealed the Immune Microenvironment and Gene Markers of Acute Respiratory Distress Syndrome

Background

Acute respiratory distress syndrome (ARDS) is caused by severe pulmonary inflammation and the leading cause of death in the intensive care unit.

Methods

We used single-cell RNA sequencing to compare peripheral blood mononuclear cells from sepsis-induced ARDS (SEP-ARDS) and pneumonic ARDS (PNE-ARDS) patient. Then, we used the GSE152978 and GSE152979 datasets to identify molecular dysregulation mechanisms at the transcriptional level in ARDS.

Results

Markedly increased CD14 cells were the predominant immune cell type observed in SEP-ARDS and PNE-ARDS patients. Cytotoxic cells and natural killer (NK) T cells were exclusively identified in patients with PNE-ARDS. An enrichment analysis of differentially expressed genes (DEGs) suggested that Th1 cell differentiation and Th2 cell differentiation were enriched in cytotoxic cells, and that the IL-17 signaling pathway, NOD receptor signaling pathway, and complement and coagulation cascades were enriched in CD14 cells. Furthermore, according to GSE152978 and GSE152979, 1939 DEGs were identified in patients with ARDS and controls; they were mainly enriched in the Kyoto Encyclopedia of Genes and Genomes pathways. RBP7 had the highest area under the curve values among the 12 hub genes and was mainly expressed in CD14 cells. Additionally, hub genes were negatively correlated with NK cells and positively correlated with neutrophils, cytotoxic cells, B cells, and macrophages.

Conclusion

A severe imbalance in the proportion of immune cells and immune dysfunction were observed in SEP-ARDS and PNE-ARDS patients. RBP7 may be immunologically associated with CD14 cells and serve as a potential marker of ARDS.

Transcriptomic profiles of multiple organ dysfunction syndrome phenotypes in pediatric critical influenza

Background

Influenza virus is responsible for a large global burden of disease, especially in children. Multiple Organ Dysfunction Syndrome (MODS) is a life-threatening and fatal complication of severe influenza infection.

Methods

We measured RNA expression of 469 biologically plausible candidate genes in children admitted to North American pediatric intensive care units with severe influenza virus infection with and without MODS. Whole blood samples from 191 influenza-infected children (median age 6.4 years, IQR: 2.2, 11) were collected a median of 27 hours following admission; for 45 children a second blood sample was collected approximately seven days later. Extracted RNA was hybridized to NanoString mRNA probes, counts normalized, and analyzed using linear models controlling for age and bacterial co-infections (FDR q<0.05).

Results

Comparing pediatric samples collected near admission, children with Prolonged MODS for ≥7 days (n=38; 9 deaths) had significant upregulation of nine mRNA transcripts associated with neutrophil degranulation (RETN, TCN1, OLFM4, MMP8, LCN2, BPI, LTF, S100A12, GUSB) compared to those who recovered more rapidly from MODS (n=27). These neutrophil transcripts present in early samples predicted Prolonged MODS or death when compared to patients who recovered, however in paired longitudinal samples, they were not differentially expressed over time. Instead, five genes involved in protein metabolism and/or adaptive immunity signaling pathways (RPL3, MRPL3, HLA-DMB, EEF1G, CD8A) were associated with MODS recovery within a week.

Conclusion

Thus, early increased expression of neutrophil degranulation genes indicated worse clinical outcomes in children with influenza infection, consistent with reports in adult cohorts with influenza, sepsis, and acute respiratory distress syndrome.

Network analysis identifies a gene biomarker panel for sepsis-induced acute respiratory distress syndrome

BACKGROUND

Acute respiratory distress syndrome (ARDS) is characterized by non-cardiogenic pulmonary edema caused by inflammation, which can lead to serious respiratory complications. Due to the high mortality of ARDS caused by sepsis, biological markers that enable early diagnosis are urgently needed for clinical treatment.

METHODS

In the present study, we used the public microarray data of whole blood from patients with sepsis-induced ARDS, patients with sepsis-alone and healthy controls to perform an integrated analysis based on differential expressed genes (DEGs) and co-expression network to identify the key genes and pathways related to the development of sepsis into ARDS that may be key targets for diagnosis and treatment.

RESULTS

Compared with controls, we identified 180 DEGs in the sepsis-alone group and 152 DEGs in the sepsis-induced ARDS group. About 70% of these genes were unique to the two groups. Functional analysis of DEGs showed that neutrophil-mediated inflammation and mitochondrial dysfunction are the main features of ARDS induced by sepsis. Gene network analysis identified key modules and screened out key regulatory genes related to ARDS. The key genes and their upstream regulators comprised a gene panel, including EOMES, LTF, CSF1R, HLA-DRA, IRF8 and MPEG1. Compared with the healthy controls, the panel had an area under the curve (AUC) of 0.900 and 0.914 for sepsis-alone group and sepsis-induced ARDS group, respectively. The AUC was 0.746 between the sepsis-alone group and sepsis-induced ARDS group. Moreover, the panel of another independent blood transcriptional expression profile dataset showed the AUC was 0.769 in diagnosing sepsis-alone group and sepsis-induced ARDS group.

CONCLUSIONS

Taken together, our method contributes to the diagnosis of sepsis and sepsis-induced ARDS. The biological pathway involved in this gene biomarker panel may also be a critical target in combating ARDS caused by sepsis.

Longitudinal Analysis of Contrasts in Gene Expression Data

We are interested in detecting a departure from the baseline in a longitudinal analysis in the context of multiple organ dysfunction syndrome (MODS). In particular, we are given gene expression reads at two time points for a fixed number of genes and individuals. The individuals can be subdivided into two groups, denoted as groups A and B. Using the two time points, we compute a contrast of gene expression reads per individual and gene. The age of each individual is known and it is used to compute, for each gene separately, a linear regression of the gene expression contrasts on the individual's age. Looking at the intercept of the linear regression to detect a departure from the baseline, we aim to reliably single out those genes for which there is a difference in the intercept among those individuals in group A and not in group B. In this work, we develop testing methodology for this setting based on two hypothesis tests-one under the null and one under an appropriately formulated alternative. We demonstrate the validity of our approach using a dataset created by bootstrapping from a real data application in the context of multiple organ dysfunction syndrome (MODS).

Biological Phenotyping in Sepsis and Acute Respiratory Distress Syndrome

Heterogeneity in sepsis and acute respiratory distress syndrome (ARDS) is increasingly being recognized as one of the principal barriers to finding efficacious targeted therapies. The advent of multiple high-throughput biological data ("omics"), coupled with the widespread access to increased computational power, has led to the emergence of phenotyping in critical care. Phenotyping aims to use a multitude of data to identify homogenous subgroups within an otherwise heterogenous population. Increasingly, phenotyping schemas are being applied to sepsis and ARDS to increase understanding of these clinical conditions and identify potential therapies. Here we present a selective review of the biological phenotyping schemas applied to sepsis and ARDS. Further, we outline some of the challenges involved in translating these conceptual findings to bedside clinical decision-making tools.

Predicting severity in COVID-19 disease using sepsis blood gene expression signatures

Severely-afflicted COVID-19 patients can exhibit disease manifestations representative of sepsis, including acute respiratory distress syndrome and multiple organ failure. We hypothesized that diagnostic tools used in managing all-cause sepsis, such as clinical criteria, biomarkers, and gene expression signatures, should extend to COVID-19 patients. Here we analyzed the whole blood transcriptome of 124 early (1-5 days post-hospital admission) and late (6-20 days post-admission) sampled patients with confirmed COVID-19 infections from hospitals in Quebec, Canada. Mechanisms associated with COVID-19 severity were identified between severity groups (ranging from mild disease to the requirement for mechanical ventilation and mortality), and established sepsis signatures were assessed for dysregulation. Specifically, gene expression signatures representing pathophysiological events, namely cellular reprogramming, organ dysfunction, and mortality, were significantly enriched and predictive of severity and lethality in COVID-19 patients. Mechanistic endotypes reflective of distinct sepsis aetiologies and therapeutic opportunities were also identified in subsets of patients, enabling prediction of potentially-effective repurposed drugs. The expression of sepsis gene expression signatures in severely-afflicted COVID-19 patients indicates that these patients should be classified as having severe sepsis. Accordingly, in severe COVID-19 patients, these signatures should be strongly considered for the mechanistic characterization, diagnosis, and guidance of treatment using repurposed drugs.

Combination of transcriptional biomarkers and clinical parameters for early prediction of sepsis indued acute respiratory distress syndrome

Objective

As a common yet intractable complication of severe sepsis, acute respiratory distress syndrome (ARDS) is closely associated with poor clinical outcomes and elevated medical expenses. The aim of the current study is to generate a model combining transcriptional biomarkers and clinical parameters to alarm the development of ARDS in septic patients.

Methods

Gene expression profile (GSE66890) was downloaded from the Gene Expression Omnibus database and clinical data were extracted. Differentially expressed genes (DEGs) from whole blood leukocytes were identified between patients with sepsis alone and septic patients who develop ARDS. ARDS prediction model was constructed using backward stepwise regression and Akaike Information Criterion (AIC). Meanwhile, a nomogram based on this model was established, with subsequent internal validation.

Results

A total of 57 severe septic patients were enrolled in this study, and 28 (49.1%) developed ARDS. Based on the differential expression analysis, six DEGs (BPI, OLFM4, LCN2, CD24, MMP8 and MME) were screened. According to the outcome prediction model, six valuable risk factors (direct lung injury, shock, tumor, BPI, MME and MMP8) were incorporated into a nomogram, which was used to predict the onset of ARDS in septic patients. The calibration curves of the nomogram showed good consistency between the probabilities and observed values. The decision curve analysis also revealed the potential clinical usefulness of the nomogram. The area under the receiver operating characteristic (AUROC) for the prediction of ARDS occurrence in septic patients by the nomogram was 0.86 (95% CI = 0.767-0.952). A sensitivity analysis showed that the AUROC for the prediction of ARDS development in septic patients without direct lung injury was 0.967 (95% CI = 0.896-1.0).

Conclusions

The nomogram based on transcriptional biomarkers and clinical parameters showed a good performance for the prediction of ARDS occurrence in septic patients.

Drug Repurposing Using Gene Co-Expression and Module Preservation Analysis in Acute Respiratory Distress Syndrome (ARDS), Systemic Inflammatory Response Syndrome (SIRS), Sepsis, and COVID-19

SARS-CoV-2 infections are highly correlated with the overexpression of pro-inflammatory cytokines in what is known as a cytokine storm, leading to high fatality rates. Such infections are accompanied by SIRS, ARDS, and sepsis, suggesting a potential link between the three phenotypes. Currently, little is known about the transcriptional similarity between these conditions. Herein, weighted gene co-expression network analysis (WGCNA) clustering was applied to RNA-seq datasets (GSE147902, GSE66890, GSE74224, GSE177477) to identify modules of highly co-expressed and correlated genes, cross referenced with dataset GSE160163, across the samples. To assess the transcriptome similarities between the conditions, module preservation analysis was performed and functional enrichment was analyzed in DAVID webserver. The hub genes of significantly preserved modules were identified, classified into upregulated or downregulated, and used to screen candidate drugs using Connectivity Map (CMap) to identify repurposed drugs. Results show that several immune pathways (chemokine signaling, NOD-like signaling, and Th1 and Th2 cell differentiation) are conserved across the four diseases. Hub genes screened using intramodular connectivity show significant relevance with the pathogenesis of cytokine storms. Transcriptomic-driven drug repurposing identified seven candidate drugs (SB-202190, eicosatetraenoic-acid, loratadine, TPCA-1, pinocembrin, mepacrine, and CAY-10470) that targeted several immune-related processes. These identified drugs warrant further study into their efficacy for treating cytokine storms, and in vitro and in vivo experiments are recommended to confirm the findings of this study.

Neutrophil functional heterogeneity is a fixed phenotype and is associated with distinct gene expression profiles

Differences in the ability of neutrophils to perform relevant effector functions has been identified in a variety of disease states. Although neutrophil functional heterogeneity is increasingly recognized during disease, few studies have examined neutrophil functional heterogeneity during periods of health. In this study, we systematically characterize neutrophil functional heterogeneity in a cohort of healthy human subjects using a range of biologically relevant agonists including immune complexes, bacterial ligands, and pathogens. With repeated testing over several years, we show that neutrophil functional capability represents a fixed phenotype for each individual. This neutrophil phenotype is preserved across a range of agonists and extends to a variety of effector functions including degranulation, neutrophil extracellular trap release, reactive oxygen species generation, phagocytosis, and bacterial killing. Using well-phenotyped healthy human subjects, we demonstrate that neutrophil functional heterogeneity is characterized by differences in neutrophil gene expression patterns. Altogether, our findings demonstrate that while neutrophil function is highly heterogeneous among healthy subjects, each individual's functional capability represents a fixed phenotype defined by a distinct neutrophil gene expression profile. These findings may be relevant during disease states where the ability to perform relevant neutrophil effector functions may impact disease course and/or clinical outcome.

Personalized medicine using omics approaches in acute respiratory distress syndrome to identify biological phenotypes

In the last decade, research on acute respiratory distress syndrome (ARDS) has made considerable progress. However, ARDS remains a leading cause of mortality in the intensive care unit. ARDS presents distinct subphenotypes with different clinical and biological features. The pathophysiologic mechanisms of ARDS may contribute to the biological variability and partially explain why some pharmacologic therapies for ARDS have failed to improve patient outcomes. Therefore, identifying ARDS variability and heterogeneity might be a key strategy for finding effective treatments. Research involving studies on biomarkers and genomic, metabolomic, and proteomic technologies is increasing. These new approaches, which are dedicated to the identification and quantitative analysis of components from biological matrixes, may help differentiate between different types of damage and predict clinical outcome and risk. Omics technologies offer a new opportunity for the development of diagnostic tools and personalized therapy in ARDS. This narrative review assesses recent evidence regarding genomics, proteomics, and metabolomics in ARDS research.

Deep Learning Chest CT for Clinically Precise Prediction of Sepsis-Induced Acute Respiratory Distress Syndrome: A Protocol for an Observational Ambispective Cohort Study

Background: Sepsis commonly causes acute respiratory distress syndrome (ARDS), and ARDS contributes to poor prognosis in sepsis patients. Early prediction of ARDS for sepsis patients remains a clinical challenge. This study aims to develop and validate chest computed tomography (CT) radiomic-based signatures for early prediction of ARDS and assessment of individual severity in sepsis patients. Methods: In this ambispective observational cohort study, a deep learning model, a sepsis-induced acute respiratory distress syndrome (SI-ARDS) prediction neural network, will be developed to extract radiomics features of chest CT from sepsis patients. The datasets will be collected from these retrospective and prospective cohorts, including 400 patients diagnosed with sepsis-3 definition during a period from 1 May 2015 to 30 May 2022. 160 patients of the retrospective cohort will be selected as a discovering group to reconstruct the model and 40 patients of the retrospective cohort will be selected as a testing group for internal validation. Additionally, 200 patients of the prospective cohort from two hospitals will be selected as a validating group for external validation. Data pertaining to chest CT, clinical information, immune-associated inflammatory indicators and follow-up will be collected. The primary outcome is to develop and validate the model, predicting in-hospital incidence of SI-ARDS. Finally, model performance will be evaluated using the area under the curve (AUC) of receiver operating characteristic (ROC), sensitivity and specificity, using internal and external validations. Discussion: Present studies reveal that early identification and classification of the SI-ARDS is essential to improve prognosis and disease management. Chest CT has been sought as a useful diagnostic tool to identify ARDS. However, when characteristic imaging findings were clearly presented, delays in diagnosis and treatment were impossible to avoid. In this ambispective cohort study, we hope to develop a novel model incorporating radiomic signatures and clinical signatures to provide an easy-to-use and individualized prediction of SI-ARDS occurrence and severe degree in patients at early stage.

Integrated Analysis of Gene Co-Expression Network and Prediction Model Indicates Immune-Related Roles of the Identified Biomarkers in Sepsis and Sepsis-Induced Acute Respiratory Distress Syndrome

Sepsis is a series of clinical syndromes caused by immunological response to severe infection. As the most important and common complication of sepsis, acute respiratory distress syndrome (ARDS) is associated with poor outcomes and high medical expenses. However, well-described studies of analysis-based researches, especially related bioinformatics analysis on revealing specific targets and underlying molecular mechanisms of sepsis and sepsis-induced ARDS (sepsis/se-ARDS), still remain limited and delayed despite the era of data-driven medicine. In this report, weight gene co-expression network based on data from a public database was constructed to identify the key modules and screen the hub genes. Functional annotation by enrichment analysis of the modular genes also demonstrated the key biological processes and signaling pathway; among which, extensive immune-involved enrichment was remarkably associated with sepsis/se-ARDS. Based on the differential expression analysis, least absolute shrink and selection operator, and multivariable logistic regression analysis of the screened hub genes, SIGLEC9, TSPO, CKS1B and PTTG3P were identified as the candidate biomarkers for the further analysis. Accordingly, a four-gene-based model for diagnostic prediction assessment was established and then developed by sepsis/se-ARDS risk nomogram, whose efficiency was verified by calibration curves and decision curve analyses. In addition, various machine learning algorithms were also applied to develop extra models based on the four genes. Receiver operating characteristic curve analysis proved the great diagnostic and predictive performance of these models, and the multivariable logistic regression of the model was still found to be the best as further verified again by the internal test, training, and external validation cohorts. During the development of sepsis/se-ARDS, the expressions of the identified biomarkers including SIGLEC9, TSPO, CKS1B and PTTG3P were all regulated remarkably and generally exhibited notable correlations with the stages of sepsis/se-ARDS. Moreover, the expression levels of these four genes were substantially correlated during sepsis/se-ARDS. Analysis of immune infiltration showed that multiple immune cells, neutrophils and monocytes in particular, might be closely involved in the process of sepsis/se-ARDS. Besides, SIGLEC9, TSPO, CKS1B and PTTG3P were considerably correlated with the infiltration of various immune cells including neutrophils and monocytes during sepsis/se-ARDS. The discovery of relevant gene co-expression network and immune signatures might provide novel insights into the pathophysiology of sepsis/se-ARDS.

Key Signature Genes of Early Terminal Granulocytic Differentiation Distinguish Sepsis From Systemic Inflammatory Response Syndrome on Intensive Care Unit Admission

Infection can induce granulopoiesis. This process potentially contributes to blood gene classifiers of sepsis in systemic inflammatory response syndrome (SIRS) patients. This study aimed to identify signature genes of blood granulocytes from patients with sepsis and SIRS on intensive care unit (ICU) admission. CD15+ cells encompassing all stages of terminal granulocytic differentiation were analyzed. CD15 transcriptomes from patients with sepsis and SIRS on ICU admission and presurgical controls (discovery cohort) were subjected to differential gene expression and pathway enrichment analyses. Differential gene expression was validated by bead array in independent sepsis and SIRS patients (validation cohort). Blood counts of granulocyte precursors were determined by flow cytometry in an extension of the validation cohort. Despite similar transcriptional CD15 responses in sepsis and SIRS, enrichment of canonical pathways known to decline at the metamyelocyte stage (mitochondrial, lysosome, cell cycle, and proteasome) was associated with sepsis but not SIRS. Twelve of 30 validated genes, from 100 selected for changes in response to sepsis rather than SIRS, were endo-lysosomal. Revisiting the discovery transcriptomes revealed an elevated expression of promyelocyte-restricted azurophilic granule genes in sepsis and myelocyte-restricted specific granule genes in sepsis followed by SIRS. Blood counts of promyelocytes and myelocytes were higher in sepsis than in SIRS. Sepsis-induced granulopoiesis and signature genes of early terminal granulocytic differentiation thus provide a rationale for classifiers of sepsis in patients with SIRS on ICU admission. Yet, the distinction of this process from noninfectious tissue injury-induced granulopoiesis remains to be investigated.

Mechanistic Understanding of Lung Inflammation: Recent Advances and Emerging Techniques

Acute respiratory distress syndrome (ARDS) is a life-threatening lung injury characterized by an acute inflammatory response in the lung parenchyma. Hence, it is considered as the most appropriate clinical syndrome to study pathogenic mechanisms of lung inflammation. ARDS is associated with increased morbidity and mortality in the intensive care unit (ICU), while no effective pharmacological treatment exists. It is very important therefore to fully characterize the underlying pathobiology and the related mechanisms, in order to develop novel therapeutic approaches. In vivo and in vitro models are important pre-clinical tools in biological and medical research in the mechanistic and pathological understanding of the majority of diseases. In this review, we will present data from selected experimental models of lung injury/acute lung inflammation, which have been based on clinical disorders that can lead to the development of ARDS and related inflammatory lung processes in humans, including ventilation-induced lung injury (VILI), sepsis, ischemia/reperfusion, smoke, acid aspiration, radiation, transfusion-related acute lung injury (TRALI), influenza, Streptococcus (S.) pneumoniae and coronaviruses infection. Data from the corresponding clinical conditions will also be presented. The mechanisms related to lung inflammation that will be covered are oxidative stress, neutrophil extracellular traps, mitogen-activated protein kinase (MAPK) pathways, surfactant, and water and ion channels. Finally, we will present a brief overview of emerging techniques in the field of omics research that have been applied to ARDS research, encompassing genomics, transcriptomics, proteomics, and metabolomics, which may recognize factors to help stratify ICU patients at risk, predict their prognosis, and possibly, serve as more specific therapeutic targets.

Comprehensive Analysis of Potential ceRNA Network and Different Degrees of Immune Cell Infiltration in Acute Respiratory Distress Syndrome

Acute respiratory distress syndrome (ARDS) is a leading cause of death in critically ill patients due to hypoxemic respiratory failure. The specific pathogenesis underlying ARDS has not been fully elucidated. In this study, we constructed a triple regulatory network involving competing endogenous RNA (ceRNA) to investigate the potential mechanism of ARDS and evaluated the immune cell infiltration patterns in ARDS patients. Overall, we downloaded three microarray datasets that included 60 patients with sepsis-induced ARDS and 79 patients with sepsis alone from the public Gene Expression Omnibus (GEO) database and identified differentially expressed genes (DEGs, including 9 DElncRNAs, 9 DEmiRNAs, and 269 DEmRNAs) by R software. The DEGs were subjected to the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional enrichment analysis, and a protein–protein interaction (PPI) network was generated for uncovering interactive relationships among DEmRNAs. Then, a ceRNA network that contained 5 DElncRNAs, 7 DEmiRNAs, and 71 DEmRNAs was established according to the overlapping genes in both DEGs and predicted genes by public databases. Finally, we identified the TUG1/miR-140-5p/NFE2L2 pathway as the hub pathway in the whole network through Cytoscape. In addition, we evaluated the distribution of 22 subtypes of immune cells and recognized three differentially expressed immune cells in patients with sepsis-induced ARDS by “Cell Type Identification by Estimating Relative Subsets of Known RNA Transcripts (CIBERSORT)” algorithm, namely, naive B cells, regulatory T cells, and eosinophils. Correlations between differentially expressed immune cells and hub genes in the ceRNA network were also performed. In conclusion, we demonstrated a new potential regulatory mechanism underlying ARDS (the TUG1/miR-140-5p/NFE2L2 ceRNA regulatory pathway), which may help in further exploring the pathogenesis of ARDS.

Functional Transcriptomic Studies of Immune Responses and Endotoxin Tolerance in Early Human Sepsis

BACKGROUND

Limited studies have functionally evaluated the heterogeneity in early ex vivo immune responses during sepsis. Our aim was to characterize early sepsis ex vivo functional immune response heterogeneity by studying whole blood endotoxin responses and derive a transcriptional metric of ex vivo endotoxin response.

METHODS

Blood collected within 24 h of hospital presentation from 40 septic patients was divided into two fractions and incubated with media (unstimulated) or endotoxin. Supernatants and cells were isolated, and responses measured using: supernatant cytokines, lung endothelial permeability after supernatant exposure, and RNA expression. A transcriptomic signature was derived in unstimulated cells to predict the ex vivo endotoxin response. The signature was tested in a separate cohort of 191 septic patients to evaluate for association with clinical outcome. Plasma biomarkers were quantified to measure in vivo host inflammation.

RESULTS

Ex vivo response to endotoxin varied and was unrelated to immunosuppression, white blood cell count, or the causative pathogen. Thirty-five percent of patients demonstrated a minimal response to endotoxin, suggesting early immunosuppression. High ex vivo cytokine production by stimulated blood cells correlated with increased in vitro pulmonary endothelial cell permeability and was associated with attenuated in vivo host inflammation. A four-gene signature of endotoxin response detectable without the need for a functional assay was identified. When tested in a separate cohort of septic patients, its expression was inversely associated with hospital mortality.

CONCLUSIONS

An attenuated ex vivo endotoxin response in early sepsis is associated with greater host in vivo inflammation and a worse clinical outcome.

Huangkui Capsule Attenuates Lipopolysaccharide-Induced Acute Lung Injury and Macrophage Activation by Suppressing Inflammation and Oxidative Stress in Mice

Background

Huangkui capsule (HKC) comprises the total flavonoid extract of flowers of Abelmoschus manihot (L.) Medicus. This study aimed to explore the effects of HKC on lipopolysaccharide- (LPS-) induced acute lung injury (ALI) in mice and LPS-stimulated RAW 264.7 cells.

Methods

Enzyme-linked immunosorbent assay, histopathology, spectrophotometry, and quantitative real-time polymerase chain reaction were used for the assessments. Statistical analysis was performed using a one-way analysis of variance.

Results

LPS significantly increased lung inflammation, neutrophil infiltration, and oxidative stress and downregulated lung miR-451 expression. Treatment with HKC dramatically, reduced the total cell count in the bronchoalveolar lavage fluid (BALF), and inhibited myeloperoxidase activity in the lung tissues 24 h after LPS challenge. Histopathological analysis demonstrated that HKC attenuated LPS-induced tissue oedema and neutrophil infiltration in the lung tissues. Additionally, the concentrations of tumour necrosis factor- (TNF-) α and interleukin- (IL-) 6 in BALF and IL-6 in the plasma reduced after HKC administration. Moreover, HKC could enhance glutathione peroxidase and catalase activities and upregulate the expression of miR-451 in the lung tissues. In vitro experiments revealed that HKC inhibited the production of nitric oxide, TNF-α, and IL-6 in LPS-induced RAW 264.7 cells and mouse primary peritoneal macrophages. Additionally, HKC downregulated LPS-induced transcription of TNF-α and IL-6 in RAW 264.7 cells.

Conclusions

These findings suggest that HKC has anti-inflammatory and antioxidative effects that may protect mice against LPS-induced ALI and macrophage activation.

Cigarette Smoke Exposure and Acute Respiratory Distress Syndrome in Sepsis: Epidemiology, Clinical Features, and Biologic Markers

Rationale: Cigarette smoke exposure is associated with an increased risk of developing acute respiratory distress syndrome (ARDS) in trauma, transfusion, and nonpulmonary sepsis. It is unknown whether this relationship exists in the general sepsis population. Furthermore, it is unknown if patients with ARDS have differences in underlying biology based on smoking status. Objectives: To assess the relationship between cigarette smoke exposure and ARDS in sepsis and identify tobacco-related biomarkers of lung injury. Methods: We studied a prospective cohort of 592 patients with sepsis from 2009 to 2017. Plasma cotinine and urine NNAL [urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol] were measured to categorize smoking status. Plasma biomarkers of inflammation and lung injury were measured, including in a smaller cohort of trauma patients with ARDS to increase generalizability. Measurements and Main Results: Passive and active smoking were associated with increased odds of developing ARDS in patients with sepsis. Among patients with sepsis and ARDS, active cigarette smokers were younger and had lower severity of illness than nonsmokers. Patients with ARDS with cigarette smoke exposure had lower plasma levels of IL-8 (P = 0.01) and sTNFR-1 (soluble tumor necrosis factor 1; P = 0.01) compared with those without exposure. Similar biomarker patterns were observed in blunt trauma patients with ARDS. Conclusions: Passive and active smoking are associated with an increased risk of developing ARDS in patients with pulmonary and nonpulmonary sepsis. Among patients with ARDS, those with cigarette smoke exposure have less systemic inflammation, while active smokers also have lower severity of illness compared with nonsmokers, suggesting that smoking contributes to biological heterogeneity in ARDS.

Olfactomedin 4 Is a Biomarker for the Severity of Infectious Diseases

Biomarkers of infectious diseases are essential tools for patient monitoring, diagnostics, and prognostics. Here we review recent advances in our understanding of olfactomedin 4 (OLFM4) in neutrophil biology and of OLFM4 as a new biomarker for certain infectious diseases. OLFM4 is a neutrophil-specific granule protein that is expressed in a subset of human and mouse neutrophils. OLFM4 expression is upregulated in many viral and bacterial infections, as well as in malaria. OLFM4 appears to play an important role in regulating host innate immunity against bacterial infection. Further, higher expression of OLFM4 is associated with severity of disease for dengue virus, respiratory syncytial virus, and malaria infections. In addition, higher expression of OLFM4 or a higher percentage of OLFM4 + neutrophils is associated with poorer outcomes in septic patients. OLFM4 is a promising biomarker and potential therapeutic target in certain infectious diseases.

Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis

BACKGROUND

Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS.

METHODS

In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable.

FINDINGS

The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90-0·95) in EARLI and 0·88 (0·84-0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81-0·94] vs 0·92 [0·88-0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group).

INTERPRETATION

Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated.

FUNDING

US National Institutes of Health and European Society of Intensive Care Medicine.

Lung-brain 'cross-talk': systemic propagation of cytokines in the ARDS via the bloodstream using a blood transfusion model does not influence cerebral inflammatory response in pigs

Background

Interorgan cross-talk describes the phenomenon in which a primarily injured organ causes secondary damage to a distant organ. This cross-talk is well known between the lung and brain. One theory suggests that the release and systemic distribution of cytokines via the bloodstream from the primarily affected organ sets in motion proinflammatory cascades in distant organs. In this study, we analysed the role of the systemic distribution of cytokines via the bloodstream in a porcine ARDS model for organ cross-talk and possible inflammatory changes in the brain.

Methods

After approval of the State and Institutional Animal Care Committee, acute respiratory distress syndrome (ARDS) induction with oleic acid injection was performed in seven animals. Eight hours after ARDS induction, blood (35-40 ml kg^-1) was taken from these seven 'ARDS donor' pigs. The collected 'ARDS donor' blood was transfused into seven healthy 'ARDS-recipient' pigs. Three animals served as a control group, and blood from these animals was transfused into three healthy pigs after an appropriate ventilation period. All animals were monitored for 8 h using advanced cardiorespiratory monitoring. Postmortem assessment included cerebral (hippocampal and cortex) mediators of early inflammatory response (IL-6, TNF-alpha, iNOS, sLCN-2), wet-to-dry ratio and lung histology. TNF-alpha serum concentration was measured in all groups.

Results

ARDS was successfully induced in the 'ARDS donor' group, and serum TNF-alpha levels were elevated compared with the 'ARDS-recipient' group. In the 'ARDS-recipient' group, neither significant ARDS alterations nor upregulation of inflammatory mediators in the brain tissue were detected after high-volume random allogenic 'ARDS-blood' transfusion. The role of the systemic distribution of inflammatory cytokines from one affected organ to another could not be confirmed in this study.

Immune transcriptome analysis of COVID-19 patients infected with SARS-CoV-2 variants carrying the E484K escape mutation identifies a distinct gene module

Fast-spreading variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) energize the COVID-19 pandemic. While viral infections elicit a conserved immune response, it is not known whether SARS-CoV-2 variants, which display enhanced binding to the ACE2 receptor and reduced neutralizing activity by vaccine-elicited antibodies, prompt specific genomic immune responses. To test this, we generated and investigated the transcriptomes in BCs from hospitalized patients infected with either the Alpha variant (n = 36) or with the Alpha variant that had acquired the E484K escape mutation (Alpha+E484K) (n = 13). We identified a gene module preferentially activated in patients infected with the Alpha+E484K variant and in patients infected with the Beta (n = 9) and Gamma (n = 3) variants that also carry by the E484K escape mutation. The E484K signature was enriched for genes preferentially expressed in monocytes and linked to severe viral infection. However, both cohorts had undergone similar treatments and no differences in disease severity were reported suggesting that this signature reflects a variant response and does not necessarily associate with disease outcome. Additionally, longitudinal transcriptome analyses revealed a more persistent retention of immune signatures in Alpha+E484K patients throughout the entire course of COVID-19 disease and convalescence. While the OAS1 Neanderthal mutation has been linked to a milder COVID-19 pathology, we did not identify significant immune transcriptomes differences in the 25 patients homozygous for this mutation. Our study offers insights into distinct molecular immune responses elicited by SARS-CoV-2 variants carrying the E484K escape mutation throughout the COVID-19 disease.

Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study

RATIONALE

Using latent class analysis (LCA), two subphenotypes of acute respiratory distress syndrome (ARDS) have consistently been identified in five randomised controlled trials (RCTs), with distinct biological characteristics, divergent outcomes and differential treatment responses to randomised interventions. Their existence in unselected populations of ARDS remains unknown. We sought to identify subphenotypes in observational cohorts of ARDS using LCA.

METHODS

LCA was independently applied to patients with ARDS from two prospective observational cohorts of patients admitted to the intensive care unit, derived from the Validating Acute Lung Injury markers for Diagnosis (VALID) (n=624) and Early Assessment of Renal and Lung Injury (EARLI) (n=335) studies. Clinical and biological data were used as class-defining variables. To test for concordance with prior ARDS subphenotypes, the performance metrics of parsimonious classifier models (interleukin 8, bicarbonate, protein C and vasopressor-use), previously developed in RCTs, were evaluated in EARLI and VALID with LCA-derived subphenotypes as the gold-standard.

RESULTS

A 2-class model best fit the population in VALID (p=0.0010) and in EARLI (p<0.0001). Class 2 comprised 27% and 37% of the populations in VALID and EARLI, respectively. Consistent with the previously described 'hyperinflammatory' subphenotype, Class 2 was characterised by higher proinflammatory biomarkers, acidosis and increased shock and worse clinical outcomes. The similarities between these and prior RCT-derived subphenotypes were further substantiated by the performance of the parsimonious classifier models in both cohorts (area under the curves 0.92-0.94). The hyperinflammatory subphenotype was associated with increased prevalence of chronic liver disease and neutropenia and reduced incidence of chronic obstructive pulmonary disease. Measurement of novel biomarkers showed significantly higher levels of matrix metalloproteinase-8 and markers of endothelial injury in the hyperinflammatory subphenotype, whereas, matrix metalloproteinase-9 was significantly lower.

CONCLUSION

Previously described subphenotypes are generalisable to unselected populations of non-trauma ARDS.

OLFM4 Regulates Lung Epithelial Cell Function in Sepsis-Associated ARDS/ALI via LDHA-Mediated NF-κB Signaling

BACKGROUND

Acute respiratory distress syndrome (ARDS) is one of the leading causes of death in patients with sepsis. As such, early and accurate identification of sepsis-related ARDS is critical.

METHODS

Bioinformatic analysis was used to explore the GEO datasets. ELISA method was used to detect the plasma or cellular supernatant of relevant proteins. Quantitative real-time PCR was used for mRNA measurements and Western blot was applied for protein measurements. Immunohistochemistry staining and Immunofluorescence staining were used to identify the localization of OLFM4. Cecal ligation and puncture (CLP) model was used to establish sepsis model.

RESULTS

The bioinformatic analysis results identified ten genes (CAMP, LTF, RETN, LCN2, ELANE, PGLYRP1, BPI, DEFA4, MPO, and OLFM4) as critical in sepsis and sepsis-related ARDS. OLFM4, LCN2, and BPI were further demonstrated to have diagnostic values in sepsis-related ARDS. Plasma expression of OLFM4 and LCN2 was also upregulated in sepsis-related ARDS patients compared to septic patients alone. OLFM4 expression was significantly increased in the lung tissues of septic mice and was co-localized with Ly6G+ neutrophils, F4/80+ macrophages and pro-surfactant C+ lung epithelial cells. In vitro data showed that OLFM4 expression in lung epithelial cells was downregulated upon LPS stimulation, whereas neutrophil media induced OLFM4 expression in lung epithelial cells. Overexpression of OLFM4 and treatment with recombinant OLFM4 effectively suppressed LPS-induced pro-inflammatory responses in lung epithelial cells. Furthermore, the increased levels of LDHA phosphorylation and the downstream NF-κB activation induced by LPS in epithelial cells were effectively diminished by OLFM4 overexpression and recombinant OLFM4 treatment via a reduction in ROS production and HIF1α expression.

CONCLUSION

OLFM4 may regulate the pro-inflammatory response of lung epithelial cells in sepsis-related ARDS by modulating metabolic disorders; this result could provide new insights into the treatment of sepsis-induced ARDS.

Predicting candidate therapeutic drugs for sepsis-induced acute respiratory distress syndrome based on transcriptome profiling

Sepsis-induced acute respiratory distress syndrome (ARDS) remains a major threat to human health without effective therapeutic drugs. Previous studies demonstrated the power of gene expression profiling to reveal pathological changes associated with sepsis-induced ARDS. However, there is still a lack of systematic data mining framework for identifying potential targets for treatment. In this study, we demonstrated the feasibility of druggable targets prediction based on gene expression data. Through the functional enrichment analysis of microarray-based expression profiles between sepsis-induced ARDS and non-sepsis ARDS samples, we revealed genes involved in anti-microbial infection immunity were significantly altered in sepsis-induced ARDS. Protein-protein interaction (PPI) network analysis highlighted TOP2A gene as the key regulator in the dysregulated gene network of sepsis-induced ARDS. We were also able to predict several therapeutic drug candidates for sepsis-induced ARDS using Connectivity Map (Cmap) database, among which doxorubicin was identified to interact with TOP2A with a high affinity similar to its endogenous ligand. Overall, our findings suggest that doxorubicin could be a potential therapeutic for sepsis-induced ARDS by targeting TOP2A, which requires further investigation and validation. The whole study relies on publicly available dataset and publicly accessible database or bioinformatic tools for data mining. Therefore, our study benchmarks a workflow for druggable target prediction which can be widely applicable in the search of targets in other pathological conditions.

Comprehensive review of lipocalin 2-mediated effects in lung inflammation

Lipocalin-2 (LCN2) is an inflammatory mediator best known for its role as an innate acute-phase protein. LCN2 mediates the innate immune response to pathogens by sequestering iron, thereby inhibiting pathogen growth. Although LCN2 and its bacteriostatic properties are well studied, other LCN2 functions in the immune response to inflammatory stimuli are less well understood, such as its role as a chemoattractant and involvement in the regulation of cell migration and apoptosis. In the lungs, most studies thus far investigating the role of LCN2 in the immune response have looked at pathogenic inflammatory stimuli. Here, we compile data that explore the role of LCN2 in the immune response to various inflammatory stimuli in an effort to differentiate between protective versus detrimental roles of LCN2.

Genetics of Acute Respiratory Distress Syndrome: Pathways to Precision

Clinical risk factors alone fail to fully explain acute respiratory distress syndrome (ARDS) risk or ARDS death, suggesting that individual risk factors contribute. The goals of genomic ARDS studies include better mechanistic understanding, identifying dysregulated pathways that may be amenable to pharmacologic targeting, using genomic causal inference techniques to find measurable traits with meaning, and deconvoluting ARDS heterogeneity by proving reproducible subpopulations that may share a unique biology. This article discusses the latest advances in ARDS genomics, provides historical perspective, and highlights some of the ways that the coronavirus disease 2019 (COVID-19) pandemic is accelerating genomic ARDS research.

Tumor necrosis factor α‑induced protein 8‑like 2 contributes to penehyclidine hydrochloride pretreatment against lipopolysaccharide‑induced acute lung injury in a mouse model

The aim of the present study was to investigate the effect of penehyclidine hydrochloride (PHC) pretreatment on mice with lipopolysaccharide (LPS)‑induced acute lung injury (ALI) and its possible underlying mechanisms. Mice were randomly separated into six groups: i) Sham group; ii) LPS group; iii) LPS + PHC group; iv) tumor necrosis factor a‑induced protein 8‑like protein 2 (TIPE2) group; v) LPS + TIPE2 group; and vi) LPS + TIPE2 + PHC group. The ALI model was induced using LPS through intratracheal injection. The mice received adenovirus gene to induce the overexpression of TIPE2. After mice were sacrificed, lung injury indices were assessed, and arterial blood, bronchoalveolar lavage fluid and lung tissues were collected for subsequent assays. Expression levels of related proteins were detected by using western blotting. It was found that compared with the sham group, the mice treated with LPS showed increased lung injury and dysfunctions of gas exchange. However, these trends were significantly ameliorated in the LPS + PHC group. Evaluation of protein expression in lung tissues showed that the increased expression of nuclear NF‑κB p65 and p‑c‑Jun N‑terminal kinase (JNK) induced by LPS were suppressed in the LPS + PHC group and the expression of TIPE2 was increased. The mice that received adenovirus gene to induce TIPE2 overexpression could also showed protective effects compared with the mice in the LPS group. However, the expression of TIPE2 decreased rather than increased in LPS group. In the mice pretreated with PHC, the expression of TIPE2 increased in mice with LPS‑induced ALI. To conclude, PHC pretreatment could inhibit the occurrence of inflammation and apoptosis in LPS‑induced ALI. This process may be related to the activation of TIPE2 and the inhibition of NF‑κB and JNK signaling pathway in the lungs of mice.

Lipocalin-2: Structure, function, distribution and role in metabolic disorders

Lipocalin-2 (LCN-2) is a novel, 198 amino acid adipocytokine also referred to as neutrophil gelatinase-associated lipocalin (NGAL). LCN-2 is a circulatory protein responsible for the transportation of small and hydrophobic molecules (steroid, free fatty acids, prostaglandins and hormones) to target organs after binding to megalin/glycoprotein and GP330 SLC22A17 or 24p3R LCN-2 receptors. LCN-2 has been used as a biomarker for acute and chronic renal injury. It is present in a large variety of cells including neutrophil, hepatocytes, lung, bone marrow, adipose tissue, macrophages, thymus, non-neoplastic breast duct, prostate, and renal cells. Different functions have been associated with LCN-2. These functions include antibacterial, anti-inflammatory, and protection against cell and tissue stress. Moreover, LCN-2 can increase the pool of matrix metalloproteinase 9 in human neutrophil granulocytes. Other reported functions of LCN-2 include its ability to destroy the extracellular matrix, which could enable cancer progression and spread of metastasis. Recent reports show that the tissue level of LCN-2 is increased in metabolic disorders such as obesity and type 2 diabetes, suggesting an association between LCN-2 and insulin sensitivity and glucose homeostasis. The precise role of LCN-2 in the modulation of insulin sensitivity, glucose and lipid metabolism is still unclear. This review explores the structure of LCN-2, tissue distribution, and its interaction with important metabolic pathways.

Dysregulated hematopoiesis in bone marrow marks severe COVID-19

Severe coronavirus disease 2019 (COVID-19) is often indicated by lymphopenia and increased myelopoiesis; however, the underlying mechanism is still unclear, especially the alteration of hematopoiesis. It is important to explore to what extent and how hematopoietic stem cells contribute to the impairment of peripheral lymphoid and myeloid compartments in COVID-19 patients. In this study, we used single-cell RNA sequencing to assess bone marrow mononuclear cells from COVID-19 patients with peripheral blood mononuclear cells as control. The results showed that the hematopoietic stem cells in these patients were mainly in the G1 phase and prone to apoptosis, with immune activation and anti-viral responses. Importantly, a significant accumulation of immature myeloid progenitors and a dramatic reduction of lymphoid progenitors in severe cases were identified, along with the up-regulation of transcription factors (such as SPI1, LMO4, ETS2, FLI1, and GATA2) that are important for the hematopoietic stem cell or multipotent progenitor to differentiate into downstream progenitors. Our results indicate a dysregulated hematopoiesis in patients with severe COVID-19.

Association between Advanced Lung Inflammation Index and 30-Day Mortality in Patients with Acute Respiratory Distress Syndrome

Background and objectives: The advanced lung inflammation index (ALI) was developed to assess the degree of systemic inflammation and has an association with prognosis in patients with lung malignancy. The prognostic value of ALI has not yet been evaluated in patients with acute respiratory distress syndrome (ARDS). Materials and Methods: Between January 2014 and May 2018, patients with ARDS in the medical intensive care unit (ICU) were reviewed retrospectively. The ALI value was calculated as the (body mass index × serum albumin level)/neutrophil-lymphocyte ratio. The cut-off value for distinguishing low from high ALI was defined according to receiver-operating characteristic curve analysis. Results: A total of 164 patients were analyzed. Their median age was 73 years, and 73% was male. The main cause of ARDS was pneumonia (95.7%, 157/164). ICU and in-hospital mortality rates were 59.8% (98/164) and 64% (105/164), respectively. The 30 day mortality was 60.9% (100/164). The median ALI value in non-survivors was lower than that in survivors at 30 day (3.81 vs. 7.39, p = 0.005). In multivariate analysis, low ALI value (≤5.38) was associated with increased 30 day mortality (odds ratio, 2.944, confidence interval 1.178–7.355, p = 0.021). Conclusions: A low ALI value was associated with increased 30 day mortality in patients with ARDS.

A Five-Genes Based Diagnostic Signature for Sepsis-Induced ARDS

Background: Acute respiratory distress syndrome (ARDS) is a frequent and serious complication of sepsis without specific and sensitive diagnostic signatures.

Methods: The mRNA profiles, including 60 blood samples with sepsis-induced ARDS and 86 blood samples with sepsis alone, were obtained from the Gene Expression Omnibus (GEO). The differently expressed genes (DEGs) were analyzed by limma package of R language. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out using the clusterProfiler package of R. Eventually, multivariate logistic regression model was established through the glm function of R, and support vector machine (SVM) model was constructed via the e1071 package of R.

Results: A total of 242 DEGs in GSE32707 and 102 DEGs in GSE66890 were identified. Notably, five genes exhibited significant differences between the two datasets and were considered to be closely associated with the occurrence of ARDS induced by sepsis. Furthermore, functional enrichment analysis based on the DEGs showed there were 80 overlapped GO terms and one KEGG pathway which were significantly enriched in the two datasets. The logistic regression model and SVM model constructed could efficiently distinguish sepsis patients with or without ARDS.

Conclusion: In brief, our study suggested that NKG7, SPTA1, FGL2, RGS2, and IFI27 might be potential diagnostic signatures for sepsis-induced ARDS, which contributed to the future exploration in mechanism of ARDS occurrence and development.

Chronic Critical Illness Elicits a Unique Circulating Leukocyte Transcriptome in Sepsis Survivors

Surgical sepsis has evolved into two major subpopulations: patients who rapidly recover, and those who develop chronic critical illness (CCI). Our primary aim was to determine whether CCI sepsis survivors manifest unique blood leukocyte transcriptomes in late sepsis that differ from transcriptomes among sepsis survivors with rapid recovery. In a prospective cohort study of surgical ICU patients, genome-wide expression analysis was conducted on total leukocytes in human whole blood collected on days 1 and 14 from sepsis survivors who rapidly recovered or developed CCI, defined as ICU length of stay ≥ 14 days with persistent organ dysfunction. Both sepsis patients who developed CCI and those who rapidly recovered exhibited marked changes in genome-wide expression at day 1 which remained abnormal through day 14. Although summary changes in gene expression were similar between CCI patients and subjects who rapidly recovered, CCI patients exhibited differential expression of 185 unique genes compared with rapid recovery patients at day 14 (p < 0.001). The transcriptomic patterns in sepsis survivors reveal an ongoing immune dyscrasia at the level of the blood leukocyte transcriptome, consistent with persistent inflammation and immune suppression. Furthermore, the findings highlight important genes that could compose a prognostic transcriptomic metric or serve as therapeutic targets among sepsis patients that develop CCI.

Circulating mitochondrial DNA-triggered autophagy dysfunction via STING underlies sepsis-related acute lung injury

The STING pathway and its induction of autophagy initiate a potent immune defense response upon the recognition of pathogenic DNA. However, this protective response is minimal, as STING activation worsens organ damage, and abnormal autophagy is observed during progressive sepsis. Whether and how the STING pathway affects autophagic flux during sepsis-induced acute lung injury (sALI) are currently unknown. Here, we demonstrate that the level of circulating mtDNA and degree of STING activation are increased in sALI patients. Furthermore, STING activation was found to play a pivotal role in mtDNA-mediated lung injury by evoking an inflammatory storm and disturbing autophagy. Mechanistically, STING activation interferes with lysosomal acidification in an interferon (IFN)-dependent manner without affecting autophagosome biogenesis or fusion, aggravating sepsis. Induction of autophagy or STING deficiency alleviated lung injury. These findings provide new insights into the role of STING in the regulatory mechanisms behind extrapulmonary sALI.

Endothelial Dysfunction and Neutrophil Degranulation as Central Events in Sepsis Physiopathology

Sepsis is a major health problem worldwide. It is a time-dependent disease, with a high rate of morbidity and mortality. In this sense, an early diagnosis is essential to reduce these rates. The progressive increase of both the incidence and prevalence of sepsis has translated into a significant socioeconomic burden for health systems. Currently, it is the leading cause of noncoronary mortality worldwide and represents one of the most prevalent pathologies both in hospital emergency services and in intensive care units. In this article, we review the role of both endothelial dysfunction and neutrophil dysregulation in the physiopathology of this disease. The lack of a key symptom in sepsis makes it difficult to obtain a quick and accurate diagnosis of this condition. Thus, it is essential to have fast and reliable diagnostic tools. In this sense, the use of biomarkers can be a very important alternative when it comes to achieving these goals. Both new biomarkers and treatments related to endothelial dysfunction and neutrophil dysregulation deserve to be further investigated in order to open new venues for the diagnosis, treatment and prognosis of sepsis.

Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data

We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI ArrayExpress to create a comprehensive meta-analysis microarray expression set (meta-expression set). Measurements had to be obtained via microarray-technique from whole blood samples of adult or pediatric patients with sepsis diagnosed based on international consensus definition immediately after admission to the intensive care unit. We aggregate trauma patients, systemic inflammatory response syndrome (SIRS) patients, and healthy controls in a non-septic entity. Differential expression (DE) analysis is compared with machine-learning-based solutions like decision tree (DT), random forest (RF), support vector machine (SVM), and deep-learning neural networks (DNNs). We evaluated classifier training and discrimination performance in 100 independent iterations. To test diagnostic resilience, we gradually degraded expression data in multiple levels. Clustering of expression values based on DE genes results in partial identification of sepsis samples. In contrast, RF, SVM, and DNN provide excellent diagnostic performance measured in terms of accuracy and area under the curve (>0.96 and >0.99, respectively). We prove DNNs as the most resilient methodology, virtually unaffected by targeted removal of DE genes. By surpassing most other published solutions, the presented approach substantially augments current diagnostic capability in intensive care medicine.

Sepsis-Pathophysiology and Therapeutic Concepts

Sepsis is a life-threatening condition and a global disease burden. Today, the heterogeneous syndrome is defined as severe organ dysfunction caused by a dysregulated host response to infection, with renewed emphasis on immune pathophysiology. Despite all efforts of experimental and clinical research during the last three decades, the ability to positively influence course and outcome of the syndrome remains limited. Evidence-based therapy still consists of basic causal and supportive measures, while adjuvant interventions such as blood purification or targeted immunotherapy largely remain without proof of effectiveness so far. With this review, we aim to provide an overview of sepsis immune pathophysiology, to update the choice of therapeutic approaches targeting different immunological mechanisms in the course of sepsis and septic shock, and to call for a paradigm shift from the pathogen to the host response as a potentially more promising angle.