On 31 December 2019 the Wuhan Health Commission reported a cluster of atypical pneumonia cases that was linked to a wet market in the city of Wuhan, China. The first patients began experiencing symptoms of illness in mid-December 2019. Clinical isolates were found to contain a novel coronavirus with similarity to bat coronaviruses. As of 28 January 2020, there are in excess of 4,500 laboratory-confirmed cases, with > 100 known deaths. As with the SARS-CoV, infections in children appear to be rare. Travel-related cases have been confirmed in multiple countries and regions outside mainland China including Germany, France, Thailand, Japan, South Korea, Vietnam, Canada, and the United States, as well as Hong Kong and Taiwan. Domestically in China, the virus has also been noted in several cities and provinces with cases in all but one provinence. While zoonotic transmission appears to be the original source of infections, the most alarming development is that human-to-human transmission is now prevelant. Of particular concern is that many healthcare workers have been infected in the current epidemic. There are several critical clinical questions that need to be resolved, including how efficient is human-to-human transmission? What is the animal reservoir? Is there an intermediate animal reservoir? Do the vaccines generated to the SARS-CoV or MERS-CoV or their proteins offer protection against 2019-nCoV? We offer a research perspective on the next steps for the generation of vaccines. We also present data on the use of in silico docking in gaining insight into 2019-nCoV Spike-receptor binding to aid in therapeutic development. Diagnostic PCR protocols can be found at https://www.who.int/health-topics/coronavirus/laboratory-diagnostics-for-novel-coronavirus.

Critically ill COVID-19 patients start developing single respiratory organ failure that often evolves into multiorgan failure. Understanding the immune mechanisms in severe forms of an infectious disease (either critical COVID-19 or bacterial septic shock) would help to achieve a better understanding of the patient’s clinical trajectories and the success of potential therapies. We hypothesized that a dysregulated immune response manifested by the abnormal activation of innate and adaptive immunity might be present depending on the severity of the clinical presentation in both COVID-19 and bacterial sepsis. We found that critically ill COVID-19 patients demonstrated a different clinical endotype that resulted in an inflammatory dysregulation in mild forms of the disease. Mild cases (COVID-19 and bacterial non severe sepsis) showed significant differences in the expression levels of CD8 naïve T cells, CD4 naïve T cells, and CD4 memory T cells. On the other hand, in the severe forms of infection (critical COVID-19 and bacterial septic shock), patients shared immune patterns with upregulated single-cell transcriptome sequencing at the following levels: B cells, monocyte classical, CD4 and CD8 naïve T cells, and natural killers. In conclusion, we identified significant gene expression differences according to the etiology of the infection (COVID-19 or bacterial sepsis) in the mild forms; however, in the severe forms (critical COVID-19 and bacterial septic shock), patients tended to share some of the same immune profiles related to adaptive and innate immune response. Severe forms of the infections were similar independent of the etiology. Our findings might promote the implementation of co-adjuvant therapies and interventions to avoid the development of severe forms of disease that are associated with high mortality rates worldwide.

A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pathways that can lead to widespread inflammation, tissue damage, and organ failure. A dysregulated immune response can manifest as changes in differentiated immune cell populations and concentrations of circulating biomarkers. To propose an early diagnostic system that enables differentiation and identifies the severity of immune-dysregulated syndromes, we built an artificial intelligence tool that uses input data from single-cell RNA sequencing. In our results, single-cell transcriptomics successfully distinguished between mild and severe sepsis and COVID-19 infections. Moreover, by interpreting the decision patterns of our classification system, we identified that different immune cells upregulating or downregulating the expression of the genes CD3, CD14, CD16, FOSB, S100A12, and TCRɣδ can accurately differentiate between different degrees of infection. Our research has identified genes of significance that effectively distinguish between infections, offering promising prospects as diagnostic markers and providing potential targets for therapeutic intervention.

Background: Severe COVID-19 is characterized by clinical and biological manifestations typically observed in sepsis. SARS-CoV-2 RNA is commonly detected in nasopharyngeal swabs, however viral RNA can be found also in peripheral blood and other tissues. Whether systemic spreading of the virus or viral components plays a role in the pathogenesis of the sepsis-like disease observed in severe COVID-19 is currently unknown. Methods: We determined the association of plasma SARS-CoV-2 RNA with the biological responses and the clinical severity of patients with COVID-19. 250 patients with confirmed COVID-19 infection were recruited (50 outpatients, 100 hospitalised ward patients, and 100 critically ill). The association between plasma SARS-CoV-2 RNA and laboratory parameters was evaluated using multivariate GLM with a gamma distribution. The association between plasma SARS-CoV-2 RNA and severity was evaluated using multivariate ordinal logistic regression analysis and Generalized Linear Model (GLM) analysis with a binomial distribution. Results: The presence of SARS-CoV-2-RNA viremia was independently associated with a number of features consistently identified in sepsis: 1) high levels of cytokines (including CXCL10, CCL-2, IL-10, IL-1ra, IL-15, and G-CSF); 2) higher levels of ferritin and LDH; 3) low lymphocyte and monocyte counts 4) and low platelet counts. In hospitalised patients, the presence of SARS-CoV-2-RNA viremia was independently associated with critical illness: (adjusted OR= 8.30 [CI95%=4.21 - 16.34], p < 0.001). CXCL10 was the most accurate identifier of SARS-CoV-2-RNA viremia in plasma (area under the curve (AUC), [CI95%], p) = 0.85 [0.80 - 0.89), <0.001]), suggesting its potential role as a surrogate biomarker of viremia. The cytokine IL-15 most accurately differentiated clinical ward patients from ICU patients (AUC: 0.82 [0.76 - 0.88], <0.001). Conclusions: systemic dissemination of genomic material of SARS-CoV-2 is associated with a sepsis-like biological response and critical illness in patients with COVID-19. RNA viremia could represent an important link between SARS-CoV-2 infection, host response dysfunction and the transition from moderate illness to severe, sepsis-like COVID-19 disease.

During infectious disease outbreaks, acquiring genetic data across various kingdoms offers essential information to tailor precise treatment methodologies and bolster clinical, epidemiological, and public health awareness. Metagenomics sequencing has paved the way for personalized treatment approaches and streamlined the monitoring process for both co-infections and opportunistic infections. In this study, we conducted long-read metagenomic DNA sequencing on mpox-like lesion pustules from six suspected patients who were positive and confirmed to be infected with MPXV during the MPXV subclade Ib outbreak in the Eastern Democratic Republic of the Congo. The sequenced data were taxonomically classified as bacterial, fungal, and viral in composition. Our results show a wide spectrum of microorganisms present in the lesions. Bacteria such as Corynebacterium amycolatum, Gardnerella vaginalis, Enterococcus faecium, Enterobacter clocae, Staphylococcus epidermidis, and Stenotrophomonas maltophilia were found in the lesions. The viral classification of the reads pointed out the absolute predominance of the monkeypox virus. Taken together, the outcomes of this investigation underscore the potential involvement of microorganisms in mpox lesions and the possible role that co-infections played in exacerbating disease severity and transmission during the MPXV subclade Ib outbreak.

Background: Poxviruses constitute a family of large dsDNA viruses that can infect a plethora of species including humans. Historically, poxviruses have caused a health burden in multiple outbreaks. The large genome of poxviruses favors reverse vaccinology approaches that can determine potential antigens and epitopes. Here, we propose the modeling of a user-friendly database containing the predicted antigens and epitopes of a large cohort of poxvirus proteomes using the existing PoxiPred method for reverse vaccinology of poxviruses. Methods: In the present study, we obtained the whole proteomes of as many as 37 distinct poxviruses. We utilized each proteome to predict both antigenic proteins and T-cell epitopes of poxviruses with the aid of an Artificial Intelligence method, namely the PoxiPred method. Results: In total, we predicted 3966 proteins as potential antigen targets. Of note, we considered that this protein may exist in a set of proteoforms. Subsets of these proteins constituted a comprehensive repository of 54,291 linear T-cell epitopes. We combined the outcome of the predictions in the format of a web tool that delivers a database of antigens and epitopes of poxviruses. We also developed a comprehensive repository dedicated to providing access to end-users to obtain AI-based screened antigens and T-cell epitopes of poxviruses in a user-friendly manner. These antigens and epitopes can be utilized to design experiments for the development of effective vaccines against a plethora of poxviruses. Conclusions: The TCEPVDB repository, already deployed to the web under an open-source coding philosophy, is free to use, does not require any login, does not store any information from its users.