Reporter mice for isolating and auditing cell type‐specific extracellular vesicles in vivo

Extracellular vesicles (EVs) are abundant, lipid‐enclosed vectors that contain nucleic acids and proteins, they can be secreted from donor cells and freely circulate, and they can be engulfed by recipient cells thus enabling systemic communication between heterotypic cell types. However, genetic tools for labeling, isolating, and auditing cell type‐specific EVs in vivo, without prior in vitro manipulation, are lacking. We have used CRISPR‐Cas9‐mediated genome editing to generate mice bearing a CD63‐emGFP^{loxP/stop/loxP} knock‐in cassette that enables the specific labeling of circulating CD63⁺ vesicles from any cell type when crossed with lineage‐specific Cre recombinase driver mice. As proof‐of‐principle, we have crossed these mice with Cdh5‐Cre^ERT2 mice to generate CD63^emGFP+ vasculature. Using these mice, we show that developing vasculature is marked with emerald GFP (emGFP) following tamoxifen administration to pregnant females. In adult mice, quiescent vasculature and angiogenic vasculature (in tumors) is also marked with emGFP. Moreover, whole plasma‐purified EVs contain a subpopulation of emGFP⁺ vesicles that are derived from the endothelium, co‐express additional EV (e.g., CD9 and CD81) and endothelial cell (e.g., CD105) markers, and they harbor specific miRNAs (e.g., miR‐126, miR‐30c, and miR‐125b). This new mouse strain should be a useful genetic tool for generating cell type‐specific, CD63⁺ EVs that freely circulate in serum and can subsequently be isolated and characterized using standard methodologies.     

The Relationship Between GPS Sampling Interval and Estimated Daily Travel Distances in Chacma Baboons (Papio ursinus)

International Journal of Primatology - Modern studies of animal movement use the Global Positioning System (GPS) to estimate animals’ distance traveled. The temporal resolution of GPS fixes...     

Neutral Genomic Microevolution of a Recently Emerged Pathogen,Salmonella enterica Serovar Agona

Salmonella enterica serovar Agona has caused multiple food-borne outbreaks of gastroenteritis since it was first isolated in 1952. We analyzed the genomes of 73 isolates from global sources, comparing five distinct outbreaks with sporadic infections as well as food contamination and the environment. Agona consists of three lineages with minimal mutational diversity: only 846 single nucleotide polymorphisms (SNPs) have accumulated in the non-repetitive, core genome since Agona evolved in 1932 and subsequently underwent a major population expansion in the 1960s. Homologous recombination with other serovars of S. enterica imported 42 recombinational tracts (360 kb) in 5/143 nodes within the genealogy, which resulted in 3,164 additional SNPs. In contrast to this paucity of genetic diversity, Agona is highly diverse according to pulsed-field gel electrophoresis (PFGE), which is used to assign isolates to outbreaks. PFGE diversity reflects a highly dynamic accessory genome associated with the gain or loss (indels) of 51 bacteriophages, 10 plasmids, and 6 integrative conjugational elements (ICE/IMEs), but did not correlate uniquely with outbreaks. Unlike the core genome, indels occurred repeatedly in independent nodes (homoplasies), resulting in inaccurate PFGE genealogies. The accessory genome contained only few cargo genes relevant to infection, other than antibiotic resistance. Thus, most of the genetic diversity within this recently emerged pathogen reflects changes in the accessory genome, or is due to recombination, but these changes seemed to reflect neutral processes rather than Darwinian selection. Each outbreak was caused by an independent clade, without universal, outbreak-associated genomic features, and none of the variable genes in the pan-genome seemed to be associated with an ability to cause outbreaks.     

Hybridization kinetics on microarray surfaces

Kinetic hybridization data are compared to a number of different models. A first-order Langmuir model provides the best fit to the data.     

The potential role of cofilin-1 in promoting triple negative breast cancer (TNBC) metastasis via the extracellular vesicles (EVs)

Triple negative breast cancer (TNBC) is an aggressive cancer, particularly prone to metastasis and is associated with poor survival outcomes. The key to unravelling the aggressiveness of TNBC lies in decoding the mechanism by which it metastasises. Cofilin-1 is a well-studied member of the cofilin family, involved in actin depolymerisation. Studies have described the diverse roles of cofilin-1 including cell motility, apoptosis and lipid metabolism. Levels of cofilin-1 have been shown to be increased in many different types of malignant cells, with increased cofilin-1 protein levels associated with poor prognosis in patients with TNBC. Extracellular vesicles (EVs) are microvesicles typically around 100 nm in size, found in all biological fluids examined to date (Lötvall et al., 2014). Proteomic studies on extracellular vesicles (EVs) have shown that cofilin-1 is amongst the most frequently detected. Moreover, decreased levels of cofilin-1 potentially inhibit the release of EVs from cells. Additionally, Cofilin-1 is essential for the maturation of EVs and may also play a key role in the establishment of the pre-metastatic niche, thus promoting tumour cell migration. Further work into the exact mechanism by which cofilin-1 advances TNBC metastasis, may potentially prevent disease progression and improve outcomes for patients with TNBC.     

Non-local concepts and models in biology

In this paper, we consider local and non-local spatially explicit mathematical models for biological phenomena. We show that, when rate differences between fast and slow local dynamics are great enough, non-local models are adequate simplifications of local models. Non-local models thus avoid describing fast processes in mechanistic detail, instead describing the effects of fast processes on slower ones. As a consequence, non-local models are helpful to biologists because they describe biological systems on scales that are convenient to observation, data collection, and insight. We illustrate these arguments by comparing local and non-local models for the aggregation of hypothetical organisms, and we support theoretical ideas with concrete examples from cell biology and animal behavior.