Sampling bias and incorrect rooting make phylogenetic network tracing of SARS-COV-2 infections unreliable.

There is obvious interest in gaining insights into the epidemiology and evolution of the virus that has recently emerged in humans as the cause of the coronavirus disease 2019 (COVID-19) pandemic. The recent paper by Forster et al. (1), analyzed 160 SARS-CoV-2 full genomes available (https://www.gisaid.org/) in early March 2020. The central claim is the identification of three main SARS-CoV-2 types, named A, B, and C, circulating in different proportions among Europeans and Americans (types A and C) and East Asian (type B). According to a median-joining network analysis, variant A is proposed to be the ancestral type because it links to the sequence of a coronavirus from bats, used as an outgroup to trace the ancestral origin of the human strains. The authors further suggest that the “ancestral Wuhan B-type virus is immunologically or environmentally adapted to a large section of the East Asian population, and may need to mutate to overcome resistance outside East Asia”. There are several serious flaws with their findings and interpretation. First, and most obviously, the sequence identity between SARS-CoV-2 and the bat virus is only 96.2%, implying that these viral genomes (which are nearly 30,000 nucleotides long) differ by more than 1,000 mutations. Such a distant outgroup is unlikely to provide a reliable root for the network. Yet, strangely, the branch to the bat virus, in Figure 1 of the paper, is only 16 or 17 mutations in length. Indeed, the network seems to be mis-rooted because (see Supplementary Figure 4) a virus from Wuhan from week 0 (24th December 2019) is portrayed as a descendant of a clade of viruses collected in weeks 1-9 (presumably from many places outside China), which makes no evolutionary (2), nor epidemiological sense (3).N

Effects of a ciliate protozoa predator on microbial communities in pitcher plant (Sarracenia purpurea) leaves.

The aquatic communities found within the water filled leaves of the pitcher plant, Sarracenia purpurea, have a simple trophic structure providing an ideal system to study microscale interactions between protozoan predators and their bacterial prey. In this study, replicate communities were maintained with and without the presence of the bactivorous protozoan, Colpoda steinii, to determine the effects of grazing on microbial communities. Changes in microbial (Archaea and Bacteria) community structure were assessed using iTag sequencing of 16S rRNA genes. The microbial communities were similar with and without the protozoan predator, with>1000 species. Of these species, Archaea were negligible, with Bacteria comprising 99.99% of the microbial community. The Proteobacteria and Bacteroidetes were the most dominant phyla. The addition of a protozoan predator did not have a significant effect on microbial evenness nor richness. However, the presence of the protozoan did cause a significant shift in the relative abundances of a number of bacterial species. This suggested that bactivorous protozoan may target specific bacterial species and/or that certain bacterial species have innate mechanisms by which they evade predators. These findings help to elucidate the effect that trophic structure perturbations have on predator prey interactions in microbial systems

Alpha diversity statistics of microbial 16S rRNA gene sequence data in <i>S. purpurea</i> samples, with (+CS) and without <i>C. steinii</i> (−CS).

<p>Alpha diversity statistics of microbial 16S rRNA gene sequence data in <i>S. purpurea</i> samples, with (+CS) and without <i>C. steinii</i> (−CS).</p

Non-metric multidimensional scaling (NMDS) ordination of normalized 16S rRNA iTag sequence data.

<p>(A) NMDS ordination of the first and third axes showing sample grouping based on the presence or absence of the protozoan predator. (B) NMDS ordination with those OTUs that were statistically significantly correlated with an axis and had a p-value < 0.03 shown by vectors.</p

Heatmap of OTUs that were statistically significantly more abundant in samples with or without the protozoan predator (only the top ten OTUs that changed the most with protozoa and without protozoa are shown).

<p>Those OTUs that were statistically significantly correlated with an NMDS axis are indicated by an *.</p

Rarefaction curve of the number of observed OTUs from 16S rRNA iTag sequence data.

<p>Rarefaction curve of the number of observed OTUs from 16S rRNA iTag sequence data.</p

Bar graph of normalized 16S rRNA iTag sequence data.

<p>The most abundant classes are shown. Less abundant classes are summed under “Other.” Samples with <i>C. steinii</i> are referred to as +CS and those without as –CS.</p

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Mixed CPE in Vero E6 cells inoculated with filtered plasma specimens.

<p>Panels (A) and (B), mock-infected Vero E6 cells. Panels (C) and (D), Vero E6 cells inoculated with virus-containing plasma. Original image magnifications are indicated beneath the panel identification letters.</p

Chikungunya virus-induced CPE in Vero E6 cells with filtered plasma specimens.

<p>(A) Mock-infected Vero E6 cells, and (B) and (C), CHIKV-induced CPE in Vero E6 cells inoculated with CHIKV-positive plasma samples. All images at 200X original magnification.</p