Rapid humoral immune responses are required for recovery from haemorrhagic fever with renal syndrome patients

The differences in cytokine signatures between severe fever with thrombocytopenia syndrome (SFTS) and hemorrhagic fever with renal syndrome (HFRS)

Severe fever with thrombocytopenia syndrome (SFTS) virus and hantavirus are categorized under the Bunyavirales order. The severe disease progression in both SFTS and hemorrhagic fever with renal syndrome (HFRS) is associated with cytokine storms. This study aimed to explore the differences in cytokine profiles and immune responses between the two diseases. A cross-sectional, single-center study involved 100 participants, comprising 46 SFTS patients, 48 HFRS patients, and 6 healthy controls. The study employed the Luminex cytokine detection platform to measure 48 cytokines. The differences in cytokine profiles and immune characteristics between the two diseases were further analyzed using multiple linear regression, principal component analysis, and random forest method. Among the 48 cytokines tested, 30 showed elevated levels in SFTS and/or HFRS compared to the healthy control group. Furthermore, there were 19 cytokines that exhibited significant differences between SFTS and HFRS. Random forest analysis suggested that TRAIL and CTACK were predictive of SFTS, while IL2Ralpha, MIG, IL-8, IFNalpha2, HGF, SCF, MCP-3, and PDGFBB were more common with HFRS. It was further verified by the receiver operating characteristic with area under the curve >0.8 and P-values <0.05, except for TRAIL. Significant differences were observed in the cytokine profiles of SFTS and HFRS, with TRAIL, IL2Ralpha, MIG, and IL-8 being the top 4 cytokines that most clearly distinguished the two diseases.

IMPORTANCE

SFTS and HFRS differ in terms of cytokine immune characteristics. TRAIL, IL-2Ralpha, MIG, and IL-8 were the top 4 that differed markedly between SFTS and HFRS.

Discovering common pathogenic processes between COVID-19 and HFRS by integrating RNA-seq differential expression analysis with machine learning

Zoonotic virus spillover in human hosts including outbreaks of Hantavirus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) imposes a serious impact on the quality of life of patients. Recent studies provide a shred of evidence that patients with Hantavirus-caused hemorrhagic fever with renal syndrome (HFRS) are at risk of contracting SARS-CoV-2. Both RNA viruses shared a higher degree of clinical features similarity including dry cough, high fever, shortness of breath, and certain reported cases with multiple organ failure. However, there is currently no validated treatment option to tackle this global concern. This study is attributed to the identification of common genes and perturbed pathways by combining differential expression analysis with bioinformatics and machine learning approaches. Initially, the transcriptomic data of hantavirus-infected peripheral blood mononuclear cells (PBMCs) and SARS-CoV-2 infected PBMCs were analyzed through differential gene expression analysis for identification of common differentially expressed genes (DEGs). The functional annotation by enrichment analysis of common genes demonstrated immune and inflammatory response biological processes enriched by DEGs. The protein-protein interaction (PPI) network of DEGs was then constructed and six genes named RAD51, ALDH1A1, UBA52, CUL3, GADD45B, and CDKN1A were identified as the commonly dysregulated hub genes among HFRS and COVID-19. Later, the classification performance of these hub genes were evaluated using Random Forest (RF), Poisson Linear Discriminant Analysis (PLDA), Voom-based Nearest Shrunken Centroids (voomNSC), and Support Vector Machine (SVM) classifiers which demonstrated accuracy >70%, suggesting the biomarker potential of the hub genes. To our knowledge, this is the first study that unveiled biological processes and pathways commonly dysregulated in HFRS and COVID-19, which could be in the next future used for the design of personalized treatment to prevent the linked attacks of COVID-19 and HFRS.

The Role of Nucleoprotein in Immunity to Human Negative-Stranded RNA Viruses—Not Just Another Brick in the Viral Nucleocapsid

Negative-stranded RNA viruses (NSVs) are important human pathogens, including emerging and reemerging viruses that cause respiratory, hemorrhagic and other severe illnesses. Vaccine design traditionally relies on the viral surface glycoproteins. However, surface glycoproteins rarely elicit effective long-term immunity due to high variability. Therefore, an alternative approach is to include conserved structural proteins such as nucleoprotein (NP). NP is engaged in myriad processes in the viral life cycle: coating and protection of viral RNA, regulation of transcription/replication processes and induction of immunosuppression of the host. A broad heterosubtypic T-cellular protection was ascribed very early to this protein. In contrast, the understanding of the humoral immunity to NP is very limited in spite of the high titer of non-neutralizing NP-specific antibodies raised upon natural infection or immunization. In this review, the data with important implications for the understanding of the role of NP in the immune response to human NSVs are revisited. Major implications of the elicited T-cell immune responses to NP are evaluated, and the possible multiple mechanisms of the neglected humoral response to NP are discussed. The intention of this review is to remind that NP is a very promising target for the development of future vaccines.

Preprocessing of Public RNA-Sequencing Datasets to Facilitate Downstream Analyses of Human Diseases

Publicly available RNA-sequencing (RNA-seq) data are a rich resource for elucidating the mechanisms of human disease; however, preprocessing these data requires considerable bioinformatic expertise and computational infrastructure. Analyzing multiple datasets with a consistent computational workflow increases the accuracy of downstream meta-analyses. This collection of datasets represents the human intracellular transcriptional response to disorders and diseases such as acute lymphoblastic leukemia (ALL), B-cell lymphomas, chronic obstructive pulmonary disease (COPD), colorectal cancer, lupus erythematosus; as well as infection with pathogens including Borrelia burgdorferi, hantavirus, influenza A virus, Middle East respiratory syndrome coronavirus (MERS-CoV), Streptococcus pneumoniae, respiratory syncytial virus (RSV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We calculated the statistically significant differentially expressed genes and Gene Ontology terms for all datasets. In addition, a subset of the datasets also includes results from splice variant analyses, intracellular signaling pathway enrichments as well as read mapping and quantification. All analyses were performed using well-established algorithms and are provided to facilitate future data mining activities, wet lab studies, and to accelerate collaboration and discovery.