McAN: a novel computational algorithm and platform for constructing and visualizing haplotype networks.

Haplotype networks are graphs used to represent evolutionary relationships between a set of taxa and are characterized by intuitiveness in analyzing genealogical relationships of closely related genomes. We here propose a novel algorithm termed McAN that considers mutation spectrum history (mutations in ancestry haplotype should be contained in descendant haplotype), node size (corresponding to sample count for a given node) and sampling time when constructing haplotype network. We show that McAN is two orders of magnitude faster than state-of-the-art algorithms without losing accuracy, making it suitable for analysis of a large number of sequences. Based on our algorithm, we developed an online web server and offline tool for haplotype network construction, community lineage determination, and interactive network visualization. We demonstrate that McAN is highly suitable for analyzing and visualizing massive genomic data and is helpful to enhance the understanding of genome evolution. Availability: Source code is written in C/C++ and available at https://github.com/Theory-Lun/McAN and https://ngdc.cncb.ac.cn/biocode/tools/BT007301 under the MIT license. Web server is available at https://ngdc.cncb.ac.cn/bit/hapnet/. SARS-CoV-2 dataset are available at https://ngdc.cncb.ac.cn/ncov/. Contact: songshh@big.ac.cn (Song S), zhaowm@big.ac.cn (Zhao W), baoym@big.ac.cn (Bao Y), zhangzhang@big.ac.cn (Zhang Z), ybxue@big.ac.cn (Xue Y).

Dynamics of the Upper Respiratory Tract Microbiota and Its Association with Mortality in COVID-19.

Rationale: Alteration of human respiratory microbiota had been observed in coronavirus disease (COVID-19). How the microbiota is associated with the prognosis in COVID-19 is unclear. Objectives: To characterize the feature and dynamics of the respiratory microbiota and its associations with clinical features in patients with COVID-19. Methods: We conducted metatranscriptome sequencing on 588 longitudinal oropharyngeal swab specimens collected from 192 patients with COVID-19 (including 39 deceased patients) and 95 healthy controls from the same geographic area. Meanwhile, the concentration of 27 cytokines and chemokines in plasma was measured for patients with COVID-19. Measurements and Main Results: The upper respiratory tract (URT) microbiota in patients with COVID-19 differed from that in healthy controls, whereas deceased patients possessed a more distinct microbiota, both on admission and before discharge/death. The alteration of URT microbiota showed a significant correlation with the concentration of proinflammatory cytokines and mortality. Specifically, Streptococcus-dominated microbiota was enriched in recovered patients, and showed high temporal stability and resistance against pathogens. In contrast, the microbiota in deceased patients was more susceptible to secondary infections and became more deviated from the norm after admission. Moreover, the abundance of S. parasanguinis on admission was significantly correlated with prognosis in nonsevere patients (lower vs. higher abundance, odds ratio, 7.80; 95% CI, 1.70-42.05). Conclusions: URT microbiota dysbiosis is a remarkable manifestation of COVID-19; its association with mortality suggests it may reflect the interplay between pathogens, symbionts, and the host immune status. Whether URT microbiota could be used as a biomarker for diagnosis and prognosis of respiratory diseases merits further investigation.

Genomic Diversity of Severe Acute Respiratory Syndrome-Coronavirus 2 in Patients With Coronavirus Disease 2019.

BACKGROUND: A novel coronavirus (CoV), severe acute respiratory syndrome (SARS)-CoV-2, has infected >75 000 individuals and spread to >20 countries. It is still unclear how fast the virus evolved and how it interacts with other microorganisms in the lung. METHODS: We have conducted metatranscriptome sequencing for bronchoalveolar lavage fluid samples from 8 patients with SARS-CoV-2, and also analyzed data from 25 patients with community-acquired pneumonia (CAP), and 20 healthy controls for comparison. RESULTS: The median number of intrahost variants was 1-4 in SARS-CoV-2-infected patients, ranged from 0 to 51 in different samples. The distribution of variants on genes was similar to those observed in the population data. However, very few intrahost variants were observed in the population as polymorphisms, implying either a bottleneck or purifying selection involved in the transmission of the virus, or a consequence of the limited diversity represented in the current polymorphism data. Although current evidence did not support the transmission of intrahost variants in a possible person-to-person spread, the risk should not be overlooked. Microbiotas in SARS-CoV-2-infected patients were similar to those in CAP, either dominated by the pathogens or with elevated levels of oral and upper respiratory commensal bacteria. CONCLUSION: SARS-CoV-2 evolves in vivo after infection, which may affect its virulence, infectivity, and transmissibility. Although how the intrahost variant spreads in the population is still elusive, it is necessary to strengthen the surveillance of the viral evolution in the population and associated clinical changes.