Bioinformatics and system biology approach to identify the influences among COVID-19, ARDS and sepsis.

Background Severe coronavirus disease 2019 (COVID -19) has led to severe pneumonia or acute respiratory distress syndrome (ARDS) worldwide. we have noted that many critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. The molecular mechanisms that underlie COVID-19, ARDS and sepsis are not well understood. The objectives of this study were to analyze potential molecular mechanisms and identify potential drugs for the treatment of COVID-19, ARDS and sepsis using bioinformatics and a systems biology approach. Methods Three RNA-seq datasets (GSE171110, GSE76293 and GSE137342) from Gene Expression Omnibus (GEO) were employed to detect mutual differentially expressed genes (DEGs) for the patients with the COVID-19, ARDS and sepsis for functional enrichment, pathway analysis, and candidate drugs analysis. Results We obtained 110 common DEGs among COVID-19, ARDS and sepsis. ARG1, FCGR1A, MPO, and TLR5 are the most influential hub genes. The infection and immune-related pathways and functions are the main pathways and molecular functions of these three diseases. FOXC1, YY1, GATA2, FOXL, STAT1 and STAT3 are important TFs for COVID-19. mir-335-5p, miR-335-5p and hsa-mir-26a-5p were associated with COVID-19. Finally, the hub genes retrieved from the DSigDB database indicate multiple drug molecules and drug-targets interaction. Conclusion We performed a functional analysis under ontology terms and pathway analysis and found some common associations among COVID-19, ARDS and sepsis. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs were also identified on the datasets. We believe that the candidate drugs obtained in this study may contribute to the effective treatment of COVID-19.

Facilitating systems-level analyses of all-cause and Covid-mediated sepsis through SeptiSearch, a manually-curated compendium of dysregulated gene sets.

Background: Sepsis is a dysfunctional host response to infection. The syndrome leads to millions of deaths annually (19.7% of all deaths in 2017) and is the cause of most deaths from severe Covid infections. High throughput sequencing or 'omics' experiments in molecular and clinical sepsis research have been widely utilized to identify new diagnostics and therapies. Transcriptomics, quantifying gene expression, has dominated these studies, due to the efficiency of measuring gene expression in tissues and the technical accuracy of technologies like RNA-Seq. Objective: Most of these studies seek to uncover novel mechanistic insights into sepsis pathogenesis and diagnostic gene signatures by identifying genes differentially expressed between two or more relevant conditions. However, little effort has been made, to date, to aggregate this knowledge from such studies. In this study we sought to build a compendium of previously described gene sets that combines knowledge gained from sepsis-associated studies. This would enable the identification of genes most associated with sepsis pathogenesis, and the description of the molecular pathways commonly associated with sepsis. Methods: PubMed was searched for studies using transcriptomics to characterize acute infection/sepsis and severe sepsis (i.e., sepsis combined with organ failure). Several studies were identified that used transcriptomics to identify differentially expressed (DE) genes, predictive/prognostic signatures, and underlying molecular responses and pathways. The molecules included in each gene set were collected, in addition to the relevant study metadata (e.g., patient groups used for comparison, sample collection time point, tissue type, etc.). Results: After performing extensive literature curation of 74 sepsis-related publications involving transcriptomics, 103 unique gene sets (comprising 20,899 unique genes) from thousands of patients were collated together with associated metadata. Frequently described genes included in gene sets as well as the molecular mechanisms they were involved in were identified. These mechanisms included neutrophil degranulation, generation of second messenger molecules, IL-4 and -13 signaling, and IL-10 signaling among many others. The database, which we named SeptiSearch, is made available in a web application created using the Shiny framework in R, (available at https://septisearch.ca). Conclusions: SeptiSearch provides members of the sepsis community the bioinformatic tools needed to leverage and explore the gene sets contained in the database. This will allow the gene sets to be further scrutinized and analyzed for their enrichment in user-submitted gene expression data and used for validation of in-house gene sets/signatures.

Identification of a sub-group of critically ill patients with high risk of intensive care unit-acquired infections and poor clinical course using a transcriptomic score.

BACKGROUND: The development of stratification tools based on the assessment of circulating mRNA of genes involved in the immune response is constrained by the heterogeneity of septic patients. The aim of this study is to develop a transcriptomic score based on a pragmatic combination of immune-related genes detected with a prototype multiplex PCR tool. METHODS: As training cohort, we used the gene expression dataset obtained from 176 critically ill patients enrolled in the REALISM study (NCT02638779) with various etiologies and still hospitalized in intensive care unit (ICU) at day 5-7. Based on the performances of each gene taken independently to identify patients developing ICU-acquired infections (ICU-AI) after day 5-7, we built an unweighted score assuming the independence of each gene. We then determined the performances of this score to identify a subgroup of patients at high risk to develop ICU-AI, and both longer ICU length of stay and mortality of this high-risk group were assessed. Finally, we validated the effectiveness of this score in a retrospective cohort of 257 septic patients. RESULTS: This transcriptomic score (TScore) enabled the identification of a high-risk group of patients (49%) with an increased rate of ICU-AI when compared to the low-risk group (49% vs. 4%, respectively), with longer ICU length of stay (13 days [95% CI 8-30] vs. 7 days [95% CI 6-9], p < 0.001) and higher ICU mortality (15% vs. 2%). High-risk patients exhibited biological features of immune suppression with low monocytic HLA-DR levels, higher immature neutrophils rates and higher IL10 concentrations. Using the TScore, we identified 160 high-risk patients (62%) in the validation cohort, with 30% of ICU-AI (vs. 18% in the low-risk group, p = 0.06), and significantly higher mortality and longer ICU length of stay. CONCLUSIONS: The transcriptomic score provides a useful and reliable companion diagnostic tool to further develop immune modulating drugs in sepsis in the context of personalized medicine.

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

Introduction: Severe COVID-19 and non-COVID-19 pulmonary sepsis share pathophysiological, immunological, and clinical features. To what extent they share mechanistically-based gene expression trajectories throughout hospitalization was unknown. Our objective was to compare gene expression trajectories between severe COVID-19 patients and contemporaneous non-COVID-19 severe sepsis patients in the intensive care unit (ICU). Methods: In this prospective single-center observational cohort study, whole blood was drawn from 20 COVID-19 patients and 22 non-COVID-19 adult sepsis patients at two timepoints: ICU admission and approximately a week later. RNA-Seq was performed on whole blood to identify differentially expressed genes and significantly enriched pathways. Results: At ICU admission, despite COVID-19 patients being almost clinically indistinguishable from non-COVID-19 sepsis patients, COVID-19 patients had 1,215 differentially expressed genes compared to non-COVID-19 sepsis patients. After one week in the ICU, the number of differentially expressed genes dropped to just 9 genes. This drop coincided with decreased expression of antiviral genes and relatively increased expression of heme metabolism genes over time in COVID-19 patients, eventually reaching expression levels seen in non-COVID-19 sepsis patients. Both groups also had similar underlying immune dysfunction, with upregulation of immune processes such as "Interleukin-1 signaling" and "Interleukin-6/JAK/STAT3 signaling" throughout disease compared to healthy controls. Discussion: Early on, COVID-19 patients had elevated antiviral responses and suppressed heme metabolism processes compared to non-COVID-19 severe sepsis patients, although both had similar underlying immune dysfunction. However, after one week in the ICU, these diseases became indistinguishable on a gene expression level. These findings highlight the importance of early antiviral treatment for COVID-19, the potential for heme-related therapeutics, and consideration of immunomodulatory therapies for both diseases to treat shared immune dysfunction.

Therapeutic potential of IL6R blockade for the treatment of sepsis and sepsis-related death: A Mendelian randomisation study.

BACKGROUND: Sepsis is characterised by dysregulated, life-threatening immune responses, which are thought to be driven by cytokines such as interleukin 6 (IL-6). Genetic variants in IL6R known to down-regulate IL-6 signalling are associated with improved Coronavirus Disease 2019 (COVID-19) outcomes, a finding later confirmed in randomised trials of IL-6 receptor antagonists (IL6RAs). We hypothesised that blockade of IL6R could also improve outcomes in sepsis. METHODS AND FINDINGS: We performed a Mendelian randomisation (MR) analysis using single nucleotide polymorphisms (SNPs) in and near IL6R to evaluate the likely causal effects of IL6R blockade on sepsis (primary outcome), sepsis severity, other infections, and COVID-19 (secondary outcomes). We weighted SNPs by their effect on CRP and combined results across them in inverse variance weighted meta-analysis, proxying the effect of IL6RA. Our outcomes were measured in UK Biobank, FinnGen, the COVID-19 Host Genetics Initiative (HGI), and the GenOSept and GainS consortium. We performed several sensitivity analyses to test assumptions of our methods, including utilising variants around CRP and gp130 in a similar analysis. In the UK Biobank cohort (N = 486,484, including 11,643 with sepsis), IL6R blockade was associated with a decreased risk of our primary outcome, sepsis (odds ratio (OR) = 0.80; 95% confidence interval (CI) 0.66 to 0.96, per unit of natural log-transformed CRP decrease). The size of this effect increased with severity, with larger effects on 28-day sepsis mortality (OR = 0.74; 95% CI 0.47 to 1.15); critical care admission with sepsis (OR = 0.48, 95% CI 0.30 to 0.78) and critical care death with sepsis (OR = 0.37, 95% CI 0.14 to 0.98). Similar associations were seen with severe respiratory infection: OR for pneumonia in critical care 0.69 (95% CI 0.49 to 0.97) and for sepsis survival in critical care (OR = 0.22; 95% CI 0.04 to 1.31) in the GainS and GenOSept consortium, although this result had a large degree of imprecision. We also confirm the previously reported protective effect of IL6R blockade on severe COVID-19 (OR = 0.69, 95% CI 0.57 to 0.84) in the COVID-19 HGI, which was of similar magnitude to that seen in sepsis. Sensitivity analyses did not alter our primary results. These results are subject to the limitations and assumptions of MR, which in this case reflects interpretation of these SNP effects as causally acting through blockade of IL6R, and reflect lifetime exposure to IL6R blockade, rather than the effect of therapeutic IL6R blockade. CONCLUSIONS: IL6R blockade is causally associated with reduced incidence of sepsis. Similar but imprecisely estimated results supported a causal effect also on sepsis related mortality and critical care admission with sepsis. These effects are comparable in size to the effect seen in severe COVID-19, where IL-6 receptor antagonists were shown to improve survival. These data suggest that a randomised trial of IL-6 receptor antagonists in sepsis should be considered.

The relationship between high density lipoprotein cholesterol and sepsis: A clinical and genetic approach.

Sepsis accounts for one in three hospital deaths. Higher concentrations of high-density lipoprotein cholesterol (HDL-C) are associated with apparent protection from sepsis, suggesting a potential therapeutic role for HDL-C or drugs, such as cholesteryl ester transport protein (CETP) inhibitors that increase HDL-C. However, these beneficial clinical associations might be due to confounding; genetic approaches can address this possibility. We identified 73,406 White adults admitted to Vanderbilt University Medical Center with infection; 11,612 had HDL-C levels, and 12,377 had genotype information from which we constructed polygenic risk scores (PRS) for HDL-C and the effect of CETP on HDL-C. We tested the associations between predictors (measured HDL-C, HDL-C PRS, CETP PRS, and rs1800777) and outcomes: sepsis, septic shock, respiratory failure, and in-hospital death. In unadjusted analyses, lower measured HDL-C concentrations were significantly associated with increased risk of sepsis (p = 2.4 × 10-23 ), septic shock (p = 4.1 × 10-12 ), respiratory failure (p = 2.8 × 10-8 ), and in-hospital death (p = 1.0 × 10-8 ). After adjustment (age, sex, electronic health record length, comorbidity score, LDL-C, triglycerides, and body mass index), these associations were markedly attenuated: sepsis (p = 2.6 × 10-3 ), septic shock (p = 8.1 × 10-3 ), respiratory failure (p = 0.11), and in-hospital death (p = 4.5 × 10-3 ). HDL-C PRS, CETP PRS, and rs1800777 significantly predicted HDL-C (p < 2 × 10-16 ), but none were associated with sepsis outcomes. Concordant findings were observed in 13,254 Black patients hospitalized with infections. Lower measured HDL-C levels were significantly associated with increased risk of sepsis and related outcomes in patients with infection, but a causal relationship is unlikely because no association was found between the HDL-C PRS or the CETP PRS and the risk of adverse sepsis outcomes.

Peripheral Blood Genes Crosstalk between COVID-19 and Sepsis.

Severe coronavirus disease 2019 (COVID-19) has led to a rapid increase in death rates all over the world. Sepsis is a life-threatening disease associated with a dysregulated host immune response. It has been shown that COVID-19 shares many similarities with sepsis in many aspects. However, the molecular mechanisms underlying sepsis and COVID-19 are not well understood. The aim of this study was to identify common transcriptional signatures, regulators, and pathways between COVID-19 and sepsis, which may provide a new direction for the treatment of COVID-19 and sepsis. First, COVID-19 blood gene expression profile (GSE179850) data and sepsis blood expression profile (GSE134347) data were obtained from GEO. Then, we intersected the differentially expressed genes (DEG) from these two datasets to obtain common DEGs. Finally, the common DEGs were used for functional enrichment analysis, transcription factor and miRNA prediction, pathway analysis, and candidate drug analysis. A total of 307 common DEGs were identified between the sepsis and COVID-19 datasets. Protein-protein interactions (PPIs) were constructed using the STRING database. Subsequently, hub genes were identified based on PPI networks. In addition, we performed GO functional analysis and KEGG pathway analysis of common DEGs, and found a common association between sepsis and COVID-19. Finally, we identified transcription factor-gene interaction, DEGs-miRNA co-regulatory networks, and protein-drug interaction, respectively. Through ROC analysis, we identified 10 central hub genes as potential biomarkers. In this study, we identified SARS-CoV-2 infection as a high risk factor for sepsis. Our study may provide a potential therapeutic direction for the treatment of COVID-19 patients suffering from sepsis.

Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach.

Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with a high mortality rate. In clinical practice, we have noted that many critically ill or critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. In addition, it has been demonstrated that severe COVID-19 has some pathological similarities with sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted and neutrophil dysfunction. Considering the parallels between COVID-19 and non-SARS-CoV-2 induced sepsis (hereafter referred to as sepsis), the aim of this study was to analyze the underlying molecular mechanisms between these two diseases by bioinformatics and a systems biology approach, providing new insights into the pathogenesis of COVID-19 and the development of new treatments. Specifically, the gene expression profiles of COVID-19 and sepsis patients were obtained from the Gene Expression Omnibus (GEO) database and compared to extract common differentially expressed genes (DEGs). Subsequently, common DEGs were used to investigate the genetic links between COVID-19 and sepsis. Based on enrichment analysis of common DEGs, many pathways closely related to inflammatory response were observed, such as Cytokine-cytokine receptor interaction pathway and NF-kappa B signaling pathway. In addition, protein-protein interaction networks and gene regulatory networks of common DEGs were constructed, and the analysis results showed that ITGAM may be a potential key biomarker base on regulatory analysis. Furthermore, a disease diagnostic model and risk prediction nomogram for COVID-19 were constructed using machine learning methods. Finally, potential therapeutic agents, including progesterone and emetine, were screened through drug-protein interaction networks and molecular docking simulations. We hope to provide new strategies for future research and treatment related to COVID-19 by elucidating the pathogenesis and genetic mechanisms between COVID-19 and sepsis.

Transcriptional differences between coronavirus disease 2019 and bacterial sepsis.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has led to major public health crises worldwide. Several studies have reported the comprehensive mRNA expression analysis of immune-related genes in patients with COVID-19, using blood samples, to understand its pathogenesis; however, the characteristics of RNA expression in COVID-19 and bacterial sepsis have not been compared. The current study aimed to address this gap. METHODS: RNA-sequencing and bioinformatics analyses were used to compare the transcriptome expression of whole blood samples from patients with COVID-19 and patients with sepsis who were admitted to the intensive care unit of Osaka University Graduate School of Medicine. RESULTS: The COVID-19 and sepsis cohorts showed upregulation of mitochondrial- and neutrophil-related transcripts, respectively. Compared with that in the control cohort, neutrophil-related transcripts were upregulated in both the COVID-19 and sepsis cohorts. In contrast, mitochondrial-related transcripts were upregulated in the COVID-19 cohort and downregulated in the sepsis cohort, compared to those in the control cohort. Moreover, transcript levels of the pro-apoptotic genes BAK1, CYCS, BBC3, CASP7, and CASP8 were upregulated in the COVID-19 cohort, whereas those of anti-apoptotic genes, such as BCL2L11 and BCL2L1, were upregulated in the sepsis cohort. CONCLUSIONS: This study clarified the differential expression of transcripts related to neutrophils and mitochondria in sepsis and COVID-19 conditions. Mitochondrial-related transcripts were downregulated in sepsis than in COVID-19 conditions, and our results indicated suboptimal intrinsic apoptotic features in sepsis samples compared with that in COVID-19 samples. This study is expected to contribute to the development of specific treatments for COVID-19.

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

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

Iron status and the risk of sepsis and severe COVID-19: a two-sample Mendelian randomization study.

Observational studies have indicated an association between iron status and risk of sepsis and COVID-19. We estimated the effect of genetically-predicted iron biomarkers on risk of sepsis and risk of being hospitalized with COVID-19, performing a two-sample Mendelian randomization study. For risk of sepsis, one standard deviation increase in genetically-predicted serum iron was associated with odds ratio (OR) of 1.14 (95% confidence interval [CI] 1.01-1.29, P = 0.031). The findings were supported in the analyses for transferrin saturation and total iron binding capacity, while the estimate for ferritin was inconclusive. We found a tendency of higher risk of hospitalization with COVID-19 for serum iron; OR 1.29 (CI 0.97-1.72, P = 0.08), whereas sex-stratified analyses showed OR 1.63 (CI 0.94-2.86, P = 0.09) for women and OR 1.21 (CI 0.92-1.62, P = 0.17) for men. Sensitivity analyses supported the main findings and did not suggest bias due to pleiotropy. Our findings suggest a causal effect of genetically-predicted higher iron status and risk of hospitalization due to sepsis and indications of an increased risk of being hospitalized with COVID-19. These findings warrant further studies to assess iron status in relation to severe infections, including the potential of improved management.

Role of miRNA dysregulation in sepsis.

BACKGROUND: Sepsis is defined as a state of multisystem organ dysfunction secondary to a dysregulated host response to infection and causes millions of deaths worldwide annually. Novel ways to counteract this disease are needed and such tools may be heralded by a detailed understanding of its molecular pathogenesis. MiRNAs are small RNA molecules that target mRNAs to inhibit or degrade their translation and have important roles in several disease processes including sepsis. MAIN BODY: The current review adopted a strategic approach to analyzing the widespread literature on the topic of miRNAs and sepsis. A pubmed search of "miRNA or microRNA or small RNA and sepsis not review" up to and including January 2021 led to 1140 manuscripts which were reviewed. Two hundred and thirty-three relevant papers were scrutinized for their content and important themes on the topic were identified and subsequently discussed, including an in-depth look at deregulated miRNAs in sepsis in peripheral blood, myeloid derived suppressor cells and extracellular vesicles. CONCLUSION: Our analysis yielded important observations. Certain miRNAs, namely miR-150 and miR-146a, have consistent directional changes in peripheral blood of septic patients across numerous studies with strong data supporting a role in sepsis pathogenesis. Furthermore, a large body of literature show miRNA signatures of clinical relevance, and lastly, many miRNAs deregulated in sepsis are associated with the process of endothelial dysfunction. This review offers a widespread, up-to-date and detailed discussion of the role of miRNAs in sepsis and is meant to stimulate further work in the field due to the potential of these small miRNAs in prompt diagnostics, prognostication and therapeutic agency.

APOL1 risk variants in individuals of African genetic ancestry drive endothelial cell defects that exacerbate sepsis.

The incidence and severity of sepsis is higher among individuals of African versus European ancestry. We found that genetic risk variants (RVs) in the trypanolytic factor apolipoprotein L1 (APOL1), present only in individuals of African ancestry, were associated with increased sepsis incidence and severity. Serum APOL1 levels correlated with sepsis and COVID-19 severity, and single-cell sequencing in human kidneys revealed high expression of APOL1 in endothelial cells. Analysis of mice with endothelial-specific expression of RV APOL1 and in vitro studies demonstrated that RV APOL1 interfered with mitophagy, leading to cytosolic release of mitochondrial DNA and activation of the inflammasome (NLRP3) and the cytosolic nucleotide sensing pathways (STING). Genetic deletion or pharmacological inhibition of NLRP3 and STING protected mice from RV APOL1-induced permeability defects and proinflammatory endothelial changes in sepsis. Our studies identify the inflammasome and STING pathways as potential targets to reduce APOL1-associated health disparities in sepsis and COVID-19.

Combined Transcriptome and Proteome Leukocyte's Profiling Reveals Up-Regulated Module of Genes/Proteins Related to Low Density Neutrophils and Impaired Transcription and Translation Processes in Clinical Sepsis.

Sepsis is a global health emergency, which is caused by various sources of infection that lead to changes in gene expression, protein-coding, and metabolism. Advancements in "omics" technologies have provided valuable tools to unravel the mechanisms involved in the pathogenesis of this disease. In this study, we performed shotgun mass spectrometry in peripheral blood mononuclear cells (PBMC) from septic patients (N=24) and healthy controls (N=9) and combined these results with two public microarray leukocytes datasets. Through combination of transcriptome and proteome profiling, we identified 170 co-differentially expressed genes/proteins. Among these, 122 genes/proteins displayed the same expression trend. Ingenuity Pathway Analysis revealed pathways related to lymphocyte functions with decreased status, and defense processes that were predicted to be strongly increased. Protein-protein interaction network analyses revealed two densely connected regions, which mainly included down-regulated genes/proteins that were related to the transcription of RNA, translation of proteins, and mitochondrial translation. Additionally, we identified one module comprising of up-regulated genes/proteins, which were mainly related to low-density neutrophils (LDNs). LDNs were reported in sepsis and in COVID-19. Changes in gene expression level were validated using quantitative real-time PCR in PBMCs from patients with sepsis. To further support that the source of the upregulated module of genes/proteins found in our results were derived from LDNs, we identified an increase of this population by flow cytometry in PBMC samples obtained from the same cohort of septic patients included in the proteomic analysis. This study provides new insights into a reprioritization of biological functions in response to sepsis that involved a transcriptional and translational shutdown of genes/proteins, with exception of a set of genes/proteins related to LDNs and host-defense system.

Bacteriostatic Potential of Melatonin: Therapeutic Standing and Mechanistic Insights.

Diseases caused by pathogenic bacteria in animals (e.g., bacterial pneumonia, meningitis and sepsis) and plants (e.g., bacterial wilt, angular spot and canker) lead to high prevalence and mortality, and decomposition of plant leaves, respectively. Melatonin, an endogenous molecule, is highly pleiotropic, and accumulating evidence supports the notion that melatonin's actions in bacterial infection deserve particular attention. Here, we summarize the antibacterial effects of melatonin in vitro, in animals as well as plants, and discuss the potential mechanisms. Melatonin exerts antibacterial activities not only on classic gram-negative and -positive bacteria, but also on members of other bacterial groups, such as Mycobacterium tuberculosis. Protective actions against bacterial infections can occur at different levels. Direct actions of melatonin may occur only at very high concentrations, which is at the borderline of practical applicability. However, various indirect functions comprise activation of hosts' defense mechanisms or, in sepsis, attenuation of bacterially induced inflammation. In plants, its antibacterial functions involve the mitogen-activated protein kinase (MAPK) pathway; in animals, protection by melatonin against bacterially induced damage is associated with inhibition or activation of various signaling pathways, including key regulators such as NF-κB, STAT-1, Nrf2, NLRP3 inflammasome, MAPK and TLR-2/4. Moreover, melatonin can reduce formation of reactive oxygen and nitrogen species (ROS, RNS), promote detoxification and protect mitochondrial damage. Altogether, we propose that melatonin could be an effective approach against various pathogenic bacterial infections.

Gasdermin D inhibition prevents multiple organ dysfunction during sepsis by blocking NET formation.

Multiple organ dysfunction is the most severe outcome of sepsis progression and is highly correlated with a worse prognosis. Excessive neutrophil extracellular traps (NETs) are critical players in the development of organ failure during sepsis. Therefore, interventions targeting NET release would likely effectively prevent NET-based organ injury associated with this disease. Herein, we demonstrate that the pore-forming protein gasdermin D (GSDMD) is active in neutrophils from septic humans and mice and plays a crucial role in NET release. Inhibition of GSDMD with disulfiram or genic deletion abrogated NET formation, reducing multiple organ dysfunction and sepsis lethality. Mechanistically, we demonstrate that during sepsis, activation of the caspase-11/GSDMD pathway controls NET release by neutrophils during sepsis. In summary, our findings uncover a novel therapeutic use for disulfiram and suggest that GSDMD is a therapeutic target to improve sepsis treatment.

Interplay between interleukin-6 signaling and the vascular endothelium in cytokine storms.

Interleukin-6 (IL-6) plays a crucial role in host defense against infection and tissue injuries and is a bioindicator of multiple distinct types of cytokine storms. In this review, we present the current understanding of the diverse roles of IL-6, its receptors, and its signaling during acute severe systemic inflammation. IL-6 directly affects vascular endothelial cells, which produce several types of cytokines and chemokines and activate the coagulation cascade. Endothelial cell dysregulation, characterized by abnormal coagulation and vascular leakage, is a common complication in cytokine storms. Emerging evidence indicates that a humanized anti-IL-6 receptor antibody, tocilizumab, can effectively block IL-6 signaling and has beneficial effects in rheumatoid arthritis, juvenile systemic idiopathic arthritis, and Castleman's disease. Recent work has also demonstrated the beneficial effect of tocilizumab in chimeric antigen receptor T-cell therapy-induced cytokine storms as well as coronavirus disease 2019 (COVID-19). Here, we highlight the distinct contributions of IL-6 signaling to the pathogenesis of several types of cytokine storms and discuss potential therapeutic strategies for the management of cytokine storms, including those associated with sepsis and COVID-19.

Comparison of renal histopathology and gene expression profiles between severe COVID-19 and bacterial sepsis in critically ill patients.

BACKGROUND: The mechanisms driving acute kidney injury (AKI) in critically ill COVID-19 patients are unclear. We collected kidney biopsies from COVID-19 AKI patients within 30 min after death in order to examine the histopathology and perform mRNA expression analysis of genes associated with renal injury. METHODS: This study involved histopathology and mRNA analyses of postmortem kidney biopsies collected from patients with COVID-19 (n = 6) and bacterial sepsis (n = 27). Normal control renal tissue was obtained from patients undergoing total nephrectomy (n = 12). The mean length of ICU admission-to-biopsy was 30 days for COVID-19 and 3-4 days for bacterial sepsis patients. RESULTS: We did not detect SARS-CoV-2 RNA in kidney biopsies from COVID-19-AKI patients yet lung tissue from the same patients was PCR positive. Extensive acute tubular necrosis (ATN) and peritubular thrombi were distinct histopathology features of COVID-19-AKI compared to bacterial sepsis-AKI. ACE2 mRNA levels in both COVID-19 (fold change 0.42, p = 0.0002) and bacterial sepsis patients (fold change 0.24, p < 0.0001) were low compared to control. The mRNA levels of injury markers NGAL and KIM-1 were unaltered compared to control tissue but increased in sepsis-AKI patients. Markers for inflammation and endothelial activation were unaltered in COVID-19 suggesting a lack of renal inflammation. Renal mRNA levels of endothelial integrity markers CD31, PV-1 and VE-Cadherin did not differ from control individuals yet were increased in bacterial sepsis patients (CD31 fold change 2.3, p = 0.0006, PV-1 fold change 1.5, p = 0.008). Angiopoietin-1 mRNA levels were downregulated in renal tissue from both COVID-19 (fold change 0.27, p < 0.0001) and bacterial sepsis patients (fold change 0.67, p < 0.0001) compared to controls. Moreover, low Tie2 mRNA expression (fold change 0.33, p = 0.037) and a disturbed VEGFR2/VEGFR3 ratio (fold change 0.09, p < 0.0001) suggest decreased microvascular flow in COVID-19. CONCLUSIONS: In a small cohort of postmortem kidney biopsies from COVID-19 patients, we observed distinct histopathological and gene expression profiles between COVID-19-AKI and bacterial sepsis-AKI. COVID-19 was associated with more severe ATN and microvascular thrombosis coupled with decreased microvascular flow, yet minimal inflammation. Further studies are required to determine whether these observations are a result of true pathophysiological differences or related to the timing of biopsy after disease onset.

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

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