LRRC8 family proteins within lysosomes regulate cellular osmoregulation and enhance cell survival to multiple physiological stresses

LRRC8 family proteins on the plasma membrane play a critical role in cellular osmoregulation by forming volume-regulated anion channels (VRACs) necessary to prevent necrotic cell death.We demonstrate that intracellular LRRC8 proteins acting within lysosomes also play an essential role in cellular osmoregulation. LRRC8 proteins on lysosome membranes generate large lysosomal volume-regulated anion channel (Lyso-VRAC) currents in response to low cytoplasmic ionic strength conditions. When a double-leucine L706L707 motif at the C terminus of LRRC8A was mutated to alanines, normal plasma membrane VRAC currents were still observed, but Lyso-VRAC currents were absent. We used this targeting mutant, as well as pharmacological tools, to demonstrate that Lyso-VRAC currents are necessary for the formation of large lysosome-derived vacuoles, which store and then expel excess water to maintain cytosolic water homeostasis. Thus, Lyso-VRACs allow lysosomes of mammalian cells to act as the cell`s “bladder.” When Lyso-VRAC current was selectively eliminated, the extent of necrotic cell death to sustained stress was greatly increased, not only in response to hypoosmotic stress, but also to hypoxic and hypothermic stresses. Thus Lyso-VRACs play an essential role in enabling cells to mount successful homeostatic responses to multiple stressors

Effects of Bamboo (Phyllostachys praecox) Cultivation on Soil Nitrogen Fractions and Mineralization

The mineralization of soil organic nitrogen (N) is the key process in the cycling of N in terrestrial ecosystems. Land-use change to bamboo (Phyllostachys praecox) cultivation that later entails organic material mulching combined with chemical fertilizer application will inevitably influence soil N mineralization (Nmin) and availability dynamics. However, the soil Nmin rates associated with various N fractions of P. praecox in response to land-use change and mulching are not well understood. The present study aimed to understand the effects of land-use change to P. praecox bamboo cultivation and organic material mulching on soil Nmin and availability. Soil properties and organic N fractions were measured in a P. praecox field planted on former paddy fields, a mulched P. praecox field, and a rice (Oryza sativa L.) field. Soil Nmin was determined using a batch incubation method, with mathematical models used to predict soil Nmin kinetics and potential. The conversion from a paddy field to P. praecox plantation decreased the soil pH, soil total N, and soil organic matter (SOM) content significantly (p < 0.05); the mulching method induced further soil acidification. The mulching treatment significantly augmented the SOM content by 7.08% compared with the no-mulching treatment (p < 0.05), but it decreased soil hydrolyzable N and increased the nonhydrolyzable N (NHN) content. Both the Nmin rate and cumulative mineralized N were lowest in the mulched bamboo field. The kinetics of Nmin was best described by the ‘two-pool model’ and ‘special model’. The Pearson’s correlation analysis and the Mantel test suggested soil pH was the dominant factor controlling the soil cumulative mineralized N and mineralization potential in the bamboo fields. These findings could help us better understand the N cycling and N availability under mulching conditions for shifts in land use, and provide a scientific basis for the sustainable management of bamboo plantations

An Integrated Method for Tracking and Monitoring Stomata Dynamics from Microscope Videos

Patchy stomata are a common and characteristic phenomenon in plants. Understanding and studying the regulation mechanism of patchy stomata are of great significance to further supplement and improve the stomatal theory. Currently, the common methods for stomatal behavior observation are based on static images, which makes it difficult to reflect dynamic changes of stomata. With the rapid development of portable microscopes and computer vision algorithms, it brings new chances for stomatal movement observation. In this study, a stomatal behavior observation system (SBOS) was proposed for real-time observation and automatic analysis of each single stoma in wheat leaf using object tracking and semantic segmentation methods. The SBOS includes two modules: the real-time observation module and the automatic analysis module. The real-time observation module can shoot videos of stomatal dynamic changes. In the automatic analysis module, object tracking locates every single stoma accurately to obtain stomatal pictures arranged in time-series; semantic segmentation can precisely quantify the stomatal opening area (SOA), with a mean pixel accuracy (MPA) of 0.8305 and a mean intersection over union (MIoU) of 0.5590 in the testing set. Moreover, we designed a graphical user interface (GUI) so that researchers could use this automatic analysis module smoothly. To verify the performance of the SBOS, the dynamic changes of stomata were observed and analyzed under chilling. Finally, we analyzed the correlation between gas exchange and SOA under drought stress, and the correlation coefficients between mean SOA and net photosynthetic rate (Pn), intercellular CO2 concentration (Ci), stomatal conductance (Gs), and transpiration rate (Tr) are 0.93, 0.96, 0.96, and 0.97

Cross-Regulation between the phz1 and phz2 Operons Maintain a Balanced Level of Phenazine Biosynthesis in Pseudomonas aeruginosa PAO1.

Gene duplication often provides selective advantages for the survival of microorganisms in adapting to varying environmental conditions. P. aeruginosa PAO1 possesses two seven-gene operons [phz1 (phzA1B1C1D1E1F1G1) and phz2 (phzA2B2C2D2E2F2G2)] that are involved in the biosynthesis of phenazine-1-carboxylic acid and its derivatives. Although the two operons are highly homologous and their functions are well known, it is unclear how the two phz operons coordinate their expressions to maintain the phenazine biosynthesis. By constructing single and double deletion mutants of the two phz operons, we found that the phz1-deletion mutant produced the same or less amount of phenazine-1-carboxylic acid and pyocyanin in GA medium than the phz2-knockout mutant while the phz1-phz2 double knockout mutant did not produce any phenazines. By generating phzA1 and phzA2 translational and transcriptional fusions with a truncated lacZ reporter, we found that the expression of the phz1 operon increased significantly at the post-transcriptional level and did not alter at the transcriptional level in the absence of the phz2 operon. Surprisingly, the expression the phz2 operon increased significantly at the post-transcriptional level and only moderately at the transcriptional level in the absence of the phz1 operon. Our findings suggested that a complex cross-regulation existed between the phz1 and phz2 operons. By mediating the upregulation of one phz operon expression while the other was deleted, this crosstalk would maintain the homeostatic balance of phenazine biosynthesis in P. aeruginosa PAO1

Comparison of Different Machine Learning Algorithms for the Prediction of the Wheat Grain Filling Stage Using RGB Images

Grain filling is essential for wheat yield formation, but is very susceptible to environmental stresses, such as high temperatures, especially in the context of global climate change. Grain RGB images include rich color, shape, and texture information, which can explicitly reveal the dynamics of grain filling. However, it is still challenging to further quantitatively predict the days after anthesis (DAA) from grain RGB images to monitor grain development. Results: The WheatGrain dataset revealed dynamic changes in color, shape, and texture traits during grain development. To predict the DAA from RGB images of wheat grains, we tested the performance of traditional machine learning, deep learning, and few-shot learning on this dataset. The results showed that Random Forest (RF) had the best accuracy of the traditional machine learning algorithms, but it was far less accurate than all deep learning algorithms. The precision and recall of the deep learning classification model using Vision Transformer (ViT) were the highest, 99.03% and 99.00%, respectively. In addition, few-shot learning could realize fine-grained image recognition for wheat grains, and it had a higher accuracy and recall rate in the case of 5-shot, which were 96.86% and 96.67%, respectively. Materials and Methods: In this work, we proposed a complete wheat grain dataset, WheatGrain, which covers thousands of wheat grain images from 6 DAA to 39 DAA, which can characterize the complete dynamics of grain development. At the same time, we built different algorithms to predict the DAA, including traditional machine learning, deep learning, and few-shot learning, in this dataset, and evaluated the performance of all models. Conclusions: To obtain wheat grain filling dynamics promptly, this study proposed an RGB dataset for the whole growth period of grain development. In addition, detailed comparisons were conducted between traditional machine learning, deep learning, and few-shot learning, which provided the possibility of recognizing the DAA of the grain timely. These results revealed that the ViT could improve the performance of deep learning in predicting the DAA, while few-shot learning could reduce the need for a number of datasets. This work provides a new approach to monitoring wheat grain filling dynamics, and it is beneficial for disaster prevention and improvement of wheat production

Chitosan Hydrogel Supplemented with Metformin Promotes Neuron–like Cell Differentiation of Gingival Mesenchymal Stem Cells

Human gingival mesenchymal stem cells (GMSCs) are derived from migratory neural crest stem cells and have the potential to differentiate into neurons. Metformin can inhibit stem–cell aging and promotes the regeneration and development of neurons. In this study, we investigated the potential of metformin as an enhancer on neuronal differentiation of GMSCs in the growth environment of chitosan hydrogel. The crosslinked chitosan/β–glycerophosphate hydrogel can form a perforated microporous structure that is suitable for cell growth and channels to transport water and macromolecules. GMSCs have powerful osteogenic, adipogenic and chondrogenic abilities in the induction medium supplemented with metformin. After induction in an induction medium supplemented with metformin, Western blot and immunofluorescence results showed that GMSCs differentiated into neuron–like cells with a significantly enhanced expression of neuro–related markers, including Nestin (NES) and β–Tubulin (TUJ1). Proteomics was used to construct protein profiles in neural differentiation, and the results showed that chitosan hydrogels containing metformin promoted the upregulation of neural regeneration–related proteins, including ATP5F1, ATP5J, NADH dehydrogenase (ubiquinone) Fe–S protein 3 (NDUFS3), and Glutamate Dehydrogenase 1 (GLUD1). Our results help to promote the clinical application of stem–cell neural regeneration

Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing

Accurate, efficient, and timely yield estimation is critical for crop variety breeding and management optimization. However, the contributions of proximal sensing data characteristics (spectral, temporal, and spatial) to yield estimation have not been systematically evaluated. We collected long-term, hyper temporal,and large-volume light detection and ranging (LiDAR) and multispectral data to (i) identify the best machine learning method and prediction stage for wheat yield estimation, (ii) characterize the contribution of multisource data fusion and the dynamic importance of structural and spectral traits to yield estimation, and (iii) elucidate the contribution of time-series data fusion and 3D spatial information to yield estimation. Wheat yield could be accurately (R-2 = 0.891) and timely (approximately-two months before harvest) estimated from fused LiDAR and multispectral data. The artificial neural network model and the flowering stage were always the best method and prediction stage, respectively. Spectral traits (such as CIgreen) dominated yield estimation, especially in the early stage, whereas the contribution of structural traits (such as height) was more stable in the late stage. Fusing spectral and structural traits increased estimation accuracy at all growth stages. Better yield estimation was realized from traits derived from complete 3D points than from canopy surface points and from integrated multi-stage (especially from jointing to heading and flowering stages) data than from single-stage data. We suggest that this study offers a novel perspective on deciphering the contributions of spectral, structural, and time-series information to wheat yield estimation and can guide accurate, efficient, and timely estimation of wheat yield.(C) 2022 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V

PCA produced by <i>P</i>. <i>aeruginosa</i> PAO1 and its derivatives in LB medium.

<p>All strains including the wild-type strain PAO1 (solid circle), the single-deletion mutant Δ<i>phz1</i> (solid square) and Δ<i>phz2</i> (solid triangle), the double-deletion mutant Δ<i>phz1phz2</i> (solid diamond), the Δ<i>phz1</i> mutant complemented with pME10Z1 (open square) and the Δ<i>phz2</i> mutant harboring pME10Z2 (open triangle) were grown in LB broth. All experiments were performed in triplicate, and each value was presented as the average ± standard deviation.</p

Structures of two <i>phz</i> operons in <i>P</i>. <i>aeruginosa</i> PAO1 and its derivatives and two types of plasmid fusions with the truncated <i>lacZ</i>.

<p>(A) <i>phz1</i> (light grey arrows) and <i>phz2</i> (heavy grey arrows) indicate two phenazine operons of <i>phzA1B1C1D1E1F1G1</i> and <i>phzA2B2C2D2E2F2G2</i>, respectively. <i>aacC1</i> (horizontally striped arrow) and <i>aph</i> (vertically striped arrow) indicate the gentamycin and kanamycin resistance cassettes inserted into chromosome, respectively. <i>lacZ</i> (black arrow) indicates the truncated β-galactosidase gene inserted and fused in frame with the first several codons of <i>phzA1</i> or <i>phzA2</i> and their upstream region in the chromosome. The translational plasmid fusion (B) and the transcriptional plasmid fusion (C) were generated in plasmids pME6015 and pME6522, respectively. MCS stands for the multi-cloning site.</p

Bacterial growth curves of <i>P</i>. <i>aeruginosa</i> PAO1 and its derivatives in LB and GA medium.

<p>Each of the wild-type strain PAO1 and its derivatives was respectively inoculated in 150 ml of LB medium (A) or GA medium (B). Optical density 600 nm was determined at 12 hour intervals. All experiments were performed in triplicate, and each value was presented as the average ± standard deviation. * indicates <i>P</i> > 0.05, two-tailed paired Student <i>t</i> test.</p