Identification of driver genes for critical forms of COVID-19 in a deeply phenotyped young patient cohort

Blood proteomics of COVID-19 infection: An update

Despite significant progress in understanding SARS-CoV-2 and its impact, a deeper comprehension of the molecular changes in the host following infection is still lacking. Proteomic analysis of COVID-19 infected patients can provide valuable data about the events and mechanisms related to the initial, progression, and terminal stages of the disease. Such information can lead to better prevention, treatment, and convalescence strategies. In this review, we discuss blood proteomic studies based on mass spectrometry in COVID-19 patients. The analysis focuses on various blood components including plasma, platelets, serum, red blood cells, and peripheral blood mononuclear cells. Scientific papers published since 2022 are covered, with approaches categorised based on whether they use in-gel, in-solution, or on-beads/on-filter digestion modes. Additionally, candidate protein biomarkers related to COVID-19 are surveyed and discussed.

Hub genes identification and validation of ferroptosis in SARS-CoV-2 induced ARDS: perspective from transcriptome analysis

Introduction

Acute Respiratory Distress Syndrome (ARDS) poses a significant health challenge due to its high incidence and mortality rates. The emergence of SARS-CoV-2 has added complexity, with evidence suggesting a correlation between COVID-19 induced ARDS and post-COVID symptoms. Understanding the underlying mechanisms of ARDS in COVID-19 patients is crucial for effective clinical treatment.

Method

To investigate the potential role of ferroptosis in SARS-CoV-2 induced ARDS, we conducted a comprehensive analysis using bioinformatics methods. Datasets from the Gene Expression Omnibus (GEO) were utilized, focusing on COVID-19 patients with varying ARDS severity. We employed weighted gene co-expression network analysis (WGCNA), differential gene expression analysis, and single-cell sequencing to identify key genes associated with ferroptosis in ARDS. Hub genes were validated using additional GEO datasets and cell experiment.

Result

The analysis discerned 916 differentially expressed genes in moderate/severe ARDS patients compared to non-critical individuals. Weighted Gene Co-expression Network Analysis (WGCNA) unveiled two modules that exhibited a positive correlation with ARDS, subsequently leading to the identification of 15 hub genes associated with ferroptosis. Among the noteworthy hub genes were MTF1, SAT1, and TXN. Protein-protein interaction analysis, and pathway analysis further elucidated their roles. Immune infiltrating analysis highlighted associations between hub genes and immune cells. Validation in additional datasets confirmed the upregulation of MTF1, SAT1, and TXN in SARS-CoV-2-induced ARDS. This was also demonstrated by qRT-PCR results in the BEAS-2B cells vitro model, suggesting their potential as diagnostic indicators.

Discussion

This study identifies MTF1, SAT1, and TXN as hub genes associated with ferroptosis in SARS-CoV-2-induced ARDS. These findings provide novel insights into the molecular mechanisms underlying ARDS in COVID-19 patients and offer potential targets for immune therapy and targeted treatment. Further experimental validation is warranted to solidify these findings and explore therapeutic interventions for ARDS in the context of COVID-19.

Context-specific eQTLs reveal causal genes underlying shared genetic architecture of critically ill COVID-19 and idiopathic pulmonary fibrosis

Most genetic variants identified through genome-wide association studies (GWAS) are suspected to be regulatory in nature, but only a small fraction colocalize with expression quantitative trait loci (eQTLs, variants associated with expression of a gene). Therefore, it is hypothesized but largely untested that integration of disease GWAS with context-specific eQTLs will reveal the underlying genes driving disease associations. We used colocalization and transcriptomic analyses to identify shared genetic variants and likely causal genes associated with critically ill COVID-19 and idiopathic pulmonary fibrosis. We first identified five genome-wide significant variants associated with both diseases. Four of the variants did not demonstrate clear colocalization between GWAS and healthy lung eQTL signals. Instead, two of the four variants colocalized only in cell-type and disease-specific eQTL datasets. These analyses pointed to higher ATP11A expression from the C allele of rs12585036, in monocytes and in lung tissue from primarily smokers, which increased risk of IPF and decreased risk of critically ill COVID-19. We also found lower DPP9 expression (and higher methylation at a specific CpG) from the G allele of rs12610495, acting in fibroblasts and in IPF lungs, and increased risk of IPF and critically ill COVID-19. We further found differential expression of the identified causal genes in diseased lungs when compared to non-diseased lungs, specifically in epithelial and immune cell types. These findings highlight the power of integrating GWAS, context-specific eQTLs, and transcriptomics of diseased tissue to harness human genetic variation to identify causal genes and where they function during multiple diseases.

Proteomic lung analysis revealed hyper-activation of neutrophil extracellular trap formation in cases of fatal COVID-19

The molecular pathology of lung injury in patients with Corona Virus Disease 2019 (COVID-19) remain unclear. In this study, we performed a proteomic study of lung tissues from seven patients with COVID-19, and eight without. Lung parenchymal tissues with COVID-19 were obtained from autopsy samples, while control samples were obtained from paracancerous tissues. Proteins were extracted using phenol extraction. A tandem mass tag-based quantitative proteomic approach combined with bioinformatic analysis was used to detect proteomic changes in the SARS-CoV-2-infected lung tissues. A total of 6,602, and 6,549 proteins were identified in replicates 1 and 2, respectively. Of these, 307, and 278, respectively, were identified as differentially expressed proteins (DEPs). In total, 216 DEPs were identified in this study. These proteins were enriched in 189 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The downregulated proteins are mainly involved in focal adhesion (n = 5), and the PI3K-Akt signaling pathway (n = 4). The upregulated proteins were related to neutrophil extracellular trap (NET) formation (n = 16), and the phagosome pathway (n = 11). The upregulated proteins in these two pathways interact with one another. Further immunohistochemistry verified NET enrichment in the tissues with COVID-19 compared to the controls. Our results systematically outlined the proteomic profiles of the lung's response to SARS-CoV-2 infection and indicated that NET formation was hyper-activated. These results will hopefully provide new evidence for understanding the mechanism behind fatal COVID-19.

Identifications of novel host cell factors that interact with the receptor-binding domain of the SARS-CoV-2 spike protein

SARS-CoV-2 entry into host cells is facilitated by the interaction between the receptor-binding domain of its spike protein (CoV2-RBD) and host cell receptor, ACE2, promoting viral membrane fusion. The virus also uses endocytic pathways for entry, but the mediating host factors remain largely unknown. It is also unknown whether mutations in the RBD of SARS-CoV-2 variants promote interactions with additional host factors to promote viral entry. Here, we used the GST pull-down approach to identify novel surface-located host factors that bind to CoV2-RBD. One of these factors, SH3BP4, regulates internalization of CoV2-RBD in an ACE2-independent but integrin- and clathrin-dependent manner and mediates SARS-CoV-2 pseudovirus entry, suggesting that SH3BP4 promotes viral entry via the endocytic route. Many of the identified factors, including SH3BP4, ADAM9, and TMEM2, show stronger affinity to CoV2-RBD than to RBD of the less infective SARS-CoV, suggesting SARS-CoV-2-specific utilization. We also found factors preferentially binding to the RBD of the SARS-CoV-2 Delta variant, potentially enhancing its entry. These data identify the repertoire of host cell surface factors that function in the events leading to the entry of SARS-CoV-2.

Comprehensive analysis of immunogenic cell death-related gene and construction of prediction model based on WGCNA and multiple machine learning in severe COVID-19

The death of coronavirus disease 2019 (COVID-19) is primarily due to from critically ill patients, especially from ARDS complications caused by SARS-CoV-2. Therefore, it is essential to contribute an in-depth understanding of the pathogenesis of the disease and to identify biomarkers for predicting critically ill patients at the molecular level. Immunogenic cell death (ICD), as a specific variant of regulatory cell death driven by stress, can induce adaptive immune responses against cell death antigens in the host. Studies have confirmed that both innate and adaptive immune pathways are involved in the pathogenesis of SARS-CoV-2 infection. However, the role of ICD in the pathogenesis of severe COVID-19 has rarely been explored. In this study, we systematically evaluated the role of ICD-related genes in COVID-19. We conducted consensus clustering, immune infiltration analysis, and functional enrichment analysis based on ICD differentially expressed genes. The results showed that immune infiltration characteristics were altered in severe and non-severe COVID-19. In addition, we used multiple machine learning methods to screen for five risk genes (KLF5, NSUN7, APH1B, GRB10 and CD4), which are used to predict COVID-19 severity. Finally, we constructed a nomogram to predict the risk of severe COVID-19 based on the classification and recognition model, and validated the model with external data sets. This study provides a valuable direction for the exploration of the pathogenesis and progress of COVID-19, and helps in the early identification of severe cases of COVID-19 to reduce mortality.

The Initial COVID-19 Reliable Interactive DNA Methylation Markers and Biological Implications

Earlier research has established the existence of reliable interactive genomic biomarkers. However, reliable DNA methylation biomarkers, not to mention interactivity, have yet to be identified at the epigenetic level. This study, drawing from 865,859 methylation sites, discovered two miniature sets of Infinium MethylationEPIC sites, each having eight CpG sites (genes) to interact with each other and disease subtypes. They led to the nearly perfect (96.87-100% accuracy) prediction of COVID-19 patients from patients with other diseases or healthy controls. These CpG sites can jointly explain some post-COVID-19-related conditions. These CpG sites and the optimally performing genomic biomarkers reported in the literature become potential druggable targets. Among these CpG sites, cg16785077 (gene MX1), cg25932713 (gene PARP9), and cg22930808 (gene PARP9) at DNA methylation levels indicate that the initial SARS-CoV-2 virus may be better treated as a transcribed viral DNA into RNA virus, i.e., not as an RNA virus that has concerned scientists in the field. Such a discovery can significantly change the scientific thinking and knowledge of viruses.

Integrated analysis of RNA-seq datasets reveals novel targets and regulators of COVID-19 severity

During the COVID-19 pandemic, RNA-seq datasets were produced to investigate the virus-host relationship. However, much of these data remains underexplored. To improve the search for molecular targets and biomarkers, we performed an integrated analysis of multiple RNA-seq datasets, expanding the cohort and including patients from different countries, encompassing severe and mild COVID-19 patients. Our analysis revealed that severe COVID-19 patients exhibit overexpression of genes coding for proteins of extracellular exosomes, endomembrane system, and neutrophil granules (e.g., S100A9, LY96, and RAB1B), which may play an essential role in the cellular response to infection. Concurrently, these patients exhibit down-regulation of genes encoding components of the T cell receptor complex and nucleolus, including TP53, IL2RB, and NCL Finally, SPI1 may emerge as a central transcriptional factor associated with the up-regulated genes, whereas TP53, MYC, and MAX were associated with the down-regulated genes during COVID-19. This study identified targets and transcriptional factors, lighting on the molecular pathophysiology of syndrome coronavirus 2 infection.

In search of a function for human type III interferons: insights from inherited and acquired deficits

The essential and redundant functions of human type I and II interferons (IFNs) have been delineated over the last three decades by studies of patients with inborn errors of immunity or their autoimmune phenocopies, but much less is known about type III IFNs. Patients with cells that do not respond to type III IFNs due to inherited IL10RB deficiency display no overt viral disease, and their inflammatory disease phenotypes can be explained by defective signaling via other interleukine10RB-dependent pathways. Moreover, patients with inherited deficiencies of interferon-stimulated gene factor 3 (ISGF-3) (STAT1, STAT2, IRF9) present viral diseases also seen in patients with inherited deficiencies of the type I IFN receptor (IFNAR1/2). Finally, patients with autoantibodies neutralizing type III IFNs have no obvious predisposition to viral disease. Current findings thus suggest that type III IFNs are largely redundant in humans. The essential functions of human type III IFNs, particularly in antiviral defenses, remain to be discovered.

Type I and type III interferons: From basic biology and genetics to clinical development for COVID-19 and beyond

Type I and type III interferons (IFNs) constitute a key antiviral defense systems of the body, inducing viral resistance to cells and mediating diverse innate and adaptive immune functions. Defective type I and type III IFN responses have recently emerged as the 'Achilles heel' in COVID-19, with such patients developing severe disease and exhibiting a high risk for critical pneumonia and death. Here, we review the biology of type I and type III IFNs, their similarities and important functional differences, and their roles in SARS-CoV-2 infection. We also appraise the various mechanisms proposed to drive defective IFN responses in COVID-19 with particular emphasis to the ability of SARS-CoV-2 to suppress IFN production and activities, the genetic factors involved and the presence of autoantibodies neutralizing IFNs and accounting for a large proportion of individuals with severe COVID-19. Finally, we discuss the long history of the type I IFN therapeutics for the treatment of viral diseases, cancer and multiple sclerosis, the various efforts to use them in respiratory infections, and the newly emerging type III IFN therapeutics, with emphasis to the more recent studies on COVID-19 and their potential use as broad spectrum antivirals for future epidemics or pandemics.

Human autoantibodies neutralizing type I IFNs: From 1981 to 2023

Human autoantibodies (auto-Abs) neutralizing type I IFNs were first discovered in a woman with disseminated shingles and were described by Ion Gresser from 1981 to 1984. They have since been found in patients with diverse conditions and are even used as a diagnostic criterion in patients with autoimmune polyendocrinopathy syndrome type 1 (APS-1). However, their apparent lack of association with viral diseases, including shingles, led to wide acceptance of the conclusion that they had no pathological consequences. This perception began to change in 2020, when they were found to underlie about 15% of cases of critical COVID-19 pneumonia. They have since been shown to underlie other severe viral diseases, including 5%, 20%, and 40% of cases of critical influenza pneumonia, critical MERS pneumonia, and West Nile virus encephalitis, respectively. They also seem to be associated with shingles in various settings. These auto-Abs are present in all age groups of the general population, but their frequency increases with age to reach at least 5% in the elderly. We estimate that at least 100 million people worldwide carry auto-Abs neutralizing type I IFNs. Here, we briefly review the history of the study of these auto-Abs, focusing particularly on their known causes and consequences.

SARS-CoV-2 rapidly evolves lineage-specific phenotypic differences when passaged repeatedly in immune-naïve mice

The persistence of SARS-CoV-2 despite the development of vaccines and a degree of herd immunity is partly due to viral evolution reducing vaccine and treatment efficacy. Serial infections of wild-type (WT) SARS-CoV-2 in Balb/c mice yield mouse-adapted strains with greater infectivity and mortality. We investigate if passaging unmodified B.1.351 (Beta) and B.1.617.2 (Delta) 20 times in K18-ACE2 mice, expressing the human ACE2 receptor, in a BSL-3 laboratory without selective pressures, drives human health-relevant evolution and if evolution is lineage-dependent. Late-passage virus causes more severe disease, at organism and lung tissue scales, with late-passage Delta demonstrating antibody resistance and interferon suppression. This resistance co-occurs with a de novo spike S371F mutation, linked with both traits. S371F, an Omicron-characteristic mutation, is co-inherited at times with spike E1182G per Nanopore sequencing, existing in different within-sample viral variants at others. Both S371F and E1182G are linked to mammalian GOLGA7 and ZDHHC5 interactions, which mediate viral-cell entry and antiviral response. This study demonstrates SARS-CoV-2's tendency to evolve with phenotypic consequences, its evolution varying by lineage, and suggests non-dominant quasi-species contribution.

Higher COVID-19 pneumonia risk associated with anti-IFN-α than with anti-IFN-ω auto-Abs in children

We found that 19 (10.4%) of 183 unvaccinated children hospitalized for COVID-19 pneumonia had autoantibodies (auto-Abs) neutralizing type I IFNs (IFN-α2 in 10 patients: IFN-α2 only in three, IFN-α2 plus IFN-ω in five, and IFN-α2, IFN-ω plus IFN-β in two; IFN-ω only in nine patients). Seven children (3.8%) had Abs neutralizing at least 10 ng/ml of one IFN, whereas the other 12 (6.6%) had Abs neutralizing only 100 pg/ml. The auto-Abs neutralized both unglycosylated and glycosylated IFNs. We also detected auto-Abs neutralizing 100 pg/ml IFN-α2 in 4 of 2,267 uninfected children (0.2%) and auto-Abs neutralizing IFN-ω in 45 children (2%). The odds ratios (ORs) for life-threatening COVID-19 pneumonia were, therefore, higher for auto-Abs neutralizing IFN-α2 only (OR [95% CI] = 67.6 [5.7-9,196.6]) than for auto-Abs neutralizing IFN-ω only (OR [95% CI] = 2.6 [1.2-5.3]). ORs were also higher for auto-Abs neutralizing high concentrations (OR [95% CI] = 12.9 [4.6-35.9]) than for those neutralizing low concentrations (OR [95% CI] = 5.5 [3.1-9.6]) of IFN-ω and/or IFN-α2.

Epigenetic and transcriptional responses in circulating leukocytes are associated with future decompensation during SARS-CoV-2 infection

To elucidate host response elements that define impending decompensation during SARS-CoV-2 infection, we enrolled subjects hospitalized with COVID-19 who were matched for disease severity and comorbidities at the time of admission. We performed combined single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) on peripheral blood mononuclear cells (PBMCs) at admission and compared subjects who improved from their moderate disease with those who later clinically decompensated and required invasive mechanical ventilation or died. Chromatin accessibility and transcriptomic immune profiles were markedly altered between the two groups, with strong signals in CD4+ T cells, inflammatory T cells, dendritic cells, and NK cells. Multiomic signature scores at admission were tightly associated with future clinical deterioration (auROC 1.0). Epigenetic and transcriptional changes in PBMCs reveal early, broad immune dysregulation before typical clinical signs of decompensation are apparent and thus may act as biomarkers to predict future severity in COVID-19.

Role of autoantibodies targeting interferon type 1 in COVID-19 severity: A systematic review and meta-analysis

Introduction

Impairment of the type I interferon (IFN-I) signaling pathway is associated with increased severity of COVID-19 disease. Here we have undertaken a systematic review and meta = analysis on the association between the severity of COVID-19 and IFN-1 autoantibodies (AAbs; aIFN-1, aIFN-α, aIFN-ω, and aIFN-β).

Methods

Four databases, including Medline [PubMed], Embase, Web of Science, and Scopus, were systematically searched to find papers on the role of aIFN-1 and its subtype AAbs in the severity of COVID-19 infection. Data on the prevalence of aIFN-1, aIFN-α, aIFN-ω, and aIFN-β were pooled using random- or fixed-effects models. Subgroup analysis was performed based on disease severity. Odds ratios (OR) for COVID-19 disease outcome, including length of hospital stay, ICU admission and death, were calculated in relation to positive or negative plasma IFN-1 AAbs.

Results

A total of 33 studies with 13023 patients were included. The overall prevalence of circulating aIFN-1, aIFN-α, and aIFN-ω AAbs was 17.8 % [13.8, 22.8], 7.2 % [4.7, 10.9], and 4.4 % [2.1, 8.6], respectively, and the overall prevalence of neutralizing aIFN-1, aIFN-α, aIFN-ω, and aIFN-β AAbs was 7.1 % [4.9, 10.1], 7.5 % [5.9, 9.5], 8.0 % [5.7, 11.1] and 1.2 % [0.4, 3.5], respectively. Circulating aIFN-α (OR = 4.537 [2.247, 9.158]), neutralizing aIFN-α (O = 17.482 [8.899, 34.342]), and neutralizing aIFN-ω (OR = 12.529 [7.397, 21.222]) were significantly more frequent in critical/severe patients than in moderate/mild patients (p < 0.001 for all). Anti-IFN-1 was more common in male subjects (OR = 2.248 [1.366, 3.699], p = 0.001) and two COVID-19 outcomes including ICU admission (OR = 2.485 [1.409, 4.385], p = 0.002) and death (OR = 2.593 [1.199, 5.604], p = 0.015) occurred more frequently in patients with positive anti-IFN-1.Conclusion: aIFN-1 and its subtypes AAbs are associated with severe and critical COVID-19 disease and may be a predictive marker for a poor prognosis, particularly in men.

Identification of toll-like receptor 5 and acyl-CoA synthetase long chain family member 1 as hub genes are correlated with the severe forms of COVID-19 by Weighted gene co-expression network analysis

Since a 25% mortality rate occurred in critical Coronavirus disease 2019 (COVID-19) patients, investigating the potential drivers remains to be important. Here, the authors applied Weighted Gene Co-expression Network Analysis to identify the potential drivers in the blood samples of multiple COVID-19 expression profiles. The authors found that the darkslateblue module was significantly correlated with critical COVID-19, and Gene Ontology analysis indicated terms associated with the inflammation pathway and apoptotic process. The authors intersected differentially expressed genes, Maximal Clique Centrality calculated hub genes, and COVID-19 related genes in the Genecards dataset, and two genes, toll-like receptor 5 (TLR5) and acyl-CoA synthetase long chain family member 1 (ACSL1), were screened out. The Gene Set Enrichment Analysis further supports their core role in the inflammatory pathway. Furthermore, the cell-type identification by estimating relative subsets of RNA transcript demonstrated that TLR5 and ACSL1 were associated with neutrophil enrichment in critical COVID-19 patients. Collectively, the aurthors identified two hub genes that were strongly correlated with critical COVID-19. These may help clarify the pathogenesis and assist the immunotherapy development.

Serum CRP interacts with SPARC and regulate immune response in severe cases of COVID-19 infection

Serum C-reactive protein (CRP) has been found elevated during COVID-19 infection, and associated with systematic inflammation as well as a poor clinical outcome. However, how did CRP participated in the COVID-19 pathogenesis remains poorly understood. Here, we report that serum C-reactive protein (CRP) levels are correlated with megakaryocyte marker genes and could regulate immune response through interaction with megakaryocytes. Molecular dynamics simulation through ColabFold showed a reliable interaction between monomeric form of CRP (mCRP) and the secreted protein acidic and rich in cysteine (SPARC). The interaction does not affect the physiological activities of SPARC while would be disturbed by pentamerization of CRP. Interplay between SPARC and mCRP results in a more intense immune response which may led to poor prognosis. This study highlights the complex interplay between inflammatory markers, megakaryocytes, and immune regulation in COVID-19 and sheds light on potential therapeutic targets.

The two-stage molecular scenery of SARS-CoV-2 infection with implications to disease severity: An in-silico quest

Introduction

The two-stage molecular profile of the progression of SARS-CoV-2 (SCOV2) infection is explored in terms of five key biological/clinical questions: (a) does SCOV2 exhibits a two-stage infection profile? (b) SARS-CoV-1 (SCOV1) vs. SCOV2: do they differ? (c) does and how SCOV2 differs from Influenza/INFL infection? (d) does low viral-load and (e) does COVID-19 early host response relate to the two-stage SCOV2 infection profile? We provide positive answers to the above questions by analyzing the time-series gene-expression profiles of preserved cell-lines infected with SCOV1/2 or, the gene-expression profiles of infected individuals with different viral-loads levels and different host-response phenotypes.

Methods

Our analytical methodology follows an in-silico quest organized around an elaborate multi-step analysis pipeline including: (a) utilization of fifteen gene-expression datasets from NCBI's gene expression omnibus/GEO repository; (b) thorough designation of SCOV1/2 and INFL progression stages and COVID-19 phenotypes; (c) identification of differentially expressed genes (DEGs) and enriched biological processes and pathways that contrast and differentiate between different infection stages and phenotypes; (d) employment of a graph-based clustering process for the induction of coherent groups of networked genes as the representative core molecular fingerprints that characterize the different SCOV2 progression stages and the different COVID-19 phenotypes. In addition, relying on a sensibly selected set of induced fingerprint genes and following a Machine Learning approach, we devised and assessed the performance of different classifier models for the differentiation of acute respiratory illness/ARI caused by SCOV2 or other infections (diagnostic classifiers), as well as for the prediction of COVID-19 disease severity (prognostic classifiers), with quite encouraging results.

Results

The central finding of our experiments demonstrates the down-regulation of type-I interferon genes (IFN-1), interferon induced genes (ISGs) and fundamental innate immune and defense biological processes and molecular pathways during the early SCOV2 infection stages, with the inverse to hold during the later ones. It is highlighted that upregulation of these genes and pathways early after infection may prove beneficial in preventing subsequent uncontrolled hyperinflammatory and potentially lethal events.

Discussion

The basic aim of our study was to utilize in an intuitive, efficient and productive way the most relevant and state-of-the-art bioinformatics methods to reveal the core molecular mechanisms which govern the progression of SCOV2 infection and the different COVID-19 phenotypes.

Multi-omic profiling reveals early immunological indicators for identifying COVID-19 Progressors

We sought to better understand the immune response during the immediate post-diagnosis phase of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by identifying molecular associations with longitudinal disease outcomes. Multi-omic analyses identified differences in immune cell composition, cytokine levels, and cell subset-specific transcriptomic and epigenomic signatures between individuals on a more serious disease trajectory (Progressors) as compared to those on a milder course (Non-progressors). Higher levels of multiple cytokines were observed in Progressors, with IL-6 showing the largest difference. Blood monocyte cell subsets were also skewed, showing a comparative decrease in non-classical CD14-CD16+ and intermediate CD14+CD16+ monocytes. In lymphocytes, the CD8+ T effector memory cells displayed a gene expression signature consistent with stronger T cell activation in Progressors. These early stage observations could serve as the basis for the development of prognostic biomarkers of disease risk and interventional strategies to improve the management of severe COVID-19. BACKGROUND: Much of the literature on immune response post-SARS-CoV-2 infection has been in the acute and post-acute phases of infection. TRANSLATIONAL SIGNIFICANCE: We found differences at early time points of infection in approximately 160 participants. We compared multi-omic signatures in immune cells between individuals progressing to needing more significant medical intervention and non-progressors. We observed widespread evidence of a state of increased inflammation associated with progression, supported by a range of epigenomic, transcriptomic, and proteomic signatures. The signatures we identified support other findings at later time points and serve as the basis for prognostic biomarker development or to inform interventional strategies.

Understanding COVID-19 progression with longitudinal peripheral blood mononuclear cell proteomics: Changes in the cellular proteome over time

The clinical presentation of COVID-19 is highly variable, and understanding the underlying biological processes is crucial. This study utilized a proteomic analysis to investigate dysregulated processes in the peripheral blood mononuclear cells of patients with COVID-19 compared to healthy volunteers. Samples were collected at different stages of the disease, including hospital admission, after 7 days of hospitalization, and 30 days after discharge. Metabolic pathway alterations and increased abundance of neutrophil-related proteins were observed in patients. Patients progressing to critical illness had significantly low-abundance proteins in the pentose phosphate and glycolysis pathways compared with those presenting clinical recovery. Important biological processes, such as fatty acid concentration and glucose metabolism disorder, remained altered even after 30 days of hospital discharge. Temporal proteomic changes revealed distinct pathways in critically ill and non-critically ill patients. Our study emphasizes the significance of longitudinal cellular proteomic studies in identifying disease progression-related pathways and persistent protein changes post-hospitalization.

COVIDpro: Database for Mining Protein Dysregulation in Patients with COVID-19

The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 still has limited treatment options. Our understanding of the molecular dysregulations that occur in response to infection remains incomplete. We developed a web application COVIDpro (https://www.guomics.com/covidPro/) that includes proteomics data obtained from 41 original studies conducted in 32 hospitals worldwide, involving 3077 patients and covering 19 types of clinical specimens, predominantly plasma and serum. The data set encompasses 53 protein expression matrices, comprising a total of 5434 samples and 14,403 unique proteins. We identified a panel of proteins that exhibit significant dysregulation, enabling the classification of COVID-19 patients into severe and non-severe disease categories. The proteomic signatures achieved promising results in distinguishing severe cases, with a mean area under the curve of 0.87 and accuracy of 0.80 across five independent test sets. COVIDpro serves as a valuable resource for testing hypotheses and exploring potential targets for novel treatments in COVID-19 patients.

Human Genomics of COVID-19 Pneumonia: Contributions of Rare and Common Variants

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection is silent or benign in most infected individuals, but causes hypoxemic COVID-19 pneumonia in about 10% of cases. We review studies of the human genetics of life-threatening COVID-19 pneumonia, focusing on both rare and common variants. Large-scale genome-wide association studies have identified more than 20 common loci robustly associated with COVID-19 pneumonia with modest effect sizes, some implicating genes expressed in the lungs or leukocytes. The most robust association, on chromosome 3, concerns a haplotype inherited from Neanderthals. Sequencing studies focusing on rare variants with a strong effect have been particularly successful, identifying inborn errors of type I interferon (IFN) immunity in 1-5% of unvaccinated patients with critical pneumonia, and their autoimmune phenocopy, autoantibodies against type I IFN, in another 15-20% of cases. Our growing understanding of the impact of human genetic variation on immunity to SARS-CoV-2 is enabling health systems to improve protection for individuals and populations.

Autoantibodies Neutralizing Type I IFNs in the Bronchoalveolar Lavage of at Least 10% of Patients During Life-Threatening COVID-19 Pneumonia

Autoantibodies (auto-Abs) neutralizing type I interferons (IFNs) are found in the blood of at least 15% of unvaccinated patients with life-threatening COVID-19 pneumonia. We report here the presence of auto-Abs neutralizing type I IFNs in the bronchoalveolar lavage (BAL) of 54 of the 415 unvaccinated patients (13%) with life-threatening COVID-19 pneumonia tested. The 54 individuals with neutralizing auto-Abs in the BAL included 45 (11%) with auto-Abs against IFN-α2, 37 (9%) with auto-Abs against IFN-ω, 54 (13%) with auto-Abs against IFN-α2 and/or ω, and five (1%) with auto-Abs against IFN-β, including three (0.7%) with auto-Abs neutralizing IFN-α2, IFN-ω, and IFN-β, and two (0.5%) with auto-Abs neutralizing IFN-α2 and IFN-β. Auto-Abs against IFN-α2 also neutralize the other 12 subtypes of IFN-α. Paired plasma samples were available for 95 patients. All seven patients with paired samples who had detectable auto-Abs in BAL also had detectable auto-Abs in plasma, and one patient had auto-Abs detectable only in blood. Auto-Abs neutralizing type I IFNs are, therefore, present in the alveolar space of at least 10% of patients with life-threatening COVID-19 pneumonia. These findings suggest that these auto-Abs impair type I IFN immunity in the lower respiratory tract, thereby contributing to hypoxemic COVID-19 pneumonia.

Airway Epithelial-Derived Immune Mediators in COVID-19

The airway epithelium, which lines the conducting airways, is central to the defense of the lungs against inhaled particulate matter and pathogens such as SARS-CoV-2, the virus that causes COVID-19. Recognition of pathogens results in the activation of an innate and intermediate immune response which involves the release of cytokines and chemokines by the airway epithelium. This response can inhibit further viral invasion and influence adaptive immunity. However, severe COVID-19 is characterized by a hyper-inflammatory response which can give rise to clinical presentations including lung injury and lead to acute respiratory distress syndrome, viral pneumonia, coagulopathy, and multi-system organ failure. In response to SARS-CoV-2 infection, the airway epithelium can mount a maladaptive immune response which can delay viral clearance, perpetuate excessive inflammation, and contribute to the pathogenesis of severe COVID-19. In this article, we will review the barrier and immune functions of the airway epithelium, how SARS-CoV-2 can interact with the epithelium, and epithelial-derived cytokines and chemokines and their roles in COVID-19 and as biomarkers. Finally, we will discuss these immune mediators and their potential as therapeutic targets in COVID-19.

The role of interleukin 13 and the type 2 immune pathway in COVID-19: A review

Although much has been learned about severe acute respiratory syndrome coronavirus 2 since December 2019, uneven global vaccine distribution, rapid viral spread, and variant evasion of preventative measures have led to its persistence in the population for the foreseeable future. Additional therapies are needed to support patients through their acute, immune-mediated disease process that continues to lead to considerable morbidity and mortality. Data revealing the involvement of type 2 immune pathway in acute coronavirus disease 2019 and post-recovery conditions represent a potential additional area for intervention. Herein, we review the current understanding of interleukin 13 in acute severe acute respiratory syndrome coronavirus 2 infection, the clinical outcomes associated with type 2 immune processes, and the impact of type 2 blockade on acute and long-term coronavirus disease 2019 conditions.

SEMtree: tree-based structure learning methods with structural equation models

MOTIVATION

With the exponential growth of expression and protein-protein interaction (PPI) data, the identification of functional modules in PPI networks that show striking changes in molecular activity or phenotypic signatures becomes of particular interest to reveal process-specific information that is correlated with cellular or disease states. This requires both the identification of network nodes with reliability scores and the availability of an efficient technique to locate the network regions with the highest scores. In the literature, a number of heuristic methods have been suggested. We propose SEMtree(), a set of tree-based structure discovery algorithms, combining graph and statistically interpretable parameters together with a user-friendly R package based on structural equation models framework.

RESULTS

Condition-specific changes from differential expression and gene-gene co-expression are recovered with statistical testing of node, directed edge, and directed path difference between groups. In the end, from a list of seed (i.e. disease) genes or gene P-values, the perturbed modules with undirected edges are generated with five state-of-the-art active subnetwork detection methods. The latter are supplied to causal additive trees based on Chu-Liu-Edmonds' algorithm (Chow and Liu, Approximating discrete probability distributions with dependence trees. IEEE Trans Inform Theory 1968;14:462-7) in SEMtree() to be converted in directed trees. This conversion allows to compare the methods in terms of directed active subnetworks. We applied SEMtree() to both Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) and simulated datasets with various differential expression patterns. Compared to existing methods, SEMtree() is able to capture biologically relevant subnetworks with simple visualization of directed paths, good perturbation extraction, and classifier performance.

AVAILABILITY AND IMPLEMENTATION

SEMtree() function is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph.

Multi-omic Profiling Reveals Early Immunological Indicators for Identifying COVID-19 Progressors

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a rapid response by the scientific community to further understand and combat its associated pathologic etiology. A focal point has been on the immune responses mounted during the acute and post-acute phases of infection, but the immediate post-diagnosis phase remains relatively understudied. We sought to better understand the immediate post-diagnosis phase by collecting blood from study participants soon after a positive test and identifying molecular associations with longitudinal disease outcomes. Multi-omic analyses identified differences in immune cell composition, cytokine levels, and cell subset-specific transcriptomic and epigenomic signatures between individuals on a more serious disease trajectory (Progressors) as compared to those on a milder course (Non-progressors). Higher levels of multiple cytokines were observed in Progressors, with IL-6 showing the largest difference. Blood monocyte cell subsets were also skewed, showing a comparative decrease in non-classical CD14-CD16+ and intermediate CD14+CD16+ monocytes. Additionally, in the lymphocyte compartment, CD8+ T effector memory cells displayed a gene expression signature consistent with stronger T cell activation in Progressors. Importantly, the identification of these cellular and molecular immune changes occurred at the early stages of COVID-19 disease. These observations could serve as the basis for the development of prognostic biomarkers of disease risk and interventional strategies to improve the management of severe COVID-19.

The Longitudinal Analysis of Convergent Antibody VDJ Regions in SARS-CoV-2-Positive Patients Using RNA-Seq

Severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) is an ongoing pandemic that continues to evolve and reinfect individuals. To understand the convergent antibody responses that evolved over the course of the pandemic, we evaluated the immunoglobulin repertoire of individuals infected by different SARS-CoV-2 variants for similarity between patients. We utilized four public RNA-seq data sets collected between March 2020 and March 2022 from the Gene Expression Omnibus (GEO) in our longitudinal analysis. This covered individuals infected with Alpha and Omicron variants. In total, from 269 SARS-CoV-2-positive patients and 26 negative patients, 629,133 immunoglobulin heavy-chain variable region V(D)J sequences were reconstructed from sequencing data. We grouped samples based on the SARS-CoV-2 variant type and/or the time they were collected from patients. Our comparison of patients within each SARS-CoV-2-positive group found 1011 common V(D)Js (same V gene, J gene and CDR3 amino acid sequence) shared by more than one patient and no common V(D)Js in the noninfected group. Taking convergence into account, we clustered based on similar CDR3 sequence and identified 129 convergent clusters from the SARS-CoV-2-positive groups. Within the top 15 clusters, 4 contain known anti-SARS-CoV-2 immunoglobulin sequences with 1 cluster confirmed to cross-neutralize variants from Alpha to Omicron. In our analysis of longitudinal groups that include Alpha and Omicron variants, we find that 2.7% of the common CDR3s found within groups were also present in more than one group. Our analysis reveals common and convergent antibodies, which include anti-SARS-CoV-2 antibodies, in patient groups over various stages of the pandemic.

Autoantibodies to Interferons in Infectious Diseases

Anti-cytokine autoantibodies and, in particular, anti-type I interferons are increasingly described in association with immunodeficient, autoimmune, and immune-dysregulated conditions. Their presence in otherwise healthy individuals may result in a phenotype characterized by a predisposition to infections with several agents. For instance, anti-type I interferon autoantibodies are implicated in Coronavirus Disease 19 (COVID-19) pathogenesis and found preferentially in patients with critical disease. However, autoantibodies were also described in the serum of patients with viral, bacterial, and fungal infections not associated with COVID-19. In this review, we provide an overview of anti-cytokine autoantibodies identified to date and their clinical associations; we also discuss whether they can act as enemies or friends, i.e., are capable of acting in a beneficial or harmful way, and if they may be linked to gender or immunosenescence. Understanding the mechanisms underlying the production of autoantibodies could improve the approach to treating some infections, focusing not only on pathogens, but also on the possibility of a low degree of autoimmunity in patients.

Uncovering a neurological protein signature for severe COVID-19

Coronavirus disease of 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has sparked a global pandemic with severe complications and high morbidity rate. Neurological symptoms in COVID-19 patients, and neurological sequelae post COVID-19 recovery have been extensively reported. Yet, neurological molecular signature and signaling pathways that are affected in the central nervous system (CNS) of COVID-19 severe patients remain still unknown and need to be identified. Plasma samples from 49 severe COVID-19 patients, 50 mild COVID-19 patients, and 40 healthy controls were subjected to Olink proteomics analysis of 184 CNS-enriched proteins. By using a multi-approach bioinformatics analysis, we identified a 34-neurological protein signature for COVID-19 severity and unveiled dysregulated neurological pathways in severe cases. Here, we identified a new neurological protein signature for severe COVID-19 that was validated in different independent cohorts using blood and postmortem brain samples and shown to correlate with neurological diseases and pharmacological drugs. This protein signature could potentially aid the development of prognostic and diagnostic tools for neurological complications in post-COVID-19 convalescent patients with long term neurological sequelae.

Interfering with Interferons: A Critical Mechanism for Critical COVID-19 Pneumonia

Infection with SARS-CoV-2 results in clinical outcomes ranging from silent or benign infection in most individuals to critical pneumonia and death in a few. Genetic studies in patients have established that critical cases can result from inborn errors of TLR3- or TLR7-dependent type I interferon immunity, or from preexisting autoantibodies neutralizing primarily IFN-α and/or IFN-ω. These findings are consistent with virological studies showing that multiple SARS-CoV-2 proteins interfere with pathways of induction of, or response to, type I interferons. They are also congruent with cellular studies and mouse models that found that type I interferons can limit SARS-CoV-2 replication in vitro and in vivo, while their absence or diminution unleashes viral growth. Collectively, these findings point to insufficient type I interferon during the first days of infection as a general mechanism underlying critical COVID-19 pneumonia, with implications for treatment and directions for future research.

Classification of COVID-19 Patients into Clinically Relevant Subsets by a Novel Machine Learning Pipeline Using Transcriptomic Features

The persistent impact of the COVID-19 pandemic and heterogeneity in disease manifestations point to a need for innovative approaches to identify drivers of immune pathology and predict whether infected patients will present with mild/moderate or severe disease. We have developed a novel iterative machine learning pipeline that utilizes gene enrichment profiles from blood transcriptome data to stratify COVID-19 patients based on disease severity and differentiate severe COVID cases from other patients with acute hypoxic respiratory failure. The pattern of gene module enrichment in COVID-19 patients overall reflected broad cellular expansion and metabolic dysfunction, whereas increased neutrophils, activated B cells, T-cell lymphopenia, and proinflammatory cytokine production were specific to severe COVID patients. Using this pipeline, we also identified small blood gene signatures indicative of COVID-19 diagnosis and severity that could be used as biomarker panels in the clinical setting.

Early peripheral blood MCEMP1 and HLA-DRA expression predicts COVID-19 prognosis

BACKGROUND

Mass vaccination has dramatically reduced the incidence of severe COVID-19, with most cases now presenting as self-limiting upper respiratory tract infections. However, those with co-morbidities, the elderly and immunocompromised, as well as the unvaccinated, remain disproportionately vulnerable to severe COVID-19 and its sequelae. Furthermore, as the effectiveness of vaccination wanes with time, immune escape SARS-CoV-2 variants could emerge to cause severe COVID-19. Reliable prognostic biomarkers for severe disease could be used as early indicator of re-emergence of severe COVID-19 as well as for triaging of patients for antiviral therapy.

METHODS

We performed a systematic review and re-analysis of 7 publicly available datasets, analysing a total of 140 severe and 181 mild COVID-19 patients, to determine the most consistent differentially regulated genes in peripheral blood of severe COVID-19 patients. In addition, we included an independent cohort where blood transcriptomics of COVID-19 patients were prospectively and longitudinally monitored previously, to track the time in which these gene expression changes occur before nadir of respiratory function. Single cell RNA-sequencing of peripheral blood mononuclear cells from publicly available datasets was then used to determine the immune cell subsets involved.

FINDINGS

The most consistent differentially regulated genes in peripheral blood of severe COVID-19 patients were MCEMP1, HLA-DRA and ETS1 across the 7 transcriptomics datasets. Moreover, we found significantly heightened MCEMP1 and reduced HLA-DRA expression as early as four days before the nadir of respiratory function, and the differential expression of MCEMP1 and HLA-DRA occurred predominantly in CD14+ cells. The online platform which we developed is publicly available at https://kuanrongchan-covid19-severity-app-t7l38g.streamlitapp.com/, for users to query gene expression differences between severe and mild COVID-19 patients in these datasets.

INTERPRETATION

Elevated MCEMP1 and reduced HLA-DRA gene expression in CD14+ cells during the early phase of disease are prognostic of severe COVID-19.

FUNDING

K.R.C is funded by the National Medical Research Council (NMRC) of Singapore under the Open Fund Individual Research Grant (MOH-000610). E.E.O. is funded by the NMRC Senior Clinician-Scientist Award (MOH-000135-00). J.G.H.L. is funded by the NMRC under the Clinician-Scientist Award (NMRC/CSAINV/013/2016-01). S.K. is funded by the NMRC under the Transition Award. This study was sponsored in part by a generous gift from The Hour Glass.

Nanonitrator: novel enhancer of inorganic nitrate’s protective effects, predicated on swarm learning approach

Inorganic nitrate is an indispensable nutrient that has been used in experimental studies for the prevention and treatment of several diseases. However, the short half-life of nitrate limits its clinical application. To increase the usability of nitrate and overcome the challenges of traditional combination drug discovery through large-scale high-throughput biological experiments, we developed a swarm learning-based combination drug prediction system that identified vitamin C as the drug of choice to be combined with nitrate. Employing microencapsulation technology, we used vitamin C, sodium nitrate, and chitosan 3000 as the core materials to prepare a nitrate nanoparticle, which we named Nanonitrator. The long-circulating delivery ability of nitrate by Nanonitrator significantly increased the efficacy and effect duration of nitrate in irradiation-induced salivary gland injury, without compromising safety. Nanonitrator at the same dose could better maintain intracellular homeostasis than nitrate (with or without vitamin C), emphasizing its potential for clinical use. More importantly, our work provides a method for incorporating inorganic compounds into sustained-release nanoparticles.

How the Competition for Cysteine May Promote Infection of SARS-CoV-2 by Triggering Oxidative Stress

SARS-CoV-2 induces a broad range of clinical manifestations. Besides the main receptor, ACE2, other putative receptors and co-receptors have been described and could become genuinely relevant to explain the different tropism manifested by new variants. In this study, we propose a biochemical model envisaging the competition for cysteine as a key mechanism promoting the infection and the selection of host receptors. The SARS-CoV-2 infection produces ROS and triggers a massive biosynthesis of proteins rich in cysteine; if this amino acid becomes limiting, glutathione levels are depleted and cannot control oxidative stress. Hence, infection succeeds. A receptor should be recognized as a marker of suitable intracellular conditions, namely the full availability of amino acids except for low cysteine. First, we carried out a comparative investigation of SARS-CoV-2 proteins and human ACE2. Then, using hierarchical cluster protein analysis, we searched for similarities between all human proteins and spike produced by the latest variant, Omicron BA.1. We found 32 human proteins very close to spike in terms of amino acid content. Most of these potential SARS-CoV-2 receptors have less cysteine than spike. We suggest that these proteins could signal an intracellular shortage of cysteine, predicting a burst of oxidative stress when used as viral entry mediators.

Unlocking life-threatening COVID-19 through two types of inborn errors of type I IFNs

Since 2003, rare inborn errors of human type I IFN immunity have been discovered, each underlying a few severe viral illnesses. Autoantibodies neutralizing type I IFNs due to rare inborn errors of autoimmune regulator (AIRE)-driven T cell tolerance were discovered in 2006, but not initially linked to any viral disease. These two lines of clinical investigation converged in 2020, with the discovery that inherited and/or autoimmune deficiencies of type I IFN immunity accounted for approximately 15%-20% of cases of critical COVID-19 pneumonia in unvaccinated individuals. Thus, insufficient type I IFN immunity at the onset of SARS-CoV-2 infection may be a general determinant of life-threatening COVID-19. These findings illustrate the unpredictable, but considerable, contribution of the study of rare human genetic diseases to basic biology and public health.

Machine learning-driven blood transcriptome-based discovery of SARS-CoV-2 specific severity biomarkers

The Coronavirus disease 2019 (COVID-19) pandemic, caused by rapidly evolving variants of severe acute respiratory syndrome coronavirus (SARS-CoV-2), continues to be a global health threat. SARS-CoV-2 infection symptoms often intersect with other nonsevere respiratory infections, making early diagnosis challenging. There is an urgent need for early diagnostic and prognostic biomarkers to predict severity and reduce mortality when a sudden outbreak occurs. This study implemented a novel approach of integrating bioinformatics and machine learning algorithms over publicly available clinical COVID-19 transcriptome data sets. The robust 7-gene biomarker identified through this analysis can not only discriminate SARS-CoV-2 associated acute respiratory illness (ARI) from other types of ARIs but also can discriminate severe COVID-19 patients from nonsevere COVID-19 patients. Validation of the 7-gene biomarker in an independent blood transcriptome data set of longitudinal analysis of COVID-19 patients across various stages of the disease showed that the dysregulation of the identified biomarkers during severe disease is restored during recovery, showing their prognostic potential. The blood biomarkers identified in this study can serve as potential diagnostic candidates and help reduce COVID-19-associated mortality.

Towards precision medicine: Omics approach for COVID-19

The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on human society. Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19. Here, we reviewed how omics, including genomics, proteomics, single-cell multi-omics, and clinical phenomics, play roles in answering biological and clinical questions about COVID-19. Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification. Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement. Furthermore, decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.

Patterns of differential expression by association in omic data using a new measure based on ensemble learning

The ongoing development of high-throughput technologies is allowing the simultaneous monitoring of the expression levels for hundreds or thousands of biological inputs with the proliferation of what has been coined as omic data sources. One relevant issue when analyzing such data sources is concerned with the detection of differential expression across two experimental conditions, clinical status or two classes of a biological outcome. While a great deal of univariate data analysis approaches have been developed to address the issue, strategies for assessing interaction patterns of differential expression are scarce in the literature and have been limited to ad hoc solutions. This paper contributes to the problem by exploiting the facilities of an ensemble learning algorithm like random forests to propose a measure that assesses the differential expression explained by the interaction of the omic variables so subtle biological patterns may be uncovered as a result. The out of bag error rate, which is an estimate of the predictive accuracy of a random forests classifier, is used as a by-product to propose a new measure that assesses interaction patterns of differential expression. Its performance is studied in synthetic scenarios and it is also applied to real studies on SARS-CoV-2 and colon cancer data where it uncovers associations that remain undetected by other methods. Our proposal is aimed at providing a novel approach that may help the experts in biomedical and life sciences to unravel insightful interaction patterns that may decipher the molecular mechanisms underlying biological and clinical outcomes.

A two-gene marker for the two-tiered innate immune response in COVID-19 patients

For coronavirus disease 2019 (COVID-19), a pandemic disease characterized by strong immune dysregulation in severe patients, convenient and efficient monitoring of the host immune response is critical. Human hosts respond to viral and bacterial infections in different ways, the former is characterized by the activation of interferon stimulated genes (ISGs) such as IFI27, while the latter is characterized by the activation of anti-bacterial associated genes (ABGs) such as S100A12. This two-tiered innate immune response has not been examined in COVID-19. In this study, the activation patterns of this two-tiered innate immune response represented by IFI27 and S100A12 were explored based on 1421 samples from 17 transcriptome datasets derived from the blood of COVID-19 patients and relevant controls. It was found that IFI27 activation occurred in most of the symptomatic patients and displayed no correlation with disease severity, while S100A12 activation was more restricted to patients under severe and critical conditions with a stepwise activation pattern. In addition, most of the S100A12 activation was accompanied by IFI27 activation. Furthermore, the activation of IFI27 was most pronounced within the first week of symptom onset, but generally waned after 2-3 weeks. On the other hand, the activation of S100A12 displayed no apparent correlation with disease duration and could last for several months in certain patients. These features of the two-tiered innate immune response can further our understanding on the disease mechanism of COVID-19 and may have implications to the clinical triage. Development of a convenient two-gene protocol for the routine serial monitoring of this two-tiered immune response will be a valuable addition to the existing laboratory tests.

Novel chimeric proteins mimicking SARS-CoV-2 spike epitopes with broad inhibitory activity

SARS-CoV-2 spike (S) protein mediates virus attachment to the cells and fusion between viral and cell membranes. Membrane fusion is driven by mutual interaction between the highly conserved heptad-repeat regions 1 and 2 (HR1 and HR2) of the S2 subunit of the spike. For this reason, these S2 regions are interesting therapeutic targets for COVID-19. Although HR1 and HR2 have been described as transiently exposed during the fusion process, no significant antibody responses against these S2 regions have been reported. Here we designed chimeric proteins that imitate highly stable HR1 helical trimers and strongly bind to HR2. The proteins have broad inhibitory activity against WT B.1 and BA.1 viruses. Sera from COVID-19 convalescent donors showed significant levels of reactive antibodies (IgG and IgA) against the HR1 mimetic proteins, whereas these antibody responses were absent in sera from uninfected donors. Moreover, both inhibitory activity and antigenicity of the proteins correlate positively with their structural stability but not with the number of amino acid changes in their HR1 sequences, indicating a conformational and conserved nature of the involved epitopes. Our results reveal previously undetected spike epitopes that may guide the design of new robust COVID-19 vaccines and therapies.

Identification and differential usage of a host metalloproteinase entry pathway by SARS-CoV-2 Delta and Omicron

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) spike glycoprotein (S) binds to angiotensin-converting enzyme 2 (ACE2) to mediate membrane fusion via two distinct pathways: 1) a surface, serine protease-dependent or 2) an endosomal, cysteine protease-dependent pathway. In this study, we found that SARS-CoV-2 S has a wider protease usage and can also be activated by TMPRSS13 and matrix metalloproteinases (MMPs). We found that MMP-2 and MMP-9 played roles in SARS-CoV-2 S cell-cell fusion and TMPRSS2- and cathepsin-independent viral entry in cells expressing high MMP levels. MMP-dependent viral entry required cleavage at the S1/S2 junction in viral producer cells, and differential processing of variants of concern S dictated its usage; the efficiently processed Delta S preferred metalloproteinase-dependent entry when available, and less processed Omicron S was unable to us metalloproteinases for entry. As MMP-2/9 are released during inflammation, they may play roles in S-mediated cytopathic effects, tropism, and disease outcome.

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MMP-2 and MMP-9 enable SARS-CoV-2 S-mediated syncytia in the absence of TMPRSS2

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SARS-CoV-2 can enter cells via MMPs in a TMPRSS2- and cathepsin-independent manner

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MMP-dependent S activation requires prior S1/S2 processing

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Delta S can readily use MMPs for entry while Omicron S cannot

Autoantibodies against type I IFNs in patients with critical influenza pneumonia

Autoantibodies neutralizing type I interferons (IFNs) can underlie critical COVID-19 pneumonia and yellow fever vaccine disease. We report here on 13 patients harboring autoantibodies neutralizing IFN-α2 alone (five patients) or with IFN-ω (eight patients) from a cohort of 279 patients (4.7%) aged 6-73 yr with critical influenza pneumonia. Nine and four patients had antibodies neutralizing high and low concentrations, respectively, of IFN-α2, and six and two patients had antibodies neutralizing high and low concentrations, respectively, of IFN-ω. The patients' autoantibodies increased influenza A virus replication in both A549 cells and reconstituted human airway epithelia. The prevalence of these antibodies was significantly higher than that in the general population for patients <70 yr of age (5.7 vs. 1.1%, P = 2.2 × 10-5), but not >70 yr of age (3.1 vs. 4.4%, P = 0.68). The risk of critical influenza was highest in patients with antibodies neutralizing high concentrations of both IFN-α2 and IFN-ω (OR = 11.7, P = 1.3 × 10-5), especially those <70 yr old (OR = 139.9, P = 3.1 × 10-10). We also identified 10 patients in additional influenza patient cohorts. Autoantibodies neutralizing type I IFNs account for ∼5% of cases of life-threatening influenza pneumonia in patients <70 yr old.

Blood Transcriptomes of SARS-CoV-2-Infected Kidney Transplant Recipients Associated with Immune Insufficiency Proportionate to Severity

BACKGROUND

Among patients with COVID-19, kidney transplant recipients (KTRs) have poor outcomes compared with non-KTRs. To provide insight into management of immunosuppression during acute illness, we studied immune signatures from the peripheral blood during and after COVID-19 infection from a multicenter KTR cohort.

METHODS

We ascertained clinical data by chart review. A single sample of blood was collected for transcriptome analysis. Total RNA was poly-A selected and RNA was sequenced to evaluate transcriptome changes. We also measured cytokines and chemokines of serum samples collected during acute infection.

RESULTS

A total of 64 patients with COVID-19 in KTRs were enrolled, including 31 with acute COVID-19 (<4 weeks from diagnosis) and 33 with post-acute COVID-19 (>4 weeks postdiagnosis). In the blood transcriptome of acute cases, we identified genes in positive or negative association with COVID-19 severity scores. Functional enrichment analyses showed upregulation of neutrophil and innate immune pathways but downregulation of T cell and adaptive immune activation pathways. This finding was independent of lymphocyte count, despite reduced immunosuppressant use in most KTRs. Compared with acute cases, post-acute cases showed "normalization" of these enriched pathways after 4 weeks, suggesting recovery of adaptive immune system activation despite reinstitution of immunosuppression. Analysis of the non-KTR cohort with COVID-19 showed significant overlap with KTRs in these functions. Serum inflammatory cytokines followed an opposite trend (i.e., increased with disease severity), indicating that blood lymphocytes are not the primary source.

CONCLUSIONS

The blood transcriptome of KTRs affected by COVID-19 shows decreases in T cell and adaptive immune activation pathways during acute disease that, despite reduced immunosuppressant use, associate with severity. These pathways show recovery after acute illness.

COVIDpro: Database for mining protein dysregulation in patients with COVID-19

Background

The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has limited treatment options partially due to our incomplete understanding of the molecular dysregulations of the COVID-19 patients. We aimed to generate a repository and data analysis tools to examine the modulated proteins underlying COVID-19 patients for the discovery of potential therapeutic targets and diagnostic biomarkers.

Methods

We built a web server containing proteomic expression data from COVID-19 patients with a toolset for user-friendly data analysis and visualization. The web resource covers expert-curated proteomic data from COVID-19 patients published before May 2022. The data were collected from ProteomeXchange and from select publications via PubMed searches and aggregated into a comprehensive dataset. Protein expression by disease subgroups across projects was compared by examining differentially expressed proteins. We also visualize differentially expressed pathways and proteins. Moreover, circulating proteins that differentiated severe cases were nominated as predictive biomarkers.

Findings

We built and maintain a web server COVIDpro ( https://www.guomics.com/covidPro/ ) containing proteomics data generated by 41 original studies from 32 hospitals worldwide, with data from 3077 patients covering 19 types of clinical specimens, the majority from plasma and sera. 53 protein expression matrices were collected, for a total of 5434 samples and 14,403 unique proteins. Our analyses showed that the lipopolysaccharide-binding protein, as identified in the majority of the studies, was highly expressed in the blood samples of patients with severe disease. A panel of significantly dysregulated proteins was identified to separate patients with severe disease from non-severe disease. Classification of severe disease based on these proteomic signatures on five test sets reached a mean AUC of 0.87 and ACC of 0.80.

Interpretation

COVIDpro is an online database with an integrated analysis toolkit. It is a unique and valuable resource for testing hypotheses and identifying proteins or pathways that could be targeted by new treatments of COVID-19 patients.

Funding

National Key R&D Program of China: Key PDPM technologies (2021YFA1301602, 2021YFA1301601, 2021YFA1301603), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04), National Natural Science Foundation of China (81972492) and National Science Fund for Young Scholars (21904107), National Resource for Network Biology (NRNB) from the National Institute of General Medical Sciences (NIGMS-P41 GM103504).

Research in context

Evidence before this study: Although an increasing number of therapies against COVID-19 are being developed, they are still insufficient, especially with the rise of new variants of concern. This is partially due to our incomplete understanding of the disease’s mechanisms. As data have been collected worldwide, several questions are now worth addressing via meta-analyses. Most COVID-19 drugs function by targeting or affecting proteins. Effectiveness and resistance to therapeutics can be effectively assessed via protein measurements. Empowered by mass spectrometry-based proteomics, protein expression has been characterized in a variety of patient specimens, including body fluids (e.g., serum, plasma, urea) and tissue (i.e., formalin-fixed and paraffin-embedded (FFPE)). We expert-curated proteomic expression data from COVID-19 patients published before May 2022, from the largest proteomic data repository ProteomeXhange as well as from literature search engines. Using this resource, a COVID-19 proteome meta-analysis could provide useful insights into the mechanisms of the disease and identify new potential drug targets.Added value of this study: We integrated many published datasets from patients with COVID-19 from 11 nations, with over 3000 patients and more than 5434 proteome measurements. We collected these datasets in an online database, and generated a toolbox to easily explore, analyze, and visualize the data. Next, we used the database and its associated toolbox to identify new proteins of diagnostic and therapeutic value for COVID-19 treatment. In particular, we identified a set of significantly dysregulated proteins for distinguishing severe from non-severe patients using serum samples.Implications of all the available evidence: COVIDpro will support the navigation and analysis of patterns of dysregulated proteins in various COVID-19 clinical specimens for identification and verification of protein biomarkers and potential therapeutic targets.

Apolipoprotein-A-I for severe COVID-19-induced hyperinflammatory states: A prospective case study

Viral infections can promote cytokine storm and multiorgan failure in individuals with an underlying immunosuppression or specific genetic background. Hyperinflammatory states, including critical forms of COVID-19, are characterized by a remodeling of the lipid profile including a dramatic decrease of the serum levels of apolipoprotein-A-I (ApoA-I), a protein known for its capacity to reduce systemic and lung inflammation, modulate innate and adaptive immunity, and prevent endothelial dysfunction and blood coagulation. In this study, four immunocompromised patients with severe COVID-19 cytokine storm that progressed despite standard-of-care therapy [Omicron (n = 3) and Delta (n = 1) variants] received 2– 4 infusions (10 mg/kg) of CER-001, an ApoA-I-containing HDL mimetic. Injections were well-tolerated with no serious adverse events. Three patients treated while not on mechanical ventilation had early clinical and biological improvement (oxygen withdrawal and correction of hematological and inflammatory parameters, including serum levels of interleukin-8) and were discharged from the hospital 3–4 days after CER-001 infusions. In the fourth patient who received CER-001 after orotracheal intubation for acute respiratory distress syndrome, infusions were followed by transient respiratory improvement before secondary worsening related to ventilation-associated pneumonia. This pilot uncontrolled exploratory compassionate study provides initial safety and proof-of-concept data from patients with a COVID-19 cytokine storm receiving ApoA-I. Further randomized controlled trial evaluation is now required to ascertain whether ApoA-I has any beneficial effects on patients with a COVID-19 cytokine storm.

The current status of gene expression profilings in COVID‐19 patients

Background

The global pandemic of coronavirus disease 2019 (COVID‐19) caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has swept through every part of the world. Because of its impact, international efforts have been underway to identify the variants of SARS‐CoV‐2 by genome sequencing and to understand the gene expression changes in COVID‐19 patients compared to healthy donors using RNA sequencing (RNA‐seq) assay. Within the last two and half years since the emergence of SARS‐CoV‐2, a large number of OMICS data of COVID‐19 patients have accumulated. Yet, we are still far from understanding the disease mechanism. Further, many people suffer from long‐term effects of COVID‐19; calling for a more systematic way to data mine the generated OMICS data, especially RNA‐seq data.

Methods

By searching gene expression omnibus (GEO) using the key terms, COVID‐19 and RNA‐seq, 108 GEO entries were identified. Each of these studies was manually examined to categorize the studies into bulk or single‐cell RNA‐seq (scRNA‐seq) followed by an inspection of their original articles.

Results

The currently available RNA‐seq data were generated from various types of patients’ samples, and COVID‐19 related sample materials have been sequenced at the level of RNA, including whole blood, different components of blood [e.g., plasma, peripheral blood mononuclear cells (PBMCs), leukocytes, lymphocytes, monocytes, T cells], nasal swabs, and autopsy samples (e.g., lung, heart, liver, kidney). Of these, RNA‐seq studies using whole blood, PBMCs, nasal swabs and autopsy/biopsy samples were reviewed to highlight the major findings from RNA‐seq data analysis.

Conclusions

Based on the bulk and scRNA‐seq data analysis, severe COVID‐19 patients display shifts in cell populations, especially those of leukocytes and monocytes, possibly leading to cytokine storms and immune silence. These RNA‐seq data form the foundation for further gene expression analysis using samples from individuals suffering from long COVID.

Distinct evolutionary trajectories of SARS-CoV-2-interacting proteins in bats and primates identify important host determinants of COVID-19

The coronavirus disease 19 (COVID-19) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a coronavirus that spilled over from the bat reservoir. However, the host genetic determinants that drive SARS-CoV-2 susceptibility and COVID-19 severity are largely unknown. Understanding how cellular proteins interacting with SARS-CoV-2 have evolved in primates and bats is of primary importance to decipher differences in the infection outcome between humans and the viral reservoir in bats. Here, we performed comparative functional genetic analyses of hundreds of SARS-CoV-2-interacting proteins to study virus–host interface adaptation over millions of years, pointing to genes similarly—or differentially—engaged in evolutionary arms races and that may be at the basis of in vivo pathogenic differences.

The coronavirus disease 19 (COVID-19) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a coronavirus that spilled over from the bat reservoir. Despite numerous clinical trials and vaccines, the burden remains immense, and the host determinants of SARS-CoV-2 susceptibility and COVID-19 severity remain largely unknown. Signatures of positive selection detected by comparative functional genetic analyses in primate and bat genomes can uncover important and specific adaptations that occurred at virus–host interfaces. We performed high-throughput evolutionary analyses of 334 SARS-CoV-2-interacting proteins to identify SARS-CoV adaptive loci and uncover functional differences between modern humans, primates, and bats. Using DGINN (Detection of Genetic INNovation), we identified 38 bat and 81 primate proteins with marks of positive selection. Seventeen genes, including the ACE2 receptor, present adaptive marks in both mammalian orders, suggesting common virus–host interfaces and past epidemics of coronaviruses shaping their genomes. Yet, 84 genes presented distinct adaptations in bats and primates. Notably, residues involved in ubiquitination and phosphorylation of the inflammatory RIPK1 have rapidly evolved in bats but not primates, suggesting different inflammation regulation versus humans. Furthermore, we discovered residues with typical virus–host arms race marks in primates, such as in the entry factor TMPRSS2 or the autophagy adaptor FYCO1, pointing to host-specific in vivo interfaces that may be drug targets. Finally, we found that FYCO1 sites under adaptation in primates are those associated with severe COVID-19, supporting their importance in pathogenesis and replication. Overall, we identified adaptations involved in SARS-CoV-2 infection in bats and primates, enlightening modern genetic determinants of virus susceptibility and severity.

From rare disorders of immunity to common determinants of infection: Following the mechanistic thread

The immense interindividual clinical variability during any infection is a long-standing enigma. Inborn errors of IFN-γ and IFN-α/β immunity underlying rare infections with weakly virulent mycobacteria and seasonal influenza virus have inspired studies of two common infections: tuberculosis and COVID-19. A TYK2 genotype impairing IFN-γ production accounts for about 1% of tuberculosis cases, and autoantibodies neutralizing IFN-α/β account for about 15% of critical COVID-19 cases. The discovery of inborn errors and mechanisms underlying rare infections drove the identification of common monogenic or autoimmune determinants of related common infections. This "rare-to-common" genetic and mechanistic approach to infectious diseases may be of heuristic value.