Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is the pathogen that causes coronavirus disease 2019 (COVID-19). As of 25 May 2020, the outbreak of COVID-19 has caused 347,192 deaths around the world. The current evidence showed that severely ill patients tend to have a high concentration of pro-inflammatory cytokines, such as interleukin (IL)-6, compared to those who are moderately ill. The high level of cytokines also indicates a poor prognosis in COVID-19. Besides, excessive infiltration of pro-inflammatory cells, mainly involving macrophages and T-helper 17 cells, has been found in lung tissues of patients with COVID-19 by postmortem examination. Recently, increasing studies indicate that the “cytokine storm” may contribute to the mortality of COVID-19. Here, we summarize the clinical and pathologic features of the cytokine storm in COVID-19. Our review shows that SARS-Cov-2 selectively induces a high level of IL-6 and results in the exhaustion of lymphocytes. The current evidence indicates that tocilizumab, an IL-6 inhibitor, is relatively effective and safe. Besides, corticosteroids, programmed cell death protein (PD)-1/PD-L1 checkpoint inhibition, cytokine-adsorption devices, intravenous immunoglobulin, and antimalarial agents could be potentially useful and reliable approaches to counteract cytokine storm in COVID-19 patients.

As an alternative to modern western medicine, Traditional Chinese Medicine (TCM) is receiving increasingly attention worldwide. Great efforts have been paid to TCM’s modernization, which tries to bridge the gap between TCM and modern western medicine. As TCM and modern western medicine share a common aspect at molecular level that the compound(s) perturb human’s dysfunction network and restore human normal physiological condition, the relationship between compounds (in herb, refer to ingredients) and their targets (proteins) should be the key factor to connect TCM and modern medicine. Accordingly, we construct this Traditional Chinese Medicine Integrated Database (TCMID, http://www.megabionet.org/tcmid/), which records TCM-related information collected from different resources and through text-mining method. To enlarge the scope of the TCMID, the data have been linked to common drug and disease databases, including Drugbank, OMIM and PubChem. Currently, our TCMID contains ∼47 000 prescriptions, 8159 herbs, 25 210 compounds, 6828 drugs, 3791 diseases and 17 521 related targets, which is the largest data set for related field. Our web-based software displays a network for integrative relationships between herbs and their treated diseases, the active ingredients and their targets, which will facilitate the study of combination therapy and understanding of the underlying mechanisms for TCM at molecular level.

By engaging angiotensin-converting enzyme 2 (ACE2 or Ace2), the novel pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) invades host cells and affects many organs, including the brain. However, the distribution of ACE2 in the brain is still obscure. Here, we investigated the ACE2 expression in the brain by analyzing data from publicly available brain transcriptome databases. According to our spatial distribution analysis, ACE2 was relatively highly expressed in some brain locations, such as the choroid plexus and paraventricular nuclei of the thalamus. According to cell-type distribution analysis, nuclear expression of ACE2 was found in many neurons (both excitatory and inhibitory neurons) and some non-neuron cells (mainly astrocytes, oligodendrocytes, and endothelial cells) in the human middle temporal gyrus and posterior cingulate cortex. A few ACE2-expressing nuclei were found in a hippocampal dataset, and none were detected in the prefrontal cortex. Except for the additional high expression of Ace2 in the olfactory bulb areas for spatial distribution as well as in the pericytes and endothelial cells for cell-type distribution, the distribution of Ace2 in the mouse brain was similar to that in the human brain. Thus, our results reveal an outline of ACE2/Ace2 distribution in the human and mouse brains, which indicates that the brain infection of SARS-CoV-2 may be capable of inducing central nervous system symptoms in coronavirus disease 2019 (COVID-19) patients. Potential species differences should be considered when using mouse models to study the neurological effects of SARS-CoV-2 infection.

BackgroundThe assessment and characterization of the gut microbiome has become a focus of research in the area of human autoimmune diseases. Ankylosing spondylitis is an inflammatory autoimmune disease and evidence showed that ankylosing spondylitis may be a microbiome-driven disease.ResultsTo investigate the relationship between the gut microbiome and ankylosing spondylitis, a quantitative metagenomics study based on deep shotgun sequencing was performed, using gut microbial DNA from 211 Chinese individuals. A total of 23,709 genes and 12 metagenomic species were shown to be differentially abundant between ankylosing spondylitis patients and healthy controls. Patients were characterized by a form of gut microbial dysbiosis that is more prominent than previously reported cases with inflammatory bowel disease. Specifically, the ankylosing spondylitis patients demonstrated increases in the abundance of Prevotella melaninogenica, Prevotella copri, and Prevotella sp. C561 and decreases in Bacteroides spp. It is noteworthy that the Bifidobacterium genus, which is commonly used in probiotics, accumulated in the ankylosing spondylitis patients. Diagnostic algorithms were established using a subset of these gut microbial biomarkers.ConclusionsAlterations of the gut microbiome are associated with development of ankylosing spondylitis. Our data suggest biomarkers identified in this study might participate in the pathogenesis or development process of ankylosing spondylitis, providing new leads for the development of new diagnostic tools and potential treatments.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1271-6) contains supplementary material, which is available to authorized users.

As a traditional medical intervention in Asia and a complementary and alternative medicine in western countries, Traditional Chinese Medicine (TCM) is capturing worldwide attention in life science field. Traditional Chinese Medicine Integrated Database (TCMID), which was originally launched in 2013, was a comprehensive database aiming at TCM’s modernization and standardization. It has been highly recognized among pharmacologists and scholars in TCM researches. The latest release, TCMID 2.0 (http://www.megabionet.org/tcmid/), replenished the preceding database with 18 203 herbal ingredients, 15 prescriptions, 82 related targets, 1356 drugs, 842 diseases and numerous new connections between them. Considering that chemical changes might take place in decocting process of prescriptions, which may result in new ingredients, new data containing the prescription ingredients was collected in current version. In addition, 778 herbal mass spectrometry (MS) spectra related to 170 herbs were appended to show the variation of herbal quality in different origin and distinguish genuine medicinal materials from common ones while 3895 MS spectra of 729 ingredients were added as the supplementary materials of component identification. With the significant increase of data, TCMID 2.0 will further facilitate TCM’s modernization and enhance the exploration of underlying biological processes that are response to the diverse pharmacologic actions of TCM.

By engaging angiotensin-converting enzyme 2 (ACE2 or Ace2), the novel pathogenic SARS-coronavirus 2 (SARS-CoV-2) may invade host cells in many organs, including the brain. However, the distribution of ACE2 in the brain is still obscure. Here we investigated the ACE2 expression in the brain by analyzing data from publicly available brain transcriptome databases. According to our spatial distribution analysis, ACE2 was relatively highly expressed in some important brain areas, such as the substantia nigra and brain ventricles. According to our cell-type distribution analysis, the expression of ACE2 were found in many neurons (both excitatory and inhibitory neurons) and some non-neuron cells (mainly astrocytes and oligodendrocytes) in human middle temporal gyrus and posterior cingulate cortex, but the ACE2-expressing cells was none in the prefrontal cortex and very few in the hippocampus. Except for the additional high expression of Ace2 in the olfactory bulb areas for spatial distribution as well as in the pericytes and endothelial cells for cell-type distribution, the distribution of Ace2 in mouse brain was similar to that in the human brain.Thus, our results reveal an outline of ACE2/Ace2 distribution in the human and mouse brain, which indicates the brain infection of SARS-CoV-2 may be capable to result in serious central nervous system symptoms in coronavirus disease 2019 (COVID-19) patients.

BackgroundSystemic lupus erythematosus (SLE) in patients from Spain is associated with intestinal dysbiosis. This study explores whether the alteration of the gut microbiome in SLE patients from China is consistent with the intestinal dysbiosis of SLE patients from Spain.ResultsThe depletion of Firmicutes and the enrichment of Bacteroidetes in SLE patients from China were consistent with the SLE patients from Spain. Furthermore, we found that nine genera of gut microbiota were SLE-related microorganisms in Chinese subjects. Genera Rhodococcus, Eggerthella, Klebsiella, Prevotella, Eubacterium, Flavonifractor and Incertae sedis were significantly enriched, while genera Dialister and Pseudobutyrivibrio were significantly depleted in SLE patients. Receiver operating characteristic analysis indicated that the nine genera have the potential to distinguish SLE patients from healthy controls.ConclusionsComparing the dysbiosis of the gut microbiome among SLE patients from China or Spain, may indicate that the gut microbiome profiles of SLE patients are more influenced by disease than ethnicity.

This study employed microbiome and metabolome analysis to explore the fecal signatures of gout patients. Fecal samples from 52 male individuals (26 healthy controls and 26 gout patients) were analyzed by 1H NMR spectroscopy and Illumina Miseq sequencing. The signatures of microbiome showed being up-regulation of opportunistic pathogens, such as Bacteroides, Porphyromonadaceae Rhodococcus, Erysipelatoclostridium and Anaerolineaceae. The signatures of metabolome were some altered metabolites which may involve uric acid excretion, purine metabolism, and inflammatory responses. Meanwhile, the correlation between discrepant metabolites and microbial taxa indicated that they could be the combined signatures of gout. This study suggests that the combined analysis of the fecal microbiome and metabolome may effectively characterize diseases.