Metabolic scaling in modular animals

Metabolic scaling is the relationship between organismal metabolic rate and body mass. Understanding the patterns and causes of metabolic scaling provides a powerful foundation for predicting biological processes at the level of individuals, populations, communities, and ecosystems. Despite intense interest in, and debate on, the mechanistic basis of metabolic scaling, relatively little attention has been paid to metabolic scaling in clonal animals with modular construction, such as colonial cnidarians, bryozoans, and colonial ascidians. Unlike unitary animals, modular animals are structural individuals subdivided into repeated morphological units, or modules, each able to acquire, process, and share resources. A modular design allows flexibility in organism size and shape with consequences for metabolic scaling. Furthermore, with careful consideration of the biology of modular animals, the size and shape of individual colonies can be experimentally manipulated to test competing theories pertaining to metabolic scaling. Here, we review metabolic scaling in modular animals and find that a wide range of scaling exponents, rather than a single value, has been reported for a variety of modular animals. We identify factors influencing variation in intraspecific scaling in this group that relate to the general observation that not all modules within a colony are identical. We highlight current gaps in our understanding of metabolic scaling in modular animals, and suggest future research directions, such as manipulating metabolic states and comparisons among species that differ in extent of module integration.     

Learning to protect your genome on the fly

Piwi-interacting RNAs (piRNAs) help defend host genomes against germline transposons. In this issue of Cell, Khurana et al. show how alterations in the piRNA-encoding loci within a single generation allow a naive fly genome to overcome the initially insurmountable challenge imposed by a newly encountered mobile element.     

Enteric Pathogens in Stored Drinking Water and on Caregiver’s Hands in Tanzanian Households with and without Reported Cases of Child Diarrhea

Background

Diarrhea is one of the leading causes of mortality in young children. Diarrheal pathogens are transmitted via the fecal-oral route, and for children the majority of this transmission is thought to occur within the home. However, very few studies have documented enteric pathogens within households of low-income countries.

Methods and Findings

The presence of molecular markers for three enteric viruses (enterovirus, adenovirus, and rotavirus), seven Escherichia coli virulence genes (ECVG), and human-specific Bacteroidales was assessed in hand rinses and household stored drinking water in Bagamoyo, Tanzania. Using a matched case-control study design, we examined the relationship between contamination of hands and water with these markers and child diarrhea. We found that the presence of ECVG in household stored water was associated with a significant decrease in the odds of a child within the home having diarrhea (OR = 0.51; 95% confidence interval 0.27–0.93). We also evaluated water management and hygiene behaviors. Recent hand contact with water or food was positively associated with detection of enteric pathogen markers on hands, as was relatively lower volumes of water reportedly used for daily hand washing. Enteropathogen markers in stored drinking water were more likely found among households in which the markers were also detected on hands, as well as in households with unimproved water supply and sanitation infrastructure.

Conclusions

The prevalence of enteric pathogen genes and the human-specific Bacteroidales fecal marker in stored water and on hands suggests extensive environmental contamination within homes both with and without reported child diarrhea. Better stored water quality among households with diarrhea indicates caregivers with sick children may be more likely to ensure safe drinking water in the home. Interventions to increase the quantity of water available for hand washing, and to improve food hygiene, may reduce exposure to enteric pathogens in the domestic environment.     

Pannexin 1 Channels Play Essential Roles in Urothelial Mechanotransduction and Intercellular Signaling

Urothelial cells respond to bladder distension with ATP release, and ATP signaling within the bladder and from the bladder to the CNS is essential for proper bladder function. In other cell types, pannexin 1 (Panx1) channels provide a pathway for mechanically-induced ATP efflux and for ATP-induced ATP release through interaction with P2X₇ receptors (P2X₇Rs). We report that Panx1 and P2X₇R are functionally expressed in the bladder mucosa and in immortalized human urothelial cells (TRT-HU1), and participate in urothelial ATP release and signaling. ATP release from isolated rat bladders induced by distention was reduced by the Panx1 channel blocker mefloquine (MFQ) and was blunted in mice lacking Panx1 or P2X₇R expression. Hypoosmotic shock induced YoPro dye uptake was inhibited by MFQ and the P2X₇R blocker A438079 in TRT-HU1 cells, and was also blunted in primary urothelial cells derived from mice lacking Panx1 or P2X₇R expression. Rinsing-induced mechanical stimulation of TRT-HU1 cells triggered ATP release, which was reduced by MFQ and potentiated in low divalent cation solution (LDPBS), a condition known to enhance P2X₇R activation. ATP signaling evaluated as intercellular Ca²⁺ wave radius was significantly larger in LDPBS, reduced by MFQ and by apyrase (ATP scavenger). These findings indicate that Panx1 participates in urothelial mechanotransduction and signaling by providing a direct pathway for mechanically-induced ATP release and by functionally interacting with P2X₇Rs.     

Behavioural and Physiological Effects of Finely Balanced Decision-Making in Chickens

In humans, more difficult decisions result in behavioural and physiological changes suggestive of increased arousal, but little is known about the effect of decision difficulty in other species. A difficult decision can have a number of characteristics; we aimed to monitor how finely balanced decisions, compared to unbalanced ones, affected the behaviour and physiology of chickens. An unbalanced decision was one in which the two options were of unequal net value (1 (Q1) vs. 6 (Q6) pieces of sweetcorn with no cost associated with either option); a finely balanced decision was one in which the options were of equal net value (i.e. hens were "indifferent" to both options). To identify hens'' indifference, a titration procedure was used in which a cost (electromagnetic weight on an access door) was applied to the Q6 option, to find the individual point at which hens chose this option approximately equally to Q1 via a non-weighted door. We then compared behavioural and physiological indicators of arousal (head movements, latency to choose, heart-rate variability and surface body temperature) when chickens made decisions that were unbalanced or finely balanced. Significant physiological (heart-rate variability) and behavioural (latency to pen) differences were found between the finely balanced and balanced conditions, but these were likely to be artefacts of the greater time and effort required to push through the weighted doors. No other behavioural and physiological measures were significantly different between the decision categories. We suggest that more information is needed on when best to monitor likely changes in arousal during decision-making and that future studies should consider decisions defined as difficult in other ways.     

Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy,Sparse, and Irregular Clinical Data

Inferring precise phenotypic patterns from population-scale clinical data is a core computational task in the development of precision, personalized medicine. The traditional approach uses supervised learning, in which an expert designates which patterns to look for (by specifying the learning task and the class labels), and where to look for them (by specifying the input variables). While appropriate for individual tasks, this approach scales poorly and misses the patterns that we don’t think to look for. Unsupervised feature learning overcomes these limitations by identifying patterns (or features) that collectively form a compact and expressive representation of the source data, with no need for expert input or labeled examples. Its rising popularity is driven by new deep learning methods, which have produced high-profile successes on difficult standardized problems of object recognition in images. Here we introduce its use for phenotype discovery in clinical data. This use is challenging because the largest source of clinical data – Electronic Medical Records – typically contains noisy, sparse, and irregularly timed observations, rendering them poor substrates for deep learning methods. Our approach couples dirty clinical data to deep learning architecture via longitudinal probability densities inferred using Gaussian process regression. From episodic, longitudinal sequences of serum uric acid measurements in 4368 individuals we produced continuous phenotypic features that suggest multiple population subtypes, and that accurately distinguished (0.97 AUC) the uric-acid signatures of gout vs. acute leukemia despite not being optimized for the task. The unsupervised features were as accurate as gold-standard features engineered by an expert with complete knowledge of the domain, the classification task, and the class labels. Our findings demonstrate the potential for achieving computational phenotype discovery at population scale. We expect such data-driven phenotypes to expose unknown disease variants and subtypes and to provide rich targets for genetic association studies.     

Dynamic prediction of cumulative incidence functions by direct binomial regression

In recent years there have been a series of advances in the field of dynamic prediction. Among those is the development of methods for dynamic prediction of the cumulative incidence function in a competing risk setting. These models enable the predictions to be updated as time progresses and more information becomes available, for example when a patient comes back for a follow‐up visit after completing a year of treatment, the risk of death, and adverse events may have changed since treatment initiation. One approach to model the cumulative incidence function in competing risks is by direct binomial regression, where right censoring of the event times is handled by inverse probability of censoring weights. We extend the approach by combining it with landmarking to enable dynamic prediction of the cumulative incidence function. The proposed models are very flexible, as they allow the covariates to have complex time‐varying effects, and we illustrate how to investigate possible time‐varying structures using Wald tests. The models are fitted using generalized estimating equations. The method is applied to bone marrow transplant data and the performance is investigated in a simulation study.