Identifying wildlife reservoirs of neglected taeniid tapeworms : non-invasive diagnosis of endemic Taenia serialis infection in a wild primate population

Despite the global distribution and public health consequences of Taenia tapeworms, the life cycles of taeniids infecting wildlife hosts remain largely undescribed. The larval stage of Taenia serialis commonly parasitizes rodents and lagomorphs, but has been reported in a wide range of hosts that includes geladas (Theropithecus gelada), primates endemic to Ethiopia. Geladas exhibit protuberant larval cysts indicative of advanced T. serialis infection that are associated with high mortality. However, non-protuberant larvae can develop in deep tissue or the abdominal cavity, leading to underestimates of prevalence based solely on observable cysts. We adapted a non-invasive monoclonal antibody-based enzyme-linked immunosorbent assay (ELISA) to detect circulating Taenia spp. antigen in dried gelada urine. Analysis revealed that this assay was highly accurate in detecting Taenia antigen, with 98.4% specificity, 98.5% sensitivity, and an area under the curve of 0.99. We used this assay to investigate the prevalence of T. serialis infection in a wild gelada population, finding that infection is substantially more widespread than the occurrence of visible T. serialis cysts (16.4% tested positive at least once, while only 6% of the same population exhibited cysts). We examined whether age or sex predicted T. serialis infection as indicated by external cysts and antigen presence. Contrary to the female-bias observed in many Taenia-host systems, we found no significant sex bias in either cyst presence or antigen presence. Age, on the other hand, predicted cyst presence (older individuals were more likely to show cysts) but not antigen presence. We interpret this finding to indicate that T. serialis may infect individuals early in life but only result in visible disease later in life. This is the first application of an antigen ELISA to the study of larval Taenia infection in wildlife, opening the doors to the identification and description of infection dynamics in reservoir populations

CREB Is Activated by Muscle Injury and Promotes Muscle Regeneration

The cAMP response element binding protein (CREB) plays key roles in differentiation of embryonic skeletal muscle progenitors and survival of adult skeletal muscle. However, little is known about the physiologic signals that activate CREB in normal muscle. Here we show that CREB phosphorylation and target genes are induced after acute muscle injury and during regeneration due to genetic mutation. Activated CREB localizes to both myogenic precursor cells and newly regenerating myofibers within regenerating areas. Moreover, we found that signals from damaged skeletal muscle tissue induce CREB phosphorylation and target gene expression in primary mouse myoblasts. An activated CREB mutant (CREBY134F) potentiates myoblast proliferation as well as expression of early myogenic transcription factors in cultured primary myocytes. Consistently, activated CREB-YF promotes myoblast proliferation after acute muscle injury in vivo and enhances muscle regeneration in dystrophic mdx mice. Our findings reveal a new physiologic function for CREB in contributing to skeletal muscle regeneration

Consensus guidelines for the use and interpretation of angiogenesis assays

The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference

The Independent Evolution Method Is Not a Viable Phylogenetic Comparative Method

<div><p>Phylogenetic comparative methods (PCMs) use data on species traits and phylogenetic relationships to shed light on evolutionary questions. Recently, Smaers and Vinicius suggested a new PCM, Independent Evolution (IE), which purportedly employs a novel model of evolution based on Felsenstein’s Adaptive Peak Model. The authors found that IE improves upon previous PCMs by producing more accurate estimates of ancestral states, as well as separate estimates of evolutionary rates for each branch of a phylogenetic tree. Here, we document substantial theoretical and computational issues with IE. When data are simulated under a simple Brownian motion model of evolution, IE produces severely biased estimates of ancestral states and changes along individual branches. We show that these branch-specific changes are essentially ancestor-descendant or “directional” contrasts, and draw parallels between IE and previous PCMs such as “minimum evolution”. Additionally, while comparisons of branch-specific changes between variables have been interpreted as reflecting the relative strength of selection on those traits, we demonstrate through simulations that regressing IE estimated branch-specific changes against one another gives a biased estimate of the scaling relationship between these variables, and provides no advantages or insights beyond established PCMs such as phylogenetically independent contrasts. In light of our findings, we discuss the results of previous papers that employed IE. We conclude that Independent Evolution is not a viable PCM, and should not be used in comparative analyses.</p></div

The Independent Evolution method.

<p>Steps for the algorithm are detailed in the main text. Modified from Smaers and Vinicius [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144147#pone.0144147.ref004" target="_blank">4</a>].</p

Comparison of distance metrics.

<p>The IE distance metric asymptotes at |2|, leading to severely underestimated distances when the difference between values is large. In contrast, the difference between logged values does not asymptote, and is an unbiased estimator of proportional distances.</p

Simulated and estimated ancestral states.

<p>Box and whisker plots for (A) simulations of ancestral states, (B) PIDC ancestral state reconstructions, and (C) IE ancestral state reconstructions at each node. Nodes are shown in increasing order based on their distance from the root of the tree. If estimated ancestral states are unbiased, they should be centered on 0. PIDC ancestral state estimates are unbiased (B), while IE ancestral state estimates are biased toward positive values near the root of the tree (C).</p

Isometric and allometric scaling relationships for raw values, logged values, and proportions of both types of values.

<p>Both <i>x</i> and <i>y</i> are of the same dimensionality, as in a brain mass ~ body mass relationship. In an isometric relationship, the scaling exponent does not change when raw values in arithmetic space (A) are plotted compared to raw proportions in arithmetic space (B). When data are log-transformed (C), the scaling exponent becomes the slope of the line. The same is true when proportions are plotted in log-space (D). For raw data (E) or raw proportions (F), an allometric relationship can be recognized by a scaling exponent that is not equal to 1 (again assuming equivalent dimensionality of <i>x</i> and <i>y</i>). For logged data (G) or logged proportions (H), allometry is characterized by a slope not equal to 1. In all iterations, isometric relationships remain isometric and allometric relationships remain allometric. There is no “collapse” of allometry into isometry as suggested by Smaers et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144147#pone.0144147.ref006" target="_blank">6</a>]. The authors either misidentify (A) as an allometric relationship, or do not appropriately convert logged data (C) into logged proportions (D).</p