PredictONCO: a web tool supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning.

PredictONCO 1.0 is a unique web server that analyzes effects of mutations on proteins frequently altered in various cancer types. The server can assess the impact of mutations on the protein sequential and structural properties and apply a virtual screening to identify potential inhibitors that could be used as a highly individualized therapeutic approach, possibly based on the drug repurposing. PredictONCO integrates predictive algorithms and state-of-the-art computational tools combined with information from established databases. The user interface was carefully designed for the target specialists in precision oncology, molecular pathology, clinical genetics and clinical sciences. The tool summarizes the effect of the mutation on protein stability and function and currently covers 44 common oncological targets. The binding affinities of Food and Drug Administration/ European Medicines Agency -approved drugs with the wild-type and mutant proteins are calculated to facilitate treatment decisions. The reliability of predictions was confirmed against 108 clinically validated mutations. The server provides a fast and compact output, ideal for the often time-sensitive decision-making process in oncology. Three use cases of missense mutations, (i) K22A in cyclin-dependent kinase 4 identified in melanoma, (ii) E1197K mutation in anaplastic lymphoma kinase 4 identified in lung carcinoma and (iii) V765A mutation in epidermal growth factor receptor in a patient with congenital mismatch repair deficiency highlight how the tool can increase levels of confidence regarding the pathogenicity of the variants and identify the most effective inhibitors. The server is available at https://loschmidt.chemi.muni.cz/predictonco.

A multi-modal deep neural network for multi-class liver cancer diagnosis.

Liver disease is a potentially asymptomatic clinical entity that may progress to patient death. This study proposes a multi-modal deep neural network for multi-class malignant liver diagnosis. In parallel with the portal venous computed tomography (CT) scans, pathology data is utilized to prognosticate primary liver cancer variants and metastasis. The processed CT scans are fed to the deep dilated convolution neural network to explore salient features. The residual connections are further added to address vanishing gradient problems. Correspondingly, five pathological features are learned using a wide and deep network that gives a benefit of memorization with generalization. The down-scaled hierarchical features from CT scan and pathology data are concatenated to pass through fully connected layers for classification between liver cancer variants. In addition, the transfer learning of pre-trained deep dilated convolution layers assists in handling insufficient and imbalanced dataset issues. The fine-tuned network can predict three-class liver cancer variants with an average accuracy of 96.06% and an Area Under Curve (AUC) of 0.832. To the best of our knowledge, this is the first study to classify liver cancer variants by integrating pathology and image data, hence following the medical perspective of malignant liver diagnosis. The comparative analysis on the benchmark dataset shows that the proposed multi-modal neural network outperformed most of the liver diagnostic studies and is comparable to others.

Transcriptome and anatomical studies reveal alterations in leaf thickness under long-term drought stress in tobacco.

Drought is one of the foremost environmental factors that limit the growth of plants. Leaf thickness (LT) is an important quantitative trait in plant physiology. The experiment was carried out in a growth room and the plants were divided into two groups such as well-watered and drought-stressed. This work investigated leaf growth in terms of leaf surface growth and expansion rate, leaf stomata traits, LT, anticlinal growth, and leaf cell layers. The results showed that the leaf area and leaf surface expansion rate were decreased by drought stress (DS). Similarly, LT, anticlinal expansion rate, palisade and spongy tissue thickness, and their related expansion rates were also decreased at different days' time points (DTP) of DS. However, a steady increase was observed in the aforementioned parameters after 12 DTP of DS. The stomatal density increased while stomata size decreased at 3 DTP and 12 DTP (low leaf water potential and relative leaf water content at these time points) and vice versa at 24 DTP compared with the well-watered plants indicating adaptations in these traits in response to DS, and thus the leaf water status played a role in the regulation of leaf stomata traits. The cell length decreased in the upper epidermis, palisade and spongy tissues by DS up to 12 DTP led to lower LT while an increase was observed after 12 DTP that resulted in higher LT. The increase in the LT was supported by the upregulation of starch and sucrose metabolism, glycerolipid metabolism, protein processing in endoplasmic reticulum pathways at 18 DTP along with the differentially expressed genes induced that were related to cell wall remodeling (cellulose, expansin, xyloglucans) and cell expansion (auxin response factors and aquaporin). The results explain the response of leaf thickness to drought stress and show alterations in LT and leaf stomatal traits. This study might serve as a valuable source of gene information for functional studies and provide a theoretical basis to understand leaf growth in terms of leaf anatomy and leaf stomatal traits under drought stress.

Leaf senescence attributes: the novel and emerging role of sugars as signaling molecules and the overlap of sugars and hormones signaling nodes.

Sugars are the primary products of photosynthesis and play multiple roles in plants. Although sugars are usually considered to be the building blocks of energy storage and carbon transport molecules, they have also gradually come to be acknowledged as signaling molecules that can initiate senescence. Senescence is an active and essential process that occurs at the last developmental stage and corresponds to programmed degradation of: cells, tissues, organs, and entire organisms. It is a complex process involving: numerous biochemical changes, transporters, genes, and transcription factors. The process is controlled by multiple developmental signals, among which sugar signals are considered to play a vital role; however, the regulatory pathways involved are not fully understood. The dynamic mechanistic framework of sugar accumulation has an inconsistent effect on senescence through the sugar signaling pathway. Key metabolizing enzymes produce different sugar signals in response to the onset of senescence. Diverse sugar signal transduction pathways and a variety of sugar sensors are involved in controlling leaf senescence. This review highlights the processes underlying initiation of sugar signaling and crosstalk between sugars and hormones signal transduction pathways affecting leaf senescence. This summary of the state of current knowledge across different plants aids in filling knowledge gaps and raises key questions that remain to be answered with respect to regulation of leaf senescence by sugar signaling pathways.

Combined Application of Manure and Chemical Fertilizers Alters Soil Environmental Variables and Improves Soil Fungal Community Composition and Rice Grain Yield.

Soil microorganisms play vital roles in energy flow and soil nutrient cycling and, thus, are important for crop production. A detailed understanding of the complex responses of microbial communities to diverse organic manure and chemical fertilizers (CFs) is crucial for agroecosystem sustainability. However, little is known about the response of soil fungal communities and soil nutrients to manure and CFs, especially under double-rice cropping systems. In this study, we investigated the effects of the application of combined manure and CFs to various fertilization strategies, such as no N fertilizer (Neg-CF); 100% chemical fertilizer (Pos-CF); 60% cattle manure (CM) + 40% CF (high-CM); 30% CM + 70% CF (low-CM); 60% poultry manure (PM) + 40% CF (high-PM), and 30% PM + 70% CF (low-PM) on soil fungal communities' structure and diversity, soil environmental variables, and rice yield. Results showed that synthetic fertilizer plus manure addition significantly increased the soil fertility and rice grain yield compared to sole CFs' application. Moreover, the addition of manure significantly changed the soil fungal community structure and increased the relative abundance of fungi such as phyla Ascomycota, Basidiomycota, Mortierellomycota, and Rozellomycota. The relative abundances dramatically differed at each taxonomic level, especially between manured and non-manured regimes. Principal coordinates analysis (PCoA) exhibited greater impacts of the addition of manure amendments than CFs on fungal community distributions. Redundancy analysis showed that the dominant fungal phyla were positively correlated with soil pH, soil organic C (SOC), total N, and microbial biomass C, and the fungal community structure was strongly affected by SOC. Network analysis explored positive relationships between microorganisms and could increase their adaptability in relevant environments. In addition, the structural equation model (SEM) shows the relationship between microbial biomass, soil nutrients, and rice grain yield. The SEM showed that soil nutrient contents and their availability directly affect rice grain yield, while soil fungi indirectly affect grain yield through microbial biomass production and nutrient levels. Our results suggest that manure application combined with CFs altered soil biochemical traits and soil fungal community structure and counteracted some of the adverse effects of the synthetic fertilizer. Overall, the findings of this research suggest that the integrated application of CF and manure is a better approach for improving soil health and rice yield.

Application of 2,4-Epibrassinolide Improves Drought Tolerance in Tobacco through Physiological and Biochemical Mechanisms.

Drought stress is a major abiotic stress that hinders plant growth and development. Brassinosteroids (BR), including 2,4-epibrassinolide (EBR), play important roles in plant growth, development, and responses to abiotic stresses, including drought stress. This work investigates exogenous EBR application roles in improving drought tolerance in tobacco. Tobacco plants were divided into three groups: WW (well-watered), DS (drought stress), and DSB (drought stress + 0.05 mM EBR). The results revealed that DS decreased the leaf thickness (LT), whereas EBR application upregulated genes related to cell expansion, which were induced by the BR (DWF4, HERK2, and BZR1) and IAA (ARF9, ARF6, PIN1, SAUR19, and ABP1) signaling pathway. This promoted LT by 28%, increasing plant adaptation. Furthermore, EBR application improved SOD (22%), POD (11%), and CAT (5%) enzyme activities and their related genes expression (FeSOD, POD, and CAT) along with a higher accumulation of osmoregulatory substances such as proline (29%) and soluble sugars (14%) under DS and conferred drought tolerance. Finally, EBR application augmented the auxin (IAA) (21%) and brassinolide (131%) contents and upregulated genes related to drought tolerance induced by the BR (BRL3 and BZR2) and IAA (YUCCA6, SAUR32, and IAA26) signaling pathways. These results suggest that it could play an important role in improving mechanisms of drought tolerance in tobacco.

Genome-Wide Identification and Expression Analysis of SnRK2 Gene Family in Dormant Vegetative Buds of Liriodendron chinense in Response to Abscisic Acid, Chilling, and Photoperiod.

Protein kinases play an essential role in plants' responses to environmental stress signals. SnRK2 (sucrose non-fermenting 1-related protein kinase 2) is a plant-specific protein kinase that plays a crucial role in abscisic acid and abiotic stress responses in some model plant species. In apple, corn, rice, pepper, grapevine, Arabidopsis thaliana, potato, and tomato, a genome-wide study of the SnRK2 protein family was performed earlier. The genome-wide comprehensive investigation was first revealed to categorize the SnRK2 genes in the Liriodendron chinense (L. chinense). The five SnRK2 genes found in the L. chinense genome were highlighted in this study. The structural gene variants, 3D structure, chromosomal distributions, motif analysis, phylogeny, subcellular localization, cis-regulatory elements, expression profiles in dormant buds, and photoperiod and chilling responses were all investigated in this research. The five SnRK2 genes from L. chinense were grouped into groups (I-IV) based on phylogeny analysis, with three being closely related to other species. Five hormones-, six stress-, two growths and biological process-, and two metabolic-related responsive elements were discovered by studying the cis-elements in the promoters. According to the expression analyses, all five genes were up- and down-regulated in response to abscisic acid (ABA), photoperiod, chilling, and chilling, as well as photoperiod treatments. Our findings gave insight into the SnRK2 family genes in L. chinense and opened up new study options.

Mathematical Modeling Reveals That Sucrose Regulates Leaf Senescence via Dynamic Sugar Signaling Pathways.

Sucrose (Suc) accumulation is one of the key indicators of leaf senescence onset, but little is known about its regulatory role. Here, we found that application of high (120-150 mM) and low levels (60 mM) of Suc to young leaf (YL) and fully expanded leaf (FEL) discs, respectively, decreased chlorophyll content and maximum photosynthetic efficiency. Electrolyte leakage and malondialdehyde levels increased at high Suc concentrations (90-120 mM in YL and 60 and 150 mM in FEL discs). In FEL discs, the senescence-associated gene NtSAG12 showed a gradual increase in expression with increased Suc application; in contrast, in YL discs, NtSAG12 was upregulated with low Suc treatment (60 mM) but downregulated at higher levels of Suc. In YL discs, trehalose-6-phosphate (T6P) accumulated at a low half-maximal effective concentration (EC50) of Suc (1.765 mM). However, T6P levels declined as trehalose 6 phosphate synthase (TPS) content decreased, resulting in the maximum velocity of sucrose non-fermenting-1-related protein kinase (SnRK) and hexokinase (HXK) occurring at higher level of Suc. We therefore speculated that senescence was induced by hexose accumulation. In FEL discs, the EC50 of T6P occurred at a low concentration of Suc (0.9488 mM); T6P levels progressively increased with higher TPS content, which inhibited SnRK activity with a dissociation constant (Kd) of 0.001475 U/g. This confirmed that the T6P-SnRK complex induced senescence in detached FEL discs.

Investigation of sugar signaling behaviors involved in sucrose-induced senescence initiation and progression in N. tabacum.

Sugar is involved in initiating leaf senescence. However, its regulatory role, especially as a signal in the senescence process, is unclear. Therefore, this study was designed to illustrate how sugar stimulates the onset of leaf senescence and controls sugar homeostasis through the T6P-SnRK (sucrose non-fermenting (SNF)-related kinase) and HXK (hexokinase) signaling pathways. We used a leaf disc system detached from fully expanded leaves of Nicotiana tabacum cv. K326 and designed a time-course study (days 3, 5, 7, and 9) with exogenously gradient concentrations (0, 30, 60, 90, 120, and 150 mM) of sucrose (Suc) treatment to identify how Suc application affects sugar metabolism and induces senescence. Our results revealed that early decreases of Fv/Fm and increases in electrolyte leakage responded to Suc on day 3. Furthermore, a substantial increase in lipid peroxidation and up-regulated expression of senescence marker genes (NtSAG12) (except 60 mM on day 3) responded sequentially by day 5. The glucose, G6P, and HXK contents were first induced by Suc on day 3 and then repressed from day 5 to day 7. However, exogenous Suc treatment significantly improved the TPS content and the subsequent precursor T6P from day 3 to day 7. Following exogenous Suc treatments, the transcript level of NtSnRK1 was markedly down-regulated from day 3 to day 7. On the other hand, a linear regression analysis demonstrated that the T6P-NtSnRK1 signaling pathway was strongly associated with senescence initiation, and was accompanied by membrane degradation and NtCP1/NtSAG12 up-regulation by day 3. The T6P-NtSnRK1 signaling pathway experienced membrane and chloroplast degradation by day 5. HXK functioned as a metabolic enzyme promoting Glc-G6P and as a Glc sensor, accelerating the initiation of senescence through the HXK-dependent pathway by repressing PSII by day 3 and the senescence process through the Glycolytic pathway by day 7. These physiological, biochemical, and molecular analyses demonstrate that exogenous Suc regulates T6P accumulation, inducing senescence through the NtSnRK signaling pathway. These results illustrate the role of Suc and the transition of the sugar signaling pathway during the progression of senescence initiation.

LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI.

Brain-computer interface (BCI) systems based on functional near-infrared spectroscopy (fNIRS) have been used as a way of facilitating communication between the brain and peripheral devices. The BCI provides an option to improve the walking pattern of people with poor walking dysfunction, by applying a rehabilitation process. A state-of-the-art step-wise BCI system includes data acquisition, pre-processing, channel selection, feature extraction, and classification. In fNIRS-based BCI (fNIRS-BCI), channel selection plays a vital role in enhancing the classification accuracy of the BCI problem. In this study, the concentration of blood oxygenation (HbO) in a resting state and in a walking state was used to decode the walking activity and the resting state of the subject, using channel selection by Least Absolute Shrinkage and Selection Operator (LASSO) homotopy-based sparse representation classification. The fNIRS signals of nine subjects were collected from the left hemisphere of the primary motor cortex. The subjects performed the task of walking on a treadmill for 10 s, followed by a 20 s rest. Appropriate filters were applied to the collected signals to remove motion artifacts and physiological noises. LASSO homotopy-based sparse representation was used to select the most significant channels, and then classification was performed to identify walking and resting states. For comparison, the statistical spatial features of mean, peak, variance, and skewness, and their combination, were used for classification. The classification results after channel selection were then compared with the classification based on the extracted features. The classifiers used for both methods were linear discrimination analysis (LDA), support vector machine (SVM), and logistic regression (LR). The study found that LASSO homotopy-based sparse representation classification successfully discriminated between the walking and resting states, with a better average classification accuracy (p < 0.016) of 91.32%. This research provides a step forward in improving the classification accuracy of fNIRS-BCI systems. The proposed methodology may also be used for rehabilitation purposes, such as controlling wheelchairs and prostheses, as well as an active rehabilitation training technique for patients with motor dysfunction.

Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks.

This research presents a brain-computer interface (BCI) framework for brain signal classification using deep learning (DL) and machine learning (ML) approaches on functional near-infrared spectroscopy (fNIRS) signals. fNIRS signals of motor execution for walking and rest tasks are acquired from the primary motor cortex in the brain's left hemisphere for nine subjects. DL algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM) are used to achieve average classification accuracies of 88.50%, 84.24%, and 85.13%, respectively. For comparison purposes, three conventional ML algorithms, support vector machine (SVM), k-nearest neighbor (k-NN), and linear discriminant analysis (LDA) are also used for classification, resulting in average classification accuracies of 73.91%, 74.24%, and 65.85%, respectively. This study successfully demonstrates that the enhanced performance of fNIRS-BCI can be achieved in terms of classification accuracy using DL approaches compared to conventional ML approaches. Furthermore, the control commands generated by these classifiers can be used to initiate and stop the gait cycle of the lower limb exoskeleton for gait rehabilitation.

RMS-UNet: Residual multi-scale UNet for liver and lesion segmentation.

Precise segmentation is in demand for hepatocellular carcinoma or metastasis clinical diagnosis due to the heterogeneous appearance and diverse anatomy of the liver on scanned abdominal computed tomography (CT) images. In this study, we present an automatic unified registration-free deep-learning-based model with residual block and dilated convolution for training end-to-end liver and lesion segmentation. A multi-scale approach has also been utilized to explore novel inter-slice features with multi-channel input images. A novel objective function is introduced to deal with fore- and background pixels imbalance based on the joint metric of dice coefficient and absolute volumetric difference. Further, batch normalization is used to improve the learning without any loss of useful information. The proposed methodology is extensively validated and tested on 30% of the publicly available Dircadb, LiTS, Sliver07, and Chaos datasets. A comparative analysis is conducted based on multiple evaluation metrics frequently used in segmentation competitions. The results show substantial improvement, with mean dice scores of 97.31, 97.38, 97.39 and 95.49% for the Dircadb, LiTS, Sliver07, and Chaos liver test sets, and 91.92 and 86.70% for Dircadb and LiTS lesion segmentation. It should be noted that we achieve the best lesion segmentation performance on common datasets. The obtained qualitative and quantitative results demonstrate that our proposed model outperform other state-of-the-art methods for liver and lesion segmentation, with competitive performance on additional datasets. Henceforth, it is envisaged as being applicable to pertinent medical segmentation applications.

Combined deep learning and molecular docking simulations approach identifies potentially effective FDA approved drugs for repurposing against SARS-CoV-2.

The ongoing pandemic of Coronavirus Disease 2019 (COVID-19) has posed a serious threat to global public health. Drug repurposing is a time-efficient approach to finding effective drugs against SARS-CoV-2 in this emergency. Here, we present a robust experimental design combining deep learning with molecular docking experiments to identify the most promising candidates from the list of FDA-approved drugs that can be repurposed to treat COVID-19. We have employed a deep learning-based Drug Target Interaction (DTI) model, called DeepDTA, with few improvements to predict drug-protein binding affinities, represented as KIBA scores, for 2440 FDA-approved and 8168 investigational drugs against 24 SARS-CoV-2 viral proteins. FDA-approved drugs with the highest KIBA scores were selected for molecular docking simulations. We ran around 50,000 docking simulations for 168 selected drugs against 285 total predicted and/or experimentally proven active sites of all 24 SARS-CoV-2 viral proteins. A list of 49 most promising FDA-approved drugs with the best consensus KIBA scores and binding affinity values against selected SARS-CoV-2 viral proteins was generated. Most importantly, 16 drugs including anidulafungin, velpatasvir, glecaprevir, rifapentine, flavin adenine dinucleotide (FAD), terlipressin, and selinexor demonstrated the highest predicted inhibitory potential against key SARS-CoV-2 viral proteins. We further measured the inhibitory activity of 5 compounds (rifapentine, velpatasvir, glecaprevir, anidulafungin, and FAD disodium) on SARS-CoV-2 PLpro using Ubiquitin-Rhodamine 110 Gly fluorescent intensity assay. The highest inhibition of PLpro activity was seen with rifapentine (IC50: 15.18 µM) and FAD disodium (IC50: 12.39 µM), the drugs with high predicted KIBA scores and binding affinities.

Application, advancement and green aspects of magnetic molecularly imprinted polymers in pesticide residue detection.

Molecularly imprinted polymers (MIPs) have added a vital contribution to food quality and safety with the effective extraction of pesticide residues due to their unique properties. Magnetic molecularly imprinted polymers (MMIPs) are a superior approach to overcome stereotypical limitations due to their unique core-shell and novel composite structure, including high chemothermal stability, rapid extraction, and high selectivity. Over the past two decades, different MMIPs have been developed for pesticide extraction in actual food samples with a complex matrix. Nevertheless, such developments are desirable, yet the synthesis and mode of application of MMIP have great potential as a green chemistry approach that can significantly reduce environmental pollution and minimize resource utilization. In this review, the MMIP application for single or multipesticide detection has been summarized by critiquing each method's uniqueness and efficiency in real sample analysis and providing a possible green chemistry exploration procedure for MMIP synthesis and application for escalated food and environmental safety.

Maize-soybean intercropping at optimal N fertilization increases the N uptake, N yield and N use efficiency of maize crop by regulating the N assimilatory enzymes.

Introduction: Surplus use of chemical nitrogen (N) fertilizers to increase agricultural Q9 production causes severe problems to the agricultural ecosystem and environment. This is contrary to N use efficiency and sustainable agricultural production. Methods: Hence, this study was designed to investigate the effect of maizesoybean intercropping on N uptake, N yield, N utilization use efficiency, and the associated nitrogen assimilatory enzymes of maize crops under different N fertilization for two consecutive years 2021-2022. Results: The findings of the study showed that intercropping at the optimal N rate (N1) (250 kg N ha-1) increased significantly maize grain yield by 30 and 34%, residue yield by 30 and 37%, and 100-grain weight by 33 and 39% in the year 2021 and 2022, respectively. As compared with mono-cropping, at this optimal N rate, the respective increase (of maize's crop N yield indices) for 2021 and 2022 were 53 and 64% for grain N yield, and 53 and 68% for residue N yield. Moreover, intercropping at N1 resulted in higher grain N content by 28 and 31%, residue N content by 18 and 22%, and total N uptake by 65 and 75% in 2021 and 2022, respectively. The values for the land equivalent ratio for nitrogen yield (LERN) were greater than 1 in intercropping, indicating better utilization of N under the intercropping over mono-cropping. Similarly, intercropping increased the N assimilatory enzymes of maize crops such as nitrate reductase (NR) activity by 19 and 25%, nitrite reductase (NiR) activity by 20 and 23%, and glutamate synthase activity (GOGAT) by 23 and 27% in 2021 and 2022, respectively. Consequently, such increases resulted in improved nitrogen use efficiency indices such as N use efficiency (NUE), partial factor nitrogen use efficiency (PFNUE), nitrogen uptake efficiency (NUpE), and nitrogen agronomic efficiency (NAE) under intercropping than mono-cropping. Conclusion: Thus, this suggests that maize-soybean intercropping under optimal N fertilization can improve the nitrogen status and nitrogen use efficiency of maize crops by regulating the nitrogen assimilatory enzymes, thereby enhancing its growth and yield. Therefore, prioritizing intercropping over an intensive mono-cropping system could be a better option for sustainable agricultural production.

Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm.

Background: The purpose of this project is to identify prognostic features in resectable pancreatic head adenocarcinoma and use these features to develop a machine learning algorithm that prognosticates survival for patients pursuing pancreaticoduodenectomy. Methods: A retrospective cohort study of 93 patients who underwent a pancreaticoduodenectomy was performed. The patients were analyzed in 2 groups: Group 1 (n = 38) comprised of patients who survived < 2 years, and Group 2 (n = 55) comprised of patients who survived > 2 years. After comparing the two groups, 9 categorical features and 2 continuous features (11 total) were selected to be statistically significant (p < .05) in predicting outcome after surgery. These 11 features were used to train a machine learning algorithm that prognosticates survival. Results: The algorithm obtained 75% accuracy, 41.9% sensitivity, and 97.5% specificity in predicting whether survival is less than 2 years after surgery. Conclusion: A supervised machine learning algorithm that prognosticates survival can be a useful tool to personalize treatment plans for patients with pancreatic cancer.

Transcription Factors Interact with ABA through Gene Expression and Signaling Pathways to Mitigate Drought and Salinity Stress.

Among abiotic stressors, drought and salinity seriously affect crop growth worldwide. In plants, research has aimed to increase stress-responsive protein synthesis upstream or downstream of the various transcription factors (TFs) that alleviate drought and salinity stress. TFs play diverse roles in controlling gene expression in plants, which is necessary to regulate biological processes, such as development and environmental stress responses. In general, plant responses to different stress conditions may be either abscisic acid (ABA)-dependent or ABA-independent. A detailed understanding of how TF pathways and ABA interact to cause stress responses is essential to improve tolerance to drought and salinity stress. Despite previous progress, more active approaches based on TFs are the current focus. Therefore, the present review emphasizes the recent advancements in complex cascades of gene expression during drought and salinity responses, especially identifying the specificity and crosstalk in ABA-dependent and -independent signaling pathways. This review also highlights the transcriptional regulation of gene expression governed by various key TF pathways, including AP2/ERF, bHLH, bZIP, DREB, GATA, HD-Zip, Homeo-box, MADS-box, MYB, NAC, Tri-helix, WHIRLY, WOX, WRKY, YABBY, and zinc finger, operating in ABA-dependent and -independent signaling pathways.

Exogenous application of brassinosteroids regulates tobacco leaf size and expansion via modulation of endogenous hormones content and gene expression.

Brassinosteroids (BR) play diverse roles in the regulation of plant growth and development. BR promotes plant growth by triggering cell division and expansion. However, the effect of exogenous BR application on the leaf size and expansion of tobacco is unknown. Tobacco seedlings are treated with different concentrations of exogenous 2,4-epibrassinolide (EBL) [control (CK, 0 mol L-1), T1 (0.5 × 10-7 mol L-1), and T2 (0.5 × 10-4 mol L-1)]. The results show that T1 has 17.29% and T2 has 25.99% more leaf area than control. The epidermal cell area is increased by 24.40% and 17.13% while the number of epidermal cells is 7.06% and 21.06% higher in T1 and T2, respectively, relative to control. So the exogenous EBL application improves the leaf area by increasing cell numbers and cell area. The endogenous BR (7.5 times and 68.4 times), auxin (IAA) (4.03% and 25.29%), and gibberellin (GA3) contents (84.42% and 91.76%) are higher in T1 and T2, respectively, in comparison with control. Additionally, NtBRI1, NtBIN2, and NtBES1 are upregulated showing that the brassinosteroid signaling pathway is activated. Furthermore, the expression of the key biosynthesis-related genes of BR (NtDWF4), IAA (NtYUCCA6), and GA3 (NtGA3ox-2) are all upregulated under EBL application. Finally, the exogenous EBL application also upregulated the expression of cell growth-related genes (NtCYCD3;1, NtARGOS, NtGRF5, NtGRF8, and NtXTH). The results reveal that the EBL application increases the leaf size and expansion by promoting the cell expansion and division through higher BR, IAA, and GA3 contents along with the upregulation of cell growth-related genes. The results of the study provide a scientific basis for the effect of EBL on tobacco leaf growth at morphological, anatomical, biochemical, and molecular levels. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12298-021-00971-x.

Co-incorporation of manure and inorganic fertilizer improves leaf physiological traits, rice production and soil functionality in a paddy field.

The combined use of organic manure and chemical fertilizer (CF) is considered to be a good method for sustaining high crop yields and improving soil quality. We performed a field experiment in 2019 at the research station of Guanxi University, to investigate the effects of cattle manure (CM) and poultry manure (PM) combined with CF on soil physical and biochemical properties, rice dry matter (DM) and nitrogen (N) accumulation and grain yield. We also evaluated differences in pre-and post-anthesis DM and N accumulation and their contributions to grain yield. The experiment consisted of six treatments: no N fertilizer (T1), 100% CF (T2), 60% CM + 40% CF (T3), 30% CM + 70% CF (T4), 60% PM + 40% CF (T5), and 30% PM + 70% CF (T6). All CF and organic manure treatments provided a total N of 150 kg ha-1. Results showed that the treatment T6 increased leaf net photosynthetic rate (Pn) by 11% and 13%, chlorophyll content by 13% and 15%, total biomass by 9% and 11% and grain yield by 11% and 17% in the early and late season, respectively, compared with T2. Similarly, the integrated manure and CF treatments improved post-antheis DM accumulation and soil properties, such as bulk density, organic carbon, total N, microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) relative to the CF-only treatments. Interestingly, increases in post-anthesis DM and N accumulation were further supported by enhanced leaf Pn and activity of N-metabolizing enzyme during the grain-filling period. Improvement in Pn and N-metabolizing enzyme activity were due to mainly improved soil quality in the combined manure and synthetic fertilizer treatments. Redundancy analysis (RDA) showed a strong relationship between grain yield and soil properties, and a stronger relationship was noted with soil MBC and MBN. Conclusively, a combination of 30% N from PM or CM with 70% N from CF is a promising option for improving soil quality and rice yield.