Metabolic rewiring promotes anti-inflammatory effects of glucocorticoids.

Glucocorticoids represent the mainstay of therapy for a broad spectrum of immune-mediated inflammatory diseases. However, the molecular mechanisms underlying their anti-inflammatory mode of action have remained incompletely understood1. Here we show that the anti-inflammatory properties of glucocorticoids involve reprogramming of the mitochondrial metabolism of macrophages, resulting in increased and sustained production of the anti-inflammatory metabolite itaconate and consequent inhibition of the inflammatory response. The glucocorticoid receptor interacts with parts of the pyruvate dehydrogenase complex whereby glucocorticoids provoke an increase in activity and enable an accelerated and paradoxical flux of the tricarboxylic acid (TCA) cycle in otherwise pro-inflammatory macrophages. This glucocorticoid-mediated rewiring of mitochondrial metabolism potentiates TCA-cycle-dependent production of itaconate throughout the inflammatory response, thereby interfering with the production of pro-inflammatory cytokines. By contrast, artificial blocking of the TCA cycle or genetic deficiency in aconitate decarboxylase 1, the rate-limiting enzyme of itaconate synthesis, interferes with the anti-inflammatory effects of glucocorticoids and, accordingly, abrogates their beneficial effects during a diverse range of preclinical models of immune-mediated inflammatory diseases. Our findings provide important insights into the anti-inflammatory properties of glucocorticoids and have substantial implications for the design of new classes of anti-inflammatory drugs.

Many morphs: Parsing gesture signals from the noise.

Parsing signals from noise is a general problem for signallers and recipients, and for researchers studying communicative systems. Substantial efforts have been invested in comparing how other species encode information and meaning, and how signalling is structured. However, research depends on identifying and discriminating signals that represent meaningful units of analysis. Early approaches to defining signal repertoires applied top-down approaches, classifying cases into predefined signal types. Recently, more labour-intensive methods have taken a bottom-up approach describing detailed features of each signal and clustering cases based on patterns of similarity in multi-dimensional feature-space that were previously undetectable. Nevertheless, it remains essential to assess whether the resulting repertoires are composed of relevant units from the perspective of the species using them, and redefining repertoires when additional data become available. In this paper we provide a framework that takes data from the largest set of wild chimpanzee (Pan troglodytes) gestures currently available, splitting gesture types at a fine scale based on modifying features of gesture expression using latent class analysis (a model-based cluster detection algorithm for categorical variables), and then determining whether this splitting process reduces uncertainty about the goal or community of the gesture. Our method allows different features of interest to be incorporated into the splitting process, providing substantial future flexibility across, for example, species, populations, and levels of signal granularity. Doing so, we provide a powerful tool allowing researchers interested in gestural communication to establish repertoires of relevant units for subsequent analyses within and between systems of communication.

GesturalOrigins: A bottom-up framework for establishing systematic gesture data across ape species.

Current methodologies present significant hurdles to understanding patterns in the gestural communication of individuals, populations, and species. To address this issue, we present a bottom-up data collection framework for the study of gesture: GesturalOrigins. By "bottom-up", we mean that we minimise a priori structural choices, allowing researchers to define larger concepts (such as 'gesture types', 'response latencies', or 'gesture sequences') flexibly once coding is complete. Data can easily be re-organised to provide replication of, and comparison with, a wide range of datasets in published and planned analyses. We present packages, templates, and instructions for the complete data collection and coding process. We illustrate the flexibility that our methodological tool offers with worked examples of (great ape) gestural communication, demonstrating differences in the duration of action phases across distinct gesture action types and showing how species variation in the latency to respond to gestural requests may be revealed or masked by methodological choices. While GesturalOrigins is built from an ape-centred perspective, the basic framework can be adapted across a range of species and potentially to other communication systems. By making our gesture coding methods transparent and open access, we hope to enable a more direct comparison of findings across research groups, improve collaborations, and advance the field to tackle some of the long-standing questions in comparative gesture research.

A socio-ecological perspective on the gestural communication of great ape species, individuals, and social units.

Over the last 30 years, most research on non-human primate gestural communication has been produced by psychologists, which has shaped the questions asked and the methods used. These researchers have drawn on concepts from philosophy, linguistics, anthropology, and ethology, but despite these broad influences the field has neglected to situate gestures into the socio-ecological context in which the diverse species, individuals, and social-units exist. In this review, we present current knowledge about great ape gestural communication in terms of repertoires, meanings, and development. We fold this into a conversation about variation in other types of ape social behaviour to identify areas for future research on variation in gestural communication. Given the large variation in socio-ecological factors across species and social-units (and the individuals within these groups), we may expect to find different preferences for specific gesture types; different needs for communicating specific meanings; and different rates of encountering specific contexts. New tools, such as machine-learning based automated movement tracking, may allow us to uncover potential variation in the speed and form of gesture actions or parts of gesture actions. New multi-group multi-generational datasets provide the opportunity to apply analyses, such as Bayesian modelling, which allows us to examine these rich behavioural landscapes. Together, by expanding our questions and our methods, researchers may finally be able to study great ape gestures from the perspective of the apes themselves and explore what this gestural communication system reveals about apes' thinking and experience of their world.

Current Concepts and Controversies in Innate Immunity of Cystic Fibrosis Lung Disease.

Cystic fibrosis (CF) lung disease is characterized by chronic infection and inflammation. The inflammatory response in CF is dominated by the activation of the innate immune system. Bacteria and fungi represent the key pathogens chronically colonizing the CF airways. In response, innate immune pattern recognition receptors, expressed by airway epithelial and myeloid cells, sense the microbial threat and release chemoattractants to recruit large numbers of neutrophils into CF airways. However, neutrophils fail to efficiently clear the invading pathogens, but instead release harmful proteases and oxidants and finally cause tissue injury. Here, we summarize and discuss current concepts and controversies in the field of innate immunity in CF lung disease, facing the ongoing questions of whether inflammation is good or bad in CF and how innate immune mechanisms could be harnessed therapeutically.