ZnT3 mRNA levels are reduced in Alzheimer's disease post-mortem brain

Extensive genetic, biochemical, and histological evidence has implicated the amyloid-β peptide (Aβ) in Alzheimer's disease pathogenesis, and several mechanisms have been suggested, such as metal binding, reactive oxygen species production, and membrane pore formation. However, recent evidence argues for an additional role for signaling mediated by the amyloid precursor protein, APP, in part via the caspase cleavage of APP at aspartate 664. Here we review the effects and implications of this cleavage event, and propose a model of Alzheimer's disease that focuses on the critical nature of this cleavage and its downstream effects.     

Transport lattice models of heat transport in skin with spatially heterogeneous,temperature-dependent perfusion

Background  

Investigation of bioheat transfer problems requires the evaluation of temporal and spatial distributions of temperature. This class of problems has been traditionally addressed using the Pennes bioheat equation. Transport of heat by conduction, and by temperature-dependent, spatially heterogeneous blood perfusion is modeled here using a transport lattice approach.     

Tractable Bayes of skew-elliptical link models for correlated binary data

Correlated binary response data with covariates are ubiquitous in longitudinal or spatial studies. Among the existing statistical models, the most well-known one for this type of data is the multivariate probit model, which uses a Gaussian link to model dependence at the latent level. However, a symmetric link may not be appropriate if the data are highly imbalanced. Here, we propose a multivariate skew-elliptical link model for correlated binary responses, which includes the multivariate probit model as a special case. Furthermore, we perform Bayesian inference for this new model and prove that the regression coefficients have a closed-form unified skew-elliptical posterior with an elliptical prior. The new methodology is illustrated by an application to COVID-19 data from three different counties of the state of California, USA. By jointly modeling extreme spikes in weekly new cases, our results show that the spatial dependence cannot be neglected. Furthermore, the results also show that the skewed latent structure of our proposed model improves the flexibility of the multivariate probit model and provides a better fit to our highly imbalanced dataset.     

Saccharomyces cerevisiae SSD1 orthologues are essential for host infection by the ascomycete plant pathogens Colletotrichum lagenarium and Magnaporthe grisea

Fungal plant pathogens have evolved diverse strategies to overcome the multilayered plant defence responses that confront them upon host invasion. Here we show that pathogenicity of the cucumber anthracnose fungus, Colletotrichum lagenarium, and the rice blast fungus, Magnaporthe grisea, requires a gene orthologous to Saccharomyces cerevisiae SSD1, a regulator of cell wall assembly. Screening for C. lagenarium insertional mutants deficient in pathogenicity led to the identification of ClaSSD1. Following targeted gene replacement, appressoria of classd1 mutants retained the potential for penetration but were unable to penetrate into host epidermal cells. Transmission electron microscopy suggested that appressorial penetration by classd1 mutants was restricted by plant cell wall-associated defence responses, which were observed less frequently with the wild-type strain. Interestingly, on non-host onion epidermis classd1 mutants induced papilla formation faster and more abundantly than the wild type. Similarly, colonization of rice leaves by M. grisea was severely reduced after deletion of the orthologous MgSSD1 gene and attempted infection by the mutants was accompanied by the accumulation of reactive oxygen species within the host cell. These results suggest that appropriate assembly of the fungal cell wall as regulated by SSD1 allows these pathogens to establish infection by avoiding the induction of host defence responses.     

Isolation and characterisation of a novel extremely thermophilic, anaerobic, chemo-organotrophic eubacterium

Abstract A new, extremely thermophilic, anaerobic, chemo-organotrophic bacterium was isolated from intertidal habitats where seepage of geothermally heated water occurs. The antibiotic sensitivity pattern and the presence of muramic acid strongly suggest an eubacterial nature of the novel isolate. Growth was measured between pH 4.8–8.2 (optimal pH 7.0) and at temperatures up to 90°C with a doubling time of 50 min at optimal temperatures of 80–85°C. This is the highest optimal growth temperature for an eubacterium described so far.
The Gram-negative, non-motile, non-sporulating, short rod to coccal shaped cells were enclosed in a sphere-like cell envelope protruding from either end. A wide range of carbohydrates, including xylose, glucose, fructose, maltose, starch, carboxymethylcellulose, and amylopectin were used in an obligately fermentative metabolism.
Morphological, physiological and molecular properties (mol% G + C = 46) are distinct from other known extremely thermophilic eubacterial genera.     

Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses

Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments are typically performed in a low-throughput manner, such as on Petri plates. Additionally, the BR pathway affects drought responses, but drought experiments are time consuming and difficult to control. To mitigate these issues and increase throughput, we developed the Robotic Assay for Drought (RoAD) system to perform BR and drought response experiments in soil-grown Arabidopsis plants. RoAD is equipped with a robotic arm, a rover, a bench scale, a precisely controlled watering system, an RGB camera, and a laser profilometer. It performs daily weighing, watering, and imaging tasks and is capable of administering BR response assays by watering plants with Propiconazole (PCZ), a BR biosynthesis inhibitor. We developed image processing algorithms for both plant segmentation and phenotypic trait extraction to accurately measure traits including plant area, plant volume, leaf length, and leaf width. We then applied machine learning algorithms that utilize the extracted phenotypic parameters to identify image-derived traits that can distinguish control, drought-treated, and PCZ-treated plants. We carried out PCZ and drought experiments on a set of BR mutants and Arabidopsis accessions with altered BR responses. Finally, we extended the RoAD assays to perform BR response assays using PCZ in Zea mays (maize) plants. This study establishes an automated and non-invasive robotic imaging system as a tool to accurately measure morphological and growth-related traits of Arabidopsis and maize plants in 3D, providing insights into the BR-mediated control of plant growth and stress responses.     

Phylogenetic relationships of bolitoglossine salamanders: a demonstration of the effects of combining morphological and molecular data sets

We analyzed sequence data for 555 bp of the mitochondrial gene cytochrome b in plethodontid salamanders, taken from 18 ingroup (tribe Bolitoglossini) and 4 outgroup (tribe Plethodontini) taxa. There were 257 variable sites, of which 219 were phylogenetically informative. Sequence differences among taxa exceeded 20%, and there were up to 15% amino acid differences among the sequences. We also analyzed 37 morphological (including karyological) characters, taken from the literature. Data were analyzed separately and then combined using parsimony and likelihood approaches. There is little conflict between the morphological and DNA data, and that which occurs is at nodes that are weakly supported by one or both of the data sets. Treated separately, the morphological and DNA data provide strong support for some nodes but not for others. The combined data act synergistically so that good support is obtained for nearly all of the nodes in the tree. Recent divergences are supported by silent transitions, and older divergences are supported by a combination of morphological, karyological, DNA transversion, and amino acid changes. Eliminating silent changes from the DNA data improves the consistency index and improves some bootstrap and decay index values for several deeper branches in the tree. However, the combined data set with all characters included provides a better supported tree overall. Maximum likelihood and parsimony with all of the data give not only the same topology but also remarkably similar branch lengths. Results of this analysis support the monopoly of the supergenera Hydromantes and Batrachoseps, and of a sister group relationship of Batrachoseps and the supergenus Bolitoglossa (represented in this study one species of the genus Bolitoglossa).      

Characterizing diffusion dynamics of a membrane protein associated with nanolipoproteins using fluorescence correlation spectroscopy

Nanolipoprotein particles (NLPs) represent a unique nanometer-sized scaffold for supporting membrane proteins (MP). Characterization of their dynamic shape and association with MP in solution remains a challenge. Here, we present a rapid method of analysis by fluorescence correlation spectroscopy (FCS) to characterize bacteriorhodopsin (bR), a membrane protein capable of forming a NLP complex. By selectively labeling individual components of NLPs during cell-free synthesis, FCS enabled us to measure specific NLP diffusion times and infer size information for different NLP species. The resulting bR-loaded NLPs were shown to be dynamically discoidal in solution with a mean diameter of 7.8 nm. The insertion rate of bR in the complex was ～55% based on a fit model incorporating two separate diffusion properties to best approximate the FCS data. More importantly, based on these data, we infer that membrane protein associated NLPs are thermodynamically constrained as discs in solution, while empty NLPs appear to be less constrained and dynamically spherical.