The use of comparative quantitative proteomics analysis in rice grain-filling in determining response to moderate soil drying stress

Moderate soil drying (MSD) stress at the grain filling stage can improve grain filling efficiently and thus increase grain yield. To elucidate the molecular response of grain filling to MSD stress, a labeling LC-based quantitative proteomics approach using tandem mass tags was applied to determine the changes in leaf and grain protein abundance level at 15 days after flowering. A total of 2109 leaf proteins and 3220 grain proteins were detected, and 251 leaf proteins and 220 grain proteins were differentially expressed under MSD stress. Based on MapMan ontology, differentially expressed proteins in leaf and grain were categorized within 22 and 18 functional categories, respectively. The patterns observed were interesting in that in some categories such as photosynthesis-related protein in leaf and cell division related proteins in grain showed higher expression abundant under MSD stress, which facilities increasing the source supply and sink size. In other categories, such as carbohydrate metabolism and mitochondrial electron transport, surprisingly showed a completely different expression pattern between leaf and grain under MSD stress, which led to faster and better remobilization of carbon from leaf to grain. Additionally, the complicated functional network including the small GTP-binding proteins, calmodulin, and 14-3-3 proteins play an important role in regulation carbon remobilization mediated by the stressful signals from soil after rice plants were treated with MSD at grain-filling stage. The findings provide theoretical evidence for better quality control and scientific improvement of rice in practice.     

3D Protein Dynamics in the Cell Nucleus

The three-dimensional (3D) architecture of the cell nucleus plays an important role in protein dynamics and in regulating gene expression. However, protein dynamics within the 3D nucleus are poorly understood. Here, we present, to our knowledge, a novel combination of 1) single-objective based light-sheet microscopy, 2) photoconvertible proteins, and 3) fluorescence correlation microscopy, to quantitatively measure 3D protein dynamics in the nucleus. We are able to acquire >3400 autocorrelation functions at multiple spatial positions within a nucleus, without significant photobleaching, allowing us to make reliable estimates of diffusion dynamics. Using this tool, we demonstrate spatial heterogeneity in Polymerase II dynamics in live U2OS cells. Further, we provide detailed measurements of human-Yes-associated protein diffusion dynamics in a human gastric cancer epithelial cell line.     

Hemodynamics analysis of the serial stenotic coronary arteries

Coronary arterial stenoses, particularly serial stenoses in a single branch, are responsible for complex hemodynamic properties of the coronary arterial trees, and the uncertain prognosis of invasive intervention. Critical information of the blood flow redistribution in the stenotic arterial segments is required for the adequate treatment planning. Therefore, in this study, an image based non-invasive functional assessment is performed to investigate the hemodynamic significances of serial stenoses. Twenty patient-specific coronary arterial trees with different combinations of stenoses were reconstructed from the computer tomography angiography for the evaluation of the hemodynamics. Our results showed that the computed FFR based on CTA images (FFRCT) pullback curves with wall shear stress (WSS) distribution could provide more effectively examine the physiological significance of the locations of the segmental narrowing and the curvature of the coronary arterial segments. The paper thus provides the diagnostic efficacy of FFRCT pullback curve for noninvasive quantification of the hemodynamics of stenotic coronary arteries with serial lesions, compared to the gold standard invasive FFR, to provide a reliable physiological assessment of significant amount of coronary artery stenosis. Further, we were also able to demonstrate the potential of carrying out virtual revascularization, to enable more precise PCI procedures and improve their outcomes.     

The p53 R72P polymorphism does not affect the physiological response to ionizing radiation in a mouse model

Tissue culture and mouse model studies show that the presence of the arginine (R) or proline (P) coding single nucleotide polymorphism (SNP) of the tumor suppressor gene p53 at codon 72 (p53 R72P) differentially affects the responses to genotoxic insult. Compared to the P variant, the R variant shows increased apoptosis in most cell cultures and mouse model tissues in response to genotoxins, and epidemiological studies suggest that the R variant may enhance cancer survival and reduce the risks of adverse reactions to genotoxic cancer treatment. As ionizing radiation (IR) treatment is often used in cancer therapy, we sought to test the physiological effects of IR in mouse models of the p53 R72P polymorphism. By performing blood counts, immunohistochemical (IHC) staining and survival studies in mouse populations rigorously controlled for strain background, sex and age, we discovered that p53 R72P polymorphism did not differentially affect the physiological response to IR. Our findings suggest that genotyping for this polymorphism to personalize IR therapy may have little clinical utility.     

Molecular Structure Evaluation of Wheat Gluten during Frozen Storage

The effects of frozen storage (0–120 day) on the secondary structure and molecular chain conformation of hydrated gluten were investigated using Fourier transform infrared spectroscopy (FTIR) and atomic force microscopy (AFM). After frozen storage, no changes were observed in the secondary structure of the 60% hydrated gluten; spectra were consistent with a tight ordered structure with many interchain hydrogen bond interactions. For the dehydrated gluten, more complex changes took place: during frozen storage for up to 60 days, there were distinctive changes in the low-frequency region of the amide I band (1618–1633 cm^?1) which were attributed to changes in the β-sheet structure. However, with the increase of frozen storage from 60 to 120 days, a band near 1614 cm^?1 replaced that at 1659 cm^?1 illustrate that the formation of protein aggregates during the long-time frozen storage, which along with the establishment of new intermolecular non-covalent bonds within the protein molecule or between two neighboring molecules. AFM images showed that the gluten chain formed a fibril-like branched network, and this network was weakened with increasing frozen storage time.     

Strong Fluorescence Enhancement with Silica-Coated Au Nanoshell Dimers

Fluorescence intensity is vital for fluorescence sensing and imaging because it determines the sensing sensitivity and imaging brightness. This study reports plasmon-enhanced fluorescence by engineering plasmonic nanostructures, that are SiO₂-coated Au nanoshell dimers with a high yield exceeding 60 %. With this elaborately designed nanostructure, we show that the thin SiO₂ shell can conveniently distance the fluorophore from the underneath metal, thereby effectively avoiding fluorescence quenching. Meanwhile, the inner Au nanoshell dimers create abundant hot spots at particle-particle junctions and enable near-infrared fluorescence enhancement. The largest fluorescence enhancement achieved is 69 times for the design with a 9 nm external SiO₂ shell, as is also confirmed by three-dimensional finite-difference time-domain simulations. This dramatically increased fluorescence has great significance in fluorescence-based sensing and imaging.     

Traction force microscopy by deep learning

Cells interact mechanically with their surroundings by exerting and sensing forces. Traction force microscopy (TFM), purported to map cell-generated forces or stresses, represents an important tool that has powered the rapid advances in mechanobiology. However, to solve the ill-posed mathematical problem, conventional TFM involved compromises in accuracy and/or resolution. Here, we applied neural network-based deep learning as an alternative approach for TFM. We modified a neural network designed for image processing to predict the vector field of stress from displacements. Furthermore, we adapted a mathematical model for cell migration to generate large sets of simulated stresses and displacements for training and testing the neural network. We found that deep learning-based TFM yielded results that resemble those using conventional TFM but at a higher accuracy than several conventional implementations tested. In addition, a trained neural network is appliable to a wide range of conditions, including cell size, shape, substrate stiffness, and traction output. The performance of deep learning-based TFM makes it an appealing alternative to conventional methods for characterizing mechanical interactions between adherent cells and the environment.     

Attention is shaped by semantic level of event-structure during speech comprehension: an electroencephalogram study

The present electroencephalogram study used an attention probe paradigm to investigate how semantic and acoustic structures constrain temporal attention during speech comprehension. Spoken sentences were used as stimuli, with each one containing a four-character critical phrase, of which the third character was the target character. We manipulated not only the semantic relationship between the target character and the immediately preceding two characters, but also the presence/absence of a pitch accent on the first character. In addition, an attention probe was either presented concurrently with the target character or not. The results showed that the N1 effect evoked by the attention probe was of larger amplitude and started earlier (enhanced attention) when the target character and the preceding two characters belonged to the same semantic event than when they spanned a semantic-event boundary, and this effect occurred only in the un-accented conditions. The results suggest that, during speech comprehension, the semantic level of event-structure can constrain attention allocation along the temporal dimension, and reverse the attention attenuation effect of prediction; meanwhile, the semantic and acoustic levels of event-structure interact with each other immediately to modulate auditory-temporal attention. The results were discussed with regard to the predictive coding account of attention.     

Chemical shift assignments of Ribosomal protein S1 from <Emphasis Type="Italic">Mycobacterium tuberculosis</Emphasis>

RpsA, also known as ribosomal protein S1, is an essential protein required for translation initiation of mRNAs when their Shine-Dalgarno sequence is degenerated (Sorensen et al. 1998). In addition, RpsA of Mycobacterium tuberculosis (M. tb) is involved in trans-translation, which is an effective system mediated by tmRNA-SmpB to release stalled ribosomes from mRNA in the presence of rare codons (Keiler 2008). Shi et al. found that POA binds to RpsA of Mtb and disrupts the formation of RpsA–tmRNA complex (Shi et al. 2011) and mutations at the C-terminus of RpsA confer PZA resistance. The previous work reported the pyrazinoic acid-binding domain of RpsA (Yang et al. Mol Microbiol 95:791–803, 2015). However, the HSQC spectra of the isolated S1 domain does not overlap with that of MtRpsA280-438, suggesting that substantial interactions occur between the flexible C-terminus and the S1 domain in MtRpsA .To further study the PZA resistance and how substantial interactions influence/affect protein structure, using heteronuclear NMR spectroscopy, we have completed backbone and side-chain ¹H, ¹⁵N, ¹³C chemical shift assignments of MtRpsA280-438 which contains S1 domain and the flexible C-terminus. These NMR resonance assignments provide the framework for detailed characterization of the solution-state protein structure determination, dynamic studies of this domain, as well as NMR-based drug discovery efforts.