A Cross-linking Mass Spectrometry Approach Defines Protein Interactions in Yeast Mitochondria

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Highlights

• •XL-MS reveals new PPIs in yeast mitochondria under glycerol and glucose condition.
• •Significant but limited results from quantitative XL-MS experiments.
• •Ndi1 participates in a CIII₂CIV₂ respiratory supercomplex.
• •Min8 promotes assembly of Cox12 into an intermediate complex IV.

     

Exclosure fences around nests of imperiled Florida Grasshopper Sparrows reduce rates of predation by mammals

Increasing nest survival by excluding predators is a goal of many bird conservation programs. However, new exclosure projects should be carefully evaluated to assess the potential risks of disturbance. We tested the effectiveness of predator exclosure fences (hereafter, fences) for nests of critically endangered Florida Grasshopper Sparrows (Ammodramus savannarum floridanus) at a dry prairie site (Three Lakes; 2015–2018) and a pasture site (the Ranch; 2015–2016) in Osceola County, Florida, USA. We installed fences at nests an average of 8 days after the start of incubation, and nest abandonment after fence installation was rare (2 of 149 installations). Predation was the leading cause of failure for unfenced nests at both sites (48–73%). At Three Lakes, nest cameras revealed that mammals and snakes were responsible for 61.5% and 38.5% of predation events, respectively, at unfenced nests. Fences reduced the daily probability of predation (0.016 for fenced nests vs. 0.074 for unfenced nests). The probability that a fenced nest would survive from discovery to fledging was more than double that of unfenced nests (60.4% vs. 27.7%). However, we found no difference in daily nest survival at the Ranch between the year before nests were fenced (2015; 0.874) and the year when all but one nest were fenced (2016; 0.867) because red imported fire ants (Solenopsis invicta) were responsible for 86% of predation events at fenced nests at the Ranch. The use of cameras at fenced nests revealed that site‐specific differences in nest predators explained variation in fence efficiency between sites. Our fence design may be useful for other species of grassland birds, but site‐specific predator communities and species‐specific response of target bird species to fences should be assessed before installing fences at other sites.     

Biosensors based on peptide exposure show single molecule conformations in live cells

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Effects of detergents on catalytic activity of human endometase/matrilysin 2, a putative cancer biomarker

Matrix metalloproteinases (MMPs) are a family of hydrolytic enzymes that play significant roles in development, morphogenesis, inflammation, and cancer invasion. Endometase (matrilysin 2 or MMP-26) is a putative early biomarker for human carcinomas. The effects of the ionic and nonionic detergents on catalytic activity of endometase were investigated. The hydrolytic activity of endometase was detergent concentration dependent, exhibiting a bell-shaped curve with its maximum activity near the critical micelle concentration (CMC) of nonionic detergents tested. The effect of Brij-35 on human gelatinase B (MMP-9), matrilysin (MMP-7), and membrane-type 1 MMP (MT1-MMP) was further explored. Their maximum catalysis was observed near the CMC of Brij-35 (∼ 90 μM). Their IC₅₀ values were above the CMC. The inhibition mechanism of MMP-7, MMP-9, and MT1-MMP by Brij-35 was a mixed type as determined by Dixon’s plot; however, the inhibition mechanism of endometase was noncompetitive with a K_i value of 240 μM. The catalytic activities of MMPs are influenced by detergents. Monomer of detergents may activate and stabilize MMPs to enhance catalysis, but micelle of detergents may sequester enzyme and block the substrate binding site to impede catalysis. Under physiological conditions, a lipid or membrane microenvironment may regulate enzymatic activity.     

Priors for the Bayesian star paradox

We show that the Bayesian star paradox, first proved mathematically by Steel and Matsen for a specific class of prior distributions, occurs in a wider context including less regular, possibly discontinuous, prior distributions.     

Spatial and temporal factors associated with nest survival of Gray Flycatchers in managed ponderosa pine forests

Gray Flycatchers (Empidonax wrightii) breed in a variety of habitats in the arid and semi‐arid regions of the western United States, but little is known about their breeding biology, especially in the northern portion of their range where they nest in ponderosa pine (Pinus ponderosa) forests. From May to July 2014 and 2015, we conducted surveys for singing male Gray Flycatchers along the eastern slope of the Cascade Range in Washington, U.S.A, monitored flycatcher nests, and quantified nest‐site vegetation. We used a logistic‐exposure model fit within a Bayesian framework to model the daily survival probability of flycatcher nests. During the 2 yr of our study, we monitored 141 nests, with 93% in ponderosa pines. Mean clutch size was 3.6 eggs and the mean number of young fledged per nest was 3.2. Predation accounted for 90% of failed nests. We found a positive association between daily nest survival and both nest height and distance of nest substrates from the nearest tree. Flycatchers that locate their nests higher above the ground and further from adjacent trees may be choosing the safest alternative because higher nests may be less exposed to terrestrial predators and nests in trees that are farther from other trees may be less exposed to arboreal predators such as jays (Corvidae) that may forage in patches with connected canopies. Nests in trees farther from other trees may also allow earlier detection of approaching predators and thus aid in nest defense.     

Exploring new scaffolds for angiotensin II receptor antagonism

Nowadays, AT₁ receptor (AT₁R) antagonists (ARBs) constitute the one of the most prevalent classes of antihypertensive drugs that modulate the renin-angiotensin system (RAS). Their main uses include also treatment of diabetic nephropathy (kidney damage due to diabetes) and congestive heart failure. Towards this direction, our study has been focused on the discovery of novel agents bearing different scaffolds which may evolve as a new class of AT₁ receptor antagonists. To fulfill this aim, a combination of computational approaches and biological assays were implemented. Particularly, a pharmacophore model was established and served as a 3D search query to screen the ChEMBL15 database. The reliability and accuracy of virtual screening results were improved by using molecular docking studies. In total, 4 compounds with completely diverse chemical scaffolds from potential ARBs, were picked and tested for their binding affinity to AT₁ receptor. Results revealed high nanomolar to micromolar affinity (IC₅₀) for all the compounds. Especially, compound 4 exhibited a binding affinity of 199 nM. Molecular dynamics simulations were utilized in an effort to provide a molecular basis of their binding to AT₁R in accordance to their biological activities.     

Simulation of protein misfolding using a lattice model

The structure of the tightly bound complex of the globular myosin head with F-actin is the key to understanding important details of the mechanism of how the actin-myosin motor functions. The current notion on this complex is based on the docking of known atomic structures of constituent proteins into low-resolution electron-density maps. The atomic structure of the complex was refined by the molecular mechanics method, which consists in minimizing the energy of molecular interaction and which makes it possible to optimize not only the relative position of protein backbones as rigid bodies, but also the position of side chains on the protein interface. The structure calculated using ICM-Pro software, on the one hand, is close to the model obtained using electron microscopy; on the other hand, it ensures the best calculated interaction energy and accounts for the results of mutagenesis experiments. On the basis of the structure obtained, we can suggest the molecular mechanisms underlying the actin-activated release of ATP hydrolysis products from myosin and the decrease in the affinity of myosin for actin upon binding of nucleotides.     

Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

A Temporally Explicit Production Efficiency Model for Fuel Load Allocation in Southern Africa

Abstract We present a regional fuel load model (1 km² spatial resolution) applied in the southern African savanna region. The model is based on a patch-scale production efficiency model (PEM) scaled up to the regional level using empirical relationships between patch-scale behavior and multi-source remote sensing data (spatio-temporal variability of vegetation and climatic variables). The model requires the spatial distribution of woody vegetation cover, which is used to determine separate respiration rates for tree and grass. Net primary production, grass and tree leaf death, and herbivory are also taken into account in this mechanistic modeling approach. The fuel load model has been calibrated and validated from independent measurements taken from savanna vegetation in Africa southward from the equator. A sensitivity analysis on the effect of climate variables (incoming radiation, air temperature, and precipitation) has been conducted to demonstrate the strong role that water availability has in determining productivity and subsequent fuel load over the southern African region. The model performance has been tested in four different areas representative of a regional increasing rainfall gradient—Etosha National Park, Namibia, Mongu and Kasama, Zambia, as well as in Kruger National Park, South Africa. Within each area, we analyze model output from three different magnitudes of canopy coverage (<5, 30, and 50%). We find that fuel load ranges predicted by the model are globally in agreement with field measurements for the same year. High rainfall sustains green herbaceous production late in the dry season and delays tree leaf litter production. Effect of water on production varies across the rainfall gradient with delayed start of green material production in more arid regions.