A glimpse on the pattern of rodent diversication: a phylogenetic approach

ABSTRACT: BACKGROUND: Development of phylogenetic methods that do not rely on fossils for the study of evolutionary processes through time have revolutionized the eld of evolutionary biology and resulted in an unprecedented expansion of our knowledge about the tree of life. These methods have helped to shed light on the macroevolution of many taxonomic groups such as the placentals (Mammalia). However, despite the increase of studies addressing the diversication patterns of organisms, no synthesis has addressed the case of the most diversied mammalian clade: the Rodentia. RESULTS: Here we present a rodent maximum likelihood phylogeny inferred from a molecular supermatrix. It is based on 11 mitochondrial and nuclear genes that covers 1,265 species, i.e., respectively 56 % and 81 % of the known specic and generic rodent diversity. The inferred topology recovered all Rodentia clades proposed by recent molecular works. A relaxed molecular clock dating approach provided a time framework for speciation events. We found that the Myomorpha clade shows a greater degree of variation in diversication rates than Sciuroidea, Caviomorpha, Castorimorpha and Anomaluromorpha. We identied a number of shifts in diversication rates within the major clades: two in Castorimorpha, three in Ctenohystrica, 6 within the squirrel-related clade and 24 in the Myomorpha clade. The majority of these shifts occurred within the most recent familial rodent radiations: the Cricetidae and Muridae clades. Using the topological imbalances and the time line we discuss the potential role of different diversication factors that might have shaped the rodents radiation. CONCLUSIONS: The present glimpse on the diversication pattern of rodents can be used for further comparative meta-analyses. Muroid lineages have a greater degree of variation in their diversication rates than any other 1rodent group. Different topological signatures suggest distinct diversication processes among rodent lineages. In particular, Muroidea and Sciuroidea display widespread distribution and have undergone evolutionary and adaptive radiation on most of the continents. Our results show that rodents experienced shifts in diversication rate regularly through the Tertiary, but at different periods for each clade. A comparison between the rodent fossil record and our results suggest that extinction led to the loss of diversication signal for most of the Paleogene nodes.     

Expression of apo-aequorin during embryonic development; how much is needed for calcium imaging?

Aequorin is a bioluminescent calcium indicator consisting of a 21 kDa protein (apo-aequorin) that is covalently linked to a lipophilic cofactor (coelenterazine). The aequorin gene can be expressed in a variety of cell lines and tissues, allowing non-invasive calcium imaging of specific cell types. In the present paper, we describe the possibilities and limitations of calcium imaging with genetically introduced apo-aequorin during embryonic development. By injecting aequorin into sea urchin, Drosophila and zebrafish eggs, we found that higher aequorin concentrations are needed in smaller eggs. Our results suggest that for measuring resting levels of free cytosolic calcium, one needs aequorin concentrations of at least 40 μM in sea urchin eggs, 2 μM in Drosophila eggs, and only 0.11 μM in zebrafish eggs. A simple assay was used to determine the absolute concentrations of expressed apo-aequorin and the percentage of aequorin formation in vivo. The use of this assay is illustrated by expression of the aequorin gene in Drosophila oocytes. These oocytes form up to 1 μM apo-aequorin. In our hands, only 0.3% of this apo-aequorin combined with coelenterazine entering from the medium to form aequorin, which was not enough for calcium imaging of the oocytes, but did allow in vivo imaging of the ovaries. From these studies, we conclude that coelenterazine entry into the cell is the rate limiting step in aequorin formation. Based on the rate of coelenterazine uptake in Drosophila, we estimate that complete conversion of 1 μM apo-aequorin would take 50 days in zebrafish eggs, 19 days in Drosophila eggs, 7 days in sea urchin eggs or 18 h in a 10 gm tissue culture cell. Our results suggest that work based on genetically introduced apo-aequorin will be most successful when large amounts of small cells can be incubated in coelenterazine. During embryonic development this would involve introducing coelenterazine into the circulatory system of late stage embryos. Calcium imaging in early stage embryos may be best done by injecting aequorin, which circumvents the slow process of coelenterazine entry.     

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data

The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for noise. In spite of such efforts, no consensus on best practices has been established and all current approaches vary substantially based on the available data and empirical tests. The k-Nearest Neighbor Graph (kNN-G) is often used to infer the identities of, and relationships between, cells and is the basis of many widely used dimensionality-reduction and projection methods. The kNN-G has also been the basis for imputation methods using, e.g., neighbor averaging and graph diffusion. However, due to the lack of an agreed-upon optimal objective function for choosing hyperparameters, these methods tend to oversmooth data, thereby resulting in a loss of information with regard to cell identity and the specific gene-to-gene patterns underlying regulatory mechanisms. In this paper, we investigate the tuning of kNN- and diffusion-based denoising methods with a novel non-stochastic method for optimally preserving biologically relevant informative variance in single-cell data. The framework, Denoising Expression data with a Weighted Affinity Kernel and Self-Supervision (DEWÄKSS), uses a self-supervised technique to tune its parameters. We demonstrate that denoising with optimal parameters selected by our objective function (i) is robust to preprocessing methods using data from established benchmarks, (ii) disentangles cellular identity and maintains robust clusters over dimension-reduction methods, (iii) maintains variance along several expression dimensions, unlike previous heuristic-based methods that tend to oversmooth data variance, and (iv) rarely involves diffusion but rather uses a fixed weighted kNN graph for denoising. Together, these findings provide a new understanding of kNN- and diffusion-based denoising methods. Code and example data for DEWÄKSS is available at https://gitlab.com/Xparx/dewakss/-/tree/Tjarnberg2020branch.