Tidal marsh plant responses to elevated CO2, nitrogen fertilization,and sea level rise

Elevated CO₂ and nitrogen (N) addition directly affect plant productivity and the mechanisms that allow tidal marshes to maintain a constant elevation relative to sea level, but it remains unknown how these global change drivers modify marsh plant response to sea level rise. Here we manipulated factorial combinations of CO₂ concentration (two levels), N availability (two levels) and relative sea level (six levels) using in situ mesocosms containing a tidal marsh community composed of a sedge, Schoenoplectus americanus, and a grass, Spartina patens. Our objective is to determine, if elevated CO₂ and N alter the growth and persistence of these plants in coastal ecosystems facing rising sea levels. After two growing seasons, we found that N addition enhanced plant growth particularly at sea levels where plants were most stressed by flooding (114% stimulation in the + 10 cm treatment), and N effects were generally larger in combination with elevated CO₂ (288% stimulation). N fertilization shifted the optimal productivity of S. patens to a higher sea level, but did not confer S. patens an enhanced ability to tolerate sea level rise. S. americanus responded strongly to N only in the higher sea level treatments that excluded S. patens. Interestingly, addition of N, which has been suggested to accelerate marsh loss, may afford some marsh plants, such as the widespread sedge, S. americanus, the enhanced ability to tolerate inundation. However, if chronic N pollution reduces the availability of propagules of S. americanus or other flood‐tolerant species on the landscape scale, this shift in species dominance could render tidal marshes more susceptible to marsh collapse.     

Biogeography of the ecosystems of the healthy human body

Background

Characterizing the biogeography of the microbiome of healthy humans is essential for understanding microbial associated diseases. Previous studies mainly focused on a single body habitat from a limited set of subjects. Here, we analyzed one of the largest microbiome datasets to date and generated a biogeographical map that annotates the biodiversity, spatial relationships, and temporal stability of 22 habitats from 279 healthy humans.

Results

We identified 929 genera from more than 24 million 16S rRNA gene sequences of 22 habitats, and we provide a baseline of inter-subject variation for healthy adults. The oral habitat has the most stable microbiota with the highest alpha diversity, while the skin and vaginal microbiota are less stable and show lower alpha diversity. The level of biodiversity in one habitat is independent of the biodiversity of other habitats in the same individual. The abundances of a given genus at a body site in which it dominates do not correlate with the abundances at body sites where it is not dominant. Additionally, we observed the human microbiota exhibit both cosmopolitan and endemic features. Finally, comparing datasets of different projects revealed a project-based clustering pattern, emphasizing the significance of standardization of metagenomic studies.

Conclusions

The data presented here extend the definition of the human microbiome by providing a more complete and accurate picture of human microbiome biogeography, addressing questions best answered by a large dataset of subjects and body sites that are deeply sampled by sequencing.     

Effects of Formalin-Inactivated Respiratory Syncytial Virus (FI-RSV) in the Perinatal Lamb Model of RSV

Respiratory syncytial virus (RSV) is the most frequent cause of bronchiolitis in infants and children worldwide. There are currently no licensed vaccines or effective antivirals. The lack of a vaccine is partly due to increased caution following the aftermath of a failed clinical trial of a formalin-inactivated RSV vaccine (FI-RSV) conducted in the 1960’s that led to enhanced disease, necessitating hospitalization of 80% of vaccine recipients and resulting in two fatalities. Perinatal lamb lungs are similar in size, structure and physiology to those of human infants and are susceptible to human strains of RSV that induce similar lesions as those observed in infected human infants. We sought to determine if perinatal lambs immunized with FI-RSV would develop key features of vaccine-enhanced disease. This was tested in colostrum-deprived lambs immunized at 3–5 days of age with FI-RSV followed two weeks later by RSV infection. The FI-RSV-vaccinated lambs exhibited several key features of RSV vaccine-enhanced disease, including reduced RSV titers in bronchoalveolar lavage fluid and lung, and increased infiltration of peribronchiolar and perivascular lymphocytes compared to lambs either undergoing an acute RSV infection or naïve controls; all features of RSV vaccine-enhanced disease. These results represent a first step proof-of-principle demonstration that the lamb can develop altered responses to RSV following FI-RSV vaccination. The lamb model may be useful for future mechanistic studies as well as the assessment of RSV vaccines designed for infants.     

A Metagenomic Framework for the Study of Airborne Microbial Communities

Understanding the microbial content of the air has important scientific, health, and economic implications. While studies have primarily characterized the taxonomic content of air samples by sequencing the 16S or 18S ribosomal RNA gene, direct analysis of the genomic content of airborne microorganisms has not been possible due to the extremely low density of biological material in airborne environments. We developed sampling and amplification methods to enable adequate DNA recovery to allow metagenomic profiling of air samples collected from indoor and outdoor environments. Air samples were collected from a large urban building, a medical center, a house, and a pier. Analyses of metagenomic data generated from these samples reveal airborne communities with a high degree of diversity and different genera abundance profiles. The identities of many of the taxonomic groups and protein families also allows for the identification of the likely sources of the sampled airborne bacteria.     

The effect of pH dependence of antibody-antigen interactions on subcellular trafficking dynamics

A drawback of targeting soluble antigens such as cytokines or toxins with long-lived antibodies is that such antibodies can prolong the half-life of the target antigen by a “buffering” effect. This has motivated the design of antibodies that bind to target with higher affinity at near neutral pH relative to acidic endosomal pH (~pH 6.0). Such antibodies are expected to release antigen within endosomes following uptake into cells, whereas antibody will be recycled and exocytosed in FcRn-expressing cells. To understand how the pH dependence of antibody-antigen interactions affects intracellular trafficking, we generated three antibodies that bind IL-6 with different pH dependencies in the range pH 6.0–7.4. The behavior of antigen in the presence of these antibodies has been characterized using a combination of fixed and live cell fluorescence microscopy. As the affinity of the antibody:IL-6 interaction at pH 6.0 decreases, an increasing amount of antigen dissociates from FcRn-bound antibody in early and late endosomes, and then enters lysosomes. Segregation of antibody and FcRn from endosomes in tubulovesicular transport carriers (TCs) into the recycling pathway can also be observed in live cells, and the extent of IL-6 association with TCs correlates with increasing affinity of the antibody:IL-6 interaction at acidic pH. These analyses result in an understanding, in spatiotemporal terms, of the effect of pH dependence of antibody-antigen interactions on subcellular trafficking and inform the design of antibodies with optimized binding properties for antigen elimination.     

Lagged Effect of Diurnal Temperature Range on Mortality in a Subtropical Megacity of China

Background

Many studies have found extreme temperature can increase the risk of mortality. However, it is not clear whether extreme diurnal temperature range (DTR) is associated with daily disease-specific mortality, and how season might modify any association.

Objectives

To better understand the acute effect of DTR on mortality and identify whether season is a modifier of the DTR effect.

Methods

The distributed lag nonlinear model (DLNM) was applied to assess the non-linear and delayed effects of DTR on deaths (non-accidental mortality (NAD), cardiovascular disease (CVD), respiratory disease (RD) and cerebrovascular disease (CBD)) in the full year, the cold season and the warm season.

Results

A non-linear relationship was consistently found between extreme DTR and mortality. Immediate effects of extreme low DTR on all types of mortality were stronger than those of extreme high DTR in the full year. The cumulative effects of extreme DTRs increased with the increment of lag days for all types of mortality in cold season, and they were greater for extreme high DTRs than those of extreme low DTRs. In hot season, the cumulative effects for extreme low DTRs increased with the increment of lag days, but for extreme high DTR they reached maxima at a lag of 13 days for all types of mortality except for CBD(at lag6 days), and then decreased.

Conclusions

Our findings suggest that extreme DTR is an independent risk factor of daily mortality, and season is a modifier of the association of DTR with daily mortality.     

Wrangling Phosphoproteomic Data to Elucidate Cancer Signaling Pathways

The interpretation of biological data sets is essential for generating hypotheses that guide research, yet modern methods of global analysis challenge our ability to discern meaningful patterns and then convey results in a way that can be easily appreciated. Proteomic data is especially challenging because mass spectrometry detectors often miss peptides in complex samples, resulting in sparsely populated data sets. Using the R programming language and techniques from the field of pattern recognition, we have devised methods to resolve and evaluate clusters of proteins related by their pattern of expression in different samples in proteomic data sets. We examined tyrosine phosphoproteomic data from lung cancer samples. We calculated dissimilarities between the proteins based on Pearson or Spearman correlations and on Euclidean distances, whilst dealing with large amounts of missing data. The dissimilarities were then used as feature vectors in clustering and visualization algorithms. The quality of the clusterings and visualizations were evaluated internally based on the primary data and externally based on gene ontology and protein interaction networks. The results show that t-distributed stochastic neighbor embedding (t-SNE) followed by minimum spanning tree methods groups sparse proteomic data into meaningful clusters more effectively than other methods such as k-means and classical multidimensional scaling. Furthermore, our results show that using a combination of Spearman correlation and Euclidean distance as a dissimilarity representation increases the resolution of clusters. Our analyses show that many clusters contain one or more tyrosine kinases and include known effectors as well as proteins with no known interactions. Visualizing these clusters as networks elucidated previously unknown tyrosine kinase signal transduction pathways that drive cancer. Our approach can be applied to other data types, and can be easily adopted because open source software packages are employed.     

Predator population size structure alters consumption of prey from epigeic and grazing food webs

Numerous studies have found that predators can suppress prey densities and thereby impact important ecosystem processes such as plant productivity and decomposition. However, prey suppression by spiders can be highly variable. Unlike predators that feed on prey within a single energy channel, spiders often consume prey from asynchronous energy channels, such as grazing (live plant) and epigeic (soil surface) channels. Spiders undergo few life cycle changes and thus appear to be ideally suited to link energy channels, but ontogenetic diet shifts in spiders have received little attention. For example, spider use of different food channels may be highly specialized in different life stages and thus a species may be a multichannel omnivore only when we consider all life stages. Using stable isotopes, we investigated whether wolf spider (Pardosa littoralis, henceforth Pardosa) prey consumption is driven by changes in spider size. Small spiders obtained > 80% of their prey from the epigeic channel, whereas larger spiders used grazing and epigeic prey almost equally. Changes in prey consumption were not driven by changes in prey density, but by changes in prey use by different spider size classes. Thus, because the population size structure of Pardosa changes dramatically over the growing season, changes in spider size may have important implications for the strength of trophic cascades. Our research demonstrates that life history can be an important component of predator diet, which may in turn affect community- and ecosystem-level processes.     

Negative density-dependent dispersal emerges from the joint evolution of density- and body condition-dependent dispersal strategies

Empirical studies have documented both positive and negative density-dependent dispersal, yet most theoretical models predict positive density dependence as a mechanism to avoid competition. Several hypotheses have been proposed to explain the occurrence of negative density-dependent dispersal, but few of these have been formally modeled. Here, we developed an individual-based model of the evolution of density-dependent dispersal. This model is novel in that it considers the effects of density on dispersal directly, and indirectly through effects on individual condition. Body condition is determined mechanistically, by having juveniles compete for resources in their natal patch. We found that the evolved dispersal strategy was a steep, increasing function of both density and condition. Interestingly, although populations evolved a positive density-dependent dispersal strategy, the simulated metapopulations exhibited negative density-dependent dispersal. This occurred because of the negative relationship between density and body condition: high density sites produced low-condition individuals that lacked the resources required for dispersal. Our model, therefore, generates the novel hypothesis that observed negative density-dependent dispersal can occur when high density limits the ability of organisms to disperse. We suggest that future studies consider how phenotype is linked to the environment when investigating the evolution of dispersal.