SARS-CoV-2 uses CD4 to infect T helper lymphocytes

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the agent of a major global outbreak of respiratory tract disease known as Coronavirus Disease 2019 (COVID-19). SARS-CoV-2 infects mainly lungs and may cause several immune-related complications, such as lymphocytopenia and cytokine storm, which are associated with the severity of the disease and predict mortality. The mechanism by which SARS-CoV-2 infection may result in immune system dysfunction is still not fully understood. Here, we show that SARS-CoV-2 infects human CD4+ T helper cells, but not CD8+ T cells, and is present in blood and bronchoalveolar lavage T helper cells of severe COVID-19 patients. We demonstrated that SARS-CoV-2 spike glycoprotein (S) directly binds to the CD4 molecule, which in turn mediates the entry of SARS-CoV-2 in T helper cells. This leads to impaired CD4 T cell function and may cause cell death. SARS-CoV-2-infected T helper cells express higher levels of IL-10, which is associated with viral persistence and disease severity. Thus, CD4-mediated SARS-CoV-2 infection of T helper cells may contribute to a poor immune response in COVID-19 patients.</p

SARS-CoV-2 uses CD4 to infect T helper lymphocytes

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the agent of a major global outbreak of respiratory tract disease known as Coronavirus Disease 2019 (COVID-19). SARS-CoV-2 infects mainly lungs and may cause several immune-related complications, such as lymphocytopenia and cytokine storm, which are associated with the severity of the disease and predict mortality. The mechanism by which SARS-CoV-2 infection may result in immune system dysfunction is still not fully understood. Here, we show that SARS-CoV-2 infects human CD4+ T helper cells, but not CD8+ T cells, and is present in blood and bronchoalveolar lavage T helper cells of severe COVID-19 patients. We demonstrated that SARS-CoV-2 spike glycoprotein (S) directly binds to the CD4 molecule, which in turn mediates the entry of SARS-CoV-2 in T helper cells. This leads to impaired CD4 T cell function and may cause cell death. SARS-CoV-2-infected T helper cells express higher levels of IL-10, which is associated with viral persistence and disease severity. Thus, CD4-mediated SARS-CoV-2 infection of T helper cells may contribute to a poor immune response in COVID-19 patients.</p

Protein C Pretreatment Protects Endothelial Cells from SARS-CoV-2-Induced Activation

SARS-CoV-2 can induce vascular dysfunction and thrombotic events in patients with severe COVID-19; however, the cellular and molecular mechanisms behind these effects remain largely unknown. In this study, we used a combination of experimental and in silico approaches to investigate the role of PC in vascular and thrombotic events in COVID-19. Single-cell RNA-sequencing data from patients with COVID-19 and healthy subjects were obtained from the publicly available Gene Expression Omnibus (GEO) repository. In addition, HUVECs were treated with inactive protein C before exposure to SARS-CoV-2 infection or a severe COVID-19 serum. An RT-qPCR array containing 84 related genes was used, and the candidate genes obtained were evaluated. Activated protein C levels were measured using an ELISA kit. We identified at the single-cell level the expression of several pro-inflammatory and pro-coagulation genes in endothelial cells from the patients with COVID-19. Furthermore, we demonstrated that exposure to SARS-CoV-2 promoted transcriptional changes in HUVECs that were partly reversed by the activated protein C pretreatment. We also observed that the serum of severe COVID-19 had a significant amount of activated protein C that could protect endothelial cells from serum-induced activation. In conclusion, activated protein C protects endothelial cells from pro-inflammatory and pro-coagulant effects during exposure to the SARS-CoV-2 virus

Efficient detection of Zika virus RNA in patients' blood from the 2016 outbreak in Campinas, Brazil

Abstract Infection with Zika virus (ZIKV), a mosquito-borne flavivirus has been casually linked with increased congenital microcephaly in Brazil from 2015 through 2016. Sensitive and specific diagnosis of patients with Zika fever (ZIKF) remains critical for patient management. We developed a ZIKV NS5 qRT-PCR assay by combining primers described by Balm et al. and a new Taqman probe. The assay was evaluated and compared with another assay described by Lanciotti et al. (ZIKV 1107) using 51 blood and 42 urine samples from 54 suspected ZIKV patients. ZIKV NS5 performed better in terms of sensitivity with more samples detected as ZIKV-positive (n = 37) than ZIKV 1107 (n = 34) for urine, and ZIKV-positive (n = 29) than ZIKV 1107 (n = 26) for blood. Both assays displayed good overall agreement for urine (κappa = 0.770) and blood (κappa = 0.825) samples. Improved availability of validated diagnostic tests, such ZIKV NS5 qRT-PCR, will be critical to ensure adequate and accurate ZIKV diagnosis

Oropouche Virus Infects, Persists and Induces IFN Response in Human Peripheral Blood Mononuclear Cells as Identified by RNA PrimeFlow™ and qRT-PCR Assays

Oropouche orthobunyavirus (OROV) is an emerging arbovirus with a high potential of dissemination in America. Little is known about the role of peripheral blood mononuclear cells (PBMC) response during OROV infection in humans. Thus, to evaluate human leukocytes susceptibility, permissiveness and immune response during OROV infection, we applied RNA hybridization, qRT-PCR and cell-based assays to quantify viral antigens, genome, antigenome and gene expression in different cells. First, we observed OROV replication in human leukocytes lineages as THP-1 monocytes, Jeko-1 B cells and Jurkat T cells. Interestingly, cell viability and viral particle detection are maintained in these cells, even after successive passages. PBMCs from healthy donors were susceptible but the infection was not productive, since neither antigenome nor infectious particle was found in the supernatant of infected PBMCs. In fact, only viral antigens and small quantities of OROV genome were detected at 24 hpi in lymphocytes, monocytes and CD11c+ cells. Finally, activation of the Interferon (IFN) response was essential to restrict OROV replication in human PBMCs. Increased expression of type I/III IFNs, ISGs and inflammatory cytokines was detected in the first 24 hpi and viral replication was re-established after blocking IFNAR or treating cells with glucocorticoid. Thus, in short, our results show OROV is able to infect and remain in low titers in human T cells, monocytes, DCs and B cells as a consequence of an effective IFN response after infection, indicating the possibility of leukocytes serving as a trojan horse in specific microenvironments during immunosuppression

A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus

Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, nonetheless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometric data that are inputted with the developed decision-making algorithm, we were able to provide a set of features that work as a “fingerprint” for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening—faster and more accurate—with improved cost-effectiveness when compared to existing technologies