Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS

Advancing omics technologies in acute respiratory distress syndrome: paving the way for personalized medicine

Despite advances in critical care, acute respiratory distress syndrome (ARDS) remains a potentially life-threatening condition with high mortality. The heterogeneous nature of ARDS, caused by diverse etiologies, poses considerable challenges to accurate diagnosis, treatment, and prognosis. Conventional methods often fail to elucidate the pathophysiology of ARDS, thus limiting therapeutic efficacy. However, recent advances in omics technologies, including genomics, transcriptomics, proteomics, metabolomics, lipidomics, and epigenomics, have provided deeper insights into ARDS mechanisms. Genomic studies have identified genetic variants associated with ARDS susceptibility, such as polymorphisms in genes encoding angiotensin-converting enzyme, surfactant proteins, toll-like receptor 4, interleukin-6, Fas/FasL, and vascular endothelial growth factor, offering potential therapeutic targets. Transcriptomic and proteomic reveal distinct biomarker profiles associated with ARDS pathogenesis, including dysregulated inflammatory signaling, epithelial and endothelial barrier dysfunction, and compromised immune responses. Metabolomics has highlighted biomarkers, such as phenylalanine and choline, aiding in severity assessment, subphenotype stratification, and treatment response prediction. Lipidomics has uncovered disruptions in lipid metabolism, including altered phospholipids, sphingolipids, and eicosanoids, with key lipid species such as lysophosphatidylcholine and ceramide emerging as biomarkers for severity and outcomes. Epigenomics explores DNA methylation, histone modifications, and non-coding RNAs, revealing their role in regulating inflammation, immune responses, and tissue repair in ARDS. These epigenetic changes hold promise for biomarker discovery and personalized therapy. Integrating these omics technologies advances our understanding of ARDS pathophysiology, enabling precision medicine approaches. This review examines the latest advancements in omics research related to ARDS, emphasizing its role in developing personalized diagnostics and therapeutic strategies to improve disease monitoring, prognosis, and treatment outcomes.

Plasma proteomic profiles correlate with organ dysfunction in COVID-19 ARDS

Severe COVID-19 is often complicated by hypoxemic respiratory failure and acute respiratory distress syndrome (ARDS). Mechanisms governing lung injury and repair in ARDS remain poorly understood. We hypothesized that plasma proteomics may uncover protein biomarkers correlated with COVID-19 ARDS severity. We analyzed the plasma proteome from 32 patients with ARDS and COVID-19 using an aptamer-based platform of 7289 proteins, and correlated protein measurements with sequential organ failure assessment (SOFA) scores at days 1 and 7 of ICU admission. We identified 184 differentially abundant proteins correlated with SOFA at day 1 and 46 proteins at day 7. In a longitudinal analysis, we correlated dynamic changes in protein abundance and SOFA between days 1 and 7 and identified 40 significant proteins. Pathway analysis of significant proteins identified increased ephrin signaling and acute phase response signaling correlated with increased SOFA scores between days 1 and 7, while pathways related to pulmonary fibrosis signaling and wound healing had a negative correlation. These findings suggest that persistent inflammation may drive disease severity, while repair processes correlate with improvements in organ dysfunction. This approach is generalizable to future ARDS cohorts for identification of biomarkers and disease mechanisms as we strive towards targeted therapies in ARDS.

Seasonal shifts in respiratory pathogen co-infections and the associated differential induction of cytokines in children

In the post-pandemic era, research on respiratory diseases should refocus on pathogens other than the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Respiratory pathogens, highly infectious to children, with to different modes of infection, such as single-pathogen infections and co-infections. Understanding the seasonal patterns of these pathogens, alongside identifying single infections and co-infections and their impact on the pediatric immune status, is crucial for clinical diagnosis, treatment, and prognosis in children. Our study found that from December 2023 to April 2024, the main co-infection combinations in children shifted from Mycoplasma pneumonia and influenza virus A (MP + IVA) to Bordetella pertussis and rhinovirus (BP + RhV). To explore the impact of these infections, two cohorts were established to analyze the effects of single and co-infections of four respiratory pathogens, MP, IVA, BP, and RhV, on the immune status of pediatric patients. Using multi-cytokine analysis, cytokines, such as PDGF-BB, that were differentially expressed between patients with single and co-infections were identified. Additionally, we observed that children with single-pathogen infections generally exhibited more severe conditions, as evidenced by higher overall cytokine expression than those with co-infections. Our findings provide an important theoretical basis for understanding the pathogenic mechanisms of single and co-infections of respiratory pathogens and clinically differentiating pediatric patients with various respiratory infections.

Gas-phase fractionation DDA promotes in-depth DIA phosphoproteome analysis

Data-independent acquisition (DIA) is a promising method for quantitative proteomics. Library-based DIA database searching against project-specific data-dependent acquisition (DDA) spectral libraries is the gold standard. These libraries are constructed using material-consuming pre-fractionation two dimensional DDA analysis. The alternative to this is library-free DIA analysis. Limited sample amounts restrict the use of fractionation to build spectral libraries for post-translational modifications (PTMs) DIA analysis. We present the use of gas-phase fractionation (GPF) DDA data to improve the depth of library-free DIA identification for the phosphoproteome, called GPF-DDA hybrid DIA. This method fully utilizes the remnants of samples post-DIA analysis and leverages both library-based and -free DIA database searching. GPF-DDA hybrid DIA analyzes phosphopeptides surplus sample after DIA analysis using a number of DDA injections with each scanning different mass-to-charge (m/z) windows, instead of preforming traditional off-line fractionation-based DDA. The GPF-DDA data is integrated into the library-free DIA database search to create a hybrid library, enhancing phosphopeptide identification. Two GPF-DDA injections proved to increase 18 % phosphopeptide and 13 % phosphosite identification in HEK293 cell lines, while five injections resulted in up to 28 % phosphopeptide and 21 % phosphosite increases compared to library-free DIA analysis alone. We used GPF-DDA hybrid DIA phosphoproteomics to characterize lung tissue upon direct (smoke induced) and indirect (sepsis induced) acute lung injury (ALI) in mice. The differentially expressed phosphosites (DEPsites) in direct ALI were found in proteins related to mRNA processing and RNA. DEPsites in indirect ALI were enriched in proteins related to microtubule polymerization, positive regulation of microtubule polymerization and fibroblast migration. This study demonstrates that GPF-DDA hybrid DIA analysis workflow can indeed promote depth of DIA analysis of phosphoproteome and could be extended to DIA analysis of other PTMs.

Proteomic and serologic assessments of responses to mRNA-1273 and BNT162b2 vaccines in human recipient sera

Introduction

The first vaccines approved against SARS-CoV-2, mRNA-1273 and BNT162b2, utilized mRNA platforms. However, little is known about the proteomic markers and pathways associated with host immune responses to mRNA vaccination. In this proof-of-concept study, sera from male and female vaccine recipients were evaluated for proteomic and immunologic responses 1-month and 6-months following homologous third vaccination.

Methods

An aptamer-based (7,289 marker) proteomic assay coupled with traditional serology was leveraged to generate a comprehensive evaluation of systemic responsiveness in 64 and 68 healthy recipients of mRNA-1273 and BNT162b2 vaccines, respectively.

Results

Sera from female recipients of mRNA-1273 showed upregulated indicators of inflammatory and immunological responses at 1-month post-third vaccination, and sera from female recipients of BNT162b2 demonstrated upregulated negative regulators of RNA sensors at 1-month. Sera from male recipients of mRNA-1273 showed no significant upregulation of pathways at 1-month post-third vaccination, though there were multiple significantly upregulated proteomic markers. Sera from male recipients of BNT162b2 demonstrated upregulated markers of immune response to doublestranded RNA and cell-cycle G(2)/M transition at 1-month. Random Forest analysis of proteomic data from pre-third-dose sera identified 85 markers used to develop a model predictive of robust or weaker IgG responses and antibody levels to SARS-CoV-2 spike protein at 6-months following boost; no specific markers were individually predictive of 6-month IgG response. Thirty markers that contributed most to the model were associated with complement cascade and activation; IL-17, TNFR pro-apoptotic, and PI3K signaling; and cell cycle progression.

Discussion

These results demonstrate the utility of proteomics to evaluate correlates or predictors of serological responses to SARS-CoV-2 vaccination.

Phytoconstituents as modulator of inflammatory pathways for COVID-19: A comprehensive review and recommendations

SARS-CoV-2 infection causes disruptions in inflammatory pathways, which fundamentally contribute to COVID-19 pathophysiology. The present review critically evaluates the gaps in scientific literature and presents the current status regarding the inflammatory signaling pathways in COVID-19. We propose that phytoconstituents can be used to treat COVID-19 associated inflammation, several already formulated in traditional medications. For this purpose, extensive literature analysis was conducted in the PubMed database to collect relevant in vitro, in vivo, and human patient studies where inflammation pathways were shown to be upregulated in COVID-19. Parallelly, scientific literature was screened for phytoconstituents with known cellular mechanisms implicated for inflammation or COVID-19 associated inflammation. Studies with insufficient evidence on cellular pathways for autophagy and mitophagy were considered out of scope and excluded from the study. The final analysis was visualized in figures and evaluated for accuracy. Our findings demonstrate the frequent participation of NF-κB, a transcription factor, in inflammatory signaling pathways linked to COVID-19. Moreover, the MAPK signaling pathway is also implicated in producing inflammatory molecules. Furthermore, it was also analyzed that the phytoconstituents with flavonoid and phenolic backbones could inhibit either the TLR4 receptor or its consecutive signaling molecules, thereby, decreasing NF-κB activity and suppressing cytokine production. Although, allopathy has treated the early phase of COVID-19, anti-inflammatory phytoconstituents and existing ayurvedic formulations may act on the COVID-19 associated inflammatory pathways and provide an additional treatment strategy. Therefore, we recommend the usage of flavonoids and phenolic phytoconstituents for the treatment of inflammation associated with COVID-19 infection and similar viral ailments.

Itaconate to treat acute lung injury: recent advances and insights from preclinical models

Acute lung injury (ALI) is defined as the acute onset of diffuse bilateral pulmonary infiltration, leading to PaO2/FiO2 ≤ 300 mmHg without clinical evidence of left atrial hypertension. Acute respiratory distress syndrome (ARDS) involves more severe hypoxemia (PaO2/FiO2 ≤ 200 mmHg). Treatment of ALI and ARDS has received renewed attention as the incidence of ALI caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has increased. Itaconate and its derivatives have shown therapeutic potential against ALI. This review provides an in-depth summary of the mechanistic research of itaconate in the field of acute lung injury, including inducing autophagy, preventing ferroptosis and pyroptosis, shifting macrophage polarization to an anti-inflammatory M2 phenotype, inhibiting neutrophil activation, regulating epigenetic modifications, and repressing aerobic glycolysis. These compounds merit further consideration in clinical trials. We anticipate that the clinical translation of itaconate-based drugs can be accelerated.

Compartmentalization of the inflammatory response during bacterial sepsis and severe COVID-19

Acute infections cause local and systemic disorders which can lead in the most severe forms to multi-organ failure and eventually to death. The host response to infection encompasses a large spectrum of reactions with a concomitant activation of the so-called inflammatory response aimed at fighting the infectious agent and removing damaged tissues or cells, and the anti-inflammatory response aimed at controlling inflammation and initiating the healing process. Fine-tuning at the local and systemic levels is key to preventing local and remote injury due to immune system activation. Thus, during bacterial sepsis and Coronavirus disease 2019 (COVID-19), concomitant systemic and compartmentalized pro-inflammatory and compensatory anti-inflammatory responses are occurring. Immune cells (e.g., macrophages, neutrophils, natural killer cells, and T-lymphocytes), as well as endothelial cells, differ from one compartment to another and contribute to specific organ responses to sterile and microbial insult. Furthermore, tissue-specific microbiota influences the local and systemic response. A better understanding of the tissue-specific immune status, the organ immunity crosstalk, and the role of specific mediators during sepsis and COVID-19 can foster the development of more accurate biomarkers for better diagnosis and prognosis and help to define appropriate host-targeted treatments and vaccines in the context of precision medicine.

A reduced proteomic signature in critically ill Covid-19 patients determined with plasma antibody micro-array and machine learning

BACKGROUND

COVID-19 is a complex, multi-system disease with varying severity and symptoms. Identifying changes in critically ill COVID-19 patients' proteomes enables a better understanding of markers associated with susceptibility, symptoms, and treatment. We performed plasma antibody microarray and machine learning analyses to identify novel proteins of COVID-19.

METHODS

A case-control study comparing the concentration of 2000 plasma proteins in age- and sex-matched COVID-19 inpatients, non-COVID-19 sepsis controls, and healthy control subjects. Machine learning was used to identify a unique proteome signature in COVID-19 patients. Protein expression was correlated with clinically relevant variables and analyzed for temporal changes over hospitalization days 1, 3, 7, and 10. Expert-curated protein expression information was analyzed with Natural language processing (NLP) to determine organ- and cell-specific expression.

RESULTS

Machine learning identified a 28-protein model that accurately differentiated COVID-19 patients from ICU non-COVID-19 patients (accuracy = 0.89, AUC = 1.00, F1 = 0.89) and healthy controls (accuracy = 0.89, AUC = 1.00, F1 = 0.88). An optimal nine-protein model (PF4V1, NUCB1, CrkL, SerpinD1, Fen1, GATA-4, ProSAAS, PARK7, and NET1) maintained high classification ability. Specific proteins correlated with hemoglobin, coagulation factors, hypertension, and high-flow nasal cannula intervention (P < 0.01). Time-course analysis of the 28 leading proteins demonstrated no significant temporal changes within the COVID-19 cohort. NLP analysis identified multi-system expression of the key proteins, with the digestive and nervous systems being the leading systems.

CONCLUSIONS

The plasma proteome of critically ill COVID-19 patients was distinguishable from that of non-COVID-19 sepsis controls and healthy control subjects. The leading 28 proteins and their subset of 9 proteins yielded accurate classification models and are expressed in multiple organ systems. The identified COVID-19 proteomic signature helps elucidate COVID-19 pathophysiology and may guide future COVID-19 treatment development.

Metabolic signatures of acute respiratory distress syndrome: COVID versus non-COVID

Acute respiratory distress syndrome (ARDS) is a fatal pulmonary disorder characterized by severe hypoxia and inflammation. ARDS is commonly triggered by systemic and pulmonary infections, with bacteria and viruses. Notable pathogens include Pseudomonas aeruginosa, Streptococcus aureus, Enterobacter species, coronaviruses, influenza viruses, and herpesviruses. COVID-19 ARDS represents the latest etiological phenotype of the disease. The pathogenesis of ARDS caused by bacteria and viruses exhibits variations in host immune responses and lung mesenchymal injury. We postulate that the systemic and pulmonary metabolomics profiles of ARDS induced by COVID-19 pathogens may exhibit distinctions compared with those induced by other infectious agents. This review aims to compare metabolic signatures in blood and lung specimens specifically within the context of ARDS. Both prevalent and phenotype-specific metabolomic signatures, including but not limited to glycolysis, ketone body production, lipid oxidation, and dysregulation of the kynurenine pathways, were thoroughly examined in this review. The distinctions in metabolic signatures between COVID-19 and non-COVID ARDS have the potential to reveal new biomarkers, elucidate pathogenic mechanisms, identify druggable targets, and facilitate differential diagnosis in the future.

CCR5/CXCR3 antagonist TAK-779 prevents diffuse alveolar damage of the lung in the murine model of the acute respiratory distress syndrome

Introduction: The acute respiratory distress syndrome (ARDS), secondary to viral pneumonitis, is one of the main causes of high mortality in patients with COVID-19 (novel coronavirus disease 2019)-ongoing SARS-CoV-2 infection- reached more than 0.7 billion registered cases. Methods: Recently, we elaborated a non-surgical and reproducible method of the unilateral total diffuse alveolar damage (DAD) of the left lung in ICR mice-a publicly available imitation of the ARDS caused by SARS-CoV-2. Our data read that two C-C chemokine receptor 5 (CCR5) ligands, macrophage inflammatory proteins (MIPs) MIP-1α/CCL3 and MIP-1β/CCL4, are upregulated in this DAD model up to three orders of magnitude compared to the background level. Results: Here, we showed that a nonpeptide compound TAK-779, an antagonist of CCR5/CXCR3, readily prevents DAD in the lung with a single injection of 2.5 mg/kg. Histological analysis revealed reduced peribronchial and perivascular mononuclear infiltration in the lung and mononuclear infiltration of the wall and lumen of the alveoli in the TAK-779-treated animals. Administration of TAK-779 decreased the 3-5-fold level of serum cytokines and chemokines in animals with DAD, including CCR5 ligands MIP-1α/β, MCP-1, and CCL5. Computed tomography revealed rapid recovery of the density and volume of the affected lung in TAK-779-treated animals. Discussion: Our pre-clinical data suggest that TAK-779 is more effective than the administration of dexamethasone or the anti-IL6R therapeutic antibody tocilizumab, which brings novel therapeutic modality to TAK-779 and other CCR5 inhibitors for the treatment of virus-induced hyperinflammation syndromes, including COVID-19.

Analysis of Protein Biomarkers From Hospitalized COVID-19 Patients Reveals Severity-Specific Signatures and Two Distinct Latent Profiles With Differential Responses to Corticosteroids

OBJECTIVES

To identify and validate novel COVID-19 subphenotypes with potential heterogenous treatment effects (HTEs) using electronic health record (EHR) data and 33 unique biomarkers.

DESIGN

Retrospective cohort study of adults presenting for acute care, with analysis of biomarkers from residual blood collected during routine clinical care. Latent profile analysis (LPA) of biomarker and EHR data identified subphenotypes of COVID-19 inpatients, which were validated using a separate cohort of patients. HTE for glucocorticoid use among subphenotypes was evaluated using both an adjusted logistic regression model and propensity matching analysis for in-hospital mortality.

SETTING

Emergency departments from four medical centers.

PATIENTS

Patients diagnosed with COVID-19 based on International Classification of Diseases , 10th Revision codes and laboratory test results.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Biomarker levels generally paralleled illness severity, with higher levels among more severely ill patients. LPA of 522 COVID-19 inpatients from three sites identified two profiles: profile 1 ( n = 332), with higher levels of albumin and bicarbonate, and profile 2 ( n = 190), with higher inflammatory markers. Profile 2 patients had higher median length of stay (7.4 vs 4.1 d; p < 0.001) and in-hospital mortality compared with profile 1 patients (25.8% vs 4.8%; p < 0.001). These were validated in a separate, single-site cohort ( n = 192), which demonstrated similar outcome differences. HTE was observed ( p = 0.03), with glucocorticoid treatment associated with increased mortality for profile 1 patients (odds ratio = 4.54).

CONCLUSIONS

In this multicenter study combining EHR data with research biomarker analysis of patients with COVID-19, we identified novel profiles with divergent clinical outcomes and differential treatment responses.

Transcriptomic analysis of human pulmonary microvascular endothelial cells treated with LPS

Acute respiratory distress syndrome (ARDS) has a rapid onset and progression, which lead to the severity and complexity of the primary disease and significantly increase the fatality rate of patients. Transcriptomics provides some ideas for clarifying the mechanism of ARDS, exploring prevention and treatment targets, and searching for related specific markers. In this study, RNA-Seq technology was used to observe the gene expression of human pulmonary microvascular endothelial cells (PMVECs) induced by LPS, and to excavate the key genes and signaling pathways in ARDS process. A total of 2300 up-regulated genes were detected, and a corresponding 1696 down-regulated genes were screened. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and protein-protein interaction (PPI) were also used for functional annotation of key genes. TFDP1 was identified as a cell cycle-dependent differentially expressed gene, and its reduced expression was verified in LPS-treated PMVECs and lung tissues of CLP-induced mice. In addition, the inhibition of TFDP1 on inflammation and apoptosis, and the promotion of proliferation were confirmed. The decreased expression of E2F1, Rb, CDK1 and the activation of MAPK signaling pathway were substantiated in the in vivo and in vitro models of ARDS. Moreover, SREBF1 has been demonstrated to be involved in cell cycle arrest in PMVECs by inhibiting CDK1. Our study shows that transcriptomics combined with basic research can broaden the investigation of ARDS mechanisms and may provide a basis for future mechanistic innovations.

Sepsis-Induced Acute Lung Injury Is Alleviated by Small Molecules from Dietary Plants via Pyroptosis Modulation

Sepsis-induced acute respiratory distress syndrome (ARDS) has high morbidity and mortality, and it has three major pathogeneses, namely alveolar-capillary barrier destruction, elevated gut permeability, and reduced neutrophil extracellular traps (NETS), all of which are pyroptosis-involved. Due to limitations of current agents like adverse reaction superposition, inevitable drug resistance, and relatively heavier financial burden, naturally extracted small-molecule compounds have a broad market even though chemically modified drugs have straightforward efficacy. Despite increased understanding of the molecular biology and mechanism underlying sepsis-induced ARDS, there are no specific reviews concerning how small molecules from dietary plants alleviate sepsis-induced acute lung injury (ALI) via regulating pyroptotic cell death. Herein, we traced and reviewed the molecular underpinnings of sepsis-induced ALI with a focus on small-molecule compounds from dietary plants, the top three categories of which are respectively flavonoids and flavone, terpenoids, and polyphenol and phenolic acids, and how they rescued septic ALI by restraining pyroptosis.

Endothelial dysfunction triggers acute respiratory distress syndrome in patients with sepsis: a narrative review

Acute respiratory distress syndrome (ARDS) is a severe organ failure occurring mainly in critically ill patients as a result of different types of insults such as sepsis, trauma or aspiration. Sepsis is the main cause of ARDS, and it contributes to a high mortality and resources consumption both in hospital setting and in the community. ARDS develops mainly an acute respiratory failure with severe and often refractory hypoxemia. ARDS also has long term implications and sequelae. Endothelial damage plays an important role in the pathogenesis of ARDS. Understanding the mechanisms of ARDS presents opportunities for novel diagnostic and therapeutic targets. Biochemical signals can be used in concert to identify and classify patients into ARDS phenotypes allowing earlier effective treatment with personalised therapies. This is a narrative review where we aimed to flesh out the pathogenetic mechanisms and heterogeneity of ARDS. We examine the links between endothelium damage and its contribution to organ failure. We have also investigated future strategies for treatment with a special emphasis in endothelial damage.

Chewing the fat: How lipidomics is changing our understanding of human health and disease in 2022

Lipids are biological molecules that play vital roles in all living organisms. They perform many cellular functions, such as 1) forming cellular and subcellular membranes, 2) storing and using energy, and 3) serving as chemical messengers during intra- and inter-cellular signal transduction. The large-scale study of the pathways and networks of cellular lipids in biological systems is called "lipidomics" and is one of the fastest-growing omics technologies of the last two decades. With state-of-the-art mass spectrometry instrumentation and sophisticated data handling, clinical studies show how human lipid composition changes in health and disease, thereby making it a valuable medium to collect for clinical applications, such as disease diagnostics, therapeutic decision-making, and drug development. This review gives a comprehensive overview of current workflows used in clinical research, from sample collection and preparation to data and clinical interpretations. This is followed by an appraisal of applications in 2022 and a perspective on the exciting future of clinical lipidomics.

Association and predictive value of soluble thrombomodulin with mortality in patients with acute respiratory distress syndrome: systematic review and meta-analysis

Background

Acute respiratory distress syndrome (ARDS) is a heterogeneous illness that has a high mortality rate. The role and predictive value of soluble thrombomodulin (sTM) in ARDS mortality is disputable, so the present study aimed to evaluate the association and predictive value of sTM for the in-hospital mortality of ARDS.

Methods

PubMed, Web of Science, Embase, Cochrane Library, Chongqing VIP, WanFang, China National Knowledge Infrastructure (CNKI), and Chinese Biomedical Literature databases were searched for relevant literature published before October 10, 2022. Relevant observable studies were included for analysis. The Newcastle-Ottawa Scale and QUAPAS (Quality Assessment of Prognostic Accuracy Studies) were employed to appraise the quality of the included studies.

Results

Thirteen articles were included in the present study. The eligible studies were of moderate to high quality [Newcastle-Ottawa Scale (NOS) 5-8 scores], and the high risk of bias in the included studieson predictive value was mainly distributed in participant and analysis domains of QUAPAS. There were 1,992 patients with ARDS, and 538 died. Our meta-analysis demonstrated that nonsurvivors had more significantly increased sTM levels than did survivors [standardized mean difference (SMD) =1.473; 95% CI: 0.874-2.072; P<0.001]. Elevated sTM levels had an independent correlation with higher mortality in patients with ARDS [pooled odds ratio (OR) =2.126; 95% CI: 1.548-2.920; P<0.001]. sTM showed satisfactory performance in predicting the mortality of ARDS [summary receiver operating characteristic curve (SROC) =0.78; 95% CI: 0.64-0.89]. The pooled sensitivity was 72% (95% CI: 66-77%), and the pooled specificity was 77% (95% CI: 72-82%). Subgroup analysis showed no significant difference in the sTM levels between nonsurvivors and survivors in terms of patients with direct ARDS (SMD =0.813; 95% CI: -0.673 to 2.229; P=0.253).

Conclusions

sTM is associated with hospital mortality in ARDS and shows moderate predictive performance. As a result, it is a potential candidate for predicting the mortality of ARDS. However, caution is needed when sTM is used to predict adverse outcomes in patients with direct ARDS.

Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS

BACKGROUND

Acute respiratory distress syndrome (ARDS), a life-threatening condition during critical illness, is a common complication of COVID-19. It can originate from various disease etiologies, including severe infections, major injury, or inhalation of irritants. ARDS poses substantial clinical challenges due to a lack of etiology-specific therapies, multisystem involvement, and heterogeneous, poor patient outcomes. A molecular comparison of ARDS groups holds the potential to reveal common and distinct mechanisms underlying ARDS pathogenesis.

METHODS

We performed a comparative analysis of urine-based metabolomics and proteomics profiles from COVID-19 ARDS patients (n = 42) and bacterial sepsis-induced ARDS patients (n = 17). To this end, we used two different approaches, first we compared the molecular omics profiles between ARDS groups, and second, we correlated clinical manifestations within each group with the omics profiles.

RESULTS

The comparison of the two ARDS etiologies identified 150 metabolites and 70 proteins that were differentially abundant between the two groups. Based on these findings, we interrogated the interplay of cell adhesion/extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis through a multi-omic network approach. Moreover, we identified a proteomic signature associated with mortality in COVID-19 ARDS patients, which contained several proteins that had previously been implicated in clinical manifestations frequently linked with ARDS pathogenesis.

CONCLUSION

In summary, our results provide evidence for significant molecular differences in ARDS patients from different etiologies and a potential synergy of extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis. The proteomic mortality signature should be further investigated in future studies to develop prediction models for COVID-19 patient outcomes.

I./K. COVID-19 Study Group, Michaëlsson J, Flodström-Tullberg M, Brighenti S, Buggert M, Mjösberg J, Malmberg KJ, Sandberg JK, Gredmark-Russ S, Rooyackers O, Svensson M, Chambers BJ, Eriksson LI, Pernemalm M, Björkström NK, Aleman S, Ljunggren HG, Klingström J, Strålin K, Norrby-Teglund A. Targeted plasma proteomics reveals signatures discriminating COVID-19 from sepsis with pneumonia

BACKGROUND

COVID-19 remains a major public health challenge, requiring the development of tools to improve diagnosis and inform therapeutic decisions. As dysregulated inflammation and coagulation responses have been implicated in the pathophysiology of COVID-19 and sepsis, we studied their plasma proteome profiles to delineate similarities from specific features.

METHODS

We measured 276 plasma proteins involved in Inflammation, organ damage, immune response and coagulation in healthy controls, COVID-19 patients during acute and convalescence phase, and sepsis patients; the latter included (i) community-acquired pneumonia (CAP) caused by Influenza, (ii) bacterial CAP, (iii) non-pneumonia sepsis, and (iv) septic shock patients.

RESULTS

We identified a core response to infection consisting of 42 proteins altered in both COVID-19 and sepsis, although higher levels of cytokine storm-associated proteins were evident in sepsis. Furthermore, microbiologic etiology and clinical endotypes were linked to unique signatures. Finally, through machine learning, we identified biomarkers, such as TRIM21, PTN and CASP8, that accurately differentiated COVID-19 from CAP-sepsis with higher accuracy than standard clinical markers.

CONCLUSIONS

This study extends the understanding of host responses underlying sepsis and COVID-19, indicating varying disease mechanisms with unique signatures. These diagnostic and severity signatures are candidates for the development of personalized management of COVID-19 and sepsis.

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.