Global and cell type-specific immunological hallmarks of severe dengue progression identified via a systems immunology approach.

Severe dengue (SD) is a major cause of morbidity and mortality. To define dengue virus (DENV) target cells and immunological hallmarks of SD progression in children's blood, we integrated two single-cell approaches capturing cellular and viral elements: virus-inclusive single-cell RNA sequencing (viscRNA-Seq 2) and targeted proteomics with secretome analysis and functional assays. Beyond myeloid cells, in natural infection, B cells harbor replicating DENV capable of infecting permissive cells. Alterations in cell type abundance, gene and protein expression and secretion as well as cell-cell communications point towards increased immune cell migration and inflammation in SD progressors. Concurrently, antigen-presenting cells from SD progressors demonstrate intact uptake yet impaired interferon response and antigen processing and presentation signatures, which are partly modulated by DENV. Increased activation, regulation and exhaustion of effector responses and expansion of HLA-DR-expressing adaptive-like NK cells also characterize SD progressors. These findings reveal DENV target cells in human blood and provide insight into SD pathogenesis beyond antibody-mediated enhancement.

Virus-inclusive single-cell RNA sequencing reveals the molecular signature of progression to severe dengue.

Dengue virus (DENV) infection can result in severe complications. However, the understanding of the molecular correlates of severity is limited, partly due to difficulties in defining the peripheral blood mononuclear cells (PBMCs) that contain DENV RNA in vivo. Accordingly, there are currently no biomarkers predictive of progression to severe dengue (SD). Bulk transcriptomics data are difficult to interpret because blood consists of multiple cell types that may react differently to infection. Here, we applied virus-inclusive single-cell RNA-seq approach (viscRNA-Seq) to profile transcriptomes of thousands of single PBMCs derived early in the course of disease from six dengue patients and four healthy controls and to characterize distinct leukocyte subtypes that harbor viral RNA (vRNA). Multiple IFN response genes, particularly MX2 in naive B cells and CD163 in CD14+ CD16+ monocytes, were up-regulated in a cell-specific manner before progression to SD. The majority of vRNA-containing cells in the blood of two patients who progressed to SD were naive IgM B cells expressing the CD69 and CXCR4 receptors and various antiviral genes, followed by monocytes. Bystander, non-vRNA-containing B cells also demonstrated immune activation, and IgG1 plasmablasts from two patients exhibited clonal expansions. Lastly, assembly of the DENV genome sequence revealed diversity at unexpected sites. This study presents a multifaceted molecular elucidation of natural dengue infection in humans with implications for any tissue and viral infection and proposes candidate biomarkers for prediction of SD.

PIP4K2C inhibition reverses autophagic flux impairment induced by SARS-CoV-2.

In search for broad-spectrum antivirals, we discovered a small molecule inhibitor, RMC-113, that potently suppresses the replication of multiple RNA viruses including SARS-CoV-2 in human lung organoids. We demonstrated selective dual inhibition of the lipid kinases PIP4K2C and PIKfyve by RMC-113 and target engagement by its clickable analog. Advanced lipidomics revealed alteration of SARS-CoV-2-induced phosphoinositide signature by RMC-113 and linked its antiviral effect with functional PIP4K2C and PIKfyve inhibition. We discovered PIP4K2C's roles in SARS-CoV-2 entry, RNA replication, and assembly/egress, validating it as a druggable antiviral target. Integrating proteomics, single-cell transcriptomics, and functional assays revealed that PIP4K2C binds SARS-CoV-2 nonstructural protein 6 and regulates virus-induced impairment of autophagic flux. Reversing this autophagic flux impairment is a mechanism of antiviral action of RMC-113. These findings reveal virus-induced autophagy regulation via PIP4K2C, an understudied kinase, and propose dual inhibition of PIP4K2C and PIKfyve as a candidate strategy to combat emerging viruses.

Magnitude and kinetics of the human immune cell response associated with severe dengue progression by single-cell proteomics.

Approximately 5 million dengue virus-infected patients progress to a potentially life-threatening severe dengue (SD) infection annually. To identify the immune features and temporal dynamics underlying SD progression, we performed deep immune profiling by mass cytometry of PBMCs collected longitudinally from SD progressors (SDp) and uncomplicated dengue (D) patients. While D is characterized by early activation of innate immune responses, in SDp there is rapid expansion and activation of IgG-secreting plasma cells and memory and regulatory T cells. Concurrently, SDp, particularly children, demonstrate increased proinflammatory NK cells, inadequate expansion of CD16+ monocytes, and high expression of the FcγR CD64 on myeloid cells, yet a signature of diminished antigen presentation. Syndrome-specific determinants include suppressed dendritic cell abundance in shock/hemorrhage versus enriched plasma cell expansion in organ impairment. This study reveals uncoordinated immune responses in SDp and provides insights into SD pathogenesis in humans with potential implications for prediction and treatment.

Numb-associated kinases are required for SARS-CoV-2 infection and are cellular targets for antiviral strategies.

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to pose serious threats to global health. We previously reported that AAK1, BIKE and GAK, members of the Numb-associated kinase family, control intracellular trafficking of multiple RNA viruses during viral entry and assembly/egress. Here, using both genetic and pharmacological approaches, we probe the functional relevance of NAKs for SARS-CoV-2 infection. siRNA-mediated depletion of AAK1, BIKE, GAK, and STK16, the fourth member of the NAK family, suppressed SARS-CoV-2 infection in human lung epithelial cells. Both known and novel small molecules with potent AAK1/BIKE, GAK or STK16 activity suppressed SARS-CoV-2 infection. Moreover, combination treatment with the approved anti-cancer drugs, sunitinib and erlotinib, with potent anti-AAK1/BIKE and GAK activity, respectively, demonstrated synergistic effect against SARS-CoV-2 infection in vitro. Time-of-addition experiments revealed that pharmacological inhibition of AAK1 and BIKE suppressed viral entry as well as late stages of the SARS-CoV-2 life cycle. Lastly, suppression of NAKs expression by siRNAs inhibited entry of both wild type and SARS-CoV-2 pseudovirus. These findings provide insight into the roles of NAKs in SARS-CoV-2 infection and establish a proof-of-principle that pharmacological inhibition of NAKs can be potentially used as a host-targeted approach to treat SARS-CoV-2 with potential implications to other coronaviruses.

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.

A comprehensive analysis of RHOA mutation positive and negative angioimmunoblastic T-cell lymphomas by targeted deep sequencing, expression profiling and single cell digital image analysis.

Angioimmunoblastic T­cell lymphoma (AITL) is a uniquely aggressive mature T­cell neoplasm. In recent years, recurrent genetic mutations in ras homolog family member A (RHOA), tet methylcytosine dioxygenase 2 (TET2), DNA methyltransferase 3 alpha (DNMT3A) and isocitrate dehydrogenase [NADP(+)] 2 (IDH2) have been identified as associated with AITL. However, a deep molecular study assessing both DNA mutations and RNA expression profile combined with digital image analysis is lacking. The present study aimed to evaluate the significance of molecular and morphologic features by high resolution digital image analysis in several cases of AITL. To do so, a total of 18 separate tissues from 10 patients with AITL were collected and analyzed. The results identified recurrent mutations in RHOA, TET2, DNMT3A, and IDH2, and demonstrated increased DNA mutations in coding, promoter and CCCTC binding factor (CTCF) binding sites in RHOA mutated AITLs vs. RHOA non­mutated cases, as well as increased overall survival in RHOA mutated patients. In addition, single cell computational digital image analysis morphologically characterized RHOA mutated AITL cells as distinct from cells from RHOA mutation negative patients. Computational analysis of single cell morphological parameters revealed that RHOA mutated cells have decreased eccentricity (more circular) compared with RHOA non­mutated AITL cells. In conclusion, the results from the present study expand our understanding of AITL and demonstrate that there are specific cell biological and morphological manifestations of RHOA mutations in cases of AITL.

High-throughput SARS-CoV-2 and host genome sequencing from single nasopharyngeal swabs.

During COVID19 and other viral pandemics, rapid generation of host and pathogen genomic data is critical to tracking infection and informing therapies. There is an urgent need for efficient approaches to this data generation at scale. We have developed a scalable, high throughput approach to generate high fidelity low pass whole genome and HLA sequencing, viral genomes, and representation of human transcriptome from single nasopharyngeal swabs of COVID19 patients.

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.