Background A severe form of pneumonia, named coronavirus disease 2019 (COVID-19) by the World Health Organization is widespread on the whole world. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was proved to be the main agent of COVID-19. In the present study, we conducted an in depth analysis of the SARS-COV-2 nucleocapsid to identify potential targets that may allow identification of therapeutic targets. Methods The SARS-COV-2 N protein subcellular localization and physicochemical property was analyzed by PSORT II Prediction and ProtParam tool. Then SOPMA tool and swiss-model was applied to analyze the structure of N protein. Next, the biological function was explored by mass spectrometry analysis and flow cytometry. At last, its potential phosphorylation sites were analyzed by NetPhos3.1 Server and PROVEAN PROTEIN. Results SARS-COV-2 N protein composed of 419 aa, is a 45.6 kDa positively charged unstable hydrophobic protein. It has 91 and 49% similarity to SARS-CoV and MERS-CoV and is predicted to be predominantly a nuclear protein. It mainly contains random coil (55.13%) of which the tertiary structure was further determined with high reliability (95.76%). Cells transfected with SARS-COV-2 N protein usually show a G1/S phase block company with an increased expression of TUBA1C, TUBB6. At last, our analysis of SARS-COV-2 N protein predicted a total number of 12 phosphorylated sites and 9 potential protein kinases which would significantly affect SARS-COV-2 N protein function. Conclusion In this study, we report the physicochemical properties, subcellular localization, and biological function of SARS-COV-2 N protein. The 12 phosphorylated sites and 9 potential protein kinase sites in SARS-COV-2 N protein may serve as promising targets for drug discovery and development for of a recombinant virus vaccine.

The protective effects of Kisspeptin on heat-induced oxidative stress in rats were investigated by using a combination of biochemical parameters and metabonomics. Metabonomic analyses were performed using gas chromatography/mass spectrometry in conjunction with multivariate and univariate statistical analyses. At the end point of the heat stress experiment, histological observation, ultrastructural analysis and biochemical parameters were measured. Metabonomic analysis of liver tissue revealed that Kisspeptin mainly attenuated the alteration of purine metabolism and fatty acid metabolism pathways. Futhermore, Kisspeptin also increased the levels of GSH, T-AOC as well as SOD activities, and upregulated MDA levels. These results provide important mechanistic insights into the protective effects of Kisspeptin against heat-induced oxidative stress.

Heat stress can cause systemic physiological and biochemical alterations in living organisms. In reproductive systems, heat stress induces germ cell loss and poor quality semen. However, until now, little has been known about such a complex regulation process, particularly in the perspective of metabolism. In this study, serum, hypothalamus, and epididymis samples derived from male SD (Sprague-Dawley) rats being exposed to high environmental temperature (40 °C) 2 h per day for 7 consecutive days were analyzed using metabonomics strategies based on GC/TOFMS. Differentially expressed metabolites reveal that the energy metabolism, amino acid neurotransmitters, and monoamine neurotransmitters pathways are associated with heat stress, in accordance with changes of the three upstream neuroendocrine system pathways in the SNS (sympathetic adrenergic system), hypothalamic pituitary adrenal axis (HPA), and hypothalamic pituitary testis axis (HPT) axis. Many of these metabolites, especially in the epididymis, were found to be up-regulated, presumably due to a self-preserving action to resist the environmental hot irritation to maintain normal functioning of the male reproductive system.

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Objective: To distinguish COVID-19 patients and non-COVID-19 viral pneumonia patients and classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators.Materials and methods: In this retrospective cohort, a total of 3,563 COVID-19 patients and 118 non-COVID-19 pneumonia patients were included. There are two cohorts of COVID-19 patients, including 548 patients in the training dataset, and 3,015 patients in the testing dataset. Laboratory indicators were measured during hospitalization for all patients. Based on laboratory indicators, we used the support vector machine and joint random sampling to risk stratification for COVID-19 patients at admission. Based on laboratory indicators detected within the 1st week after admission, we used logistic regression and joint random sampling to develop the survival mode. The laboratory indicators of COVID-10 and non-COVID-19 were also compared.Results: We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen with AUC >0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission in the testing dataset. Results showed that this model could stratify the patients in the testing dataset effectively (AUC = 0.89). Our model still has good performance at different times (Mean AUC: 0.71, 0.72, 0.72, respectively for 3, 5, and 7 days after admission). Moreover, laboratory indicators detected within the 1st week after admission were able to estimate the probability of death (AUC = 0.95). We identified six indicators with permutation p < 0.05, including eosinophil percentage (p = 0.007), white blood cell count (p = 0.045), albumin (p = 0.041), aspartate transaminase (p = 0.043), lactate dehydrogenase (p = 0.002), and hemoglobin (p = 0.031). We could diagnose COVID-19 and differentiate it from other kinds of viral pneumonia based on these laboratory indicators.Conclusions: Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19. In addition, laboratory findings could be used to distinguish COVID-19 and non-COVID-19.

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The progression from mild to critical illness is the main reason leading to the death of COVID-19 patients. Rapid risk-stratification at admission is important for precise management of COVID-19. Here, we developed a practical admission stratification model to predict the severity during hospitalization of COVID-19 patients using laboratory data from 3563 patients, including 548 patients in the training dataset, and 3015 patients in the testing dataset. We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage (NEUT%), lymphocytes percentage (LYMPH%), creatinine (CREA), and blood urea nitrogen (BUN) with AUC greater than 0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission. Results showed that this model could stratify the patients in the testing dataset effectively (AUC=0.89). Moreover, laboratory indicators detected in the first week after admission were able to estimate the probability of death (AUC=0.95). Besides, we could diagnose COVID-19 and differentiated it from other kinds of viral pneumonia based on laboratory indicators (accuracy=0.97). Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19.