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With the continual progress of sequencing techniques, genome-scale data are increasingly used in phylogenetic studies. With more data from throughout the genome, the relationship between genes and different kinds of characters is receiving more attention. Here, we present version 4 of RASP, a software to reconstruct ancestral states through phylogenetic trees. RASP can apply generalized statistical ancestral reconstruction methods to phylogenies, explore the phylogenetic signal of characters to particular trees, calculate distances between trees, and cluster trees into groups. RASP 4 has an improved graphic user interface and is freely available from http://mnh.scu.edu.cn/soft/blog/RASP (program) and https://github.com/sculab/RASP (source code).

Different analytical techniques used on the same data set may lead to different conclusions about the existence and strength of genetic structure. Therefore, reliable interpretation of the results from different methods depends on the efficacy and reliability of different statistical methods. In this paper, we evaluated the performance of multiple analytical methods to detect the presence of a linear barrier dividing populations. We were specifically interested in determining if simulation conditions, such as dispersal ability and genetic equilibrium, affect the power of different analytical methods for detecting barriers. We evaluated two boundary detection methods (Monmonier's algorithm and WOMBLING), two spatial Bayesian clustering methods (TESS and GENELAND), an aspatial clustering approach (STRUCTURE), and two recently developed, non-Bayesian clustering methods [PSMIX and discriminant analysis of principal components (DAPC)]. We found that clustering methods had higher success rates than boundary detection methods and also detected the barrier more quickly. All methods detected the barrier more quickly when dispersal was long distance in comparison to short-distance dispersal scenarios. Bayesian clustering methods performed best overall, both in terms of highest success rates and lowest time to barrier detection, with GENELAND showing the highest power. None of the methods suggested a continuous linear barrier when the data were generated under an isolation-by-distance (IBD) model. However, the clustering methods had higher potential for leading to incorrect barrier inferences under IBD unless strict criteria for successful barrier detection were implemented. Based on our findings and those of previous simulation studies, we discuss the utility of different methods for detecting linear barriers to gene flow.

Severe cases of coronavirus disease 2019 (COVID-19) are regularly complicated by respiratory failure. Although it has been suggested that elevated levels of blood neutrophils associate with worsening oxygenation in COVID-19, it is unknown whether neutrophils are drivers of the thrombo-inflammatory storm or simple bystanders. To better understand the potential role of neutrophils in COVID-19, we measured levels of the neutrophil activation marker S100A8/A9 (calprotectin) in hospitalized patients and determined its relationship to severity of illness and respiratory status. Patients with COVID-19 (n = 172) had markedly elevated levels of calprotectin in their blood. Calprotectin tracked with other acute phase reactants including C-reactive protein, ferritin, lactate dehydrogenase, and absolute neutrophil count, but was superior in identifying patients requiring mechanical ventilation. In longitudinal samples, calprotectin rose as oxygenation worsened. When tested on day 1 or 2 of hospitalization (n = 94 patients), calprotectin levels were significantly higher in patients who progressed to severe COVID-19 requiring mechanical ventilation (8039 ± 7031 ng/ml, n = 32) as compared to those who remained free of intubation (3365 ± 3146, P < 0.0001). In summary, serum calprotectin levels track closely with current and future COVID-19 severity, implicating neutrophils as potential perpetuators of inflammation and respiratory compromise in COVID-19.

Analysis of SARS-CoV-2 genetic diversity within infected hosts can provide insight into the generation and spread of new viral variants and may enable high resolution inference of transmission chains. However, little is known about temporal aspects of SARS-CoV-2 intrahost diversity and the extent to which shared diversity reflects convergent evolution as opposed to transmission linkage. Here we use high depth of coverage sequencing to identify within-host genetic variants in 325 specimens from hospitalized COVID-19 patients and infected employees at a single medical center. We validated our variant calling by sequencing defined RNA mixtures and identified a viral load threshold that minimizes false positives. By leveraging clinical metadata, we found that intrahost diversity is low and does not vary by time from symptom onset. This suggests that variants will only rarely rise to appreciable frequency prior to transmission. Although there was generally little shared variation across the sequenced cohort, we identified intrahost variants shared across individuals who were unlikely to be related by transmission. These variants did not precede a rise in frequency in global consensus genomes, suggesting that intrahost variants may have limited utility for predicting future lineages. These results provide important context for sequence-based inference in SARS-CoV-2 evolution and epidemiology.

In severe cases of coronavirus disease 2019 (COVID-19), viral pneumonia progresses to respiratory failure. Neutrophil extracellular traps (NETs) are extracellular webs of chromatin, microbicidal proteins, and oxidant enzymes that are released by neutrophils to contain infections. However, when not properly regulated, NETs have potential to propagate inflammation and microvascular thrombosis, including in the lungs of patients with acute respiratory distress syndrome. While elevated levels of blood neutrophils predict worse outcomes in COVID-19, the role of NETs has not been investigated. We now report that sera from patients with COVID-19 (n=50 patients, n=84 samples) have elevated levels of cell-free DNA, myeloperoxidase(MPO)-DNA, and citrullinated histone H3 (Cit-H3); the latter two are highly specific markers of NETs. Highlighting the potential clinical relevance of these findings, cell-free DNA strongly correlated with acute phase reactants including C-reactive protein, D-dimer, and lactate dehydrogenase, as well as absolute neutrophil count. MPO-DNA associated with both cell-free DNA and absolute neutrophil count, while Cit-H3 correlated with platelet levels. Importantly, both cell-free DNA and MPO-DNA were higher in hospitalized patients receiving mechanical ventilation as compared with hospitalized patients breathing room air. Finally, sera from individuals with COVID-19 triggered NET release from control neutrophils in vitro. In summary, these data reveal high levels of NETs in many patients with COVID-19, where they may contribute to cytokine release and respiratory failure. Future studies should investigate the predictive power of circulating NETs in longitudinal cohorts, and determine the extent to which NETs may be novel therapeutic targets in severe COVID-19.

Biodiversity reduction and loss continues to progress at an alarming rate, and thus, there is widespread interest in utilizing rapid and efficient methods for quantifying and delimiting taxonomic diversity. Single-locus species delimitation methods have become popular, in part due to the adoption of the DNA barcoding paradigm. These techniques can be broadly classified into tree-based and distance-based methods depending on whether species are delimited based on a constructed genealogy. Although the relative performance of these methods has been tested repeatedly with simulations, additional studies are needed to assess congruence with empirical data. We compiled a large data set of mitochondrial ND4 sequences from horned lizards (Phrynosoma) to elucidate congruence using four tree-based (single-threshold GMYC, multiple-threshold GMYC, bPTP, mPTP) and one distance-based (ABGD) species delimitation models. We were particularly interested in cases with highly uneven sampling and/or large differences in intraspecific diversity. Results showed a high degree of discordance among methods, with multiple-threshold GMYC and bPTP suggesting an unrealistically high number of species (29 and 26 species within the P. douglasii complex alone). The single-threshold GMYC model was the most conservative, likely a result of difficulty in locating the inflection point in the genealogies. mPTP and ABGD appeared to be the most stable across sampling regimes and suggested the presence of additional cryptic species that warrant further investigation. These results suggest that the mPTP model may be preferable in empirical data sets with highly uneven sampling or large differences in effective population sizes of species.