Background Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) induced Coronavirus Disease 2019 (COVID-19) has posed a global threat to public health. The immune system is crucial in defending and eliminating the virus and infected cells. However, immune dysregulation may result in the rapid progression of COVID-19. Here, we evaluated the subsets, phenotypic and functional characteristics of natural killer (NK) and T cells in patients with COVID-19 and their associations with disease severity. Methods Demographic and clinical data of COVID-19 patients enrolled in Wuhan Union Hospital from February 25 to February 27, 2020, were collected and analyzed. The phenotypic and functional characteristics of NK cells and T cells subsets in circulating blood and serum levels of cytokines were analyzed via flow cytometry. Then the LASSO logistic regression model was employed to predict risk factors for the severity of COVID-19. Results The counts and percentages of NK cells, CD4 + T cells, CD8 + T cells and NKT cells were significantly reduced in patients with severe symptoms. The cytotoxic CD3 - CD56 dim CD16 + cell population significantly decreased, while the CD3 - CD56 dim CD16 - part significantly increased in severe COVID-19 patients. More importantly, elevated expression of regulatory molecules, such as CD244 and programmed death-1 (PD-1), on NK cells and T cells, as well as decreased serum cytotoxic effector molecules including perforin and granzyme A, were detected in patients with COVID-19. The serum IL-6, IL-10, and TNF-α were significantly increased in severe patients. Moreover, the CD3 - CD56 dim CD16 - cells were screened out as an influential factor in severe cases by LASSO logistic regression. Conclusions The functional exhaustion and other subset alteration of NK and T cells may contribute to the progression and improve the prognosis of COVID-19. Surveillance of lymphocyte subsets may in the future enable early screening for signs of critical illness and understanding the pathogenesis of this disease.

GMrepo (data repository for Gut Microbiota) is a database of curated and consistently annotated human gut metagenomes. Its main purpose is to facilitate the reusability and accessibility of the rapidly growing human metagenomic data. This is achieved by consistently annotating the microbial contents of collected samples using state-of-art toolsets and by manual curation of the meta-data of the corresponding human hosts. GMrepo organizes the collected samples according to their associated phenotypes and includes all possible related meta-data such as age, sex, country, body-mass-index (BMI) and recent antibiotics usage. To make relevant information easier to access, GMrepo is equipped with a graphical query builder, enabling users to make customized, complex and biologically relevant queries. For example, to find (1) samples from healthy individuals of 18 to 25 years old with BMIs between 18.5 and 24.9, or (2) projects that are related to colorectal neoplasms, with each containing >100 samples and both patients and healthy controls. Precomputed species/genus relative abundances, prevalence within and across phenotypes, and pairwise co-occurrence information are all available at the website and accessible through programmable interfaces. So far, GMrepo contains 58 903 human gut samples/runs (including 17 618 metagenomes and 41 285 amplicons) from 253 projects concerning 92 phenotypes. GMrepo is freely available at: https://gmrepo.humangut.info.

GMrepo (data repository for Gut Microbiota) is a database of curated and consistently annotated human gut metagenomes. Its main purposes are to increase the reusability and accessibility of human gut metagenomic data, and enable cross-project and phenotype comparisons. To achieve these goals, we performed manual curation on the meta-data and organized the datasets in a phenotype-centric manner. GMrepo v2 contains 353 projects and 71,642 runs/samples, which are significantly increased from the previous version. Among these runs/samples, 45,111 and 26,531 were obtained by 16S rRNA amplicon and whole-genome metagenomics sequencing, respectively. We also increased the number of phenotypes from 92 to 133. In addition, we introduced disease-marker identification and cross-project/phenotype comparison. We first identified disease markers between two phenotypes (e.g. health versus diseases) on a per-project basis for selected projects. We then compared the identified markers for each phenotype pair across datasets to facilitate the identification of consistent microbial markers across datasets. Finally, we provided a marker-centric view to allow users to check if a marker has different trends in different diseases. So far, GMrepo includes 592 marker taxa (350 species and 242 genera) for 47 phenotype pairs, identified from 83 selected projects. GMrepo v2 is freely available at: https://gmrepo.humangut.info.

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Lipopolysaccharide (LPS) can lead to uncontrollable cytokine production and eventually cause fatal sepsis syndrome. Individual toxicity difference of LPS has been widely reported. In our study we observed that two thirds of the rats (24/36) died at a given dose of LPS, while the rest (12/36) survived. Tracking the dynamic metabolic change in survival and non-survival rats in the early stage may reveal new system information to understand the inter-individual variation in response to LPS. As the time-resolved datasets are very complex and no single method can elucidate the problem clearly and comprehensively, the static and dynamic metabolomics methods were employed in combination as cross-validation. Intriguingly, some common results have been observed. Lipids were the main different metabolites between survival and non-survival rats in pre-dose serum and in the early stage of infection with LPS. The LPS treatment led to S-adenosly-methionine and total cysteine individual difference in early stage, and subsequent significant perturbations in energy metabolism and oxidative stress. Furthermore, cytokine profiles were analyzed to identify potential biological associations between cytokines and specific metabolites. Our collective findings may provide some heuristic guidance for elucidating the underlying mechanism of individual difference in LPS-mediated disease.