Age‐related variation in reproductive traits in the wandering albatross: evidence for terminal improvement following senescence

The processes driving age‐related variation in demographic rates are central to understanding population and evolutionary ecology. An increasing number of studies in wild vertebrates find evidence for improvements in reproductive performance traits in early adulthood, followed by senescent declines in later life. However, life history theory predicts that reproductive investment should increase with age as future survival prospects diminish, and that raised reproductive investment may have associated survival costs. These non‐mutually exclusive processes both predict an increase in breeding performance at the terminal breeding attempt. Here, we use a 30‐year study of wandering albatrosses (Diomedea exulans) to disentangle the processes underpinning age‐related variation in reproduction. Whilst highlighting the importance of breeding experience, we reveal senescent declines in performance are followed by a striking increase in breeding success and a key parental investment trait at the final breeding attempt.     

A Novel In Vitro Model to Study Pericytes in the Neurovascular Unit of the Developing Cortex

Cortical function is impaired in various disorders of the central nervous system including Alzheimer’s disease, autism and schizophrenia. Some of these disorders are speculated to be associated with insults in early brain development. Pericytes have been shown to regulate neurovascular integrity in development, health and disease. Hence, precisely controlled mechanisms must have evolved in evolution to operate pericyte proliferation, repair and cell fate within the neurovascular unit (NVU). It is well established that pericyte deficiency leads to NVU injury resulting in cognitive decline and neuroinflammation in cortical layers. However, little is known about the role of pericytes in pathophysiological processes of the developing cortex. Here we introduce an in vitro model that enables to precisely study pericytes in the immature cortex and show that moderate inflammation and hypoxia result in caspase-3 mediated pericyte loss. Using heterozygous EYFP-NG2 mouse mutants we performed live imaging of pericytes for several days in vitro. In addition we show that pericytes maintain their capacity to proliferate which may allow cell-based therapies like reprogramming of pericytes into induced neuronal cells in the presented approach.     

MHC gene copy number variation in Tasmanian devils: implications for the spread of a contagious cancer

Tasmanian devils face extinction owing to the emergence of a contagious cancer. Devil facial tumour disease (DFTD) is a clonal cancer spread owing to a lack of major histocompatibility complex (MHC) barriers in Tasmanian devil populations. We present a comprehensive screen of MHC diversity in devils and identify 25 MHC types and 53 novel sequences, but conclude that overall levels of MHC diversity at the sequence level are low. The majority of MHC Class I variation can be explained by allelic copy number variation with two to seven sequence variants identified per individual. MHC sequences are divided into two distinct groups based on sequence similarity. DFTD cells and most devils have sequences from both groups. Twenty per cent of individuals have a restricted MHC repertoire and contain only group I or only group II sequences. Counterintuitively, we postulate that the immune system of individuals with a restricted MHC repertoire may recognize foreign MHC antigens on the surface of the DFTD cell. The implication of these results for management of DFTD and this endangered species are discussed.     

Modeling the Distribution of Migratory Bird Stopovers to Inform Landscape-Scale Siting of Wind Development

Conservation of migratory birds requires understanding the distribution of and potential threats to their migratory habitats. However, although migratory birds are protected under international treaties, few maps have been available to represent migration at a landscape scale useful to target conservation efforts or inform the siting of wind energy developments that may affect migratory birds. To fill this gap, we developed models that predict where four groups of birds concentrate or stopover during their migration through the state of Wyoming, USA: raptors, wetland, riparian and sparse grassland birds. The models were based on existing literature and expert knowledge concerning bird migration behavior and ecology and validated using expert ratings and known occurrences. There was significant agreement between migratory occurrence data and migration models for all groups except raptors, and all models ranked well with experts. We measured the overlap between the migration concentration models and a predictive model of wind energy development to assess the potential exposure of migratory birds to wind development and illustrate the utility of migratory concentration models for landscape-scale planning. Wind development potential is high across 15% of Wyoming, and 73% of this high potential area intersects important migration concentration areas. From 5.2% to 18.8% of each group’s important migration areas was represented within this high wind potential area, with the highest exposures for sparse grassland birds and the lowest for riparian birds. Our approach could be replicated elsewhere to fill critical data gaps and better inform conservation priorities and landscape-scale planning for migratory birds.     

Structural Bridges through Fold Space

Several protein structure classification schemes exist that partition the protein universe into structural units called folds. Yet these schemes do not discuss how these units sit relative to each other in a global structure space. In this paper we construct networks that describe such global relationships between folds in the form of structural bridges. We generate these networks using four different structural alignment methods across multiple score thresholds. The networks constructed using the different methods remain a similar distance apart regardless of the probability threshold defining a structural bridge. This suggests that at least some structural bridges are method specific and that any attempt to build a picture of structural space should not be reliant on a single structural superposition method. Despite these differences all representations agree on an organisation of fold space into five principal community structures: all-α, all-β sandwiches, all-β barrels, α/β and α + β. We project estimated fold ages onto the networks and find that not only are the pairings of unconnected folds associated with higher age differences than bridged folds, but this difference increases with the number of networks displaying an edge. We also examine different centrality measures for folds within the networks and how these relate to fold age. While these measures interpret the central core of fold space in varied ways they all identify the disposition of ancestral folds to fall within this core and that of the more recently evolved structures to provide the peripheral landscape. These findings suggest that evolutionary information is encoded along these structural bridges. Finally, we identify four highly central pivotal folds representing dominant topological features which act as key attractors within our landscapes.     

“Not for All the Tea in China!” Political Ideology and the Avoidance of Dissonance-Arousing Situations

People often avoid information and situations that have the potential to contradict previously held beliefs and attitudes (i.e., situations that arouse cognitive dissonance). According to the motivated social cognition model of political ideology, conservatives tend to have stronger epistemic needs to attain certainty and closure than liberals. This implies that there may be differences in how liberals and conservatives respond to dissonance-arousing situations. In two experiments, we investigated the possibility that conservatives would be more strongly motivated to avoid dissonance-arousing tasks than liberals. Indeed, U.S. residents who preferred more conservative presidents (George W. Bush and Ronald Reagan) complied less than Americans who preferred more liberal presidents (Barack Obama and Bill Clinton) with the request to write a counter-attitudinal essay about who made a “better president.” This difference was not observed under circumstances of low perceived choice or when the topic of the counter-attitudinal essay was non-political (i.e., when it pertained to computer or beverage preferences). The results of these experiments provide initial evidence of ideological differences in dissonance avoidance. Future work would do well to determine whether such differences are specific to political issues or topics that are personally important. Implications for political behavior are discussed.     

Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome

Identifying biomarkers for tuberculosis (TB) is an ongoing challenge in developing immunological correlates of infection outcome and protection. Biomarker discovery is also necessary for aiding design and testing of new treatments and vaccines. To effectively predict biomarkers for infection progression in any disease, including TB, large amounts of experimental data are required to reach statistical power and make accurate predictions. We took a two-pronged approach using both experimental and computational modeling to address this problem. We first collected 200 blood samples over a 2- year period from 28 non-human primates (NHP) infected with a low dose of Mycobacterium tuberculosis. We identified T cells and the cytokines that they were producing (single and multiple) from each sample along with monkey status and infection progression data. Machine learning techniques were used to interrogate the experimental NHP datasets without identifying any potential TB biomarker. In parallel, we used our extensive novel NHP datasets to build and calibrate a multi-organ computational model that combines what is occurring at the site of infection (e.g., lung) at a single granuloma scale with blood level readouts that can be tracked in monkeys and humans. We then generated a large in silico repository of in silico granulomas coupled to lymph node and blood dynamics and developed an in silico tool to scale granuloma level results to a full host scale to identify what best predicts Mycobacterium tuberculosis (Mtb) infection outcomes. The analysis of in silico blood measures identifies Mtb-specific frequencies of effector T cell phenotypes at various time points post infection as promising indicators of infection outcome. We emphasize that pairing wetlab and computational approaches holds great promise to accelerate TB biomarker discovery.