Role of anoctamins in cancer and apoptosis

Anoctamin 1 (TMEM16A, Ano1) is a recently identified Ca²⁺-activated chloride channel and a member of a large protein family comprising 10 paralogues. Before Ano1 was identified as a chloride channel protein, it was known as the cancer marker DOG1. DOG1/Ano1 is expressed in gastrointestinal stromal tumours (GIST) and particularly in head and neck squamous cell carcinoma, at very high levels never detected in other tissues. It is now emerging that Ano1 is part of the 11q13 locus, amplified in several types of tumour, where it is thought to augment cell proliferation, cell migration and metastasis. Notably, Ano1 is upregulated through histone deacetylase (HDAC), corresponding to the known role of HDAC in HNSCC. As Ano1 does not enhance proliferation in every cell type, its function is perhaps modulated by cell-specific factors, or by the abundance of other anoctamins. Thus Ano6, by regulating Ca²⁺-induced membrane phospholipid scrambling and annexin V binding, supports cellular apoptosis rather than proliferation. Current findings implicate other cellular functions of anoctamins, apart from their role as Ca²⁺-activated Cl⁻ channels.     

CXCR4 Is Dispensable for T Cell Egress from Chronically Inflamed Skin via the Afferent Lymph

T cell recirculation through extralymphoid tissues is essential to immune surveillance, host defense and inflammation. In this process, T cells enter the tissue from the blood and subsequently leave via the afferent lymph. In the absence of inflammation, T cells require CCR7 expression to egress from the skin or lung, which is consistent with the constitutive expression of the CCR7 ligand CCL21 on lymphatic endothelium. However, during chronic inflammation alternative chemoattractants come into play, allowing Ccr7-deficient (Ccr7^−/−) T cells to egress efficiently from affected skin. As T cell egress from inflamed sites is a potential control point of the inflammatory response, we aimed to determine alternative T cell exit receptors using a mouse and a sheep model. We show that CCR7⁺ and CCR7^– T cells exiting from the chronically inflamed skin were highly responsive to the CXCR4 ligand CXCL12, which was induced in the lymphatics in the inflamed site. Based on these findings, we hypothesized that CXCR4 mediates T cell egress from inflamed skin. However, pharmacological inhibition of CXCR4 did not affect the tissue egress of wildtype or Ccr7^−/− CD4 and CD8 T cells after adoptive transfer into chronically inflamed skin. Similarly, adoptively transferred Cxcr4^−/− Ccr7^−/− and Ccr7^−/− T cells egressed from the inflamed skin equally well. Based on these data, we conclude that, while CXCR4 might play an essential role for other cell types that enter the afferent lymphatics, it is dispensable for T cell egress from the chronically inflamed skin.     

Strand-specific Recognition of DNA Damages by XPD Provides Insights into Nucleotide Excision Repair Substrate Versatility

Recognition and removal of DNA damages is essential for cellular and organismal viability. Nucleotide excision repair (NER) is the sole mechanism in humans for the repair of carcinogenic UV irradiation-induced photoproducts in the DNA, such as cyclobutane pyrimidine dimers. The broad substrate versatility of NER further includes, among others, various bulky DNA adducts. It has been proposed that the 5′-3′ helicase XPD (xeroderma pigmentosum group D) protein plays a decisive role in damage verification. However, despite recent advances such as the identification of a DNA-binding channel and central pore in the protein, through which the DNA is threaded, as well as a dedicated lesion recognition pocket near the pore, the exact process of target site recognition and verification in eukaryotic NER still remained elusive. Our single molecule analysis by atomic force microscopy reveals for the first time that XPD utilizes different recognition strategies to verify structurally diverse lesions. Bulky fluorescein damage is preferentially detected on the translocated strand, whereas the opposite strand preference is observed for a cyclobutane pyrimidine dimer lesion. Both states, however, lead to similar conformational changes in the resulting specific complexes, indicating a merge to a “final” verification state, which may then trigger the recruitment of further NER proteins.     

Enhanced Passive Bat Rabies Surveillance in Indigenous Bat Species from Germany - A Retrospective Study

In Germany, rabies in bats is a notifiable zoonotic disease, which is caused by European bat lyssaviruses type 1 and 2 (EBLV-1 and 2), and the recently discovered new lyssavirus species Bokeloh bat lyssavirus (BBLV). As the understanding of bat rabies in insectivorous bat species is limited, in addition to routine bat rabies diagnosis, an enhanced passive surveillance study, i.e. the retrospective investigation of dead bats that had not been tested for rabies, was initiated in 1998 to study the distribution, abundance and epidemiology of lyssavirus infections in bats from Germany. A total number of 5478 individuals representing 21 bat species within two families were included in this study. The Noctule bat (Nyctalus noctula) and the Common pipistrelle (Pipistrellus pipistrellus) represented the most specimens submitted. Of all investigated bats, 1.17% tested positive for lyssaviruses using the fluorescent antibody test (FAT). The vast majority of positive cases was identified as EBLV-1, predominately associated with the Serotine bat (Eptesicus serotinus). However, rabies cases in other species, i.e. Nathusius'' pipistrelle bat (Pipistrellus nathusii), P. pipistrellus and Brown long-eared bat (Plecotus auritus) were also characterized as EBLV-1. In contrast, EBLV-2 was isolated from three Daubenton''s bats (Myotis daubentonii). These three cases contribute significantly to the understanding of EBLV-2 infections in Germany as only one case had been reported prior to this study. This enhanced passive surveillance indicated that besides known reservoir species, further bat species are affected by lyssavirus infections. Given the increasing diversity of lyssaviruses and bats as reservoir host species worldwide, lyssavirus positive specimens, i.e. both bat and virus need to be confirmed by molecular techniques.     

Methods to account for spatial autocorrelation in the analysis of species distributional data: a review

Species distributional or trait data based on range map (extent‐of‐occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species’ distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing spatial autocorrelation in the errors. However, we found that for presence/absence data the results and conclusions were very variable between the different methods. This is likely due to the low information content of binary maps. Also, in contrast with previous studies, we found that autocovariate methods consistently underestimated the effects of environmental controls of species distributions. Given their widespread use, in particular for the modelling of species presence/absence data (e.g. climate envelope models), we argue that this warrants further study and caution in their use. To aid other ecologists in making use of the methods described, code to implement them in freely available software is provided in an electronic appendix.