Cell specific peripheral immune responses predict survival in critical COVID-19 patients

Role of TOMM34 on NF-κB activation-related hyperinflammation in severely ill patients with COVID-19 and influenza

BACKGROUND

Highly pathogenic respiratory RNA viruses such as SARS-CoV-2 and its associated syndrome COVID-19 pose a tremendous threat to the global public health. Innate immune responses to SARS-CoV-2 depend mainly upon the NF-κB-mediated inflammation. Identifying unknown host factors driving the NF-κB activation and inflammation is crucial for the development of immune intervention strategies.

METHODS

Published single-cell RNA sequencing (scRNA-seq) data was used to analyze the differential transcriptome profiles of bronchoalveolar lavage (BAL) cells between healthy individuals (n = 27) and patients with severe COVID-19 (n = 21), as well as the differential transcriptome profiles of peripheral blood mononuclear cells (PBMCs) between healthy individuals (n = 22) and severely ill patients with COVID-19 (n = 45) or influenza (n = 16). Loss-of-function and gain-of-function assays were performed in diverse viruses-infected cells and male mice models to identify the role of TOMM34 in antiviral innate immunity.

FINDINGS

TOMM34, together with a list of genes encoding pro-inflammatory cytokines and antiviral immune proteins, was transcriptionally upregulated in circulating monocytes, lung epithelium and innate immune cells from individuals with severe COVID-19 or influenza. Deficiency of TOMM34/Tomm34 significantly impaired the type I interferon responses and NF-κB-mediated inflammation in various human/murine cell lines, murine bone marrow-derived macrophages (BMDMs) and in vivo. Mechanistically, TOMM34 recruits TRAF6 to facilitate the K63-linked polyubiquitination of NEMO upon viral infection, thus promoting the downstream NF-κB activation.

INTERPRETATION

In this study, viral induction of TOMM34 is positively correlated with the hyperinflammation in severely ill patients with COVID-19 and influenza. Our findings also highlight the physiological role of TOMM34 in the innate antiviral signallings.

FUNDING

A full list of funding sources can be found in the acknowledgements section.

Immunological characterization and comparison of children with COVID-19 from their adult counterparts at single-cell resolution

Introduction

The immunological characteristics that could protect children with coronavirus disease 2019 (COVID-19) from severe or fatal illnesses have not been fully understood yet.

Methods

Here, we performed single-cell RNA sequencing (scRNA-seq) analysis on peripheral blood samples of 15 children (8 with COVID-19) and compared them to 18 adults (13 with COVID-19).

Results

The child-adult integrated single cell data indicated that children with the disease presented a restrained response to type I interferon in most of the major immune cell types, along with suppression of upstream interferon regulatory factor and toll-like receptor expression in monocytes, which was confirmed by in vitro interferon stimulation assays. Unlike adult patients, children with COVID-19 showed lower frequencies of activated proinflammatory CD14+ monocytes, possibly explaining the rareness of cytokine storm in them. Notably, natural killer (NK) cells in pediatric patients displayed potent cytotoxicity with a rich expression of cytotoxic molecules and upregulated cytotoxic pathways, whereas the cellular senescence, along with the Notch signaling pathway, was significantly downregulated in NK cells, all suggesting more robust cytotoxicity in NK cells of children than adult patients that was further confirmed by CD107a degranulation assays. Lastly, a modest adaptive immune response was evident with more naïve T cells but less activated and proliferated T cells while less naïve B cells but more activated B cells in children over adult patients.

Conclusion

Conclusively, this preliminary study revealed distinct cell frequency and activation status of major immune cell types, particularly more robust NK cell cytotoxicity in PBMC that might help protect children from severe COVID-19.

Shared genetics and causal association between plasma levels of SARS-CoV-2 entry receptor ACE2 and Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is the highest risk of COVID-19 infection, hospitalization, and mortality. However, it remains largely unclear about the link between AD and COVID-19 outcomes. ACE2 is an entry receptor for SARS-CoV-2. Circulating ACE2 is a novel biomarker of death and associated with COVID-19 outcomes.

METHODS

Here, we explored the shared genetics and causal association between AD and plasma ACE2 levels using large-scale genome-wide association study, gene expression, expression quantitative trait loci, and high-throughput plasma proteomic profiling datasets.

RESULTS

We found a significant causal effect of genetically increased circulating ACE2 on increased risk of AD. Cross-trait association analysis identified 19 shared genetic variants, and three variants rs3104412, rs2395166, and rs3135344 at chromosome 6p21.32 were associated with COVID-19 infection, hospitalization, and severity. We mapped 19 variants to 117 genes, which were significantly upregulated in lung, spleen, and small intestine, downregulated in brain tissues, and involved in immune system, immune disease, and infectious disease pathways. The plasma proteins corresponding to LST1, AGER, TNXB, and APOC1 were predominantly associated with COVID-19 infection, ventilation, and death.

CONCLUSION

Together, our findings suggest the shared genetics and causal association between AD and plasma ACE2 levels, which may partially explain the link between AD and COVID-19.

SARS-CoV-2 inflammation durably imprints memory CD4 T cells

Memory CD4 T cells are critical to human immunity, yet it is unclear whether viral inflammation during memory formation has long-term consequences. Here, we compared transcriptional and epigenetic landscapes of Spike (S)-specific memory CD4 T cells in 24 individuals whose first exposure to S was via SARS-CoV-2 infection or mRNA vaccination. Nearly 2 years after memory formation, S-specific CD4 T cells established by infection remained enriched for transcripts related to cytotoxicity and for interferon-stimulated genes, likely because of a chromatin accessibility landscape altered by inflammation. Moreover, S-specific CD4 T cells primed by infection had reduced proliferative capacity in vitro relative to vaccine-primed cells. Furthermore, the transcriptional state of S-specific memory CD4 T cells was minimally altered by booster immunization and/or breakthrough infection. Thus, infection-associated inflammation durably imprints CD4 T cell memory, which affects the function of these cells and may have consequences for long-term immunity.

Type I interferons induce an epigenetically distinct memory B cell subset in chronic viral infection

Memory B cells (MBCs) are key providers of long-lived immunity against infectious disease, yet in chronic viral infection, they do not produce effective protection. How chronic viral infection disrupts MBC development and whether such changes are reversible remain unknown. Through single-cell (sc)ATAC-seq and scRNA-seq during acute versus chronic lymphocytic choriomeningitis viral infection, we identified a memory subset enriched for interferon (IFN)-stimulated genes (ISGs) during chronic infection that was distinct from the T-bet+ subset normally associated with chronic infection. Blockade of IFNAR-1 early in infection transformed the chromatin landscape of chronic MBCs, decreasing accessibility at ISG-inducing transcription factor binding motifs and inducing phenotypic changes in the dominating MBC subset, with a decrease in the ISG subset and an increase in CD11c+CD80+ cells. However, timing was critical, with MBCs resistant to intervention at 4 weeks post-infection. Together, our research identifies a key mechanism to instruct MBC identity during viral infection.

scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics

Single-cell RNA sequencing has achieved massive success in biological research fields. Discovering novel cell types from single-cell transcriptomics has been demonstrated to be essential in the field of biomedicine, yet is time-consuming and needs prior knowledge. With the unprecedented boom in cell atlases, auto-annotation tools have become more prevalent due to their speed, accuracy and user-friendly features. However, existing tools have mostly focused on general cell-type annotation and have not adequately addressed the challenge of discovering novel rare cell types. In this work, we introduce scNovel, a powerful deep learning-based neural network that specifically focuses on novel rare cell discovery. By testing our model on diverse datasets with different scales, protocols and degrees of imbalance, we demonstrate that scNovel significantly outperforms previous state-of-the-art novel cell detection models, reaching the most AUROC performance(the only one method whose averaged AUROC results are above 94%, up to 16.26% more comparing to the second-best method). We validate scNovel's performance on a million-scale dataset to illustrate the scalability of scNovel further. Applying scNovel on a clinical COVID-19 dataset, three potential novel subtypes of Macrophages are identified, where the COVID-related differential genes are also detected to have consistent expression patterns through deeper analysis. We believe that our proposed pipeline will be an important tool for high-throughput clinical data in a wide range of applications.

Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology

PURPOSE

Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations.

METHODS

To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins.

RESULTS

We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences.

CONCLUSION

Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

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.

Interferon response and profiling of interferon response genes in peripheral blood of vaccine-naive COVID-19 patients

Introduction

There is insufficient understanding on systemic interferon (IFN) responses during COVID-19 infection. Early reports indicated that interferon responses were suppressed by the coronavirus (SARS-CoV-2) and clinical trials of administration of various kinds of interferons had been disappointing. Expression of interferon-stimulated genes (ISGs) in peripheral blood (better known as interferon score) has been a well-established bioassay marker of systemic IFN responses in autoimmune diseases. Therefore, with archival samples of a cohort of COVID-19 patients collected before the availability of vaccination, we aimed to better understand this innate immune response by studying the IFN score and related ISGs expression in bulk and single cell RNAs sequencing expression datasets.

Methods

In this study, we recruited 105 patients with COVID-19 and 30 healthy controls in Hong Kong. Clinical risk factors, disease course, and blood sampling times were recovered. Based on a set of five commonly used ISGs (IFIT1, IFIT2, IFI27, SIGLEC1, IFI44L), the IFN score was determined in blood leukocytes collected within 10 days after onset. The analysis was confined to those blood samples collected within 10 days after disease onset. Additional public datasets of bulk gene and single cell RNA sequencing of blood samples were used for the validation of IFN score results.

Results

Compared to the healthy controls, we showed that ISGs expression and IFN score were significantly increased during the first 10 days after COVID infection in majority of patients (71%). Among those low IFN responders, they were more commonly asymptomatic patients (71% vs 25%). 22 patients did not mount an overall significant IFN response and were classified as low IFN responders (IFN score < 1). However, early IFN score or ISGs level was not a prognostic biomarker and could not predict subsequent disease severity. Both IFI27 and SIGLEC1 were monocyte-predominant expressing ISGs and IFI27 were activated even among those low IFN responders as defined by IFN score. In conclusion, a substantial IFN response was documented in this cohort of COVID-19 patients who experience a natural infection before the vaccination era. Like innate immunity towards other virus, the ISGs activation was observed largely during the early course of infection (before day 10). Single-cell RNA sequencing data suggested monocytes were the cell-type that primarily accounted for the activation of two highly responsive ISGs (IFI44L and IFI27).

Discussion

As sampling time and age were two major confounders of ISG expression, they may account for contradicting observations among previous studies. On the other hand, the IFN score was not associated with the severity of the disease.

The unfolded protein response pathway as a possible link in the pathogenesis of COVID-19 and sepsis

The global impact of the COVID-19 pandemic has been substantial. Emerging evidence underscores a strong clinical connection between COVID-19 and sepsis. Numerous studies have identified the unfolded protein response (UPR) pathway as a crucial pathogenic pathway for both COVID-19 and sepsis, but it remains to be investigated whether this signaling pathway operates as a common pathogenic mechanism for both COVID-19 and sepsis. In this study, single-cell RNA-seq data and transcriptome data for COVID-19 and sepsis cases were downloaded from GEO (Gene Expression Omnibus). By analyzing the single-cell transcriptome data, we identified B cells as the critical cell subset and the UPR pathway as the critical signaling pathway. Based on the transcriptome data, a machine learning diagnostic model was then constructed using the interleaved genes of B-cell-related and UPR-pathway-related genes. We validated the diagnostic model using both internal and external datasets and found the accuracy and stability of this model to be extremely strong. Even after integrating our algorithmic model with the patient's clinical status, it continued to yield identical results, further emphasizing the reliability of this model. This study provides a novel molecular perspective on the pathogenesis of sepsis and COVID-19 at the single-cell level and suggests that these two diseases may share a common mechanism.

Meta-analysis of single-cell RNA-sequencing data for depicting the transcriptomic landscape of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a respiratory disease characterized by airflow limitation and chronic inflammation of the lungs that is a leading cause of death worldwide. Since the complete pathological mechanisms at the single-cell level are not fully understood yet, an integrative approach to characterizing the single-cell-resolution landscape of COPD is required. To identify the cell types and mechanisms associated with the development of COPD, we conducted a meta-analysis using three single-cell RNA-sequencing datasets of COPD. Among the 154,011 cells from 16 COPD patients and 18 healthy subjects, 17 distinct cell types were observed. Of the 17 cell types, monocytes, mast cells, and alveolar type 2 cells (AT2 cells) were found to be etiologically implicated in COPD based on genetic and transcriptomic features. The most transcriptomically diversified states of the three etiological cell types showed significant enrichment in immune/inflammatory responses (monocytes and mast cells) and/or mitochondrial dysfunction (monocytes and AT2 cells). We then identified three chemical candidates that may potentially induce COPD by modulating gene expression patterns in the three etiological cell types. Overall, our study suggests the single-cell level mechanisms underlying the pathogenesis of COPD and may provide information on toxic compounds that could be potential risk factors for COPD.

Multiplexed discrimination of SARS-CoV-2 variants via plasmonic-enhanced fluorescence in a portable and automated device

Portable assays for the rapid identification of lineages of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are needed to aid large-scale efforts in monitoring the evolution of the virus. Here we report a multiplexed assay in a microarray format for the detection, via isothermal amplification and plasmonic-gold-enhanced near-infrared fluorescence, of variants of SARS-CoV-2. The assay, which has single-nucleotide specificity for variant discrimination, single-RNA-copy sensitivity and does not require RNA extraction, discriminated 12 lineages of SARS-CoV-2 (in three mutational hotspots of the Spike protein) and detected the virus in nasopharyngeal swabs from 1,034 individuals at 98.8% sensitivity and 100% specificity, with 97.6% concordance with genome sequencing in variant discrimination. We also report a compact, portable and fully automated device integrating the entire swab-to-result workflow and amenable to the point-of-care detection of SARS-CoV-2 variants. Portable, rapid, accurate and multiplexed assays for the detection of SARS-CoV-2 variants and lineages may facilitate variant-surveillance efforts.

SuSiE PCA: A scalable Bayesian variable selection technique for principal component analysis

Latent factor models, like principal component analysis (PCA), provide a statistical framework to infer low-rank representation in various biological contexts. However, feature selection is challenging when this low-rank structure manifests from a sparse subspace. We introduce SuSiE PCA, a scalable sparse latent factor approach that evaluates uncertainty in contributing variables through posterior inclusion probabilities. We validate our model in extensive simulations and demonstrate that SuSiE PCA outperforms other approaches in signal detection and model robustness. We apply SuSiE PCA to multi-tissue expression quantitative trait loci (eQTLs) data from GTEx v8 and identify tissue-specific factors and their contributing eGenes. We further investigate its performance on the large-scale perturbation data and find that SuSiE PCA identifies modules with a higher enrichment of ribosome-related genes than sparse PCA (false discovery rate [FDR] = 9.2 × 10 - 82 vs. 1.4 × 10 - 33 ), while being ∼ 18x faster. Overall, SuSiE PCA provides an efficient tool to identify relevant features in high-dimensional biological data.

Defining cardiac functional recovery in end-stage heart failure at single-cell resolution

Recovery of cardiac function is the holy grail of heart failure therapy yet is infrequently observed and remains poorly understood. In this study, we performed single-nucleus RNA sequencing from patients with heart failure who recovered left ventricular systolic function after left ventricular assist device implantation, patients who did not recover and non-diseased donors. We identified cell-specific transcriptional signatures of recovery, most prominently in macrophages and fibroblasts. Within these cell types, inflammatory signatures were negative predictors of recovery, and downregulation of RUNX1 was associated with recovery. In silico perturbation of RUNX1 in macrophages and fibroblasts recapitulated the transcriptional state of recovery. Cardiac recovery mediated by BET inhibition in mice led to decreased macrophage and fibroblast Runx1 expression and diminished chromatin accessibility within a Runx1 intronic peak and acquisition of human recovery signatures. These findings suggest that cardiac recovery is a unique biological state and identify RUNX1 as a possible therapeutic target to facilitate cardiac recovery.

Peripheral blood mononuclear cell tissue factor (F3 gene) transcript levels and circulating extracellular vesicles are elevated in severe coronavirus 2019 (COVID-19) disease

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with excessive coagulation, thrombosis, and mortality.

OBJECTIVE

To provide insight into mechanisms that contribute to excessive coagulation in coronavirus 2019 (COVID-19) disease.

PATIENTS/METHODS

Blood from COVID-19 patients was investigated for coagulation-related gene expression and functional activities.

RESULTS

Single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells from severe COVID-19 patients revealed a 5.2-fold increase in tissue factor (TF [F3 gene]) transcript expression levels (P < .05), the trigger of extrinsic coagulation; a 7.7-fold increase in C1-inhibitor (SERPING1 gene; P < .01) transcript expression levels, an inhibitor of intrinsic coagulation; and a 4.4-fold increase in anticoagulant thrombomodulin (TM [THBD gene]) transcript expression levels (P < .001). Bulk RNA-seq analysis of sorted CD14⁺ monocytes on an independent cohort of COVID-19 patients confirmed these findings (P < .05). Indicative of excessive coagulation, 41% of COVID-19 patients' plasma samples contained high D-dimer levels (P < .0001); of these, 19% demonstrated extracellular vesicle TF activity (P = .109). COVID-19 patients' ex vivo plasma-based thrombin generation correlated positively with D-dimer levels (P < .01). Plasma procoagulant extracellular vesicles were elevated ∼9-fold in COVID-19 patients (P < .01). Public scRNA-seq data sets from bronchoalveolar lung fluid and our peripheral blood mononuclear cell scRNA-seq data show CD14⁺ monocytes/macrophages TF transcript expression levels are elevated in severe but not mild or moderate COVID-19 patients.

CONCLUSIONS

Beyond local lung injury, SARS-CoV-2 infection increases systemic TF (F3) transcript levels and elevates circulating extracellular vesicles that likely contribute to disease-associated coagulation, thrombosis, and related mortality.

A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information

This study systematically reviews the Artificial Intelligence (AI) methods developed to resolve the critical process of COVID-19 gene data analysis, including diagnosis, prognosis, biomarker discovery, drug responsiveness, and vaccine efficacy. This systematic review follows the guidelines of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA). We searched PubMed, Embase, Web of Science, and Scopus databases to identify the relevant articles from January 2020 to June 2022. It includes the published studies of AI-based COVID-19 gene modeling extracted through relevant keyword searches in academic databases. This study included 48 articles discussing AI-based genetic studies for several objectives. Ten articles confer about the COVID-19 gene modeling with computational tools, and five articles evaluated ML-based diagnosis with observed accuracy of 97% on SARS-CoV-2 classification. Gene-based prognosis study reviewed three articles and found host biomarkers detecting COVID-19 progression with 90% accuracy. Twelve manuscripts reviewed the prediction models with various genome analysis studies, nine articles examined the gene-based in silico drug discovery, and another nine investigated the AI-based vaccine development models. This study compiled the novel coronavirus gene biomarkers and targeted drugs identified through ML approaches from published clinical studies. This review provided sufficient evidence to delineate the potential of AI in analyzing complex gene information for COVID-19 modeling on multiple aspects like diagnosis, drug discovery, and disease dynamics. AI models entrenched a substantial positive impact by enhancing the efficiency of the healthcare system during the COVID-19 pandemic.

Innate and Adaptive Immunity during SARS-CoV-2 Infection: Biomolecular Cellular Markers and Mechanisms

The coronavirus 2019 (COVID-19) pandemic was caused by a positive sense single-stranded RNA (ssRNA) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, other human coronaviruses (hCoVs) exist. Historical pandemics include smallpox and influenza, with efficacious therapeutics utilized to reduce overall disease burden through effectively targeting a competent host immune system response. The immune system is composed of primary/secondary lymphoid structures with initially eight types of immune cell types, and many other subtypes, traversing cell membranes utilizing cell signaling cascades that contribute towards clearance of pathogenic proteins. Other proteins discussed include cluster of differentiation (CD) markers, major histocompatibility complexes (MHC), pleiotropic interleukins (IL), and chemokines (CXC). The historical concepts of host immunity are the innate and adaptive immune systems. The adaptive immune system is represented by T cells, B cells, and antibodies. The innate immune system is represented by macrophages, neutrophils, dendritic cells, and the complement system. Other viruses can affect and regulate cell cycle progression for example, in cancers that include human papillomavirus (HPV: cervical carcinoma), Epstein-Barr virus (EBV: lymphoma), Hepatitis B and C (HB/HC: hepatocellular carcinoma) and human T cell Leukemia Virus-1 (T cell leukemia). Bacterial infections also increase the risk of developing cancer (e.g., Helicobacter pylori). Viral and bacterial factors can cause both morbidity and mortality alongside being transmitted within clinical and community settings through affecting a host immune response. Therefore, it is appropriate to contextualize advances in single cell sequencing in conjunction with other laboratory techniques allowing insights into immune cell characterization. These developments offer improved clarity and understanding that overlap with autoimmune conditions that could be affected by innate B cells (B1+ or marginal zone cells) or adaptive T cell responses to SARS-CoV-2 infection and other pathologies. Thus, this review starts with an introduction into host respiratory infection before examining invaluable cellular messenger proteins and then individual immune cell markers.

Machine Learning and COVID-19: Lessons from SARS-CoV-2

Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behavior and high-throughput technologies, associated with COVID-19 evolution.

The role of lung macrophages in acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS) is an acute and diffuse inflammatory lung injury in a short time, one of the common severe manifestations of the respiratory system that endangers human life and health. As an innate immune cell, macrophages play a key role in the inflammatory response. For a long time, the role of pulmonary macrophages in ARDS has tended to revolve around the polarization of M1/M2. However, with the development of single-cell RNA sequencing, fate mapping, metabolomics, and other new technologies, a deeper understanding of the development process, classification, and function of macrophages in the lung are acquired. Here, we discuss the function of pulmonary macrophages in ARDS from the two dimensions of anatomical location and cell origin and describe the effects of cell metabolism and intercellular interaction on the function of macrophages. Besides, we explore the treatments for targeting macrophages, such as enhancing macrophage phagocytosis, regulating macrophage recruitment, and macrophage death. Considering the differences in responsiveness of different research groups to these treatments and the tremendous dynamic changes in the gene expression of monocyte/macrophage, we discussed the possibility of characterizing the gene expression of monocyte/macrophage as the biomarkers. We hope that this review will provide new insight into pulmonary macrophage function and therapeutic targets of ARDS.

Circulating microRNA signatures associated with disease severity and outcome in COVID-19 patients

Background

SARS-CoV-2 induces a spectrum of clinical conditions ranging from asymptomatic infection to life threatening severe disease. Host microRNAs have been involved in the cytokine storm driven by SARS-CoV-2 infection and proposed as candidate biomarkers for COVID-19.

Methods

To discover signatures of circulating miRNAs associated with COVID-19, disease severity and mortality, small RNA-sequencing was performed on serum samples collected from 89 COVID-19 patients (34 severe, 29 moderate, 26 mild) at hospital admission and from 45 healthy controls (HC). To search for possible sources of miRNAs, investigation of differentially expressed (DE) miRNAs in relevant human cell types in vitro.

Results

COVID-19 patients showed upregulation of miRNAs associated with lung disease, vascular damage and inflammation and downregulation of miRNAs that inhibit pro-inflammatory cytokines and chemokines, angiogenesis, and stress response. Compared with mild/moderate disease, patients with severe COVID-19 had a miRNA signature indicating a profound impairment of innate and adaptive immune responses, inflammation, lung fibrosis and heart failure. A subset of the DE miRNAs predicted mortality. In particular, a combination of high serum miR-22-3p and miR-21-5p, which target antiviral response genes, and low miR-224-5p and miR-155-5p, targeting pro-inflammatory factors, discriminated severe from mild/moderate COVID-19 (AUROC 0.88, 95% CI 0.80-0.95, p<0.0001), while high leukocyte count and low levels of miR-1-3p, miR-23b-3p, miR-141-3p, miR-155-5p and miR-4433b-5p predicted mortality with high sensitivity and specificity (AUROC 0.95, 95% CI 0.89-1.00, p<0.0001). In vitro experiments showed that some of the DE miRNAs were modulated directly by SARS-CoV-2 infection in permissive lung epithelial cells.

Conclusions

We discovered circulating miRNAs associated with COVID-19 severity and mortality. The identified DE miRNAs provided clues on COVID-19 pathogenesis, highlighting signatures of impaired interferon and antiviral responses, inflammation, organ damage and cardiovascular failure as associated with severe disease and death.

Severe COVID-19 is characterised by inflammation and immature myeloid cells early in disease progression

SARS-CoV-2 infection causes a wide spectrum of disease severity. Identifying the immunological characteristics of severe disease and the risk factors for their development are important in the management of COVID-19. This study aimed to identify and rank clinical and immunological features associated with progression to severe COVID-19 in order to investigate an immunological signature of severe disease. One hundred and eight patients with positive SARS-CoV-2 PCR were recruited. Routine clinical and laboratory markers were measured, as well as myeloid and lymphoid whole-blood immunophenotyping and measurement of the pro-inflammatory cytokines IL-6 and soluble CD25. All analysis was carried out in a routine hospital diagnostic laboratory. Univariate analysis demonstrated that severe disease was most strongly associated with elevated CRP and IL-6, loss of DLA-DR expression on monocytes and CD10 expression on neutrophils. Unbiased machine learning demonstrated that these four features were strongly associated with severe disease, with an average prediction score for severe disease of 0.925. These results demonstrate that these four markers could be used to identify patients developing severe COVID-19 and allow timely delivery of therapeutics.

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Severe COVID-19 is characterised by a combination of emergency myelopoiesis and inflammation.

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These changes can be rapidly identified in a diagnostic laboratory, facilitating intervention.

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This disease signature was derived from a cohort of patients with a wide range of ages, frailty and COVID-19 severity.