Extensive acute and sustained changes to neutrophil proteomes post-SARS-CoV-2 infection

Predicting coronavirus disease 2019 severity using explainable artificial intelligence techniques

Predictive models for determining coronavirus disease 2019 (COVID-19) severity have been established; however, the complexity of the interactions among factors limits the use of conventional statistical methods. This study aimed to establish a simple and accurate predictive model for COVID-19 severity using an explainable machine learning approach. A total of 3,301 patients ≥ 18 years diagnosed with COVID-19 between February 2020 and October 2022 were included. The discovery cohort comprised patients whose disease onset fell before October 1, 2020 (N = 1,023), and the validation cohort comprised the remaining patients (N = 2,278). Pointwise linear and logistic regression models were used to extract 41 features. Reinforcement learning was used to generate a simple model with high predictive accuracy. The primary evaluation was the area under the receiver operating characteristic curve (AUC). The predictive model achieved an AUC of ≥ 0.905 using four features: serum albumin levels, lactate dehydrogenase levels, age, and neutrophil count. The highest AUC value was 0.906 (sensitivity, 0.842; specificity, 0.811) in the discovery cohort and 0.861 (sensitivity, 0.804; specificity, 0.675) in the validation cohort. Simple and well-structured predictive models were established, which may aid in patient management and the selection of therapeutic interventions.

Neutrophils Display Novel Partners of Cytosolic Proliferating Cell Nuclear Antigen Involved in Interferon Response in COVID-19 Patients

INTRODUCTION

Neutrophils are key players in the hyperinflammatory response during SARS-CoV-2 infection. The cytosolic proliferating cell nuclear antigen (PCNA) is a scaffolding protein highly dependent on the microenvironment status and known to interact with numerous proteins that regulate neutrophil functions. This study aimed to examine the cytosolic protein content and PCNA interactome in neutrophils from COVID-19 patients.

METHODS

Proteomic analyses were performed on neutrophil cytosols from healthy donors and patients with severe or critical COVID-19. In vitro approaches were used to explore the biological significance of the COVID-19-specific PCNA interactome.

RESULTS

Neutrophil cytosol analysis revealed a strong interferon (IFN) protein signature, with variations according to disease severity. Interactome analysis identified associations of PCNA with proteins involved in interferon signaling, cytoskeletal organization, and neutrophil extracellular trap (NET) formation, such as protein arginine deiminase type-4 (PADI4) and histone H3, particularly in critical patients. Functional studies of interferon signaling showed that T2AA, a PCNA scaffold inhibitor, downregulated IFN-related genes, including STAT1, MX1, IFIT1, and IFIT2 in neutrophils. Additionally, T2AA specifically inhibited the secretion of CXCL10, an IFN-dependent cytokine. PCNA was also found to interact with key effector proteins implicated in NET formation, such as histone H3, especially in critical COVID-19 cases.

CONCLUSION

The analysis of the PCNA interactome has unveiled new protein partners that enhance the interferon pathway, thereby modulating immune responses and contributing to hyperinflammation in COVID-19. These findings provide valuable insights into interferon dysregulation in other immune-related conditions.

INTRODUCTION

Neutrophils are key players in the hyperinflammatory response during SARS-CoV-2 infection. The cytosolic proliferating cell nuclear antigen (PCNA) is a scaffolding protein highly dependent on the microenvironment status and known to interact with numerous proteins that regulate neutrophil functions. This study aimed to examine the cytosolic protein content and PCNA interactome in neutrophils from COVID-19 patients.

METHODS

Proteomic analyses were performed on neutrophil cytosols from healthy donors and patients with severe or critical COVID-19. In vitro approaches were used to explore the biological significance of the COVID-19-specific PCNA interactome.

RESULTS

Neutrophil cytosol analysis revealed a strong interferon (IFN) protein signature, with variations according to disease severity. Interactome analysis identified associations of PCNA with proteins involved in interferon signaling, cytoskeletal organization, and neutrophil extracellular trap (NET) formation, such as protein arginine deiminase type-4 (PADI4) and histone H3, particularly in critical patients. Functional studies of interferon signaling showed that T2AA, a PCNA scaffold inhibitor, downregulated IFN-related genes, including STAT1, MX1, IFIT1, and IFIT2 in neutrophils. Additionally, T2AA specifically inhibited the secretion of CXCL10, an IFN-dependent cytokine. PCNA was also found to interact with key effector proteins implicated in NET formation, such as histone H3, especially in critical COVID-19 cases.

CONCLUSION

The analysis of the PCNA interactome has unveiled new protein partners that enhance the interferon pathway, thereby modulating immune responses and contributing to hyperinflammation in COVID-19. These findings provide valuable insights into interferon dysregulation in other immune-related conditions.

Identification of VISTA regulators in macrophages mediating cancer cell survival

Numerous human cancers have exhibited the ability to elude immune checkpoint blockade (ICB) therapies. This type of resistance can be mediated by immune-suppressive macrophages that limit antitumor immunity in the tumor microenvironment (TME). Here, we elucidate a strategy to shift macrophages into a proinflammatory state that down-regulates V domain immunoglobulin suppressor of T cell activation (VISTA) via inhibiting AhR and IRAK1. We used a high-throughput microfluidic platform combined with a genome-wide CRISPR knockout screen to identify regulators of VISTA levels. Functional characterization showed that the knockdown of these hits diminished VISTA surface levels on macrophages and sustained an antitumor phenotype. Furthermore, targeting of both AhR and IRAK1 in mouse models overcame resistance to ICB treatment. Tumor immunophenotyping indicated that infiltration of cytotoxic CD8+ cells, natural killer cells, and antitumor macrophages was substantially increased in treated mice. Collectively, AhR and IRAK1 are implicated as regulators of VISTA that coordinate a multifaceted barrier to antitumor immune responses.

Clinical Efficacy of Serum Antiglycopeptidolipid Core IgA Antibody Test for Screening Nontuberculous Mycobacterial Pulmonary Disease in Bronchiectasis: A European Multicenter Cohort Study

BACKGROUND

The serum antiglycopeptidolipid core IgA antibody test has been proposed as a diagnostic tool for Mycobacterium avium complex pulmonary diseases. Cross-reactivity with other nontuberculous mycobacteria (NTM), including Mycobacterium abscessus, indicates that it may have a role as a broader screening test for nontuberculous mycobacterial pulmonary disease (NTM-PD). NTM-PD is believed to be underdiagnosed in patients with bronchiectasis.

RESEARCH QUESTION

Can the serum antiglycopeptidolipid core IgA antibody test be used to screen for NTM-PD in bronchiectasis?

STUDY DESIGN AND METHODS

Patients from the prospective European Bronchiectasis Registry (European Multicentre Bronchiectasis Audit and Research Collaboration-Bronchiectasis Research Involving Databases, Genomics and Endotyping; ClinicalTrails.gov Identifier: NCT03791086) were enrolled. Patients from the United Kingdom, Italy, Spain, Belgium, The Netherlands, and Germany were included. A control cohort of patients without any underlying lung disease also was recruited. The levels of serum IgA antibodies against the glycopeptidolipid core were measured using an enzyme immunoassay kit, and receiver operating characteristics curve analysis was conducted to evaluate the accuracy of the antibody level in screening for NTM-PD.

RESULTS

Two hundred eighty-two patients were enrolled (151 female patients [53.6%]; median age, 68 years). Median antiglycopeptidolipid core IgA antibody levels were 0.2 U/mL (interquartile range [IQR], 0.1-0.3 U/mL) in patients without NTM isolation and NTM-PD (n = 238), 0.3 U/mL (IQR, 0.2-0.4 U/mL) in patients with NTM isolation that was incompatible with the diagnosis of NTM-PD (n = 18), and 1.5 U/mL (IQR, 0.4-6.2 U/mL) in patients with NTM-PD (n = 26; P = .0001). Antibody levels showed excellent accuracy in identifying patients with NTM-PD (area under the receiver operating characteristic curve, 0.886; 95% CI, 0.800-0.973) in the bronchiectasis cohort and also showed excellent discrimination of patients with NTM-PD from those with NTM isolation who did not meet the diagnostic criteria for NTM-PD (0.816; 95% CI, 0.687-0.945).

INTERPRETATION

The antiglycopeptidolipid core IgA antibody demonstrated excellent efficacy in screening for NTM-PD in a large cohort of patients with bronchiectasis.

CLINICAL TRIAL REGISTRY

ClinicalTrials.gov; No.: NCT03791086; URL: www.

CLINICALTRIALS

gov.

ULV: A robust statistical method for clustered data, with applications to multi-subject, single-cell omics data

Molecular and genomic technological advancements have greatly enhanced our understanding of biological processes by allowing us to quantify key biological variables such as gene expression, protein levels, and microbiome compositions. These breakthroughs have enabled us to achieve increasingly higher levels of resolution in our measurements, exemplified by our ability to comprehensively profile biological information at the single-cell level. However, the analysis of such data faces several critical challenges: limited number of individuals, non-normality, potential dropouts, outliers, and repeated measurements from the same individual. In this article, we propose a novel method, which we call U-statistic based latent variable (ULV). Our proposed method takes advantage of the robustness of rank-based statistics and exploits the statistical efficiency of parametric methods for small sample sizes. It is a computationally feasible framework that addresses all the issues mentioned above simultaneously. We show that our method controls false positives at desired significance levels. An additional advantage of ULV is its flexibility in modeling various types of single-cell data, including both RNA and protein abundance. The usefulness of our method is demonstrated in two studies: a single-cell proteomics study of acute myelogenous leukemia (AML) and a single-cell RNA study of COVID-19 symptoms. In the AML study, ULV successfully identified differentially expressed proteins that would have been missed by the pseudobulk version of the Wilcoxon rank-sum test. In the COVID-19 study, ULV identified genes associated with covariates such as age and gender, and genes that would be missed without adjusting for covariates. The differentially expressed genes identified by our method are less biased toward genes with high expression levels. Furthermore, ULV identified additional gene pathways likely contributing to the mechanisms of COVID-19 severity.

Differences between human male and female neutrophils in mRNA, translation efficiency, protein, and phosphoprotein profiles

Background

Human males and females show differences in the incidence of neutrophil-associated diseases such as systemic lupus erythematosus, rheumatoid arthritis, and reactive arthritis, and differences in neutrophil physiological responses such as a faster response to the chemorepellent SLIGKV. Little is known about the basis of sex-based differences in human neutrophils.

Methods

Starting with human neutrophils from healthy donors, we used RNA-seq to examine total mRNA profiles, mRNAs not associated with ribosomes and thus not being translated, mRNAs in monosomes, and mRNAs in polysomes and thus heavily translated. We used mass spectrometry systems to identify proteins and phosphoproteins.

Results

There were sex-based differences in the translation of 24 mRNAs. There were 132 proteins with higher levels in male neutrophils; these tended to be associated with RNA regulation, ribosome, and phosphoinositide signaling pathways, whereas 30 proteins with higher levels in female neutrophils were associated with metabolic processes, proteosomes, and phosphatase regulatory proteins. Male neutrophils had increased phosphorylation of 32 proteins. After exposure to SLIGKV, male neutrophils showed a faster response in terms of protein phosphorylation compared to female neutrophils.

Conclusions

Male neutrophils have higher levels of proteins and higher phosphorylation of proteins associated with RNA processing and signaling pathways, while female neutrophils have higher levels of proteins associated with metabolism and proteolytic pathways. This suggests that male neutrophils might be more ready to adapt to a new environment, and female neutrophils might be more effective at responding to pathogens. This may contribute to the observed sex-based differences in neutrophil behavior and neutrophil-associated disease incidence and severity.

Omics-based profiles and biomarkers of respiratory infections: are we there yet?

From the influenza pandemic of 1918–1919 to the most recent COVID-19 pandemic, respiratory infections remain a leading cause of mortality worldwide [1, 2]. Concurrently, the development of high-throughput omics technologies has revolutionised research about host responses to known and emerging respiratory pathogens [3], accelerating our understanding of highly prevalent pulmonary diseases [4]. Notably, omics technology-based characterisation of pathogens and host pathophysiology have critically supported diagnostic and therapeutic global health efforts during both the influenza A H1N1 and SARS-CoV-2 pandemics [5–7]. Nonetheless, elucidation of key immune response mechanisms and development of host-targeted therapeutics remain important unrealised research and clinical priorities in the global fight against lower respiratory tract infections (LTRIs) [8, 9].

Descriptive omics-based clinical research provides valuable early steps in understanding host immune responses to respiratory pathogens in our global efforts to mitigate the impacts of severe respiratory infections with rapidly evolving technologies https://bit.ly/4bjJsvL