Multi-cohort analysis of host immune response identifies conserved protective and detrimental modules associated with severity across viruses

International multi-cohort analysis identifies novel framework for quantifying immune dysregulation in critical illness: results of the SUBSPACE consortium

Progress in the management of critical care syndromes such as sepsis, Acute Respiratory Distress Syndrome (ARDS), and trauma has slowed over the last two decades, limited by the inherent heterogeneity within syndromic illnesses. Numerous immune endotypes have been proposed in sepsis and critical care, however the overlap of the endotypes is unclear, limiting clinical translation. The SUBSPACE consortium is an international consortium that aims to advance precision medicine through the sharing of transcriptomic data. By evaluating the overlap of existing immune endotypes in sepsis across over 6,000 samples, we developed cell-type specific signatures to quantify dysregulation in these immune compartments. Myeloid and lymphoid dysregulation were associated with disease severity and mortality across all cohorts. This dysregulation was not only observed in sepsis but also in ARDS, trauma, and burn patients, indicating a conserved mechanism across various critical illness syndromes. Moreover, analysis of randomized controlled trial data revealed that myeloid and lymphoid dysregulation is linked to differential mortality in patients treated with anakinra or corticosteroids, underscoring its prognostic and therapeutic significance. In conclusion, this novel immunology-based framework for quantifying cellular compartment dysregulation offers a valuable tool for prognosis and therapeutic decision-making in critical illness.

Sialylated IgG induces the transcription factor REST in alveolar macrophages to protect against lung inflammation and severe influenza disease

While most respiratory viral infections resolve with little harm to the host, severe symptoms arise when infection triggers an aberrant inflammatory response that damages lung tissue. Host regulators of virally induced lung inflammation have not been well defined. Here, we show that enrichment for sialylated, but not asialylated immunoglobulin G (IgG), predicted mild influenza disease in humans and was broadly protective against heterologous influenza viruses in a murine challenge model. Mechanistic studies show that sialylated IgG mediated this protection by inducing the transcription factor repressor element-1 silencing transcription factor (REST), which repressed nuclear factor κB (NF-κB)-driven responses, preventing severe lung inflammation and protecting lung function during influenza infection. Therapeutic administration of a recombinant, sialylated Fc molecule in clinical development similarly activated REST and protected against severe influenza disease, demonstrating that this pathway could be clinically harnessed. Overall, induction of REST through sialylated IgG signaling is a strategy to limit inflammatory disease sequelae in infections caused by antigenically distinct influenza strains.

Characterization of immortalized human podocytes infected with lentivirus as an in vitro model of viral infection-associated podocytopathy

A large number of studies have shown the association of kidney disease with viral infections in the body. Viral infections cause kidney injury in two manners, the systemic inflammation (cytokine storm) and the direct infection of kidney cells. Concerning direct viral infection of podocytes, the mechanism underlying virus-induced podocyte injury remains largely unknown and requires effective experimental models to facilitate its study. Here, we performed molecular characterization of immortalized human podocyte cell line (HPC) infected with lentivirus by RNA-seq. Bioinformatics analysis revealed a strong innate immune response in the cells, including interferon production and signaling. Meanwhile, activations of ferroptosis pathway and TNF-alpha signaling were also found, consistent with an impaired viability of the cells. Lentiviral infection also upregulated expression of APOL1 as observed in patients with HIV associated nephropathy (HIVAN) and diabetic nephropathy (DN). Interestingly, when the lentiviral infected cells were treated with Adriamycin (ADR), the ADR-associated signaling pathways were not interfered and remained activated as that in the cells treated with ADR only, suggesting that the virus and ADR have distinct mechanisms in damaging podocytes. Thus, the lentivirus-infected HPC cells represent a useful in vitro model of viral infection-associated podocytopathy.

Identification and transcriptomic assessment of latent profile pediatric septic shock phenotypes

BACKGROUND

Sepsis poses a grave threat, especially among children, but treatments are limited owing to heterogeneity among patients. We sought to test the clinical and biological relevance of pediatric septic shock subclasses identified using reproducible approaches.

METHODS

We performed latent profile analyses using clinical, laboratory, and biomarker data from a prospective multi-center pediatric septic shock observational cohort to derive phenotypes and trained a support vector machine model to assign phenotypes in an internal validation set. We established the clinical relevance of phenotypes and tested for their interaction with common sepsis treatments on patient outcomes. We conducted transcriptomic analyses to delineate phenotype-specific biology and inferred underlying cell subpopulations. Finally, we compared whether latent profile phenotypes overlapped with established gene-expression endotypes and compared survival among patients based on an integrated subclassification scheme.

RESULTS

Among 1071 pediatric septic shock patients requiring vasoactive support on day 1 included, we identified two phenotypes which we designated as Phenotype 1 (19.5%) and Phenotype 2 (80.5%). Membership in Phenotype 1 was associated with ~ fourfold adjusted odds of complicated course relative to Phenotype 2. Patients belonging to Phenotype 1 were characterized by relatively higher Angiopoietin-2/Tie-2 ratio, Angiopoietin-2, soluble thrombomodulin (sTM), interleukin 8 (IL-8), and intercellular adhesion molecule 1 (ICAM-1) and lower Tie-2 and Angiopoietin-1 concentrations compared to Phenotype 2. We did not identify significant interactions between phenotypes, common treatments, and clinical outcomes. Transcriptomic analysis revealed overexpression of genes implicated in the innate immune response and driven primarily by developing neutrophils among patients designated as Phenotype 1. There was no statistically significant overlap between established gene-expression endotypes, reflective of the host adaptive response, and the newly derived phenotypes, reflective of the host innate response including microvascular endothelial dysfunction. However, an integrated subclassification scheme demonstrated varying survival probabilities when comparing patient endophenotypes.

CONCLUSIONS

Our research underscores the reproducibility of latent profile analyses to identify pediatric septic shock phenotypes with high prognostic relevance. Pending validation, an integrated subclassification scheme, reflective of the different facets of the host response, holds promise to inform targeted intervention among those critically ill.

A T-Cell-Derived 3-Gene Signature Distinguishes SARS-CoV-2 from Common Respiratory Viruses

Research on the host responses to respiratory viruses could help develop effective interventions and therapies against the current and future pandemics from the host perspective. To explore the pathogenesis that distinguishes SARS-CoV-2 infections from other respiratory viruses, we performed a multi-cohort analysis with integrated bioinformatics and machine learning. We collected 3730 blood samples from both asymptomatic and symptomatic individuals infected with SARS-CoV-2, seasonal human coronavirus (sHCoVs), influenza virus (IFV), respiratory syncytial virus (RSV), or human rhinovirus (HRV) across 15 cohorts. First, we identified an enhanced cellular immune response but limited interferon activities in SARS-CoV-2 infection, especially in asymptomatic cases. Second, we identified a SARS-CoV-2-specific 3-gene signature (CLSPN, RBBP6, CCDC91) that was predominantly expressed by T cells, could distinguish SARS-CoV-2 infection, including Omicron, from other common respiratory viruses regardless of symptoms, and was predictive of SARS-CoV-2 infection before detectable viral RNA on RT-PCR testing in a longitude follow-up study. Thereafter, a user-friendly online tool, based on datasets collected here, was developed for querying a gene of interest across multiple viral infections. Our results not only identify a unique host response to the viral pathogenesis in SARS-CoV-2 but also provide insights into developing effective tools against viral pandemics from the host perspective.

Comparative single-cell RNA sequencing analysis of immune response to inactivated vaccine and natural SARS-CoV-2 infection

Uncovering the immune response to an inactivated SARS-CoV-2 vaccine (In-Vac) and natural infection is crucial for comprehending COVID-19 immunology. Here we conducted an integrated analysis of single-cell RNA sequencing (scRNA-seq) data from serial peripheral blood mononuclear cell (PBMC) samples derived from 12 individuals receiving In-Vac compared with those from COVID-19 patients. Our study reveals that In-Vac induces subtle immunological changes in PBMC, including cell proportions and transcriptomes, compared with profound changes for natural infection. In-Vac modestly upregulates IFN-α but downregulates NF-κB pathways, while natural infection triggers hyperactive IFN-α and NF-κB pathways. Both In-Vac and natural infection alter T/B cell receptor repertoires, but COVID-19 has more significant change in preferential VJ gene, indicating a vigorous immune response. Our study reveals distinct patterns of cellular communications, including a selective activation of IL-15RA/IL-15 receptor pathway after In-Vac boost, suggesting its potential role in enhancing In-Vac-induced immunity. Collectively, our study illuminates multifaceted immune responses to In-Vac and natural infection, providing insights for optimizing SARS-CoV-2 vaccine efficacy.

Integrative systems biology reveals NKG2A-biased immune responses correlate with protection in infectious disease, autoimmune disease, and cancer

Infection, autoimmunity, and cancer are principal human health challenges of the 21st century. Often regarded as distinct ends of the immunological spectrum, recent studies hint at potential overlap between these diseases. For example, inflammation can be pathogenic in infection and autoimmunity. T resident memory (TRM) cells can be beneficial in infection and cancer. However, these findings are limited by size and scope; exact immunological factors shared across diseases remain elusive. Here, we integrate large-scale deeply clinically and biologically phenotyped human cohorts of 526 patients with infection, 162 with lupus, and 11,180 with cancer. We identify an NKG2A+ immune bias as associative with protection against disease severity, mortality, and autoimmune/post-acute chronic disease. We reveal that NKG2A+ CD8+ T cells correlate with reduced inflammation and increased humoral immunity and that they resemble TRM cells. Our results suggest NKG2A+ biases as a cross-disease factor of protection, supporting suggestions of immunological overlap between infection, autoimmunity, and cancer.

Systems immunology of transcriptional responses to viral infection identifies conserved antiviral pathways across macaques and humans

Viral pandemics and epidemics pose a significant global threat. While macaque models of viral disease are routinely used, it remains unclear how conserved antiviral responses are between macaques and humans. Therefore, we conducted a cross-species analysis of transcriptomic data from over 6,088 blood samples from macaques and humans infected with one of 31 viruses. Our findings demonstrate that irrespective of primate or viral species, there are conserved antiviral responses that are consistent across infection phase (acute, chronic, or latent) and viral genome type (DNA or RNA viruses). Leveraging longitudinal data from experimental challenges, we identify virus-specific response kinetics such as host responses to Coronaviridae and Orthomyxoviridae infections peaking 1-3 days earlier than responses to Filoviridae and Arenaviridae viral infections. Our results underscore macaque studies as a powerful tool for understanding viral pathogenesis and immune responses that translate to humans, with implications for viral therapeutic development and pandemic preparedness.

A myeloid program associated with COVID-19 severity is decreased by therapeutic blockade of IL-6 signaling

Altered myeloid inflammation and lymphopenia are hallmarks of severe infections. We identified the upregulated EN-RAGE gene program in airway and blood myeloid cells from patients with acute lung injury from SARS-CoV-2 or other causes across 7 cohorts. This program was associated with greater clinical severity and predicted future mechanical ventilation and death. EN-RAGEhi myeloid cells express features consistent with suppressor cell functionality, including low HLA-DR and high PD-L1. Sustained EN-RAGE program expression in airway and blood myeloid cells correlated with clinical severity and increasing expression of T cell dysfunction markers. IL-6 upregulated many EN-RAGE program genes in monocytes in vitro. IL-6 signaling blockade by tocilizumab in a placebo-controlled clinical trial led to rapid normalization of EN-RAGE and T cell gene expression. This identifies IL-6 as a key driver of myeloid dysregulation associated with worse clinical outcomes in COVID-19 patients and provides insights into shared pathophysiological mechanisms in non-COVID-19 ARDS.

An NKG2A biased immune response confers protection for infection, autoimmune disease, and cancer

Infection, autoimmunity, and cancer are the principal human health challenges of the 21st century and major contributors to human death and disease. Often regarded as distinct ends of the immunological spectrum, recent studies have hinted there may be more overlap between these diseases than appears. For example, pathogenic inflammation has been demonstrated as conserved between infection and autoimmune settings. T resident memory (TRM) cells have been highlighted as beneficial for infection and cancer. However, these findings are limited by patient number and disease scope; exact immunological factors shared across disease remain elusive. Here, we integrate large-scale deeply clinically and biologically phenotyped human cohorts of 526 patients with infection, 162 with lupus, and 11,180 with cancer. We identify an NKG2A+ immune bias as associative with protection against disease severity, mortality, and autoimmune and post-acute chronic disease. We reveal that NKG2A+ CD8+ T cells correlate with reduced inflammation, increased humoral immunity, and resemble TRM cells. Our results suggest that an NKG2A+ bias is a pan-disease immunological factor of protection and thus supports recent suggestions that there is immunological overlap between infection, autoimmunity, and cancer. Our findings underscore the promotion of an NKG2A+ biased response as a putative therapeutic strategy.

Multi-omics analysis of mucosal and systemic immunity to SARS-CoV-2 after birth

The dynamics of immunity to infection in infants remain obscure. Here, we used a multi-omics approach to perform a longitudinal analysis of immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in infants and young children by analyzing blood samples and weekly nasal swabs collected before, during, and after infection with Omicron and non-Omicron variants. Infection stimulated robust antibody titers that, unlike in adults, showed no sign of decay for up to 300 days. Infants mounted a robust mucosal immune response characterized by inflammatory cytokines, interferon (IFN) α, and T helper (Th) 17 and neutrophil markers (interleukin [IL]-17, IL-8, and CXCL1). The immune response in blood was characterized by upregulation of activation markers on innate cells, no inflammatory cytokines, but several chemokines and IFNα. The latter correlated with viral load and expression of interferon-stimulated genes (ISGs) in myeloid cells measured by single-cell multi-omics. Together, these data provide a snapshot of immunity to infection during the initial weeks and months of life.

A machine learning classifier using 33 host immune response mRNAs accurately distinguishes viral and non-viral acute respiratory illnesses in nasal swab samples

BACKGROUND

Viral acute respiratory illnesses (viral ARIs) contribute significantly to human morbidity and mortality worldwide, but their successful treatment requires timely diagnosis of viral etiology, which is complicated by overlap in clinical presentation with the non-viral ARIs. Multiple pandemics in the twenty-first century to date have further highlighted the unmet need for effective monitoring of clinically relevant emerging viruses. Recent studies have identified conserved host response to viral infections in the blood.

METHODS

We hypothesize that a similarly conserved host response in nasal samples can be utilized for diagnosis and to rule out viral infection in symptomatic patients when current diagnostic tests are negative. Using a multi-cohort analysis framework, we analyzed 1555 nasal samples across 10 independent cohorts dividing them into training and validation.

RESULTS

Using six of the datasets for training, we identified 119 genes that are consistently differentially expressed in viral ARI patients (N = 236) compared to healthy controls (N = 146) and further down-selected 33 genes for classifier development. The resulting locked logistic regression-based classifier using the 33-mRNAs had AUC of 0.94 and 0.89 in the six training and four validation datasets, respectively. Furthermore, we found that although trained on healthy controls only, in the four validation datasets, the 33-mRNA classifier distinguished viral ARI from both healthy or non-viral ARI samples with > 80% specificity and sensitivity, irrespective of age, viral type, and viral load. Single-cell RNA-sequencing data showed that the 33-mRNA signature is dominated by macrophages and neutrophils in nasal samples.

CONCLUSION

This proof-of-concept signature has potential to be adapted as a clinical point-of-care test ('RespVerity') to improve the diagnosis of viral ARIs.

Detection of viral infection in cell lines using ViralCellDetector

Cell lines are commonly used in research to study biology, including gene expression regulation, cancer progression, and drug responses. However, cross-contaminations with bacteria, mycoplasma, and viruses are common issues in cell line experiments. Detection of bacteria and mycoplasma infections in cell lines is relatively easy but identifying viral infections in cell lines is difficult. Currently, there are no established methods or tools available for detecting viral infections in cell lines. To address this challenge, we developed a tool called ViralCellDetector that detects viruses through mapping RNA-seq data to a library of virus genome. Using this tool, we observed that around 10% of experiments with the MCF7 cell line were likely infected with viruses. Furthermore, to facilitate the detection of samples with unknown sources of viral infection, we identified the differentially expressed genes involved in viral infection from two different cell lines and used these genes in a machine learning approach to classify infected samples based on the host response gene expression biomarkers. Our model reclassifies the infected and non-infected samples with an AUC of 0.91 and an accuracy of 0.93. Overall, our mapping- and marker-based approaches can detect viral infections in any cell line simply based on readily accessible RNA-seq data, allowing researchers to avoid the use of unintentionally infected cell lines in their studies.

Prediction of HLA genotypes from single-cell transcriptome data

The human leukocyte antigen (HLA) locus plays a central role in adaptive immune function and has significant clinical implications for tissue transplant compatibility and allelic disease associations. Studies using bulk-cell RNA sequencing have demonstrated that HLA transcription may be regulated in an allele-specific manner and single-cell RNA sequencing (scRNA-seq) has the potential to better characterize these expression patterns. However, quantification of allele-specific expression (ASE) for HLA loci requires sample-specific reference genotyping due to extensive polymorphism. While genotype prediction from bulk RNA sequencing is well described, the feasibility of predicting HLA genotypes directly from single-cell data is unknown. Here we evaluate and expand upon several computational HLA genotyping tools by comparing predictions from human single-cell data to gold-standard, molecular genotyping. The highest 2-field accuracy averaged across all loci was 76% by arcasHLA and increased to 86% using a composite model of multiple genotyping tools. We also developed a highly accurate model (AUC 0.93) for predicting HLA-DRB345 copy number in order to improve genotyping accuracy of the HLA-DRB locus. Genotyping accuracy improved with read depth and was reproducible at repeat sampling. Using a metanalytic approach, we also show that HLA genotypes from PHLAT and OptiType can generate ASE ratios that are highly correlated (R2 = 0.8 and 0.94, respectively) with those derived from gold-standard genotyping.

Susceptibility of Diabetic Patients to COVID-19 Infections: Clinico-Hematological and Complications Analysis

Background: Coronavirus disease 2019 has become a global health threat resulting in a catastrophic spread and more than 3.8 million deaths worldwide. It has been suggested that there is a negative influence of diabetes mellites (DM), which is a complex chronic disease, on COVID-19 severe outcomes. Other factors in diabetic patients may also contribute to COVID-19 disease outcomes, such as older age, obesity, hyperglycaemia, hypertension, and other chronic conditions. Methods: A cohort study was conducted on the demographics, clinical information, and laboratory findings of the hospitalised COVID-19 with DM and non-DM patients were obtained from the medical records in King Faisal Specialist Hospital and Research Centre, Saudi Arabia. Results: Among the study population, 108 patients had DM, and 433 were non-DM patients. Patients with DM were more likely to present symptoms such as fever (50.48%), anorexia (19.51%), dry cough (47.96%), shortness of breath (35.29%), chest pain (16.49%), and other symptoms. There was a significant decrease in the mean of haematological and biochemical parameters, such as haemoglobin, calcium, and alkaline phosphate in people with diabetes compared to non-diabetics and a considerable increase in other parameters, such as glucose, potassium, and cardiac troponin. Conclusions: According to the findings of this study, patients who have diabetes have a greater risk of developing more severe symptoms associated with COVID-19 disease. This could result in more patients being admitted to the intensive care unit as well as higher mortality rates.

SelectBCM tool: a batch evaluation framework to select the most appropriate batch-correction methods for bulk transcriptome analysis

Bulk transcriptomes are an essential data resource for understanding basic and disease biology. However, integrating information from different experiments remains challenging because of the batch effect generated by various technological and biological variations in the transcriptome. Numerous batch-correction methods to deal with this batch effect have been developed in the past. However, a user-friendly workflow to select the most appropriate batch-correction method for the given set of experiments is still missing. We present the SelectBCM tool that prioritizes the most appropriate batch-correction method for a given set of bulk transcriptomic experiments, improving biological clustering and gene differential expression analysis. We demonstrate the applicability of the SelectBCM tool on analyses of real data for two common diseases, rheumatoid arthritis and osteoarthritis, and one example to characterize a biological state, where we performed a meta-analysis of the macrophage activation state. The R package is available at https://github.com/ebi-gene-expression-group/selectBCM.

Microscale combinatorial stimulation of human myeloid cells reveals inflammatory priming by viral ligands

Cells sense a wide variety of signals and respond by adopting complex transcriptional states. Most single-cell profiling is carried out today at cellular baseline, blind to cells' potential spectrum of functional responses. Exploring the space of cellular responses experimentally requires access to a large combinatorial perturbation space. Single-cell genomics coupled with multiplexing techniques provide a useful tool for characterizing cell states across several experimental conditions. However, current multiplexing strategies require programmatic handling of many samples in macroscale arrayed formats, precluding their application in large-scale combinatorial analysis. Here, we introduce StimDrop, a method that combines antibody-based cell barcoding with parallel droplet processing to automatically formulate cell population × stimulus combinations in a microfluidic device. We applied StimDrop to profile the effects of 512 sequential stimulation conditions on human dendritic cells. Our results demonstrate that priming with viral ligands potentiates hyperinflammatory responses to a second stimulus, and show transcriptional signatures consistent with this phenomenon in myeloid cells of patients with severe COVID-19.

Severe COVID-19 as a Possible Mediator of Autoimmunity and Sjögren’s Syndrome

We treated a 65-year-old man for COVID-19 who was hospitalized urgently due to life-threatening respiratory decompensation and later developed cardiac arrest, both of which were successfully treated. Three days prior to the patient's urgent hospitalization, he had a high fever of over 38.0°C. Viral infection was diagnosed by polymerase chain reaction (PCR) on the day of admission, which was negative on the 11th day. Blood analysis on the second day was strongly positive for COVID-19 IgG antibodies, which continued for one year. Because of the acute increase in viral IgG antibodies, we performed other immunological analyses; Sjögren's syndrome antigen A (SS-A) and Sjögren's syndrome antigen B (SS-B) antibodies were positive, although he had no history of autoimmune diseases. Subsequent salivary-gland biopsy and pathological analysis confirmed the diagnosis of Sjögren's syndrome. The severe clinical manifestations and early antibody seroconversion in this case suggest COVID-19 as a mediator of autoimmunity and Sjögren's syndrome.

Systems biological assessment of the temporal dynamics of immunity to a viral infection in the first weeks and months of life

The dynamics of innate and adaptive immunity to infection in infants remain obscure. Here, we used a multi-omics approach to perform a longitudinal analysis of immunity to SARS-CoV-2 infection in infants and young children in the first weeks and months of life by analyzing blood samples collected before, during, and after infection with Omicron and Non-Omicron variants. Infection stimulated robust antibody titers that, unlike in adults, were stably maintained for >300 days. Antigen-specific memory B cell (MCB) responses were durable for 150 days but waned thereafter. Somatic hypermutation of V-genes in MCB accumulated progressively over 9 months. The innate response was characterized by upregulation of activation markers on blood innate cells, and a plasma cytokine profile distinct from that seen in adults, with no inflammatory cytokines, but an early and transient accumulation of chemokines (CXCL10, IL8, IL-18R1, CSF-1, CX3CL1), and type I IFN. The latter was strongly correlated with viral load, and expression of interferon-stimulated genes (ISGs) in myeloid cells measured by single-cell transcriptomics. Consistent with this, single-cell ATAC-seq revealed enhanced accessibility of chromatic loci targeted by interferon regulatory factors (IRFs) and reduced accessibility of AP-1 targeted loci, as well as traces of epigenetic imprinting in monocytes, during convalescence. Together, these data provide the first snapshot of immunity to infection during the initial weeks and months of life.

Single-cell RNA-seq methods to interrogate virus-host interactions

The twenty-first century has seen the emergence of many epidemic and pandemic viruses, with the most recent being the SARS-CoV-2-driven COVID-19 pandemic. As obligate intracellular parasites, viruses rely on host cells to replicate and produce progeny, resulting in complex virus and host dynamics during an infection. Single-cell RNA sequencing (scRNA-seq), by enabling broad and simultaneous profiling of both host and virus transcripts, represents a powerful technology to unravel the delicate balance between host and virus. In this review, we summarize technological and methodological advances in scRNA-seq and their applications to antiviral immunity. We highlight key scRNA-seq applications that have enabled the understanding of viral genomic and host response heterogeneity, differential responses of infected versus bystander cells, and intercellular communication networks. We expect further development of scRNA-seq technologies and analytical methods, combined with measurements of additional multi-omic modalities and increased availability of publicly accessible scRNA-seq datasets, to enable a better understanding of viral pathogenesis and enhance the development of antiviral therapeutics strategies.

Multi-objective optimization identifies a specific and interpretable COVID-19 host response signature

The identification of a COVID-19 host response signature in blood can increase the understanding of SARS-CoV-2 pathogenesis and improve diagnostic tools. Applying a multi-objective optimization framework to both massive public and new multi-omics data, we identified a COVID-19 signature regulated at both transcriptional and epigenetic levels. We validated the signature's robustness in multiple independent COVID-19 cohorts. Using public data from 8,630 subjects and 53 conditions, we demonstrated no cross-reactivity with other viral and bacterial infections, COVID-19 comorbidities, or confounders. In contrast, previously reported COVID-19 signatures were associated with significant cross-reactivity. The signature's interpretation, based on cell-type deconvolution and single-cell data analysis, revealed prominent yet complementary roles for plasmablasts and memory T cells. Although the signal from plasmablasts mediated COVID-19 detection, the signal from memory T cells controlled against cross-reactivity with other viral infections. This framework identified a robust, interpretable COVID-19 signature and is broadly applicable in other disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.

Linking research of biomedical datasets

Biomedical data preprocessing and efficient computing can be as important as the statistical methods used to fit the data; data processing needs to consider application scenarios, data acquisition and individual rights and interests. We review common principles, knowledge and methods of integrated research according to the whole-pipeline processing mechanism diverse, coherent, sharing, auditable and ecological. First, neuromorphic and native algorithms integrate diverse datasets, providing linear scalability and high visualization. Second, the choice mechanism of different preprocessing, analysis and transaction methods from raw to neuromorphic was summarized on the node and coordinator platforms. Third, combination of node, network, cloud, edge, swarm and graph builds an ecosystem of cohort integrated research and clinical diagnosis and treatment. Looking forward, it is vital to simultaneously combine deep computing, mass data storage and massively parallel communication.

Deciphering COVID-19 host transcriptomic complexity and variations for therapeutic discovery against new variants

The molecular manifestations of host cells responding to SARS-CoV-2 and its evolving variants of infection are vastly different across the studied models and conditions, imposing challenges for host-based antiviral drug discovery. Based on the postulation that antiviral drugs tend to reverse the global host gene expression induced by viral infection, we retrospectively evaluated hundreds of signatures derived from 1,700 published host transcriptomic profiles of SARS/MERS/SARS-CoV-2 infection using an iterative data-driven approach. A few of these signatures could be reversed by known anti-SARS-CoV-2 inhibitors, suggesting the potential of extrapolating the biology for new variant research. We discovered IMD-0354 as a promising candidate to reverse the signatures globally with nanomolar IC50 against SARS-CoV-2 and its five variants. IMD-0354 stimulated type I interferon antiviral response, inhibited viral entry, and down-regulated hijacked proteins. This study demonstrates that the conserved coronavirus signatures and the transcriptomic reversal approach that leverages polypharmacological effects could guide new variant therapeutic discovery.

Host protease activity classifies pneumonia etiology

Community-acquired pneumonia (CAP) has been brought to the forefront of global health priorities due to the COVID-19 pandemic. However, classification of viral versus bacterial pneumonia etiology remains a significant clinical challenge. To this end, we have engineered a panel of activity-based nanosensors that detect the dysregulated activity of pulmonary host proteases implicated in the response to pneumonia-causing pathogens and produce a urinary readout of disease. The nanosensor targets were selected based on a human protease transcriptomic signature for pneumonia etiology generated from 33 unique publicly available study cohorts. Five mouse models of bacterial or viral CAP were developed to assess the ability of the nanosensors to produce etiology-specific urinary signatures. Machine learning algorithms were used to train diagnostic classifiers that could distinguish infected mice from healthy controls and differentiate those with bacterial versus viral pneumonia with high accuracy. This proof-of-concept diagnostic approach demonstrates a way to distinguish pneumonia etiology based solely on the host proteolytic response to infection.

Mature neutrophils and a NF-κB-to-IFN transition determine the unifying disease recovery dynamics in COVID-19

Disease recovery dynamics are often difficult to assess, as patients display heterogeneous recovery courses. To model recovery dynamics, exemplified by severe COVID-19, we apply a computational scheme on longitudinally sampled blood transcriptomes, generating recovery states, which we then link to cellular and molecular mechanisms, presenting a framework for studying the kinetics of recovery compared with non-recovery over time and long-term effects of the disease. Specifically, a decrease in mature neutrophils is the strongest cellular effect during recovery, with direct implications on disease outcome. Furthermore, we present strong indications for global regulatory changes in gene programs, decoupled from cell compositional changes, including an early rise in T cell activation and differentiation, resulting in immune rebalancing between interferon and NF-κB activity and restoration of cell homeostasis. Overall, we present a clinically relevant computational framework for modeling disease recovery, paving the way for future studies of the recovery dynamics in other diseases and tissues.

Multi-omics insights into host-viral response and pathogenesis in Crimean-Congo hemorrhagic fever viruses for novel therapeutic target

The pathogenesis and host-viral interactions of the Crimean–Congo hemorrhagic fever orthonairovirus (CCHFV) are convoluted and not well evaluated. Application of the multi-omics system biology approaches, including biological network analysis in elucidating the complex host-viral response, interrogates the viral pathogenesis. The present study aimed to fingerprint the system-level alterations during acute CCHFV-infection and the cellular immune responses during productive CCHFV-replication in vitro. We used system-wide network-based system biology analysis of peripheral blood mononuclear cells (PBMCs) from a longitudinal cohort of CCHF patients during the acute phase of infection and after one year of recovery (convalescent phase) followed by untargeted quantitative proteomics analysis of the most permissive CCHFV-infected Huh7 and SW13 cells. In the RNAseq analysis of the PBMCs, comparing the acute and convalescent-phase, we observed system-level host’s metabolic reprogramming towards central carbon and energy metabolism (CCEM) with distinct upregulation of oxidative phosphorylation (OXPHOS) during CCHFV-infection. Upon application of network-based system biology methods, negative coordination of the biological signaling systems like FOXO/Notch axis and Akt/mTOR/HIF-1 signaling with metabolic pathways during CCHFV-infection were observed. The temporal quantitative proteomics in Huh7 showed a dynamic change in the CCEM over time and concordant with the cross-sectional proteomics in SW13 cells. By blocking the two key CCEM pathways, glycolysis and glutaminolysis, viral replication was inhibited in vitro. Activation of key interferon stimulating genes during infection suggested the role of type I and II interferon-mediated antiviral mechanisms both at the system level and during progressive replication.

Crimean-Congo hemorrhagic fever (CCHF) is an emerging disease that is increasingly spreading to new populations. The condition is now endemic in almost 30 countries in sub-Saharan Africa, South-Eastern Europe, the Middle East and Central Asia. CCHF is caused by a tick-borne virus and can cause uncontrolled bleeding. It has a mortality rate of up to 40%, and there are currently no vaccines or effective treatments available.

All viruses depend entirely on their hosts for reproduction, and they achieve this through hijacking the molecular machinery of the cells they infect. However, little is known about how the CCHF virus does this and how the cells respond. To understand more about the relationship between the cell’s metabolism and viral replication, Neogi, Elaldi et al. studied immune cells taken from patients during an infection and one year later.

The gene activity of the cells showed that the virus prefers to hijack processes known as central carbon and energy metabolism. These are the main regulator of the cellular energy supply and the production of essential chemicals. By using cancer drugs to block these key pathways, Neogi, Elaldi et al. could reduce the viral reproduction in laboratory cells.

These findings provide a clearer understanding of how the CCHF virus replicates inside human cells. By interfering with these processes, researchers could develop new antiviral strategies to treat the disease. One of the cancer drugs tested in cells, 2-DG, has been approved for emergency use against COVID-19 in some countries. Neogi, Elaldi et al. are now studying this further in animals with the hope of reaching clinical trials in the future.

An 8-gene machine learning model improves clinical prediction of severe dengue progression

BACKGROUND

Each year 3-6 million people develop life-threatening severe dengue (SD). Clinical warning signs for SD manifest late in the disease course and are nonspecific, leading to missed cases and excess hospital burden. Better SD prognostics are urgently needed.

METHODS

We integrated 11 public datasets profiling the blood transcriptome of 365 dengue patients of all ages and from seven countries, encompassing biological, clinical, and technical heterogeneity. We performed an iterative multi-cohort analysis to identify differentially expressed genes (DEGs) between non-severe patients and SD progressors. Using only these DEGs, we trained an XGBoost machine learning model on public data to predict progression to SD. All model parameters were "locked" prior to validation in an independent, prospectively enrolled cohort of 377 dengue patients in Colombia. We measured expression of the DEGs in whole blood samples collected upon presentation, prior to SD progression. We then compared the accuracy of the locked XGBoost model and clinical warning signs in predicting SD.

RESULTS

We identified eight SD-associated DEGs in the public datasets and built an 8-gene XGBoost model that accurately predicted SD progression in the independent validation cohort with 86.4% (95% CI 68.2-100) sensitivity and 79.7% (95% CI 75.5-83.9) specificity. Given the 5.8% proportion of SD cases in this cohort, the 8-gene model had a positive and negative predictive value (PPV and NPV) of 20.9% (95% CI 16.7-25.6) and 99.0% (95% CI 97.7-100.0), respectively. Compared to clinical warning signs at presentation, which had 77.3% (95% CI 58.3-94.1) sensitivity and 39.7% (95% CI 34.7-44.9) specificity, the 8-gene model led to an 80% reduction in the number needed to predict (NNP) from 25.4 to 5.0. Importantly, the 8-gene model accurately predicted subsequent SD in the first three days post-fever onset and up to three days prior to SD progression.

CONCLUSIONS

The 8-gene XGBoost model, trained on heterogeneous public datasets, accurately predicted progression to SD in a large, independent, prospective cohort, including during the early febrile stage when SD prediction remains clinically difficult. The model has potential to be translated to a point-of-care prognostic assay to reduce dengue morbidity and mortality without overwhelming limited healthcare resources.

Multiple early factors anticipate post-acute COVID-19 sequelae

Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific auto-antibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes, exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time, leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.

Immune transcriptome analysis of COVID-19 patients infected with SARS-CoV-2 variants carrying the E484K escape mutation identifies a distinct gene module

Fast-spreading variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) energize the COVID-19 pandemic. While viral infections elicit a conserved immune response, it is not known whether SARS-CoV-2 variants, which display enhanced binding to the ACE2 receptor and reduced neutralizing activity by vaccine-elicited antibodies, prompt specific genomic immune responses. To test this, we generated and investigated the transcriptomes in BCs from hospitalized patients infected with either the Alpha variant (n = 36) or with the Alpha variant that had acquired the E484K escape mutation (Alpha+E484K) (n = 13). We identified a gene module preferentially activated in patients infected with the Alpha+E484K variant and in patients infected with the Beta (n = 9) and Gamma (n = 3) variants that also carry by the E484K escape mutation. The E484K signature was enriched for genes preferentially expressed in monocytes and linked to severe viral infection. However, both cohorts had undergone similar treatments and no differences in disease severity were reported suggesting that this signature reflects a variant response and does not necessarily associate with disease outcome. Additionally, longitudinal transcriptome analyses revealed a more persistent retention of immune signatures in Alpha+E484K patients throughout the entire course of COVID-19 disease and convalescence. While the OAS1 Neanderthal mutation has been linked to a milder COVID-19 pathology, we did not identify significant immune transcriptomes differences in the 25 patients homozygous for this mutation. Our study offers insights into distinct molecular immune responses elicited by SARS-CoV-2 variants carrying the E484K escape mutation throughout the COVID-19 disease.

Collapsing Focal Segmental Glomerulosclerosis in Viral Infections

Collapsing glomerulopathy represents a special variant of the proteinuric kidney disease focal segmental glomerulosclerosis (FSGS). Histologically, the collapsing form of FSGS (cFSGS) is characterized by segmental or global condensation and obliteration of glomerular capillaries, the appearance of hyperplastic and hypertrophic podocytes and severe tubulointerstitial damage. Clinically, cFSGS patients present with acute kidney injury, nephrotic-range proteinuria and are at a high risk of rapid progression to irreversible kidney failure. cFSGS can be attributed to numerous etiologies, namely, viral infections like HIV, cytomegalovirus, Epstein-Barr-Virus, and parvovirus B19 and also drugs and severe ischemia. Risk variants of the APOL1 gene, predominantly found in people of African descent, increase the risk of developing cFSGS. Patients infected with the new Corona-Virus SARS-CoV-2 display an increased rate of acute kidney injury (AKI) in severe cases of COVID-19. Besides hemodynamic instability, cytokine mediated injury and direct viral entry and infection of renal epithelial cells contributing to AKI, there are emerging reports of cFSGS associated with SARS-CoV-2 infection in patients of mainly African ethnicity. The pathogenesis of cFSGS is proposed to be linked with direct viral infection of podocytes, as described for HIV-associated glomerulopathy. Nevertheless, there is growing evidence that the systemic inflammatory cascade, activated in acute viral infections like COVID-19, is a major contributor to the impairment of basic cellular functions in podocytes. This mini review will summarize the current knowledge on cFSGS associated with viral infections with a special focus on the influence of systemic immune responses and potential mechanisms propagating the development of cFSGS.

A 6-mRNA host response classifier in whole blood predicts outcomes in COVID-19 and other acute viral infections

Predicting the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19. We developed a logistic regression-based classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N = 705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs. We selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.94 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N = 97) and retrospectively (N = 100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay. With further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.

Single-cell immunology of SARS-CoV-2 infection

Gaining a better understanding of the immune cell subsets and molecular factors associated with protective or pathological immunity against severe acute respiratory syndrome coronavirus (SARS-CoV)-2 could aid the development of vaccines and therapeutics for coronavirus disease 2019 (COVID-19). Single-cell technologies, such as flow cytometry, mass cytometry, single-cell transcriptomics and single-cell multi-omic profiling, offer considerable promise in dissecting the heterogeneity of immune responses among individual cells and uncovering the molecular mechanisms of COVID-19 pathogenesis. Single-cell immune-profiling studies reported to date have identified innate and adaptive immune cell subsets that correlate with COVID-19 disease severity, as well as immunological factors and pathways of potential relevance to the development of vaccines and treatments for COVID-19. For facilitation of integrative studies and meta-analyses into the immunology of SARS-CoV-2 infection, we provide standardized, download-ready versions of 21 published single-cell sequencing datasets (over 3.2 million cells in total) as well as an interactive visualization portal for data exploration.

Human immune diversity: from evolution to modernity

The extreme diversity of the human immune system, forged and maintained throughout evolutionary history, provides a potent defense against opportunistic pathogens. At the same time, this immune variation is the substrate upon which a plethora of immune-associated diseases develop. Genetic analysis suggests that thousands of individually weak loci together drive up to half of the observed immune variation. Intense selection maintains this genetic diversity, even selecting for the introgressed Neanderthal or Denisovan alleles that have reintroduced variation lost during the out-of-Africa migration. Variations in age, sex, diet, environmental exposure, and microbiome each potentially explain the residual variation, with proof-of-concept studies demonstrating both plausible mechanisms and correlative associations. The confounding interaction of many of these variables currently makes it difficult to assign definitive contributions. Here, we review the current state of play in the field, identify the key unknowns in the causality of immune variation, and identify the multidisciplinary pathways toward an improved understanding.

Comprehensive investigations revealed consistent pathophysiological alterations after vaccination with COVID-19 vaccines

Large-scale COVID-19 vaccinations are currently underway in many countries in response to the COVID-19 pandemic. Here, we report, besides generation of neutralizing antibodies, consistent alterations in hemoglobin A1c, serum sodium and potassium levels, coagulation profiles, and renal functions in healthy volunteers after vaccination with an inactivated SARS-CoV-2 vaccine. Similar changes had also been reported in COVID-19 patients, suggesting that vaccination mimicked an infection. Single-cell mRNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) before and 28 days after the first inoculation also revealed consistent alterations in gene expression of many different immune cell types. Reduction of CD8+ T cells and increase in classic monocyte contents were exemplary. Moreover, scRNA-seq revealed increased NF-κB signaling and reduced type I interferon responses, which were confirmed by biological assays and also had been reported to occur after SARS-CoV-2 infection with aggravating symptoms. Altogether, our study recommends additional caution when vaccinating people with pre-existing clinical conditions, including diabetes, electrolyte imbalances, renal dysfunction, and coagulation disorders.

Circulating acetylated polyamines correlate with Covid-19 severity in cancer patients

Cancer patients are particularly susceptible to the development of severe Covid-19, prompting us to investigate the serum metabolome of 204 cancer patients enrolled in the ONCOVID trial. We previously described that the immunosuppressive tryptophan/kynurenine metabolite anthranilic acid correlates with poor prognosis in non-cancer patients. In cancer patients, we observed an elevation of anthranilic acid at baseline (without Covid-19 diagnosis) and no further increase with mild or severe Covid-19. We found that, in cancer patients, Covid-19 severity was associated with the depletion of two bacterial metabolites, indole-3-proprionate and 3-phenylproprionate, that both positively correlated with the levels of several inflammatory cytokines. Most importantly, we observed that the levels of acetylated polyamines (in particular N1-acetylspermidine, N1,N8-diacetylspermidine and N1,N12-diacetylspermine), alone or in aggregate, were elevated in severe Covid-19 cancer patients requiring hospitalization as compared to uninfected cancer patients or cancer patients with mild Covid-19. N1-acetylspermidine and N1,N8-diacetylspermidine were also increased in patients exhibiting prolonged viral shedding (>40 days). An abundant literature indicates that such acetylated polyamines increase in the serum from patients with cancer, cardiovascular disease or neurodegeneration, associated with poor prognosis. Our present work supports the contention that acetylated polyamines are associated with severe Covid-19, both in the general population and in patients with malignant disease. Severe Covid-19 is characterized by a specific metabolomic signature suggestive of the overactivation of spermine/spermidine N1-acetyl transferase-1 (SAT1), which catalyzes the first step of polyamine catabolism.

Application of omics technology to combat the COVID-19 pandemic

As of August 27, 2021, the ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread to over 220 countries, areas, and territories. Thus far, 214,468,601 confirmed cases, including 4,470,969 deaths, have been reported to the World Health Organization. To combat the COVID-19 pandemic, multiomics-based strategies, including genomics, transcriptomics, proteomics, and metabolomics, have been used to study the diagnosis methods, pathogenesis, prognosis, and potential drug targets of COVID-19. In order to help researchers and clinicians to keep up with the knowledge of COVID-19, we summarized the most recent progresses reported in omics-based research papers. This review discusses omics-based approaches for studying COVID-19, summarizing newly emerged SARS-CoV-2 variants as well as potential diagnostic methods, risk factors, and pathological features of COVID-19. This review can help researchers and clinicians gain insight into COVID-19 features, providing direction for future drug development and guidance for clinical treatment, so that patients can receive appropriate treatment as soon as possible to reduce the risk of disease progression.

Mind the gap from research laboratory to clinic: Challenges and opportunities for next-generation assays in human diseases

Modern vaccinology has experienced major conceptual and technological advances over the past 30 years. These include atomic-level structures driving immunogen design, new vaccine delivery methods, powerful adjuvants, and novel animal models. In addition, utilizing advanced assays to learn how the immune system senses a pathogen and orchestrates protective immunity has been critical in the design of effective vaccines and therapeutics. The National Institute of Allergy and Infectious Diseases of the National Institutes of Health convened a workshop in September 2020 focused on next generation assays for vaccine development (Table 1). The workshop focused on four critical pathogens: severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) and human immunodeficiency virus (HIV)-which have no licensed vaccines-and tuberculosis (TB) and influenza-both of which are in critical need of improved vaccines. The goal was to share progress and lessons learned, and to identify any commonalities that can be leveraged to design vaccines and therapeutics.

Tracheal aspirate RNA sequencing identifies distinct immunological features of COVID-19 ARDS

The immunological features that distinguish COVID-19-associated acute respiratory distress syndrome (ARDS) from other causes of ARDS are incompletely understood. Here, we report the results of comparative lower respiratory tract transcriptional profiling of tracheal aspirate from 52 critically ill patients with ARDS from COVID-19 or from other etiologies, as well as controls without ARDS. In contrast to a "cytokine storm," we observe reduced proinflammatory gene expression in COVID-19 ARDS when compared to ARDS due to other causes. COVID-19 ARDS is characterized by a dysregulated host response with increased PTEN signaling and elevated expression of genes with non-canonical roles in inflammation and immunity. In silico analysis of gene expression identifies several candidate drugs that may modulate gene expression in COVID-19 ARDS, including dexamethasone and granulocyte colony stimulating factor. Compared to ARDS due to other types of viral pneumonia, COVID-19 is characterized by impaired interferon-stimulated gene (ISG) expression. The relationship between SARS-CoV-2 viral load and expression of ISGs is decoupled in patients with COVID-19 ARDS when compared to patients with mild COVID-19. In summary, assessment of host gene expression in the lower airways of patients reveals distinct immunological features of COVID-19 ARDS.

Multi-omic profiling reveals widespread dysregulation of innate immunity and hematopoiesis in COVID-19

Our understanding of protective versus pathological immune responses to SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is limited by inadequate profiling of patients at the extremes of the disease severity spectrum. Here, we performed multi-omic single-cell immune profiling of 64 COVID-19 patients across the full range of disease severity, from outpatients with mild disease to fatal cases. Our transcriptomic, epigenomic, and proteomic analyses revealed widespread dysfunction of peripheral innate immunity in severe and fatal COVID-19, including prominent hyperactivation signatures in neutrophils and NK cells. We also identified chromatin accessibility changes at NF-κB binding sites within cytokine gene loci as a potential mechanism for the striking lack of pro-inflammatory cytokine production observed in monocytes in severe and fatal COVID-19. We further demonstrated that emergency myelopoiesis is a prominent feature of fatal COVID-19. Collectively, our results reveal disease severity-associated immune phenotypes in COVID-19 and identify pathogenesis-associated pathways that are potential targets for therapeutic intervention.

Multisystem inflammatory syndrome in children: a microcosm of challenges and opportunities for translational bioinformatics in pediatric research

PURPOSE OF REVIEW

Despite significant progress in our understanding and clinical management of multisystem inflammatory syndrome in children (MIS-C), significant challenges remain. Here, we review recently published studies on the clinical diagnosis, risk stratification, and treatment of MIS-C, highlighting key gaps in research progress that are a microcosm for challenges in translational pediatric research. We then discuss potential solutions in the realm of translational bioinformatics.

RECENT FINDINGS

Current case definitions are inconsistent and do not capture the underlying pathophysiology of MIS-C, which remains poorly understood. Although overall mortality is low, some patients rapidly decompensate, and a test to identify those at risk for severe outcomes remains an unmet need. Treatment consists of various combinations of immunoglobulins, corticosteroids, and biologics, based on extrapolated data and expert opinion, while the benefits remain unclear as we await the completion of clinical trials.

SUMMARY

The small size and heterogeneity of the pediatric population contribute to unmet needs because of financial and logistical constraints of the current research infrastructure focused on eliminating most sources of heterogeneity, leading to ungeneralizable results. Data sharing and meta-analysis of gene expression shows promise to accelerate progress in the field of MIS-C as well as other childhood diseases beyond the current pandemic.

Multicohort Analysis Identifies Monocyte Gene Signatures to Accurately Monitor Subset-Specific Changes in Human Diseases

Monocytes are crucial regulators of inflammation, and are characterized by three distinct subsets in humans, of which classical and non-classical are the most abundant. Different subsets carry out different functions and have been previously associated with multiple inflammatory conditions. Dissecting the contribution of different monocyte subsets to disease is currently limited by samples and cohorts, often resulting in underpowered studies and poor reproducibility. Publicly available transcriptome profiles provide an alternative source of data characterized by high statistical power and real-world heterogeneity. However, most transcriptome datasets profile bulk blood or tissue samples, requiring the use of in silico approaches to quantify changes in cell levels. Here, we integrated 853 publicly available microarray expression profiles of sorted human monocyte subsets from 45 independent studies to identify robust and parsimonious gene expression signatures, consisting of 10 genes specific to each subset. These signatures maintain their accuracy regardless of disease state in an independent cohort profiled by RNA-sequencing and are specific to their respective subset when compared to other immune cells from both myeloid and lymphoid lineages profiled across 6160 transcriptome profiles. Consequently, we show that these signatures can be used to quantify changes in monocyte subsets levels in expression profiles from patients in clinical trials. Finally, we show that proteins encoded by our signature genes can be used in cytometry-based assays to specifically sort monocyte subsets. Our results demonstrate the robustness, versatility, and utility of our computational approach and provide a framework for the discovery of new cellular markers.