Caveolae and caveolae-like membrane domains in cellular signaling and disease: identification of downstream targets for the tumor suppressor protein caveolin-1

Caveolae are small, flask-shaped invaginations of the plasma membrane present on a large number of mammalian cells. Recent results obtained with knock-out mice for the gene caveolin-1 demonstrate that expression of caveolin-1 protein is essential for caveolae formation in vivo. Caveolae are implicated in a wide variety of cellular events including transcytosis, cholesterol trafficking and as cellular centers important in coordinating signalling events. Caveolae share this role and the property of detergent insolubility with plasma membrane assemblies rich in glycosphingolipids and cholesterol, often called lipid rafts, but preferably referred to here as caveolae-like membrane domains. Due to such widespread presence and usage in cellular function, caveolae and related domains are implicated in human diseases, including cancer. In particular, the protein caveolin-1 is suggested to function as a tumor suppressor protein. Evidence demonstrating such a role for caveolin-1 in human colon carcinoma cells will be discussed together with data from microarray experiments seeking to identify caveolin-1 target genes responsible for such behavior.     

On the role of repeated sequences 5' to variant surface glycoprotein genes in African trypanosomes

In African trypanosomes, the DNA region situated upstream from all active and some silent variant surface glycoprotein genes (VSG genes) has a repetitive structure. This region is composed of a variable number of tandem repeats of an A + T-rich sequence which lacks the recognition sites for most commonly used restriction endonucleases, and is thus called 'barren region'. The length of the barren regions varies in different trypanosome variants from 0.2 to many kb. We have characterized the barren region upstream from the active VSG gene in two independent Trypanosoma equiperdum variants expressing the same VSG gene in the same expression site. To analyse the junction point between the expression site and the inserted gene, these two barren regions were cloned and sequenced. The longer barren region contains 14 repeats and the other contains two repeats. In both cases the junction point has been shown to lie within a repeat but different repeats were used in each case. These results argue that the repeats are important for the insertion of the duplicated-transposed gene into the expression site and that any repeat can be used.     

Water quality check: macrophages setting the standards

In a recent study published in Cell,Chikina et al.uncovered a new mechanism by which colonic macrophages perform quality check of fluids absorbed by colonic epi...     

SAMA: A Method for 3D Morphological Analysis

Three-dimensional (3D) culture models are critical tools for understanding tissue morphogenesis. A key requirement for their analysis is the ability to reconstruct the tissue into computational models that allow quantitative evaluation of the formed structures. Here, we present Software for Automated Morphological Analysis (SAMA), a method by which epithelial structures grown in 3D cultures can be imaged, reconstructed and analyzed with minimum human intervention. SAMA allows quantitative analysis of key features of epithelial morphogenesis such as ductal elongation, branching and lumen formation that distinguish different hormonal treatments. SAMA is a user-friendly set of customized macros operated via FIJI (http://fiji.sc/Fiji), an open-source image analysis platform in combination with a set of functions in R (http://www.r-project.org/), an open-source program for statistical analysis. SAMA enables a rapid, exhaustive and quantitative 3D analysis of the shape of a population of structures in a 3D image. SAMA is cross-platform, licensed under the GPLv3 and available at http://montevil.theobio.org/content/sama.     

Selective Susceptibility of Human Skin Antigen Presenting Cells to Productive Dengue Virus Infection

Dengue is a growing global concern with 390 million people infected each year. Dengue virus (DENV) is transmitted by mosquitoes, thus host cells in the skin are the first point of contact with the virus. Human skin contains several populations of antigen-presenting cells which could drive the immune response to DENV in vivo: epidermal Langerhans cells (LCs), three populations of dermal dendritic cells (DCs), and macrophages. Using samples of normal human skin we detected productive infection of CD14⁺ and CD1c⁺ DCs, LCs and dermal macrophages, which was independent of DC-SIGN expression. LCs produced the highest viral titers and were less sensitive to IFN-β. Nanostring gene expression data showed significant up-regulation of IFN-β, STAT-1 and CCL5 upon viral exposure in susceptible DC populations. In mice infected intra-dermally with DENV we detected parallel populations of infected DCs originating from the dermis and migrating to the skin-draining lymph nodes. Therefore dermal DCs may simultaneously facilitate systemic spread of DENV and initiate the adaptive anti-viral immune response.     

Assessing metacommunity processes through signatures in spatiotemporal turnover of community composition

Although metacommunity ecology has been a major field of research in the last decades, with both conceptual and empirical outputs, the analysis of the temporal dynamics of metacommunities has only emerged recently and consists mostly of repeated static analyses. Here we propose a novel analytical framework to assess metacommunity processes using path analyses of spatial and temporal diversity turnovers. We detail the principles and practical aspects of this framework and apply it to simulated datasets to illustrate its ability to decipher the respective contributions of entangled drivers of metacommunity dynamics. We then apply it to four empirical datasets. Empirical results support the view that metacommunity dynamics may be generally shaped by multiple ecological processes acting in concert, with environmental filtering being variable across both space and time. These results reinforce our call to go beyond static analyses of metacommunities that are blind to the temporal part of environmental variability.     

Response to “Vast (but avoidable) underestimation of global biodiversity”

This Formal Comment provides clarifications on the authors’ recent estimates of global bacterial diversity and the current status of the field, and responds to a Formal Comment from John Wiens regarding their prior work.

We welcome Wiens’ efforts to estimate global animal-associated bacterial richness and thank him for highlighting points of confusion and potential caveats in our previous work on the topic [1]. We find Wiens’ ideas worthy of consideration, as most of them represent a step in the right direction, and we encourage lively scientific discourse for the advancement of knowledge. Time will ultimately reveal which estimates, and underlying assumptions, came closest to the true bacterial richness; we are excited and confident that this will happen in the near future thanks to rapidly increasing sequencing capabilities. Here, we provide some clarifications on our work, its relation to Wiens’ estimates, and the current status of the field.First, Wiens states that we excluded animal-associated bacterial species in our global estimates. However, thousands of animal-associated samples were included in our analysis, and this was clearly stated in our main text (second paragraph on page 3).Second, Wiens’ commentary focuses on “S1 Text” of our paper [1], which was rather peripheral, and, hence, in the Supporting information. S1 Text [1] critically evaluated the rationale underlying previous estimates of global bacterial operational taxonomic unit (OTU) richness by Larsen and colleagues [2], but the results of S1 Text [1] did not in any way flow into the analyses presented in our main article. Indeed, our estimates of global bacterial (and archaeal) richness, discussed in our main article, are based on 7 alternative well-established estimation methods founded on concrete statistical models, each developed specifically for richness estimates from multiple survey data. We applied these methods to >34,000 samples from >490 studies including from, but not restricted to, animal microbiomes, to arrive at our global estimates, independently of the discussion in S1 Text [1].Third, Wiens’ commentary can yield the impression that we proposed that there are only 40,100 animal-associated bacterial OTUs and that Cephalotes in particular only have 40 associated bacterial OTUs. However, these numbers, mentioned in our S1 Text [1], were not meant to be taken as proposed point estimates for animal-associated OTU richness, and we believe that this was clear from our text. Instead, these numbers were meant as examples to demonstrate how strongly the estimates of animal-associated bacterial richness by Larsen and colleagues [2] would decrease simply by (a) using better justified mathematical formulas, i.e., with the same input data as used by Larsen and colleagues [2] but founded on an actual statistical model; (b) accounting for even minor overlaps in the OTUs associated with different animal genera; and/or (c) using alternative animal diversity estimates published by others [3], rather than those proposed by Larsen and colleagues [2]. Specifically, regarding (b), Larsen and colleagues [2] (pages 233 and 259) performed pairwise host species comparisons within various insect genera (for example, within the Cephalotes) to estimate on average how many bacterial OTUs were unique to each host species, then multiplied that estimate with their estimated number of animal species to determine the global animal-associated bacterial richness. However, since their pairwise host species comparisons were restricted to congeneric species, their estimated number of unique OTUs per host species does not account for potential overlaps between different host genera. Indeed, even if an OTU is only found “in one” Cephalotes species, it might not be truly unique to that host species if it is also present in members of other host genera. To clarify, we did not claim that all animal genera can share bacterial OTUs, but instead considered the implications of some average microbiome overlap (some animal genera might share no bacteria, and other genera might share a lot). The average microbiome overlap of 0.1% (when clustering bacterial 16S sequences into OTUs at 97% similarity) between animal genera used in our illustrative example in S1 Text [1] is of course speculative, but it is not unreasonable (see our next point). A zero overlap (implicitly assumed by Larsen and colleagues [2]) is almost certainly wrong. One goal of our S1 Text [1] was to point out the dramatic effects of such overlaps on animal-associated bacterial richness estimates using “basic” mathematical arguments.Fourth, Wiens’ commentary could yield the impression that existing data are able to tell us with sufficient certainty when a bacterial OTU is “unique” to a specific animal taxon. However, so far, the microbiomes of only a minuscule fraction of animal species have been surveyed. One can thus certainly not exclude the possibility that many bacterial OTUs currently thought to be “unique” to a certain animal taxon are eventually also found in other (potentially distantly related) animal taxa, for example, due to similar host diets and or environmental conditions [4–7]. As a case in point, many bacteria in herbivorous fish guts were found to be closely related to bacteria in mammals [8], and Song and colleagues [6] report that bat microbiomes closely resemble those of birds. The gut microbiome of caterpillars consists mostly of dietary and environmental bacteria and is not species specific [4]. Even in animal taxa with characteristic microbiota, there is a documented overlap across host species and genera. For example, there are a small number of bacteria consistently and specifically associated with bees, but these are found across bee genera at the level of the 99.5% similar 16S rRNA OTUs [5]. To further illustrate that an average microbiome overlap between animal taxa at least as large as the one considered in our S1 Text (0.1%) [1] is not unreasonable, we analyzed 16S rRNA sequences from the Earth Microbiome Project [6,9] and measured the overlap of microbiota originating from individuals of different animal taxa. We found that, on average, 2 individuals from different host classes (e.g., 1 mammalian and 1 avian sample) share 1.26% of their OTUs (16S clustered at 100% similarity), and 2 individuals from different host genera belonging to the same class (e.g., 2 mammalian samples) share 2.84% of their OTUs (methods in S1 Text of this response). A coarser OTU threshold (e.g., 97% similarity, considered in our original paper [1]) would further increase these average overlaps. While less is known about insect microbiomes, there is currently little reason to expect a drastically different picture there, and, as explained in our S1 Text [1], even a small average microbiome overlap of 0.1% between host genera would strongly limit total bacterial richness estimates. The fact that the accumulation curve of detected bacterial OTUs over sampled insect species does not yet strongly level off says little about where the accumulation curve would asymptotically converge; rigorous statistical methods, such as the ones used for our global estimates [1], would be needed to estimate this asymptote.Lastly, we stress that while the present conversation (including previous estimates by Louca and colleagues [1], Larsen and colleagues [2], Locey and colleagues [10], Wiens’ commentary, and this response) focuses on 16S rRNA OTUs, it may well be that at finer phylogenetic resolutions, e.g., at bacterial strain level, host specificity and bacterial richness are substantially higher. In particular, future whole-genome sequencing surveys may well reveal the existence of far more genomic clusters and ecotypes than 16S-based OTUs.     

Oligonucleotide analogues containing the internucleotide linker C3′-NH-CO-CH<Subscript>2</Subscript>-N(CH<Subscript>3</Subscript>)-C5′

Oligonucleotide analogues were synthesized whose internucleoside linker contains an amide bond and a methylamino group (C3′-NH-CO-CH₂-N(CH₃)-C5′). Melting curves for duplexes formed by modified oligonucleotides and natural oligonucleotides complementary to them were measured, and the melting temperatures and thermodynamic parameters of duplex formation were calculated. The introduction of one modified dinucleoside linker into the oligonucleotide only slightly decreases the melting temperatures of these duplexes compared with unmodified ones. The CD spectra of modified duplexes were studied, and their spatial structures are discussed.