β‐amyloid model core peptides: Effects of hydrophobes and disulfides

The mechanism by which a disordered peptide nucleates and forms amyloid is incompletely understood. A central domain of β‐amyloid (Aβ21–30) has been proposed to have intrinsic structural propensities that guide the limited formation of structure in the process of fibrillization. In order to test this hypothesis, we examine several internal fragments of Aβ, and variants of these either cyclized or with an N‐terminal Cys. While Aβ21–30 and variants were always monomeric and unstructured (circular dichroism (CD) and nuclear magnetic resonance spectroscopy (NMRS)), we found that the addition of flanking hydrophobic residues in Aβ16–34 led to formation of typical amyloid fibrils. NMR showed no long‐range nuclear overhauser effect (nOes) in Aβ21–30, Aβ16–34, or their variants, however. Serial ¹H‐¹⁵N‐heteronuclear single quantum coherence spectroscopy, ¹H‐¹H nuclear overhauser effect spectroscopy, and ¹H‐¹H total correlational spectroscopy spectra were used to follow aggregation of Aβ16–34 and Cys‐Aβ16–34 at a site‐specific level. The addition of an N‐terminal Cys residue (in Cys‐Aβ16–34) increased the rate of fibrillization which was attributable to disulfide bond formation. We propose a scheme comparing the aggregation pathways for Aβ16–34 and Cys‐Aβ16–34, according to which Cys‐Aβ16–34 dimerizes, which accelerates fibril formation. In this context, cysteine residues form a focal point that guides fibrillization, a role which, in native peptides, can be assumed by heterogeneous nucleators of aggregation.     

Slr4, a newly identified S-layer protein from marine Gammaproteobacteria,is a major biofilm matrix component

S-layers are paracrystalline proteinaceous lattices that surround prokaryotic cells, forming a critical interface between the cells and their extracellular environment. Here, we report the discovery of a novel S-layer protein present in the Gram-negative marine organism, Pseudoalteromonas tunicata D2. An uncharacterized protein (EAR28894) was identified as the most abundant protein in planktonic cultures and biofilms. Bioinformatic methods predicted a beta-helical structure for EAR28894 similar to the Caulobacter S-layer protein, RsaA, despite sharing less than 20% sequence identity. Transmission electron microscopy revealed that purified EAR28894 protein assembled into paracrystalline sheets with a unique square lattice symmetry and a unit cell spacing of ~9.1 nm. An S-layer was found surrounding the outer membrane in wild-type cells and completely removed from cells in an EAR28894 deletion mutant. S-layer material also appeared to be “shed” from wild-type cells and was highly abundant in the extracellular matrix where it is associated with outer membrane vesicles and other matrix components. EAR28894 and its homologs form a new family of S-layer proteins that are widely distributed in Gammaproteobacteria including species of Pseudoalteromonas and Vibrio, and found exclusively in marine metagenomes. We propose the name Slr4 for this novel protein family.     

Spatial sampling bias and model complexity in stream‐based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin,USA

Leveraging existing presence records and geospatial datasets, species distribution modeling has been widely applied to informing species conservation and restoration efforts. Maxent is one of the most popular modeling algorithms, yet recent research has demonstrated Maxent models are vulnerable to prediction errors related to spatial sampling bias and model complexity. Despite elevated rates of biodiversity imperilment in stream ecosystems, the application of Maxent models to stream networks has lagged, as has the availability of tools to address potential sources of error and calculate model evaluation metrics when modeling in nonraster environments (such as stream networks). Herein, we use Maxent and customized R code to estimate the potential distribution of paddlefish (Polyodon spathula) at a stream‐segment level within the Arkansas River basin, USA, while accounting for potential spatial sampling bias and model complexity. Filtering the presence data appeared to adequately remove an eastward, large‐river sampling bias that was evident within the unfiltered presence dataset. In particular, our novel riverscape filter provided a repeatable means of obtaining a relatively even coverage of presence data among watersheds and streams of varying sizes. The greatest differences in estimated distributions were observed among models constructed with default versus AIC_C‐selected parameterization. Although all models had similarly high performance and evaluation metrics, the AIC_C‐selected models were more inclusive of westward‐situated and smaller, headwater streams. Overall, our results solidified the importance of accounting for model complexity and spatial sampling bias in SDMs constructed within stream networks and provided a roadmap for future paddlefish restoration efforts in the study area.     

Exploring context dependency in eco‐evolutionary patterns with the stick insect Timema cristinae

Rapid evolution can influence the ecology of populations, communities, and ecosystems, but the importance of evolution for ecological dynamics remains unclear, largely because the contexts in which evolution is powerful are poorly resolved. Here, we carry out a large observational study to test hypotheses about context dependency of eco‐evolutionary patterns previously identified on the stick insect Timema cristinae. Experiments and observations conducted in 2011 and 2012 documented predator‐mediated negative effects of camouflage maladaptation (i.e., evolutionary dynamics) on: (a) T. cristinae abundance and, (b) species richness and abundance of other arthropods. Here we show that camouflage maladaptation does not correlate with T. cristinae abundance and, instead, is associated with increased abundance and species richness of cohabitating arthropods. We furthermore find that plants with high levels of Timema maladaptation tend to have higher foliar nitrogen, that is, higher nutritional value, and more positive mass‐abundance slopes in the coexisting arthropod communities. We propose explanations for the observed contrasting results, such as negative density‐ and frequency‐dependent selection, feedbacks between herbivore abundance and plant nutritional quality, and common effects of predation pressure on selection and prey abundance. Our results demonstrate the utility of observational studies to assess the context dependency of eco‐evolutionary dynamics patterns and provide testable hypotheses for future work.     

Freshwater fish functional and taxonomic diversity above and below Niagara Falls

The Niagara River, which connects two Great Lakes (Erie and Ontario) and forms a border between Canada and the United States, has experienced decades of abiotic and biotic disturbance as well as long-term restoration efforts. Given the iconic riverscape and importance as a binational fisheries resource, a biodiversity assessment of the mainstem Niagara River fish assemblage is overdue. Here, fish assemblage and habitat data from a standardized boat electrofishing program of the Niagara River were combined with species trait data related to substrate associations, diet preferences, reproductive strategies, and body size to quantify biodiversity patterns among river sections (sites above and below Niagara Falls), seasons (spring, summer, fall), and years (2015–2017). Sixty-five species were captured representing a variety of trait combinations. Significant differences in functional dispersion and divergence (i.e., functional diversity) were observed between river sections, seasons, and (or) years. The fish community captured in the lower river in spring 2015 had both the highest average functional dispersion (2.08?±?0.32 SD) and divergence (0.88?±?0.04 SD) compared to the other seasonal sampling efforts, but relatively few fishes were captured (n?=?686). Although non-native fishes represented a small portion of the catch over the 3 years (8.6% of catch), the seasonal presence (spring and fall) of mostly introduced large-bodied salmonids expanded functional trait space in the lower river during these periods. The importance of rare species on functional diversity metrics suggests further insight on local species detection probabilities is needed to understand if differences in functional diversity reflect ecological patterns or are driven by sampling design.