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A more complete understanding of immune-mediated damage to the coronary arteries in children with Kawasaki disease (KD) is required for improvements in patient treatment and outcomes. We recently reported the transcriptional profile of KD coronary arteritis, and in this study sought to determine protein expression of transcriptionally up-regulated immune genes in KD coronary arteries from the first 2 months after disease onset. We examined the coronary arteries of 12 fatal KD cases and 13 childhood controls for expression of a set of proteins whose genes were highly up-regulated in the KD coronary artery transcriptome: allograft inflammatory factor 1 (AIF1), interleukin 18 (IL-18), CD74, CD1c, CD20 (MS4A1), Toll-like receptor 7 (TLR-7) and Z-DNA binding protein 1 (ZBP1). Immunohistochemistry and immunofluorescence studies were performed to evaluate protein expression and co-localization, respectively. AIF1 was expressed transmurally in KD arteritis and localized to macrophages and myeloid dendritic cells. CD74, which interacts with major histocompatibility complex (MHC) class II on antigen-presenting cells, localized to the intima-media. CD1c, a marker of myeloid dendritic cells, was expressed in a transmural pattern, as were IL-18 and CD20. ZBP1 and TLR-7 were up-regulated compared to controls, but less highly compared to the other proteins. These findings provide evidence of antigen presentation and interferon response in KD arteritis. In combination with prior studies demonstrating T lymphocyte activation, these results demonstrate the complexity of the KD arterial immune response., (© 2017 British Society for Immunology.)

As increasingly large molecular data sets are collected for phylogenomics, the conflicting phylogenetic signal among gene trees poses challenges to resolve some difficult nodes of the Tree of Life. Among these nodes, the phylogenetic position of the honey bees (Apini) within the corbiculate bee group remains controversial, despite its considerable importance for understanding the emergence and maintenance of eusociality. Here, we show that this controversy stems in part from pervasive phylogenetic conflicts among GC-rich gene trees. GC-rich genes typically have a high nucleotidic heterogeneity among species, which can induce topological conflicts among gene trees. When retaining only the most GC-homogeneous genes or using a nonhomogeneous model of sequence evolution, our analyses reveal a monophyletic group of the three lineages with a eusocial lifestyle (honey bees, bumble bees, and stingless bees). These phylogenetic relationships strongly suggest a single origin of eusociality in the corbiculate bees, with no reversal to solitary living in this group. To accurately reconstruct other important evolutionary steps across the Tree of Life, we suggest removing GC-rich and GC-heterogeneous genes from large phylogenomic data sets. Interpreted as a consequence of genome-wide variations in recombination rates, this GC effect can affect all taxa featuring GC-biased gene conversion, which is common in eukaryotes., (© The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Different views of the pattern of social evolution among the highly eusocial bees have arisen as a result of discordance between past molecular and morphology-based phylogenies. Here we present new data and taxa for four molecular data sets and reassess the morphological characters available to date. We show there is no significant character incongruence between four molecular data sets (two nuclear and two mitochondrial), but highly significant character incongruence leads to topological incongruence between the molecular and morphological data. We investigate the effects of using different outgroup combinations to root the estimated tree. We also consider various ways in which biases in the sequence data could be misleading, using several maximum likelihood models, LogDet corrections, and spectral analyses. Ultimately, we concede there is strong discordance between the molecular and morphological data partitions and appropriately apply the conditional combination approach in this case. We also find two equally well supported placements of the root for the molecular trees, one supported by 16S and 28S sequences, the other supported by cytochrome b and opsin. The strength of the evidence leads us to accept two equally well supported hypotheses based on analyses of the molecular data sets. These are the most rigorously supported hypotheses of corbiculate bee relationships at this time, and frame our argument that highly eusocial behavior within the corbiculate bees evolved twice independently.

Nucleotide sequences from a 434-bp region of the 16S rRNA gene were analyzed for 65 taxa of Hymenoptera (ants, bees, wasps, parasitoid wasps, sawflies) to examine the patterns of variation within the gene fragment and the taxonomic levels for which it shows maximum utility in phylogeny estimation. A hierarchical approach was adopted in the study through comparison of levels of sequence variation among taxa at different taxonomic levels. As previously reported for many holometabolous insects, the 16S data reported here for Hymenoptera are highly AT-rich and exhibit strong site-to-site variation in substitution rate. More precise estimates of the shape parameter (alpha) of the gamma distribution and the proportion of invariant sites were obtained in this study by employing a reference phylogeny and utilizing maximum-likelihood estimation. The effectiveness of this approach to recovering expected phylogenies of selected hymenopteran taxa has been tested against the use of maximum parsimony. This study finds that the 16S gene is most informative for phylogenetic analysis at two different levels: among closely related species or populations, and among tribes, subfamilies, and families. Maximization of the phylogenetic signal extracted from the 16S gene at higher taxonomic levels may require consideration of the base composition bias and the site-to-site rate variation in a maximum-likelihood framework.