Respiratory immune status and microbiome in recovered COVID-19 patients revealed by metatranscriptomic analyses.

Coronavirus disease 2019 (COVID-19) is currently a severe threat to global public health, and the immune response to COVID-19 infection has been widely investigated. However, the immune status and microecological changes in the respiratory systems of patients with COVID-19 after recovery have rarely been considered. We selected 72 patients with severe COVID-19 infection, 57 recovered from COVID-19 infection, and 65 with non-COVID-19 pneumonia, for metatranscriptomic sequencing and bioinformatics analysis. Accordingly, the differentially expressed genes between the infected and other groups were enriched in the chemokine signaling pathway, NOD-like receptor signaling pathway, phagosome, TNF signaling pathway, NF-kappa B signaling pathway, Toll-like receptor signaling pathway, and C-type lectin receptor signaling pathway. We speculate that IL17RD, CD74, and TNFSF15 may serve as disease biomarkers in COVID-19. Additionally, principal coordinate analysis revealed significant differences between groups. In particular, frequent co-infections with the genera Streptococcus, Veillonella, Gemella, and Neisseria, among others, were found in COVID-19 patients. Moreover, the random forest prediction model with differential genes showed a mean area under the curve (AUC) of 0.77, and KCNK12, IL17RD, LOC100507412, PTPRT, MYO15A, MPDZ, FLRT2, SPEG, SERPINB3, and KNDC1 were identified as the most important genes distinguishing the infected group from the recovered group. Agrobacterium tumefaciens, Klebsiella michiganensis, Acinetobacter pittii, Bacillus sp. FJAT.14266, Brevundimonas naejangsanensis, Pseudopropionibacterium propionicum, Priestia megaterium, Dialister pneumosintes, Veillonella rodentium, and Pseudomonas protegens were selected as candidate microbial markers for monitoring the recovery of COVID patients. These results will facilitate the diagnosis, treatment, and prognosis of COVID patients recovering from severe illness.

Integrated mRNA sequence optimization using deep learning.

The coronavirus disease of 2019 pandemic has catalyzed the rapid development of mRNA vaccines, whereas, how to optimize the mRNA sequence of exogenous gene such as severe acute respiratory syndrome coronavirus 2 spike to fit human cells remains a critical challenge. A new algorithm, iDRO (integrated deep-learning-based mRNA optimization), is developed to optimize multiple components of mRNA sequences based on given amino acid sequences of target protein. Considering the biological constraints, we divided iDRO into two steps: open reading frame (ORF) optimization and 5' untranslated region (UTR) and 3'UTR generation. In ORF optimization, BiLSTM-CRF (bidirectional long-short-term memory with conditional random field) is employed to determine the codon for each amino acid. In UTR generation, RNA-Bart (bidirectional auto-regressive transformer) is proposed to output the corresponding UTR. The results show that the optimized sequences of exogenous genes acquired the pattern of human endogenous gene sequence. In experimental validation, the mRNA sequence optimized by our method, compared with conventional method, shows higher protein expression. To the best of our knowledge, this is the first study by introducing deep-learning methods to integrated mRNA sequence optimization, and these results may contribute to the development of mRNA therapeutics.

Continuous population-level monitoring of SARS-CoV-2 seroprevalence in a large European metropolitan region.

Effective public health measures against SARS-CoV-2 require granular knowledge of population-level immune responses. We developed a Tripartite Automated Blood Immunoassay (TRABI) to assess the IgG response against three SARS-CoV-2 proteins. We used TRABI for continuous seromonitoring of hospital patients and blood donors (n = 72'250) in the canton of Zurich from December 2019 to December 2020 (pre-vaccine period). We found that antibodies waned with a half-life of 75 days, whereas the cumulative incidence rose from 2.3% in June 2020 to 12.2% in mid-December 2020. A follow-up health survey indicated that about 10% of patients infected with wildtype SARS-CoV-2 sustained some symptoms at least twelve months post COVID-19. Crucially, we found no evidence of a difference in long-term complications between those whose infection was symptomatic and those with asymptomatic acute infection. The cohort of asymptomatic SARS-CoV-2-infected subjects represents a resource for the study of chronic and possibly unexpected sequelae.

Respiratory immune status and microbiome in recovered COVID-19 patients revealed by metatranscriptomic analyses

Coronavirus disease 2019 (COVID-19) is currently a severe threat to global public health, and the immune response to COVID-19 infection has been widely investigated. However, the immune status and microecological changes in the respiratory systems of patients with COVID-19 after recovery have rarely been considered. We selected 72 patients with severe COVID-19 infection, 57 recovered from COVID-19 infection, and 65 with non-COVID-19 pneumonia, for metatranscriptomic sequencing and bioinformatics analysis. Accordingly, the differentially expressed genes between the infected and other groups were enriched in the chemokine signaling pathway, NOD-like receptor signaling pathway, phagosome, TNF signaling pathway, NF-kappa B signaling pathway, Toll-like receptor signaling pathway, and C-type lectin receptor signaling pathway. We speculate that IL17RD, CD74, and TNFSF15 may serve as disease biomarkers in COVID-19. Additionally, principal coordinate analysis revealed significant differences between groups. In particular, frequent co-infections with the genera Streptococcus, Veillonella, Gemella, and Neisseria, among others, were found in COVID-19 patients. Moreover, the random forest prediction model with differential genes showed a mean area under the curve (AUC) of 0.77, and KCNK12, IL17RD, LOC100507412, PTPRT, MYO15A, MPDZ, FLRT2, SPEG, SERPINB3, and KNDC1 were identified as the most important genes distinguishing the infected group from the recovered group. Agrobacterium tumefaciens, Klebsiella michiganensis, Acinetobacter pittii, Bacillus sp. FJAT.14266, Brevundimonas naejangsanensis, Pseudopropionibacterium propionicum, Priestia megaterium, Dialister pneumosintes, Veillonella rodentium, and Pseudomonas protegens were selected as candidate microbial markers for monitoring the recovery of COVID patients. These results will facilitate the diagnosis, treatment, and prognosis of COVID patients recovering from severe illness.

Shale gas exploration and development in the Sichuan Basin: Progress, challenge and countermeasures

China's shale gas production in 2020 exceeds 200 × 108 m³, which creates a miracle in the history of natural gas development in China. The Sichuan Basin has already been and will be the main battlefield of shale gas exploration and development in China. In order to further promote the large-scale efficient development of shale gas in China, under the new situation of global COVID-19 spread and domestic “carbon peak and carbon neutrality” goal, this paper analyzes the progress and challenges of shale gas exploration and development in the Sichuan Basin from four aspects, including resource exploration, gas reservoir engineering, drilling and production engineering and industry regulation, and puts forward countermeasures and suggestions for achieving large-scale efficient development of shale gas. The following research results are obtained: First, the large-scale efficient development of shale gas in the Sichuan Basin has to take the sustainable and stable production of middle–shallow shale gas and the large-scale productivity construction of deep shale gas as the base. Second, compared with the shale gas exploration and development in the North America, the Sichuan Basin has its own characteristics in terms of geographical setting, geological condition, drilling and production technology and industry regulation, which makes it difficult to copy the development pattern of large scale, high density and continuous well deployment from the North America, so it is necessary to adopt the strategy of “high production with few wells”. On the one hand, continue to apply the geology and engineering integration technology to carry out “integrated research, integrated design, integrated implementation and integrated iteration” in the whole life cycle of shale gas well;and on the other hand, carry out problem-oriented continuous researches from the aspects of geological evaluation, development policy, engineering technology and industry regulation, so as to improve geological evaluation theory and technology, innovate gas reservoir engineering theory and method, research and develop engineering technology for cost reduction and efficiency improvement, improve shale gas industry regulation, and form a new pattern of collaborative promotion of technical and non-technical elements. In conclusion, the research results provide important reference and guidance for the large-scale efficient development of shale gas in the Sichuan Basin and even the whole country.

Redesigning Vina@QNLM for Ultra-Large-Scale Molecular Docking and Screening on a Sunway Supercomputer.

Ultra-large-scale molecular docking can improve the accuracy of lead compounds in drug discovery. In this study, we developed a molecular docking piece of software, Vina@QNLM, which can use more than 4,80,000 parallel processes to search for potential lead compounds from hundreds of millions of compounds. We proposed a task scheduling mechanism for large-scale parallelism based on Vinardo and Sunway supercomputer architecture. Then, we readopted the core docking algorithm to incorporate the full advantage of the heterogeneous multicore processor architecture in intensive computing. We successfully expanded it to 10, 465, 065 cores (1,61,001 management process elements and 0, 465, 065 computing process elements), with a strong scalability of 55.92%. To the best of our knowledge, this is the first time that 10 million cores are used for molecular docking on Sunway. The introduction of the heterogeneous multicore processor architecture achieved the best speedup, which is 11x more than that of the management process element of Sunway. The performance of Vina@QNLM was comprehensively evaluated using the CASF-2013 and CASF-2016 protein-ligand benchmarks, and the screening power was the highest out of the 27 pieces of software tested in the CASF-2013 benchmark. In some existing applications, we used Vina@QNLM to dock more than 10 million molecules to nine rigid proteins related to SARS-CoV-2 within 8.5 h on 10 million cores. We also developed a platform for the general public to use the software.

A multiple-center clinical evaluation of a new real-time reverse transcriptase PCR diagnostic kit for SARS-CoV-2.

Aim: The outbreak of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has had serious repercussions worldwide. This study was aimed to evaluate the accuracy of a new kit for detection of SARS-CoV-2 compared with similar detection kit. Materials & methods: A total of 500 subjects were included and tested with both the new test and control kits. Clinical diagnosis results were taken as the reference standard. Results: Compared with clinical diagnosis, the sensitivity of the test kit was 82.64%, specificity was 98.45% and total coincidence rate was 90.80%. The total coincidence rate, sensitivity and specificity between control kit and clinical diagnosis were 89.20%, 78.10% and 99.61%, respectively. Conclusions: The new kit was comparable to the similar detection kit for detection of SARS-CoV-2 in sensitivity, specificity and total coincidence rate.

The associations between fasting plasma glucose levels and mortality of COVID-19 in patients without diabetes.

AIMS: Coronavirus disease 2019 (COVID-19) which is a novel pneumonia can rapidly progress to acute respiratory distress syndrome, septic shock, and multiple organ dysfunction syndrome. It has appeared in 196 countries around the world. We aimed to clarify the associations between fasting plasma glucose levels and mortality of COVID-19 in patients without diabetes. METHODS: We performed a retrospective, single-center study of 151 patients without diabetes in Tongji Hospital from January 1, 2020 to February 28, 2020. Past medical histories, clinical features and laboratory parameters were collected in these patients. RESULTS: Compared with survivors, non-survivors were more likely to have underlying medical conditions including hypertension and chronic pulmonary diseases. Non-survivors had higher C-reactive protein (CRP), procalcitonin (PCT), interleukin (IL)-2R, IL-6, IL-8 and, tumor necrosis factor-α (TNF-α) levels, while lower lymphocyte counts as compared with those of survivors (all P<0.05). Besides, patients with higher fasting plasma glucose (FPG) had higher IL-6, IL-8, CRP levels and mortality; while lower lymphocyte counts. After adjusting for age and gender, each tertile increment of FPG levels conferred 3.54-fold higher risks of death (odds ratio, 3.54; 95% confidential interval, 1.25-10.06, P=0.018). CONCLUSIONS: Non-survivors combined with more comorbidities, more severe infection, and worse liver, kidney and cardiac function in patients without diabetes. Additionally, fasting plasma glucose levels were significantly associated with the risk of death in patients even with normal FPG and HbA1c levels.