KLRD1-expressing natural killer cells predict influenza susceptibility

NK cell subsets and dysfunction during viral infection: a new avenue for therapeutics?

In the setting of viral challenge, natural killer (NK) cells play an important role as an early immune responder against infection. During this response, significant changes in the NK cell population occur, particularly in terms of their frequency, location, and subtype prevalence. In this review, changes in the NK cell repertoire associated with several pathogenic viral infections are summarized, with a particular focus placed on changes that contribute to NK cell dysregulation in these settings. This dysregulation, in turn, can contribute to host pathology either by causing NK cells to be hyperresponsive or hyporesponsive. Hyperresponsive NK cells mediate significant host cell death and contribute to generating a hyperinflammatory environment. Hyporesponsive NK cell populations shift toward exhaustion and often fail to limit viral pathogenesis, possibly enabling viral persistence. Several emerging therapeutic approaches aimed at addressing NK cell dysregulation have arisen in the last three decades in the setting of cancer and may prove to hold promise in treating viral diseases. However, the application of such therapeutics to treat viral infections remains critically underexplored. This review briefly explores several therapeutic approaches, including the administration of TGF-β inhibitors, immune checkpoint inhibitors, adoptive NK cell therapies, CAR NK cells, and NK cell engagers among other therapeutics.

Natural killer cells and their exosomes in viral infections and related therapeutic approaches: where are we?

Innate immunity is the first line of the host immune system to fight against infections. Natural killer cells are the innate immunity lymphocytes responsible for fighting against virus-infected and cancerous cells. They have various mechanisms to suppress viral infections. On the other hand, viruses have evolved to utilize different ways to evade NK cell-mediated responses. Viruses can balance the response by regulating the cytokine release pattern and changing the proportion of activating and inhibitory receptors on the surface of NK cells. Exosomes are a subtype of extracellular vesicles that are involved in intercellular communication. Most cell populations can release these nano-sized vesicles, and it was shown that these vesicles produce identical outcomes to the originating cell from which they are released. In recent years, the role of NK cell-derived exosomes in various diseases including viral infections has been highlighted, drawing attention to utilizing the therapeutic potential of these nanoparticles. In this article, the role of NK cells in various viral infections and the mechanisms used by viruses to evade these important immune system cells are initially examined. Subsequently, the role of NK cell exosomes in controlling various viral infections is discussed. Finally, the current position of these cells in the treatment of viral infections and the therapeutic potential of their exosomes are reviewed. Video Abstract.

Baseline innate and T cell populations are correlates of protection against symptomatic influenza virus infection independent of serology

Evidence suggests that innate and adaptive cellular responses mediate resistance to the influenza virus and confer protection after vaccination. However, few studies have resolved the contribution of cellular responses within the context of preexisting antibody titers. Here, we measured the peripheral immune profiles of 206 vaccinated or unvaccinated adults to determine how baseline variations in the cellular and humoral immune compartments contribute independently or synergistically to the risk of developing symptomatic influenza. Protection correlated with diverse and polyfunctional CD4+ and CD8+ T, circulating T follicular helper, T helper type 17, myeloid dendritic and CD16+ natural killer (NK) cell subsets. Conversely, increased susceptibility was predominantly attributed to nonspecific inflammatory populations, including γδ T cells and activated CD16- NK cells, as well as TNFα+ single-cytokine-producing CD8+ T cells. Multivariate and predictive modeling indicated that cellular subsets (1) work synergistically with humoral immunity to confer protection, (2) improve model performance over demographic and serologic factors alone and (3) comprise the most important predictive covariates. Together, these results demonstrate that preinfection peripheral cell composition improves the prediction of symptomatic influenza susceptibility over vaccination, demographics or serology alone.

Upregulated CD8+ MAIT cell differentiation and KLRD1 gene expression after inactivated SARS-CoV-2 vaccination identified by single-cell sequencing

Background

The primary strategy for reducing the incidence of COVID-19 is SARS-CoV-2 vaccination. Few studies have explored T cell subset differentiation and gene expressions induced by SARS-CoV-2 vaccines. Our study aimed to analyze T cell dynamics and transcriptome gene expression after inoculation with an inactivated SARS-CoV-2 vaccine by using single-cell sequencing.

Methods

Single-cell sequencing was performed after peripheral blood mononuclear cells were extracted from three participants at four time points during the inactivated SARS-CoV-2 vaccination process. After library preparation, raw read data analysis, quality control, dimension reduction and clustering, single-cell T cell receptor (TCR) sequencing, TCR V(D)J sequencing, cell differentiation trajectory inference, differentially expressed genes, and pathway enrichment were analyzed to explore the characteristics and mechanisms of postvaccination immunodynamics.

Results

Inactivated SARS-CoV-2 vaccination promoted T cell proliferation, TCR clone amplification, and TCR diversity. The proliferation and differentiation of CD8+ mucosal-associated invariant T (MAIT) cells were significantly upregulated, as were KLRD1 gene expression and the two pathways of nuclear-transcribed mRNA catabolic process, nonsense-mediated decay, and translational initiation.

Conclusion

Upregulation of CD8+ MAIT cell differentiation and KLRD1 expression after inactivated SARS-CoV-2 vaccination was demonstrated by single-cell sequencing. We conclude that the inactivated SARS-CoV-2 vaccine elicits adaptive T cell immunity to enhance early immunity and rapid response to the targeted virus.

Gene Expression in Cord Blood and Tuberculosis in Early Childhood: A Nested Case-Control Study in a South African Birth Cohort

BACKGROUND

Transcriptomic profiling of adults with tuberculosis (TB) has become increasingly common, predominantly for diagnostic and risk prediction purposes. However, few studies have evaluated signatures in children, particularly in identifying those at risk for developing TB disease. We investigated the relationship between gene expression obtained from umbilical cord blood and both tuberculin skin test conversion and incident TB disease through the first 5 years of life.

METHODS

We conducted a nested case-control study in the Drakenstein Child Health Study, a longitudinal, population-based birth cohort in South Africa. We applied transcriptome-wide screens to umbilical cord blood samples from neonates born to a subset of selected mothers (N = 131). Signatures identifying tuberculin conversion and risk of subsequent TB disease were identified from genome-wide analysis of RNA expression.

RESULTS

Gene expression signatures revealed clear differences predictive of tuberculin conversion (n = 26) and TB disease (n = 10); 114 genes were associated with tuberculin conversion and 30 genes were associated with the progression to TB disease among children with early infection. Coexpression network analysis revealed 6 modules associated with risk of TB infection or disease, including a module associated with neutrophil activation in immune response (P < .0001) and defense response to bacterium (P < .0001).

CONCLUSIONS

These findings suggest multiple detectable differences in gene expression at birth that were associated with risk of TB infection or disease throughout early childhood. Such measures may provide novel insights into TB pathogenesis and susceptibility.

Cysteine-rich intestinal protein 1 is a novel surface marker for human myometrial stem/progenitor cells

Myometrial stem/progenitor cells (MyoSPCs) have been proposed as the cells of origin for uterine fibroids, but the identity of the MyoSPC has not been well established. We previously identified SUSD2 as a possible MyoSPC marker, but the relatively poor enrichment in stem cell characteristics of SUSD2+ over SUSD2- cells compelled us to find better markers. We combined bulk RNA-seq of SUSD2+/- cells with single cell RNA-seq to identify markers for MyoSPCs. We observed seven distinct cell clusters within the myometrium, with the vascular myocyte cluster most highly enriched for MyoSPC characteristics and markers. CRIP1 expression was found highly upregulated by both techniques and was used as a marker to sort CRIP1+/PECAM1- cells that were both enriched for colony forming potential and able to differentiate into mesenchymal lineages, suggesting that CRIP1+/PECAM1- cells could be used to better study the etiology of uterine fibroids.

Comparative analysis of machine learning approaches for predicting respiratory virus infection and symptom severity

Respiratory diseases are among the major health problems causing a burden on hospitals. Diagnosis of infection and rapid prediction of severity without time-consuming clinical tests could be beneficial in preventing the spread and progression of the disease, especially in countries where health systems remain incapable. Personalized medicine studies involving statistics and computer technologies could help to address this need. In addition to individual studies, competitions are also held such as Dialogue for Reverse Engineering Assessment and Methods (DREAM) challenge which is a community-driven organization with a mission to research biology, bioinformatics, and biomedicine. One of these competitions was the Respiratory Viral DREAM Challenge, which aimed to develop early predictive biomarkers for respiratory virus infections. These efforts are promising, however, the prediction performance of the computational methods developed for detecting respiratory diseases still has room for improvement. In this study, we focused on improving the performance of predicting the infection and symptom severity of individuals infected with various respiratory viruses using gene expression data collected before and after exposure. The publicly available gene expression dataset in the Gene Expression Omnibus, named GSE73072, containing samples exposed to four respiratory viruses (H1N1, H3N2, human rhinovirus (HRV), and respiratory syncytial virus (RSV)) was used as input data. Various preprocessing methods and machine learning algorithms were implemented and compared to achieve the best prediction performance. The experimental results showed that the proposed approaches obtained a prediction performance of 0.9746 area under the precision-recall curve (AUPRC) for infection (i.e., shedding) prediction (SC-1), 0.9182 AUPRC for symptom class prediction (SC-2), and 0.6733 Pearson correlation for symptom score prediction (SC-3) by outperforming the best leaderboard scores of Respiratory Viral DREAM Challenge (a 4.48% improvement for SC-1, a 13.68% improvement for SC-2, and a 13.98% improvement for SC-3). Additionally, over-representation analysis (ORA), which is a statistical method for objectively determining whether certain genes are more prevalent in pre-defined sets such as pathways, was applied using the most significant genes selected by feature selection methods. The results show that pathways associated with the 'adaptive immune system' and 'immune disease' are strongly linked to pre-infection and symptom development. These findings contribute to our knowledge about predicting respiratory infections and are expected to facilitate the development of future studies that concentrate on predicting not only infections but also the associated symptoms.

Epigenetic control of CD1D expression as a mechanism of resistance to immune checkpoint therapy in poorly immunogenic melanomas

Immune Checkpoint Therapies (ICT) have revolutionized the treatment of metastatic melanoma. However, only a subset of patients reaches complete responses. Deficient β2-microglobulin (β2M) expression impacts antigen presentation to T cells, leading to ICT resistance. Here, we investigate alternative β2M-correlated biomarkers that associate with ICT resistance. We shortlisted immune biomarkers interacting with human β2M using the STRING database. Next, we profiled the transcriptomic expression of these biomarkers in association with clinical and survival outcomes in the melanoma GDC-TCGA-SKCM dataset and a collection of publicly available metastatic melanoma cohorts treated with ICT (anti-PD1). Epigenetic control of identified biomarkers was interrogated using the Illumina Human Methylation 450 dataset from the melanoma GDC-TCGA-SKCM study. We show that β2M associates with CD1d, CD1b, and FCGRT at the protein level. Co-expression and correlation profile of B2M with CD1D, CD1B, and FCGRT dissociates in melanoma patients following B2M expression loss. Lower CD1D expression is typically found in patients with poor survival outcomes from the GDC-TCGA-SKCM dataset, in patients not responding to anti-PD1 immunotherapies, and in a resistant anti-PD1 pre-clinical model. Immune cell abundance study reveals that B2M and CD1D are both enriched in tumor cells and dendritic cells from patients responding to anti-PD1 immunotherapies. These patients also show increased levels of natural killer T (NKT) cell signatures in the tumor microenvironment (TME). Methylation reactions in the TME of melanoma impact the expression of B2M and SPI1, which controls CD1D expression. These findings suggest that epigenetic changes in the TME of melanoma may impact β2M and CD1d-mediated functions, such as antigen presentation for T cells and NKT cells. Our hypothesis is grounded in comprehensive bioinformatic analyses of a large transcriptomic dataset from four clinical cohorts and mouse models. It will benefit from further development using well-established functional immune assays to support understanding the molecular processes leading to epigenetic control of β2M and CD1d. This research line may lead to the rational development of new combinatorial treatments for metastatic melanoma patients that poorly respond to ICT.

Characterizing the cellular and molecular variabilities of peripheral immune cells in healthy recipients of BBIBP-CorV inactivated SARS-CoV-2 vaccine by single-cell RNA sequencing

Over 3 billion doses of inactivated vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been administered globally. However, our understanding of the immune cell functional transcription and T cell receptor (TCR)/B cell receptor (BCR) repertoire dynamics following inactivated SARS-CoV-2 vaccination remains poorly understood. Here, we performed single-cell RNA and TCR/BCR sequencing on peripheral blood mononuclear cells at four time points after immunization with the inactivated SARS-CoV-2 vaccine BBIBP-CorV. Our analysis revealed an enrichment of monocytes, central memory CD4⁺ T cells, type 2 helper T cells and memory B cells following vaccination. Single-cell TCR-seq and RNA-seq comminating analysis identified a clonal expansion of CD4⁺ T cells (but not CD8⁺ T cells) following a booster vaccination that corresponded to a decrease in the TCR diversity of central memory CD4⁺ T cells and type 2 helper T cells. Importantly, these TCR repertoire changes and CD4⁺ T cell differentiation were correlated with the biased VJ gene usage of BCR and the antibody-producing function of B cells post-vaccination. Finally, we compared the functional transcription and repertoire dynamics in immune cells elicited by vaccination and SARS-CoV-2 infection to explore the immune responses under different stimuli. Our data provide novel molecular and cellular evidence for the CD4⁺ T cell-dependent antibody response induced by inactivated vaccine BBIBP-CorV. This information is urgently needed to develop new prevention and control strategies for SARS-CoV-2 infection. (ClinicalTrials.gov Identifier: NCT04871932).Trial registration: ClinicalTrials.gov identifier: NCT04871932..

Cysteine-Rich Intestinal Protein 1 is a Novel Surface Marker for Myometrial Stem/Progenitor Cells

Myometrial stem/progenitor cells (MyoSPCs) have been proposed as the cells of origin for uterine fibroids, which are benign tumors that develop in the myometrium of most reproductive age women, but the identity of the MyoSPC has not been well established. We previously identified SUSD2 as a possible MyoSPC marker, but the relatively poor enrichment in stem cell characteristics of SUSD2+ over SUSD2- cells compelled us to find better discerning markers for more rigorous downstream analyses. We combined bulk RNA-seq of SUSD2+/- cells with single cell RNA-seq to identify markers capable of further enriching for MyoSPCs. We observed seven distinct cell clusters within the myometrium, with the vascular myocyte cluster most highly enriched for MyoSPC characteristics and markers, including SUSD2. CRIP1 expression was found highly upregulated in both techniques and was used as a marker to sort CRIP1+/PECAM1- cells that were both enriched for colony forming potential and able to differentiate into mesenchymal lineages, suggesting that CRIP1+/PECAM1- cells could be used to better study the etiology of uterine fibroids.

Susceptibility identification for seasonal influenza A/H3N2 based on baseline blood transcriptome

Introduction

Influenza susceptibility difference is a widely existing trait that has great practical significance for the accurate prevention and control of influenza.

Methods

Here, we focused on the human susceptibility to the seasonal influenza A/H3N2 of healthy adults at baseline level. Whole blood expression data for influenza A/H3N2 susceptibility from GEO were collected firstly (30 symptomatic and 19 asymptomatic). Then to explore the differences at baseline, a suite of systems biology approaches - the differential expression analysis, co-expression network analysis, and immune cell frequencies analysis were utilized.

Results

We found the baseline condition, especially immune condition between symptomatic and asymptomatic, was different. Co-expression module that is positively related to asymptomatic is also related to immune cell type of naïve B cell. Function enrichment analysis showed significantly correlation with "B cell receptor signaling pathway", "immune response-activating cell surface receptor signaling pathway" and so on. Also, modules that are positively related to symptomatic are also correlated to immune cell type of neutrophils, with function enrichment analysis showing significantly correlations with "response to bacterium", "inflammatory response", "cAMP-dependent protein kinase complex" and so on. Responses of symptomatic and asymptomatic hosts after virus exposure show differences on resisting the virus, with more effective frontline defense for asymptomatic hosts. A prediction model was also built based on only baseline transcription information to differentiate symptomatic and asymptomatic population with accuracy of 0.79.

Discussion

The results not only improve our understanding of the immune system and influenza susceptibility, but also provide a new direction for precise and targeted prevention and therapy of influenza.

Genetic landscape and immune mechanism of monocytes associated with the progression of acute-on-chronic liver failure

OBJECTIVE

Acute-on-chronic liver failure (ACLF) has a high prevalence and short-term mortality. Monocytes play an important role in the development of ACLF. However, the monocyte subpopulations with unique features and functions in ACLF and associated with disease progression remain poorly understood. We investigated the specific monocyte subpopulations associated with ACLF progression and their roles in inflammatory responses using the single-cell RNA sequencing (scRNA-seq).

METHODS

We performed scRNA-seq on 17,310 circulating monocytes from healthy controls and ACLF patients and genetically defined their subpopulations to characterize specific monocyte subpopulations associated with ACLF progression.

RESULTS

Five monocyte subpopulations were obtained, including pro-inflammatory monocytes, CD16 monocytes, HLA monocytes, megakaryocyte-like monocytes, and NK-like monocytes. Comparisons of the monocytes between ACLF patients and healthy controls showed that the pro-inflammatory monocytes had the most significant gene changes, among which the expressions of genes related to inflammatory responses and cell metabolism were significantly increased while the genes related to cell cycle progression were significantly decreased. Furthermore, compared with the ACLF survival group, the ACLF death group had significantly higher expressions of pro-inflammatory cytokines (e.g., IL-6) and their receptors, chemokines (e.g., CCL4 and CCL5), and inflammation-inducing factors (e.g., HES4). Additionally, validation using scRNA-seq and flow cytometry revealed the presence of a cell type-specific transcriptional signature of pro-inflammatory monocytes THBS1, whose production might reflect the disease progression and poor prognosis.

CONCLUSIONS

We present the accurate classification, molecular markers, and signaling pathways of monocytes associated with ACLF progression. Therapies targeting pro-inflammatory monocytes may be a promising approach for blocking ACLF progression.

Spatial heterogeneity of infiltrating T cells in high-grade serous ovarian cancer revealed by multi-omics analysis

Tumor-infiltrating lymphocytes (TILs), especially CD8⁺ TILs, represent a favorable prognostic factor in high-grade serous ovarian cancer (HGSOC) and other tumor lineages. Here, we analyze the spatial heterogeneity of different TIL subtypes in HGSOC. We integrated RNA sequencing, whole-genome sequencing, bulk T cell receptor (TCR) sequencing, as well as single-cell RNA/TCR sequencing to investigate the characteristics and differential composition of TILs across different HGSOC sites. Two immune "cold" patterns in ovarian cancer are identified: (1) ovarian lesions with low infiltration of mainly dysfunctional T cells and immunosuppressive Treg cells and (2) omental lesions infiltrated with non-tumor-specific bystander cells. Exhausted CD8 T cells that are preferentially enriched in ovarian tumors exhibit evidence for expansion and cytotoxic activity. Inherent tumor immune microenvironment characteristics appear to be the main contributor to the spatial differences in TIL status. The landscape of spatial heterogeneity of TILs may inform potential strategies for therapeutic manipulation in HGSOC.

Integrated time-series transcriptomic and metabolomic analyses reveal different inflammatory and adaptive immune responses contributing to host resistance to PRRSV

Porcine reproductive and respiratory syndrome virus (PRRSV) is a highly contagious disease that affects the global pig industry. To understand mechanisms of susceptibility/resistance to PRRSV, this study profiled the time-serial white blood cells transcriptomic and serum metabolomic responses to PRRSV in piglets from a crossbred population of PRRSV-resistant Tongcheng pigs and PRRSV-susceptible Large White pigs. Gene set enrichment analysis (GSEA) illustrated that PRRSV infection up-regulated the expression levels of marker genes of dendritic cells, monocytes and neutrophils and inflammatory response, but down-regulated T cells, B cells and NK cells markers. CIBERSORT analysis confirmed the higher T cells proportion in resistant pigs during PRRSV infection. Resistant pigs showed a significantly higher level of T cell activation and lower expression levels of monocyte surface signatures post infection than susceptible pigs, corresponding to more severe suppression of T cell immunity and inflammatory response in susceptible pigs. Differentially expressed genes between resistant/susceptible pigs during the course of infection were significantly enriched in oxidative stress, innate immunity and humoral immunity, cell cycle, biotic stimulated cellular response, wounding response and behavior related pathways. Fourteen of these genes were distributed in 5 different QTL regions associated with PRRSV-related traits. Chemokine CXCL10 levels post PRRSV infection were differentially expressed between resistant pigs and susceptible pigs and can be a promising marker for susceptibility/resistance to PRRSV. Furthermore, the metabolomics dataset indicated differences in amino acid pathways and lipid metabolism between pre-infection/post-infection and resistant/susceptible pigs. The majority of metabolites levels were also down-regulated after PRRSV infection and were significantly positively correlated to the expression levels of marker genes in adaptive immune response. The integration of transcriptome and metabolome revealed concerted molecular events triggered by the infection, notably involving inflammatory response, adaptive immunity and G protein-coupled receptor downstream signaling. This study has increased our knowledge of the immune response differences induced by PRRSV infection and susceptibility differences at the transcriptomic and metabolomic levels, providing the basis for the PRRSV resistance mechanism and effective PRRS control.

Interactive single-cell data analysis using Cellar

Cell type assignment is a major challenge for all types of high throughput single cell data. In many cases such assignment requires the repeated manual use of external and complementary data sources. To improve the ability to uniformly assign cell types across large consortia, platforms and modalities, we developed Cellar, a software tool that provides interactive support to all the different steps involved in the assignment and dataset comparison process. We discuss the different methods implemented by Cellar, how these can be used with different data types, how to combine complementary data types and how to analyze and visualize spatial data. We demonstrate the advantages of Cellar by using it to annotate several HuBMAP datasets from multi-omics single-cell sequencing and spatial proteomics studies. Cellar is open-source and includes several annotated HuBMAP datasets.

Identification of Potential Diagnostic Biomarkers From Circulating Cells During the Course of Sleep Deprivation-Related Myocardial Infarction Based on Bioinformatics Analyses

Background

Myocardial infarction (MI) is the leading cause of death from non-infectious diseases worldwide and results in rapid deterioration due to the sudden rupture of plaques associated with atherosclerosis, a chronic inflammatory disease. Sleep is a key factor that regulates immune homeostasis of the body. The imbalance in circulating immune cells caused by sleep deprivation (SD) may represent a risk factor leading to the rapid deterioration of plaques and MI. Therefore, it is of profound significance to identify diagnostic biomarkers for preventing SD-related MI.

Methods

In the present study, we identified coexpressed differentially expressed genes (co-DEGs) between peripheral blood mononuclear cells from MI and SD samples (compared to controls) from a public database. LASSO regression analysis was applied to identify significant diagnostic biomarkers from co-DEGs. Moreover, receiver operating characteristic (ROC) curve analysis was performed to test biomarker accuracy and diagnostic ability. We further analyzed immune cell enrichment in MI and SD samples using the CIBERSORT algorithm, and the correlation between biomarkers and immune cell composition was assessed. We also investigated whether diagnostic biomarkers are involved in immune cell signaling pathways in SD-related MI processes.

Results

A total of 10 downregulated co-DEGs from the sets of MI-DEGs and SD-DEGs were overlapped. After applying LASSO regression analysis, SYTL2, KLRD1, and C12orf75 were selected and validated as diagnostic biomarkers using ROC analysis. Next, we found that resting NK cells were downregulated in both the MI samples and SD samples, which is similar to the changes noted for SYTL2. Importantly, SYTL2 was strongly positively correlated not only with resting NK cells but also with most genes related to NK cell markers in the MI and SD datasets. Moreover, SYTL2 was highly associated with genes in NK cell signaling pathways, including the MAPK signaling pathway, cytotoxic granule movement and exocytosis, and NK cell activation. Furthermore, GSEA and KEGG analyses provided evidence that the DEGs identified from MI samples with low vs. high SYTL2 expression exhibited a strong association with the regulation of the immune response and NK cell-mediated cytotoxicity.

Conclusion

In conclusion, SYTL2, KLRD1, and C12orf75 represent potential diagnostic biomarkers of MI. The association between SYTL2 and resting NK cells may be critically involved in SD-related MI development and occurrence.

Establishment of a Risk Score Model for Early Prediction of Severe H1N1 Influenza

H1N1 is the most common subtype of influenza virus circulating worldwide and can cause severe disease in some populations. Early prediction and intervention for patients who develop severe influenza will greatly reduce their mortality. In this study, we conducted a comprehensive analysis of 180 PBMC samples from three published datasets from the GEO DataSets. Differentially expressed gene (DEG) analysis and weighted correlation network analysis (WGCNA) were performed to provide candidate DEGs for model building. Functional enrichment and CIBERSORT analyses were also performed to evaluate the differences in composition and function of PBMCs between patients with severe and mild disease. Finally, a risk score model was built using lasso regression analysis, with six genes (CX3CR1, KLRD1, MMP8, PRTN3, RETN and SCD) involved. The model performed moderately in the early identification of patients that develop severe H1N1 disease.

SBT (Composed of Panax ginseng and Aconitum carmichaeli) and Stigmasterol Enhances Nitric Oxide Production and Exerts Curative Properties as a Potential Anti-Oxidant and Immunity-Enhancing Agent

Immune dysregulation is a risk factor for several diseases, including infectious diseases. Immunostimulatory agents have been used for the treatment of immune dysregulation, but deleterious adverse effects have been reported. The present study aims to establish the anti-oxidant and immunity-enhancing effects of Sambu-Tang (SBT), composed of Panax ginseng and Aconitum carmichaeli, and stigmasterol (Stig), an active compound of SBT. Immune-related factors were analyzed in RAW264.7 macrophage cells, mouse primary splenocytes, and the serum and spleen of cyclophosphamide-induced immunosuppressed mice. Results showed that the production levels of nitric oxide (NO) and expression levels of inducible NO synthase and heme oxygenase-1 were increased following SBT or Stig treatment in RAW264.7 cells. SBT or Stig increased the production levels of G-CSF, IFN-γ, IL-12, IL-2, IL-6, and TNF-α and induced the activation of NF-κB in RAW264.7 cells. SBT or Stig promoted splenic lymphocyte proliferation and increased splenic NK cell cytotoxic activity. In addition, SBT or Stig enhanced the levels of IFN-γ, IL-12, IL-2, IL-6, or TNF-α in the serum and spleen of the immunosuppressed mice. SBT or Stig increased the superoxide dismutase activity in the spleen. Collectively, SBT and Stig possess anti-oxidant and immunomodulatory activities, so they may be considered effective natural compounds for the treatment of various symptoms caused by immune dysregulation.

Neutrophil-to-lymphocyte ratio as a potential biomarker in predicting influenza susceptibility

BACKGROUND

Human population exposed to influenza viruses exhibited wide variation in susceptibility. The ratio of neutrophils to lymphocytes (NLR) has been examined to be a marker of systemic inflammation. We sought to investigate the relationship between influenza susceptibility and the NLR taken before influenza virus infection.

METHODS

We investigated blood samples from five independent influenza challenge cohorts prior to influenza inoculation at the cellular level by using digital cytometry. We used multi-cohort gene expression analysis to compare the NLR between the symptomatic infected (SI) and asymptomatic uninfected (AU) subjects. We then used a network analysis approach to identify host factors associated with NLR and influenza susceptibility.

RESULTS

The baseline NLR was significantly higher in the SI group in both discovery and validation cohorts. The NLR achieved an AUC of 0.724 on the H3N2 data, and 0.736 on the H1N1 data in predicting influenza susceptibility. We identified four key modules that were not only significantly correlated with the baseline NLR, but also differentially expressed between the SI and AU groups. Genes within these four modules were enriched in pathways involved in B cell-mediated immune responses, cellular metabolism, cell cycle, and signal transduction, respectively.

CONCLUSIONS

This study identified the NLR as a potential biomarker for predicting disease susceptibility to symptomatic influenza. An elevated NLR was detected in susceptible hosts, who may have defects in B cell-mediated immunity or impaired function in cellular metabolism, cell cycle or signal transduction. Our work can serve as a comparative model to provide insights into the COVID-19 susceptibility.

DeepFlu: a deep learning approach for forecasting symptomatic influenza A infection based on pre-exposure gene expression

BACKGROUND AND OBJECTIVE

Not everyone gets sick after an exposure to influenza A viruses (IAV). Although KLRD1 has been identified as a potential biomarker for influenza susceptibility, it remains unclear whether forecasting symptomatic flu infection based on pre-exposure host gene expression might be possible.

METHOD

To examine this hypothesis, we developed DeepFlu using the state-of-the-art deep learning approach on the human gene expression data infected with IAV subtype H1N1 or H3N2 viruses to forecast who would catch the flu prior to an exposure to IAV.

RESULTS

The results indicated that such forecast is possible and, in other words, gene expression could reflect the strength of host immunity. In the leave-one-person-out cross-validation, DeepFlu based on deep neural network outperformed the models using convolutional neural network, random forest, or support vector machine, achieving 70.0% accuracy, 0.787 AUROC, and 0.758 AUPR for H1N1 and 73.8% accuracy, 0.847 AUROC, and 0.901 AUPR for H3N2. In the external validation, DeepFlu also reached 71.4% accuracy, 0.700 AUROC, and 0.723 AUPR for H1N1 and 73.5% accuracy, 0.732 AUROC, and 0.749 AUPR for H3N2, surpassing the KLRD1 biomarker. In addition, DeepFlu which was trained only by pre-exposure data worked the best than by other time spans and mixed training data of H1N1 and H3N2 did not necessarily enhance prediction. DeepFlu is available at https://github.com/ntou-compbio/DeepFlu.

CONCLUSIONS

DeepFlu is a prognostic tool that can moderately recognize individuals susceptible to the flu and may help prevent the spread of IAV.

Identification of Biomarkers Related to Immune Cell Infiltration with Gene Coexpression Network in Myocardial Infarction

Background

There is evidence that the immune system plays a key critical role in the pathogenesis of myocardial infarction (MI). However, the exact mechanisms associated with immunity have not been systematically uncovered.

Methods

This study used the weighted gene coexpression network analysis (WGCNA) and the CIBERSORT algorithm to analyze the MI expression data from the Gene Expression Omnibus database and then identify the module associated with immune cell infiltration. In addition, we built the coexpression network and protein-protein interactions network analysis to identify the hub genes. Furthermore, the relationship between hub genes and NK cell resting was validated by using another dataset GSE123342. Finally, receiver operating characteristic (ROC) curve analyses were used to assess the diagnostic value of verified hub genes.

Results

Monocytes and neutrophils were markedly increased, and T cell CD8, T cell CD4 naive, T cell CD4 memory resting, and NK cell resting were significantly decreased in MI groups compared with stable coronary artery disease (CAD) groups. The WGCNA results showed that the pink model had the highest correlation with the NK cell resting infiltration level. We identified 11 hub genes whose expression correlated to the NK cell resting infiltration level, among which, 7 hub genes (NKG7, TBX21, PRF1, CD247, KLRD1, FASLG, and EOMES) were successfully validated in GSE123342. And these 7 genes had diagnostic value to distinguish MI and stable CAD.

Conclusions

NKG7, TBX21, PRF1, CD247, KLRD1, FASLG, and EOMES may be a diagnostic biomarker and therapeutic target associated with NK cell resting infiltration in MI.

Computational drug repositioning of atorvastatin for ulcerative colitis

OBJECTIVE

Ulcerative colitis (UC) is a chronic inflammatory disorder with limited effective therapeutic options for long-term treatment and disease maintenance. We hypothesized that a multi-cohort analysis of independent cohorts representing real-world heterogeneity of UC would identify a robust transcriptomic signature to improve identification of FDA-approved drugs that can be repurposed to treat patients with UC.

MATERIALS AND METHODS

We performed a multi-cohort analysis of 272 colon biopsy transcriptome samples across 11 publicly available datasets to identify a robust UC disease gene signature. We compared the gene signature to in vitro transcriptomic profiles induced by 781 FDA-approved drugs to identify potential drug targets. We used a retrospective cohort study design modeled after a target trial to evaluate the protective effect of predicted drugs on colectomy risk in patients with UC from the Stanford Research Repository (STARR) database and Optum Clinformatics DataMart.

RESULTS

Atorvastatin treatment had the highest inverse-correlation with the UC gene signature among non-oncolytic FDA-approved therapies. In both STARR (n = 827) and Optum (n = 7821), atorvastatin intake was significantly associated with a decreased risk of colectomy, a marker of treatment-refractory disease, compared to patients prescribed a comparator drug (STARR: HR = 0.47, P = .03; Optum: HR = 0.66, P = .03), irrespective of age and length of atorvastatin treatment.

DISCUSSION & CONCLUSION

These findings suggest that atorvastatin may serve as a novel therapeutic option for ameliorating disease in patients with UC. Importantly, we provide a systematic framework for integrating publicly available heterogeneous molecular data with clinical data at a large scale to repurpose existing FDA-approved drugs for a wide range of human diseases.

Experimental Babesia rossi infection induces hemolytic, metabolic, and viral response pathways in the canine host

Background

Babesia rossi is a leading cause of morbidity and mortality among the canine population of sub-Saharan Africa, but pathogenesis remains poorly understood. Previous studies of B. rossi infection were derived from clinical cases, in which neither the onset of infection nor the infectious inoculum was known. Here, we performed controlled B. rossi inoculations in canines and evaluated disease progression through clinical tests and whole blood transcriptomic profiling.

Results

Two subjects were administered a low inoculum (10⁴ parasites) while three received a high (10⁸ parasites). Subjects were monitored for 8 consecutive days; anti-parasite treatment with diminazene aceturate was administered on day 4. Blood was drawn prior to inoculation as well as every experimental day for assessment of clinical parameters and transcriptomic profiles. The model recapitulated natural disease manifestations including anemia, acidosis, inflammation and behavioral changes. Rate of disease onset and clinical severity were proportional to the inoculum. To analyze the temporal dynamics of the transcriptomic host response, we sequenced mRNA extracted from whole blood drawn on days 0, 1, 3, 4, 6, and 8. Differential gene expression, hierarchical clustering, and pathway enrichment analyses identified genes and pathways involved in response to hemolysis, metabolic changes, and several arms of the immune response including innate immunity, adaptive immunity, and response to viral infection.

Conclusions

This work comprehensively characterizes the clinical and transcriptomic progression of B. rossi infection in canines, thus establishing a large mammalian model of severe hemoprotozoal disease to facilitate the study of host-parasite biology and in which to test novel anti-disease therapeutics. The knowledge gained from the study of B. rossi in canines will not only improve our understanding of this emerging infectious disease threat in domestic dogs, but also provide insight into the pathobiology of human diseases caused by Babesia and Plasmodium species.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07889-4.

Downregulation of exhausted cytotoxic T cells in gene expression networks of multisystem inflammatory syndrome in children

Multisystem inflammatory syndrome in children (MIS-C) presents with fever, inflammation and pathology of multiple organs in individuals under 21 years of age in the weeks following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Although an autoimmune pathogenesis has been proposed, the genes, pathways and cell types causal to this new disease remain unknown. Here we perform RNA sequencing of blood from patients with MIS-C and controls to find disease-associated genes clustered in a co-expression module annotated to CD56dimCD57+ natural killer (NK) cells and exhausted CD8+ T cells. A similar transcriptome signature is replicated in an independent cohort of Kawasaki disease (KD), the related condition after which MIS-C was initially named. Probing a probabilistic causal network previously constructed from over 1,000 blood transcriptomes both validates the structure of this module and reveals nine key regulators, including TBX21, a central coordinator of exhausted CD8+ T cell differentiation. Together, this unbiased, transcriptome-wide survey implicates downregulation of NK cells and cytotoxic T cell exhaustion in the pathogenesis of MIS-C.

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.

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

Viral infections induce a conserved host response distinct from bacterial infections. We hypothesized that the conserved response is associated with disease severity and is distinct between patients with different outcomes. To test this, we integrated 4,780 blood transcriptome profiles from patients aged 0 to 90 years infected with one of 16 viruses, including SARS-CoV-2, Ebola, chikungunya, and influenza, across 34 cohorts from 18 countries, and single-cell RNA sequencing profiles of 702,970 immune cells from 289 samples across three cohorts. Severe viral infection was associated with increased hematopoiesis, myelopoiesis, and myeloid-derived suppressor cells. We identified protective and detrimental gene modules that defined distinct trajectories associated with mild versus severe outcomes. The interferon response was decoupled from the protective host response in patients with severe outcomes. These findings were consistent, irrespective of age and virus, and provide insights to accelerate the development of diagnostics and host-directed therapies to improve global pandemic preparedness.

Transcriptomic Analysis Reveals Host miRNAs Correlated with Immune Gene Dysregulation during Fatal Disease Progression in the Ebola Virus Cynomolgus Macaque Disease Model

Ebola virus is a continuing threat to human populations, causing a virulent hemorrhagic fever disease characterized by dysregulation of both the innate and adaptive host immune responses. Severe cases are distinguished by an early, elevated pro-inflammatory response followed by a pronounced lymphopenia with B and T cells unable to mount an effective anti-viral response. The precise mechanisms underlying the dysregulation of the host immune system are poorly understood. In recent years, focus on host-derived miRNAs showed these molecules to play an important role in the host gene regulation arsenal. Here, we describe an investigation of RNA biomarkers in the fatal Ebola virus disease (EVD) cynomolgus macaque model. We monitored both host mRNA and miRNA responses in whole blood longitudinally over the disease course in these non-human primates (NHPs). Analysis of the interactions between these classes of RNAs revealed several miRNA markers significantly correlated with downregulation of genes; specifically, the analysis revealed those involved in dysregulated immune pathways associated with EVD. In particular, we noted strong interactions between the miRNAs hsa-miR-122-5p and hsa-miR-125b-5p with immunological genes regulating both B and T-cell activation. This promising set of biomarkers will be useful in future studies of severe EVD pathogenesis in both NHPs and humans and may serve as potential prognostic targets.

Characterization of Gene Expression Signatures for the Identification of Cellular Heterogeneity in the Developing Mammary Gland

The developing mammary gland depends on several transcription-dependent networks to define cellular identities and differentiation trajectories. Recent technological advancements that allow for single-cell profiling of gene expression have provided an initial picture into the epithelial cellular heterogeneity across the diverse stages of gland maturation. Still, a deeper dive into expanded molecular signatures would improve our understanding of the diversity of mammary epithelial and non-epithelial cellular populations across different tissue developmental stages, mouse strains and mammalian species. Here, we combined differential mammary gland fractionation approaches and transcriptional profiles obtained from FACS-isolated mammary cells to improve our definitions of mammary-resident, cellular identities at the single-cell level. Our approach yielded a series of expression signatures that illustrate the heterogeneity of mammary epithelial cells, specifically those of the luminal fate, and uncovered transcriptional changes to their lineage-defined, cellular states that are induced during gland development. Our analysis also provided molecular signatures that identified non-epithelial mammary cells, including adipocytes, fibroblasts and rare immune cells. Lastly, we extended our study to elucidate expression signatures of human, breast-resident cells, a strategy that allowed for the cross-species comparison of mammary epithelial identities. Collectively, our approach improved the existing signatures of normal mammary epithelial cells, as well as elucidated the diversity of non-epithelial cells in murine and human breast tissue. Our study provides a useful resource for future studies that use single-cell molecular profiling strategies to understand normal and malignant breast development.

Flexible Mixture Model Approaches That Accommodate Footprint Size Variability for Robust Detection of Balancing Selection

Long-term balancing selection typically leaves narrow footprints of increased genetic diversity, and therefore most detection approaches only achieve optimal performances when sufficiently small genomic regions (i.e., windows) are examined. Such methods are sensitive to window sizes and suffer substantial losses in power when windows are large. Here, we employ mixture models to construct a set of five composite likelihood ratio test statistics, which we collectively term B statistics. These statistics are agnostic to window sizes and can operate on diverse forms of input data. Through simulations, we show that they exhibit comparable power to the best-performing current methods, and retain substantially high power regardless of window sizes. They also display considerable robustness to high mutation rates and uneven recombination landscapes, as well as an array of other common confounding scenarios. Moreover, we applied a specific version of the B statistics, termed B2, to a human population-genomic data set and recovered many top candidates from prior studies, including the then-uncharacterized STPG2 and CCDC169-SOHLH2, both of which are related to gamete functions. We further applied B2 on a bonobo population-genomic data set. In addition to the MHC-DQ genes, we uncovered several novel candidate genes, such as KLRD1, involved in viral defense, and SCN9A, associated with pain perception. Finally, we show that our methods can be extended to account for multiallelic balancing selection and integrated the set of statistics into open-source software named BalLeRMix for future applications by the scientific community.

Genetic landscape and autoimmunity of monocytes in developing Vogt-Koyanagi-Harada disease

Vogt-Koyanagi-Harada (VKH) disease is a systemic autoimmune disorder affecting multiple organs, including eyes, skin, and central nervous system. It is known that monocytes significantly contribute to the development of autoimmune disease. However, the subset heterogeneity with unique functions and signatures in human circulating monocytes and the identity of disease-specific monocytic populations remain largely unknown. Here, we employed an advanced single-cell RNA sequencing technology to systematically analyze 11,259 human circulating monocytes and genetically defined their subpopulations. We constructed a precise atlas of human blood monocytes, identified six subpopulations-including S100A12, HLA, CD16, proinflammatory, megakaryocyte-like, and NK-like monocyte subsets-and uncovered two previously unidentified subsets: HLA and megakaryocyte-like monocyte subsets. Relative to healthy individuals, cellular composition, gene expression signatures, and activation states were markedly alternated in VKH patients utilizing cell type-specific programs, especially the CD16 and proinflammatory monocyte subpopulations. Notably, we discovered a disease-relevant subgroup, proinflammatory monocytes, which showed a discriminative gene expression signature indicative of inflammation, antiviral activity, and pathologic activation, and converted into a pathologic activation state implicating the active inflammation during VKH disease. Additionally, we found the cell type-specific transcriptional signature of proinflammatory monocytes, ISG15, whose production might reflect the treatment response. Taken together, in this study, we present discoveries on accurate classification, molecular markers, and signaling pathways for VKH disease-associated monocytes. Therapeutically targeting this proinflammatory monocyte subpopulation would provide an attractive approach for treating VKH, as well as other autoimmune diseases.

Identification of potential mRNA panels for severe acute respiratory syndrome coronavirus 2 (COVID-19) diagnosis and treatment using microarray dataset and bioinformatics methods

The goal of the present investigation is to identify the differentially expressed genes (DEGs) between SARS-CoV-2 infected and normal control samples to investigate the molecular mechanisms of infection with SARS-CoV-2. The microarray data of the dataset E-MTAB-8871 were retrieved from the ArrayExpress database. Pathway and Gene Ontology (GO) enrichment study, protein–protein interaction (PPI) network, modules, target gene–miRNA regulatory network, and target gene–TF regulatory network have been performed. Subsequently, the key genes were validated using an analysis of the receiver operating characteristic (ROC) curve. In SARS-CoV-2 infection, a total of 324 DEGs (76 up- and 248 down-regulated genes) were identified and enriched in a number of associated SARS-CoV-2 infection pathways and GO terms. Hub and target genes such as TP53, HRAS, MAPK11, RELA, IKZF3, IFNAR2, SKI, TNFRSF13C, JAK1, TRAF6, KLRF2, CD1A were identified from PPI network, target gene–miRNA regulatory network, and target gene–TF regulatory network. Study of the ROC showed that ten genes (CCL5, IFNAR2, JAK2, MX1, STAT1, BID, CD55, CD80, HAL-B, and HLA-DMA) were substantially involved in SARS-CoV-2 patients. The present investigation identified key genes and pathways that deepen our understanding of the molecular mechanisms of SARS-CoV-2 infection, and could be used for SARS-CoV-2 infection as diagnostic and therapeutic biomarkers.

A blood-based host gene expression assay for early detection of respiratory viral infection: an index-cluster prospective cohort study

Background

Early and accurate identification of individuals with viral infections is crucial for clinical management and public health interventions. We aimed to assess the ability of transcriptomic biomarkers to identify naturally acquired respiratory viral infection before typical symptoms are present.

Methods

In this index-cluster study, we prospectively recruited a cohort of undergraduate students (aged 18–25 years) at Duke University (Durham, NC, USA) over a period of 5 academic years. To identify index cases, we monitored students for the entire academic year, for the presence and severity of eight symptoms of respiratory tract infection using a daily web-based survey, with symptoms rated on a scale of 0–4. Index cases were defined as individuals who reported a 6-point increase in cumulative daily symptom score. Suspected index cases were visited by study staff to confirm the presence of reported symptoms of illness and to collect biospecimen samples. We then identified clusters of close contacts of index cases (ie, individuals who lived in close proximity to index cases, close friends, and partners) who were presumed to be at increased risk of developing symptomatic respiratory tract infection while under observation. We monitored each close contact for 5 days for symptoms and viral shedding and measured transcriptomic responses at each timepoint each day using a blood-based 36-gene RT-PCR assay.

Findings

Between Sept 1, 2009, and April 10, 2015, we enrolled 1465 participants. Of 264 index cases with respiratory tract infection symptoms, 150 (57%) had a viral cause confirmed by RT-PCR. Of their 555 close contacts, 106 (19%) developed symptomatic respiratory tract infection with a proven viral cause during the observation window, of whom 60 (57%) had the same virus as their associated index case. Nine viruses were detected in total. The transcriptomic assay accurately predicted viral infection at the time of maximum symptom severity (mean area under the receiver operating characteristic curve [AUROC] 0·94 [95% CI 0·92–0·96]), as well as at 1 day (0·87 [95% CI 0·84–0·90]), 2 days (0·85 [0·82–0·88]), and 3 days (0·74 [0·71–0·77]) before peak illness, when symptoms were minimal or absent and 22 (62%) of 35 individuals, 25 (69%) of 36 individuals, and 24 (82%) of 29 individuals, respectively, had no detectable viral shedding.

Interpretation

Transcriptional biomarkers accurately predict and diagnose infection across diverse viral causes and stages of disease and thus might prove useful for guiding the administration of early effective therapy, quarantine decisions, and other clinical and public health interventions in the setting of endemic and pandemic infectious diseases.

Funding

US Defense Advanced Research Projects Agency.

Behavioral strategies to prevent and mitigate COVID-19 infection

The single stranded RNA virus SARS-CoV-2 has caused a massive addition to the already leading global cause of mortality, viral respiratory tract infections. Characterized by and associated with early and deleteriously enhanced production of pro-inflammatory cytokines by respiratory epithelial cells, severe COVID-19 illness has the potential to inflict acute respiratory distress syndrome and even death. Due to the fast spreading nature of COVID-19 and the current lack of a vaccine or specific pharmaceutical treatments, understanding of viral pathogenesis, behavioral prophylaxis, and mitigation tactics are of great public health concern. This review article outlines the immune response to viral pathogens, and due to the novelty of COVID-19 and the large body of evidence suggesting the respiratory and immune benefits from regular moderate intensity exercise, provides observational and mechanistic evidence from research on other viral infections that suggests strategically planned exercise regimens may help reduce susceptibility to infection, while also mitigating severe immune responses to infection commonly associated with poor COVID-19 prognosis. We propose that regular moderate intensity exercise should be considered as part of a combinatorial approach including widespread hygiene initiatives, properly planned and well-executed social distancing policies, and use of efficacious facial coverings like N95 respirators. Studies discerning COVID-19 pathogenesis mechanisms, transfer dynamics, and individual responses to pharmaceutical and adjunct treatments are needed to reduce viral transmission and bring an end to the COVID-19 pandemic.

Human immunology and immunotherapy: main achievements and challenges

The immune system is a fascinating world of cells, soluble factors, interacting cells, and tissues, all of which are interconnected. The highly complex nature of the immune system makes it difficult to view it as a whole, but researchers are now trying to put all the pieces of the puzzle together to obtain a more complete picture. The development of new specialized equipment and immunological techniques, genetic approaches, animal models, and a long list of monoclonal antibodies, among many other factors, are improving our knowledge of this sophisticated system. The different types of cell subsets, soluble factors, membrane molecules, and cell functionalities are some aspects that we are starting to understand, together with their roles in health, aging, and illness. This knowledge is filling many of the gaps, and in some cases, it has led to changes in our previous assumptions; e.g., adaptive immune cells were previously thought to be unique memory cells until trained innate immunity was observed, and several innate immune cells with features similar to those of cytokine-secreting T cells have been discovered. Moreover, we have improved our knowledge not only regarding immune-mediated illnesses and how the immune system works and interacts with other systems and components (such as the microbiome) but also in terms of ways to manipulate this system through immunotherapy. The development of different types of immunotherapies, including vaccines (prophylactic and therapeutic), and the use of pathogens, monoclonal antibodies, recombinant proteins, cytokines, and cellular immunotherapies, are changing the way in which we approach many diseases, especially cancer.

Cytotoxic lymphocytes are dysregulated in multisystem inflammatory syndrome in children

Multisystem inflammatory syndrome in children (MIS-C) presents with fever, inflammation and multiple organ involvement in individuals under 21 years following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. To identify genes, pathways and cell types driving MIS-C, we sequenced the blood transcriptomes of MIS-C cases, pediatric cases of coronavirus disease 2019, and healthy controls. We define a MIS-C transcriptional signature partially shared with the transcriptional response to SARS-CoV-2 infection and with the signature of Kawasaki disease, a clinically similar condition. By projecting the MIS-C signature onto a co-expression network, we identified disease gene modules and found genes downregulated in MIS-C clustered in a module enriched for the transcriptional signatures of exhausted CD8 + T-cells and CD56 dim CD57 + NK cells. Bayesian network analyses revealed nine key regulators of this module, including TBX21 , a central coordinator of exhausted CD8 + T-cell differentiation. Together, these findings suggest dysregulated cytotoxic lymphocyte response to SARS-Cov-2 infection in MIS-C.

Mapping a mammalian adult adrenal gland hierarchy across species by microwell-seq

Recently, single-cell RNA-seq technologies have been rapidly updated, leading to a revolution in biology. We previously developed Microwell-seq, a cost-effective and high-throughput single cell RNA sequencing(scRNA-seq) method with a very simple device. Most cDNA libraries are sequenced using an expensive Illumina platform. Here, we present the first report showing combined Microwell-seq and BGI MGISEQ2000, a less expensive sequencing platform, to profile the whole transcriptome of 11,883 individual mouse adult adrenal gland cells and identify 18 transcriptionally distinct clusters. Moreover, we performed a single-cell comparative analysis of human and mouse adult adrenal glands to reveal the conserved genetic networks in these mammalian systems. These results provide new insights into the sophisticated adrenal gland hierarchy and provide a benchmark, low-cost strategy for high-throughput single-cell RNA study.

Preexisting subtype immunodominance shapes memory B cell recall response to influenza vaccination

Influenza is a highly contagious viral pathogen with more than 200,000 cases reported in the United States during the 2017-2018 season. Annual vaccination is recommended by the World Health Organization, with the goal to reduce influenza severity and transmission. Currently available vaccines are about 60% effective, and vaccine effectiveness varies from season to season, as well as between different influenza subtypes within a single season. Immunological imprinting from early-life influenza infection can prominently shape the immune response to subsequent infections. Here, the impact of preexisting B cell memory in the response to quadrivalent influenza vaccine was assessed using blood samples collected from healthy subjects (18-85 years old) prior to and 21-28 days following influenza vaccination. Influenza vaccination increased both HA-specific antibodies and memory B cell frequency. Despite no apparent differences in antigenicity between vaccine components, most individuals were biased toward one of the vaccine strains. Specifically, responses to H3N2 were reduced in magnitude relative to the other vaccine components. Overall, this study unveils a potentially new mechanism underlying differential vaccine effectiveness against distinct influenza subtypes.

Sex Differences in the Blood Transcriptome Identify Robust Changes in Immune Cell Proportions with Aging and Influenza Infection

Sex differences in autoimmunity and infection suggest that a better understanding of molecular sex differences will improve the diagnosis and treatment of immune-related disease. We identified 144 differentially expressed genes, referred to as immune sex expression signature (iSEXS), between human males and females using an integrated multi-cohort analysis of blood transcriptome profiles from six discovery cohorts from five continents with 458 healthy individuals. We validated iSEXS in 11 additional cohorts of 524 peripheral blood samples. When we separated iSEXS into genes located on sex chromosomes (XY-iSEXS) or autosomes (autosomal-iSEXS), both modules distinguished males and females. iSEXS reflects sex differences in immune cell proportions, with female-associated genes showing higher expression by CD4+ T cells and male-associated genes showing higher expression by myeloid cells. Autosomal-iSEXS detected an increase in monocytes with age in females, reflected sex-differential immune cell dynamics during influenza infection, and predicted antibody response in males, but not females.

Simian Immunodeficiency Virus Infection Modulates CD94+ (KLRD1+) NK Cells in Rhesus Macaques

Recently, we and others have shown that natural killer (NK) cells exhibit memory-like recall responses against cytomegalovirus (CMV) and human immunodeficiency/virus simian immunodeficiency virus (HIV/SIV) infections. Although the mechanism(s) have not been fully delineated, several groups have shown that the activating receptor NKG2C is elevated on NK cells in the context of rhesus CMV (rhCMV) or human CMV (hCMV) infections. CD94, which heterodimerizes with NKG2C is also linked to adaptive NK cell responses. Because nonhuman primates (NHP) play a crucial role in modeling HIV (SIV) infections, it is crucial to be able to assess and characterize the NKG2 family in NHP. Unfortunately, it is not possible to detect CD94 using commercially available antibodies in NHP. Our work, a first for NHP, has focused on developing RNA flow cytometry using mRNA transcripts as proxies distinguishing NKG2C from NKG2A. We have expanded the application of this technology and here we show the first characterization of CD94⁺ (KLRD1⁺) NK cells in NHP using multiparametric RNA flow cytometry. Peripheral blood mononuclear cells from naive and matched acutely (n = 4) or chronically (n = 12) SIV-infected rhesus macaques were analyzed by flow cytometry using commercially available antibodies, determining expression of transcripts for NKG2A, NKG2C, and CD94 (KLRC1, KLRC2, and KLRD1, respectively) on NK cells using RNA flow cytometry. Our data show that KLRC1^+/- KLRC2⁺ KLRD1⁺ NK cells decrease following chronic, but not acute, infection with SIV. This approach will allow us to investigate the kinetics of infection and NK memory formation and will further improve our understanding of basic NK cell biology, especially in the context of SIV infection.IMPORTANCE Nonhuman primates play a crucial role in approximating human biology and many diseases that are difficult, if not impossible, to achieve in other animal models, notably HIV. Current advances in adaptive NK cell research positions us to address fundamental deficiencies in our fight against infection and disease at the earliest moments after infection or substantially earlier in disease progression. We show here that we can identify specific NK cell subpopulations that are modulated following chronic, but not acute, SIV infection. The ability to identify these subsets more precisely will inform therapeutic and vaccine strategies targeting an optimized NK cell response.

Circulating T cell-monocyte complexes are markers of immune perturbations

Our results highlight for the first time that a significant proportion of cell doublets in flow cytometry, previously believed to be the result of technical artifacts and thus ignored in data acquisition and analysis, are the result of biological interaction between immune cells. In particular, we show that cell:cell doublets pairing a T cell and a monocyte can be directly isolated from human blood, and high resolution microscopy shows polarized distribution of LFA1/ICAM1 in many doublets, suggesting in vivo formation. Intriguingly, T cell-monocyte complex frequency and phenotype fluctuate with the onset of immune perturbations such as infection or immunization, reflecting expected polarization of immune responses. Overall these data suggest that cell doublets reflecting T cell-monocyte in vivo immune interactions can be detected in human blood and that the common approach in flow cytometry to avoid studying cell:cell complexes should be re-visited.

Increased monocyte count as a cellular biomarker for poor outcomes in fibrotic diseases: a retrospective, multicentre cohort study

BACKGROUND

There is an urgent need for biomarkers to better stratify patients with idiopathic pulmonary fibrosis by risk for lung transplantation allocation who have the same clinical presentation. We aimed to investigate whether a specific immune cell type from patients with idiopathic pulmonary fibrosis could identify those at higher risk of poor outcomes. We then sought to validate our findings using cytometry and electronic health records.

METHODS

We first did a discovery analysis with transcriptome data from the Gene Expression Omnibus at the National Center for Biotechnology Information for 120 peripheral blood mononuclear cell (PBMC) samples of patients with idiopathic pulmonary fibrosis. We estimated percentages of 13 immune cell types using statistical deconvolution, and investigated the association of these cell types with transplant-free survival. We validated these results using PBMC samples from patients with idiopathic pulmonary fibrosis in two independent cohorts (COMET and Yale). COMET profiled monocyte counts in 45 patients with idiopathic pulmonary fibrosis from March 12, 2010, to March 10, 2011, using flow cytometry; we tested if increased monocyte count was associated with the primary outcome of disease progression. In the Yale cohort, 15 patients with idiopathic pulmonary fibrosis (with five healthy controls) were classed as high risk or low risk from April 28, 2014, to Aug 20, 2015, using a 52-gene signature, and we assessed whether monocyte percentage (measured by cytometry by time of flight) was higher in high-risk patients. We then examined complete blood count values in the electronic health records (EHR) of 45 068 patients with idiopathic pulmonary fibrosis, systemic sclerosis, hypertrophic cardiomyopathy, or myelofibrosis from Stanford (Jan 01, 2008, to Dec 31, 2015), Northwestern (Feb 15, 2001 to July 31, 2017), Vanderbilt (Jan 01, 2008, to Dec 31, 2016), and Optum Clinformatics DataMart (Jan 01, 2004, to Dec 31, 2016) cohorts, and examined whether absolute monocyte counts of 0·95 K/μL or greater were associated with all-cause mortality in these patients.

FINDINGS

In the discovery analysis, estimated CD14+ classical monocyte percentages above the mean were associated with shorter transplant-free survival times (hazard ratio [HR] 1·82, 95% CI 1·05-3·14), whereas higher percentages of T cells and B cells were not (0·97, 0·59-1·66; and 0·78, 0·45-1·34 respectively). In two validation cohorts (COMET trial and the Yale cohort), patients with higher monocyte counts were at higher risk for poor outcomes (COMET Wilcoxon p=0·025; Yale Wilcoxon p=0·049). Monocyte counts of 0·95 K/μL or greater were associated with mortality after adjusting for forced vital capacity (HR 2·47, 95% CI 1·48-4·15; p=0·0063), and the gender, age, and physiology index (HR 2·06, 95% CI 1·22-3·47; p=0·0068) across the COMET, Stanford, and Northwestern datasets). Analysis of medical records of 7459 patients with idiopathic pulmonary fibrosis showed that patients with monocyte counts of 0·95 K/μL or greater were at increased risk of mortality with lung transplantation as a censoring event, after adjusting for age at diagnosis and sex (Stanford HR=2·30, 95% CI 0·94-5·63; Vanderbilt 1·52, 1·21-1·89; Optum 1·74, 1·33-2·27). Likewise, higher absolute monocyte count was associated with shortened survival in patients with hypertrophic cardiomyopathy across all three cohorts, and in patients with systemic sclerosis or myelofibrosis in two of the three cohorts.

INTERPRETATION

Monocyte count could be incorporated into the clinical assessment of patients with idiopathic pulmonary fibrosis and other fibrotic disorders. Further investigation into the mechanistic role of monocytes in fibrosis might lead to insights that assist the development of new therapies.

FUNDING

Bill & Melinda Gates Foundation, US National Institute of Allergy and Infectious Diseases, and US National Library of Medicine.

A 20-Gene Set Predictive of Progression to Severe Dengue

There is a need to identify biomarkers predictive of severe dengue. Single-cohort transcriptomics has not yielded generalizable results or parsimonious, predictive gene sets. We analyzed blood samples of dengue patients from seven gene expression datasets (446 samples, five countries) using an integrated multi-cohort analysis framework and identified a 20-gene set that predicts progression to severe dengue. We validated the predictive power of this 20-gene set in three retrospective dengue datasets (84 samples, three countries) and a prospective Colombia cohort (34 patients), with an area under the receiver operating characteristic curve of 0.89, 100% sensitivity, and 76% specificity. The 20-gene dengue severity scores declined during the disease course, suggesting an infection-triggered host response. This 20-gene set is strongly associated with the progression to severe dengue and represents a predictive signature, generalizable across ages, host genetic factors, and virus strains, with potential implications for the development of a host response-based dengue prognostic assay.