Functional specialization of islet dendritic cell subsets

Dendritic cells (DC) play important roles in both tolerance and immunity to β cells in type 1 diabetes. How and why DC can have diverse and opposing functions in islets remains elusive. To answer these questions, islet DC subsets and their specialized functions were characterized. Under both homeostatic and inflammatory conditions, there were two main tissue-resident DC subsets in islets, defined as CD11b(lo/-)CD103(+)CX3CR1(-) (CD103(+) DC), the majority of which were derived from fms-like tyrosine kinase 3-dependent pre-DC, and CD11b(+)CD103(-)CX3CR1(+) (CD11b(+) DC), the majority of which were derived from monocytes. CD103(+) DC were the major migratory DC and cross-presented islet-derived Ag in the pancreatic draining lymph node, although this DC subset displayed limited phagocytic activity. CD11b(+) DC were numerically the predominant subset (60-80%) but poorly migrated to the draining lymph node. Although CD11b(+) DC had greater phagocytic activity, they poorly presented Ag to T cells. CD11b(+) DC increased in numbers and percentage during T cell-mediated insulitis, suggesting that this subset might be involved in the pathogenesis of diabetes. These data elucidate the phenotype and function of homeostatic and inflammatory islet DC, suggesting differential roles in islet immunity.     

Ionizing radiation biomarkers for potential use in epidemiological studies

Ionizing radiation is a known human carcinogen that can induce a variety of biological effects depending on the physical nature, duration, doses and dose-rates of exposure. However, the magnitude of health risks at low doses and dose-rates (below 100mSv and/or 0.1mSvmin(-1)) remains controversial due to a lack of direct human evidence. It is anticipated that significant insights will emerge from the integration of epidemiological and biological research, made possible by molecular epidemiology studies incorporating biomarkers and bioassays. A number of these have been used to investigate exposure, effects and susceptibility to ionizing radiation, albeit often at higher doses and dose rates, with each reflecting time-limited cellular or physiological alterations. This review summarises the multidisciplinary work undertaken in the framework of the European project DoReMi (Low Dose Research towards Multidisciplinary Integration) to identify the most appropriate biomarkers for use in population studies. In addition to logistical and ethical considerations for conducting large-scale epidemiological studies, we discuss the relevance of their use for assessing the effects of low dose ionizing radiation exposure at the cellular and physiological level. We also propose a temporal classification of biomarkers that may be relevant for molecular epidemiology studies which need to take into account the time elapsed since exposure. Finally, the integration of biology with epidemiology requires careful planning and enhanced discussions between the epidemiology, biology and dosimetry communities in order to determine the most important questions to be addressed in light of pragmatic considerations including the appropriate population to be investigated (occupationally, environmentally or medically exposed), and study design. The consideration of the logistics of biological sample collection, processing and storing and the choice of biomarker or bioassay, as well as awareness of potential confounding factors, are also essential.     

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.     

Testing for the Dual-Route Cascade Reading Model in the Brain: An fMRI Effective Connectivity Account of an Efficient Reading Style

Neuropsychological data about the forms of acquired reading impairment provide a strong basis for the theoretical framework of the dual-route cascade (DRC) model which is predictive of reading performance. However, lesions are often extensive and heterogeneous, thus making it difficult to establish precise functional anatomical correlates. Here, we provide a connective neural account in the aim of accommodating the main principles of the DRC framework and to make predictions on reading skill. We located prominent reading areas using fMRI and applied structural equation modeling to pinpoint distinct neural pathways. Functionality of regions together with neural network dissociations between words and pseudowords corroborate the existing neuroanatomical view on the DRC and provide a novel outlook on the sub-regions involved. In a similar vein, congruent (or incongruent) reliance of pathways, that is reliance on the word (or pseudoword) pathway during word reading and on the pseudoword (or word) pathway during pseudoword reading predicted good (or poor) reading performance as assessed by out-of-magnet reading tests. Finally, inter-individual analysis unraveled an efficient reading style mirroring pathway reliance as a function of the fingerprint of the stimulus to be read, suggesting an optimal pattern of cerebral information trafficking which leads to high reading performance.     

In vitro toxicity of bithionol and bithionol sulphoxide to Neoparamoeba spp., the causative agent of amoebic gill disease (AGD)

The objective of the present study was to evaluate the in vitro toxicity of bithionol and bithionol sulphoxide to Neoparamoeba spp., the causative agent of amoebic gill disease (AGD). The current treatment for AGD-affected Atlantic salmon involves bathing sea-caged fish in freshwater for a minimum of 3 h, a labour-intensive and costly exercise. Previous attempts to identify alternative treatments have suggested bithionol as an alternate therapeutic, but extensive in vitro efficacy testing has not yet been done. In vitro toxicity to Neoparamoeba spp. was examined using amoebae isolated from the gill of AGD-affected Atlantic salmon and exposing the parasites to freshwater, alumina (10 mg l(-1)), seawater, bithionol or bithionol sulphoxide at nominal concentrations of 0.1, 0.5, 1, 5 and 10 mg l(-1) in seawater. The numbers of viable amoebae were counted using the trypan blue exclusion method at 0, 24, 48 and 72 h. Both bithionol and bithionol sulphoxide demonstrated in vitro toxicity to Neoparamoeba spp. at all concentrations examined (0.1 to 10 mg l(-1) over 72 h), with a comparable toxicity to freshwater observed for both chemicals at concentrations > 5 mg l(-1) following a 72 h treatment. Freshwater remained the most effective treatment, with only 6% viable amoebae seen after 24 h and no viable amoebae observed after 48 h.     

Saccharomyces cerevisiae and Candida tropicalis as starter cultures for the alcoholic fermentation of tchapalo, a traditional sorghum beer

Starter cultures of Candida tropicalis and Saccharomyces cerevisiae isolated from tchapalo were tested in pure culture and co-culture of four ratios [2:1, 25:4, 1:4, 2:3 (cells/cells)] for their ability to ferment sorghum wort to produce tchapalo. All the starters showed means growth rate between 0.043 and 0.101 h^?1. Only C. tropicalis in pure culture showed growth rate lower than that of S. cerevisiae in single culture. During fermentation, according to total soluble solids depletion, yeast starters could be grouped in four different profiles. But in the beer produced, total soluble solids contents were statistically identical. The lowest values were obtained with co-culture C. tropicalis + S. cerevisiae in the ratios of 2:1 and 2:3. Starter cultures with large ratio of C. tropicalis produced a higher organic acids and 2-butanone than S. cerevisiae in pure culture. However, co-culture C. tropicalis + S. cerevisiae (2:1) was the alone starter which produced higher ethanol than S. cerevisiae in pure culture. The beers produced with C. tropicalis + S. cerevisiae (25:4), C. tropicalis + S. cerevisiae (1:4) and C. tropicalis were widely different from those produced with the others starter cultures.     

Phylogenetic species delimitation in ectomycorrhizal fungi and implications for barcoding: the case of the Tricholoma scalpturatum complex (Basidiomycota)

Population studies have revealed that the fungal ectomycorrhizal morphospecies Tricholoma scalpturatum consists of at least two genetically distinct groups that occur sympatrically in several geographical areas. This discovery prompted us to examine species boundaries and relationships between members formerly assigned to T. scalpturatum and allied taxa using phylogenetic analyses. Sequence data were obtained from three nuclear DNA regions [internal transcribed spacer (ITS), gpd and tef], from 101 carpophores collected over a large geographical range in Western Europe, and some reference sequences from public databases. The ITS was also tested for its applicability as DNA barcode for species delimitation. Four highly supported phylogenetic clades were detected. The two previously detected genetic groups of T. scalpturatum were assigned to the phylospecies Tricholoma argyraceum and T. scalpturatum. The two remaining clades were referred to as Tricholoma cingulatum and Tricholoma inocybeoides. Unexpectedly, T. cingulatum showed an accelerated rate of evolution that we attributed to narrow host specialization. This study also reveals recombinant ITS sequences in T. inocybeoides, suggesting a hybrid origin. The ITS was a useful tool for the determination of species boundaries: the mean value of intraspecific genetic distances in the entire ITS region (including 5.8S rDNA) was <0.2%, whereas interspecific divergence estimates ranged from 1.78% to 4.22%. Apart from giving insights into the evolution of the T. scalpturatum complex, this study contributes to the establishment of a library of taxonomically verified voucher specimens, an a posteriori correlation between phenotype and genotype, and DNA barcoding of ectomycorrhizal fungi.