Regulation of Glandular Size and Phytoalexin Biosynthesis by a Negative Feedback Loop in Cotton.

Plants have evolved diverse defense mechanisms encompassing physical and chemical barriers. Cotton pigment glands are known for containing various defense metabolites, but the precise regulation of gland size to modulate defense compound levels remains enigmatic. Here, it is discovered that the VQ domain-containing protein JAVL negatively regulates pigment gland size and the biosynthesis of defense compounds, while the MYC2-like transcription factor GoPGF has the opposite effect. Notably, GoPGF directly activates the expression of JAVL, whereas JAVL suppresses GoPGF transcription, establishing a negative feedback loop that maintains the expression homeostasis between GoPGF and JAVL. Furthermore, it is observed that JAVL negatively regulates jasmonate levels by inhibiting the expression of jasmonate biosynthetic genes and interacting with GoPGF to attenuate its activation effects, thereby maintaining homeostatic regulation of jasmonate levels. The increased expression ratio of GoPGF to JAVL leads to enlarged pigment glands and elevated jasmonates and defense compounds, enhancing insect and pathogen resistance in cotton. These findings unveil a new mechanism for regulating gland size and secondary metabolites biosynthesis, providing innovative strategies for strengthening plant defense.

Implementation of Thermal-Triggered Binary-Ternary Switchable Memory Performance in Zn/polysulfide/organic Complex-Based Memorizers by Finely Modulating the S62- Relaxation.

Polysulfide-based multilevel memorizers are promising as novel memorizers, in which the occurrence of Sn2- relaxation is key for their multilevel memory. However, the effects of crystal packing and the side group of organic ligands on Sn2- relaxation are still ambiguous. In this work, ionic [Zn(S6)2·Zn2(Bipy)2SO4 (1), Zn(S6)2·Zn(Pmbipy)3 (2)] and neutral [ZnS6(Ombipy) (3), ZnS6(Phen)2 (4)] Zn/polysulfide/organic complexes with different packing modes and structures of organic ligands have been synthesized and were fabricated as memory devices. In both ionic and neutral Zn complexes, the S62- relaxation will be blocked by steric hindrances due to the packing of counter-cations and hydrogen-bond restrictions. Consequently, only the binary memory performances can be seen in FTO/1/Ag, FTO/2/Ag, and FTO/4/Ag, which originate from the more condensed packing of conjugated ligands upon electrical stimulus. Interestingly, FTO/3/Ag illustrates the unique thermally triggered reversible binary-ternary switchable memory performance. In detail, after introducing a methyl group on the 6'-position of bipyridine in ZnS6(Ombipy) (3), the ring-to-chain relaxation of S62- anions at room temperature will be inhibited, but it can happen at a higher temperature of 120 °C, which has been verified by elongated S-S lengths and the strengthened C-H···S hydrogen bond upon heating. The rules drawn in this work will provide a useful guide for the design of stimulus-responsive memorizers that can be applied in special industries such as automobile, oil, and gas industries.

A dirigent protein complex directs lignin polymerization and assembly of the root diffusion barrier.

Functionally similar to the tight junctions present in animal guts, plant roots have evolved a lignified Casparian strip as an extracellular diffusion barrier in the endodermis to seal the root apoplast and maintain nutrient homeostasis. How this diffusion barrier is structured has been partially defined, but its lignin polymerization and assembly steps remain elusive. Here, we characterize a family of dirigent proteins (DPs) essential for both the localized polymerization of lignin required for Casparian strip biogenesis in the cell wall and for attachment of the strip to the plasma membrane to seal the apoplast. We reveal a Casparian strip lignification mechanism that requires cooperation between DPs and the Schengen pathway. Furthermore, we demonstrate that DPs directly mediate lignin polymerization as part of this mechanism.

A new family of proteins is required for tethering of Casparian strip membrane domain and nutrient homoeostasis in rice.

Cell-cell junctions are essential for multicellular organisms to maintain nutrient homoeostasis. A plant-type tight junction, the Casparian strip (CS)-Casparian strip membrane domain (CSD) that seals the paracellular space between adjacent endodermal cells, has been known for more than one hundred years. However, the molecular basis of this structure remains unknown. Here we report that a new family of proteins containing a glycine/alanine/proline-rich domain, a lectin domain and a secretory signal peptide (GAPLESS) mediates tethering of the plasma membrane to the CS in rice. The GAPLESS proteins are specifically localized in the CS of root endodermal cells, and loss of their functions results in a disabled cell-cell junction and disrupted nutrient homoeostasis. The GAPLESS protein forms a tight complex with OsCASP1 in the plasma membrane, thereby mediating the CS-CSD junction. This study provides valuable insights into the junctional complex of plant endodermal cells, shedding light on our understanding of nutrient homoeostasis in crops and the cell junctions of eukaryotes.

Cell type-specific mapping of ion distribution in Arabidopsis thaliana roots.

Cell type-specific mapping of element distribution is critical to fully understand how roots partition nutrients and toxic elements with aboveground parts. In this study, we developed a method that combines fluorescence-activated cell sorting (FACS) with inductively coupled plasma mass spectrometry (ICP-MS) to assess the ionome of different cell populations within Arabidopsis thaliana roots. The method reveals that most elements exhibit a radial concentration gradient increasing from the rhizodermis to inner cell layers, and detected previously unknown ionomic changes resulting from perturbed xylem loading processes. With this approach, we also identify a strong accumulation of manganese in trichoblasts of iron-deficient roots. We demonstrate that confining manganese sequestration in trichoblasts but not in endodermal cells efficiently retains manganese in roots, therefore preventing toxicity in shoots. These results indicate the existence of cell type-specific constraints for efficient metal sequestration in roots. Thus, our approach opens an avenue to investigate element compartmentation and transport pathways in plants.

Multi-copper oxidases SKU5 and SKS1 coordinate cell wall formation using apoplastic redox-based reactions in roots.

The primary cell wall is a fundamental plant constituent that is flexible but sufficiently rigid to support the plant cell shape. Although many studies have demonstrated that reactive oxygen species (ROS) serve as important signaling messengers to modify the cell wall structure and affect cellular growth, the regulatory mechanism underlying the spatial-temporal regulation of ROS activity for cell wall maintenance remains largely unclear. Here, we demonstrate the role of the Arabidopsis (Arabidopsis thaliana) multicopper oxidase-like protein skewed 5 (SKU5) and its homolog SKU5-similar 1 (SKS1) in root cell wall formation through modulating ROS homeostasis. Loss of SKU5 and SKS1 function resulted in aberrant division planes, protruding cell walls, ectopic deposition of iron, and reduced nicotinamide adeninedinucleotide phosphate (NADPH) oxidase-dependent ROS overproduction in the root epidermis-cortex and cortex-endodermis junctions. A decrease in ROS level or inhibition of NADPH oxidase activity rescued the cell wall defects of sku5 sks1 double mutants. SKU5 and SKS1 proteins were activated by iron treatment, and iron over-accumulated in the walls between the root epidermis and cortex cell layers of sku5 sks1. The glycosylphosphatidylinositol-anchored motif was crucial for membrane association and functionality of SKU5 and SKS1. Overall, our results identified SKU5 and SKS1 as regulators of ROS at the cell surface for regulation of cell wall structure and root cell growth.

Localization and circulation: vesicle trafficking in regulating plant nutrient homeostasis.

Nutrient homeostasis is essential for plant growth and reproduction. Plants, therefore, have evolved tightly regulated mechanisms for the uptake, translocation, distribution, and storage of mineral nutrients. Considering that inorganic nutrient transport relies on membrane-based transporters and channels, vesicle trafficking, one of the fundamental cell biological processes, has become a hotspot of plant nutrition studies. In this review, we summarize recent advances in the study of how vesicle trafficking regulates nutrient homeostasis to contribute to the adaptation of plants to heterogeneous environments. We also discuss new perspectives on future studies, which may inspire researchers to investigate new approaches to improve the human diet and health by changing the nutrient quality of crops.

Sec24C mediates a Golgi-independent trafficking pathway that is required for tonoplast localisation of ABCC1 and ABCC2.

Protein sorting is an essential biological process in all organisms. Trafficking membrane proteins generally relies on the sorting machinery of the Golgi apparatus. However, many proteins have been found to be delivered to target locations via Golgi-independent pathways, but the mechanisms underlying this delivery system remain unknown. Here, we report that Sec24C mediates the direct secretory trafficking of the phytochelatin transporters ABCC1 and ABCC2 from the endoplasmic reticulum (ER) to prevacuolar compartments (PVCs) in Arabidopsis thaliana. Genetic analysis showed that the sec24c mutants are hypersensitive to cadmium (Cd) and arsenic (As) treatments due to mislocalisation of ABCC1 and ABCC2, which results in defects in the vacuole compartmentalisation of the toxic metals. Furthermore, we found that Sec24C recognises ABCC1 and ABCC2 through direct interactions to mediate their exit from the ER to PVCs, which is independent of brefeldin A-sensitive post-Golgi trafficking pathway. These findings expand our understanding of Golgi-independent trafficking, which also provide key insights regarding the mechanism of tonoplast protein sorting and open a new perspective on the function of Sec24 proteins.

Decreasing nitrogen assimilation under drought stress by suppressing DST-mediated activation of Nitrate Reductase 1.2 in rice.

Nitrogen is an essential nutrient for plant growth and development, and plays vital roles in crop yield. Assimilation of nitrogen is thus fine-tuned in response to heterogeneous environments. However, the regulatory mechanism underlying this essential process remains largely unknown. Here, we report that a zinc-finger transcription factor, drought and salt tolerance (DST), controls nitrate assimilation in rice by regulating the expression of OsNR1.2. We found that loss of function of DST results in a significant decrease of nitrogen use efficiency (NUE) in the presence of nitrate. Further study revealed that DST is required for full nitrate reductase activity in rice and directly regulates the expression of OsNR1.2, a gene showing sequence similarity to nitrate reductase. Reverse genetics and biochemistry studies revealed that OsNR1.2 encodes an NADH-dependent nitrate reductase that is required for high NUE of rice. Interestingly, the DST-OsNR1.2 regulatory module is involved in the suppression of nitrate assimilation under drought stress, which contributes to drought tolerance. Considering the negative role of DST in stomata closure, as revealed previously, the positive role of DST in nitrogen assimilation suggests a mechanism coupling nitrogen metabolism and stomata movement. The discovery of this coupling mechanism will aid the engineering of drought-tolerant crops with high NUE in the future.

Long-distance blue light signalling regulates phosphate deficiency-induced primary root growth inhibition.

Although roots are mainly embedded in the soil, recent studies revealed that light regulates mineral nutrient uptake by roots. However, it remains unclear whether the change in root system architecture in response to different rhizosphere nutrient statuses involves light signaling. Here, we report that blue light regulates primary root growth inhibition under phosphate-deficient conditions through the cryptochromes and their downstream signaling factors. We showed that the inhibition of root elongation by low phosphate requires blue light signal perception at the shoot and transduction to the root. In this process, SPA1 and COP1 play a negative role while HY5 plays a positive role. Further experiments revealed that HY5 is able to migrate from the shoot to root and that the shoot-derived HY5 autoactivates root HY5 and regulates primary root growth by directly activating the expression of LPR1, a suppressor of root growth under phosphate starvation. Taken together, our study reveals a regulatory mechanism by which blue light signaling regulates phosphate deficiency-induced primary root growth inhibition, providing new insights into the crosstalk between light and nutrient signaling.

The effect of coronary slow flow on left atrial structure and function.

The coronary slow flow phenomenon (CSFP) is common in coronary angiography, however its impact on left atrial (LA) function is still controversial. This study aims to evaluate the LA structure and function of patients with CSFP using two-dimensional speckle tracking echocardiography (2D-STE). Consecutive patients scheduled for coronary angiography from January 2016 to September 2017 were enrolled in this study. Patients' demographic data, clinical histories, laboratory and angiographic findings were collected and recorded. Diagnostic criteria for CSFP is based on Beltrame et al. proposed in 2012. Meanwhile 139 patients who have no significant stenosis (≤ 40%) and normal blood flow were selected as control. All patients received an echocardiographic examination 24 h before coronary angiography. LA structure and function were measured with echocardiography and 2D-STE. Our results showed that among the 1,954 patients who had received coronary angiography, 512 patients were included in the analysis after the exclusion criteria was implemented. Of those, 101 patients met the CSFP criteria (5.5%). CSFP is mainly seen in LAD (~ 70%). There was no statistical difference in baseline characteristics between the CSFP group and control group, except for a higher proportion of smokers in the CSFP group (P = 0.001). The percentage of monocytes is an independent risk factor for the occurrence of CSFP (P = 0.036) after binary logistic regression analysis. The LA global longitudinal strain (LA-GLS, represents reservoir functions) decreased and LA strain rate at late diastole (LA-SRa, represents booster function) increased in patients with CSFP compared to the control group (P < 0.05). Correlation test of continuous variables by Pearson test suggested that LA-GLS was negatively correlated with TIMI frame count (TFC). We concluded that the percentage of monocytes is an independent risk factor for the CSFP; the LA reservoir and booster functions were impaired in patients with CSFP; LA-GLS is negatively correlated with TFC.

Phytochrome B inhibits darkness-induced hypocotyl adventitious root formation by stabilizing IAA14 and suppressing ARF7 and ARF19.

Adventitious roots (ARs) are an important root type for plants and display a high phenotypic plasticity in response to different environmental stimuli. Previous studies found that dark-light transition can trigger AR formation from the hypocotyl of etiolated Arabidopsis thaliana, which was used as a model for the identification of regulators of AR biogenesis. However, the central regulatory machinery for darkness-induced hypocotyl AR (HAR) remains elusive. Here, we report that photoreceptors suppress HAR biogenesis through regulating the molecular module essential for lateral roots. We found that hypocotyls embedded in soil or in continuous darkness are able to develop HARs, wherein photoreceptors act as negative regulators. Distinct from wound-induced ARs that require WOX11 and WOX12, darkness-induced HARs are fully dependent on ARF7, ARF19, WOX5/7, and LBD16. Further studies established that PHYB interacts with IAA14, ARF7, and ARF9. The interactions stabilize IAA14 and inhibit the transcriptional activities of ARF7 and ARF19 and thus suppress biogenesis of darkness-induced HARs. This finding not only revealed the central machinery controlling HAR biogenesis but also illustrated that AR formation could be initiated by multiple pathways.

Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks.

BACKGROUND: Network alignment is an efficient computational framework in the prediction of protein function and phylogenetic relationships in systems biology. However, most of existing alignment methods focus on aligning PPIs based on static network model, which are actually dynamic in real-world systems. The dynamic characteristic of PPI networks is essential for understanding the evolution and regulation mechanism at the molecular level and there is still much room to improve the alignment quality in dynamic networks. RESULTS: In this paper, we proposed a novel alignment algorithm, Twadn, to align dynamic PPI networks based on a strategy of time warping. We compare Twadn with the existing dynamic network alignment algorithm DynaMAGNA++ and DynaWAVE and use area under the receiver operating characteristic curve and area under the precision-recall curve as evaluation indicators. The experimental results show that Twadn is superior to DynaMAGNA++ and DynaWAVE. In addition, we use protein interaction network of Drosophila to compare Twadn and the static network alignment algorithm NetCoffee2 and experimental results show that Twadn is able to capture timing information compared to NetCoffee2. CONCLUSIONS: Twadn is a versatile and efficient alignment tool that can be applied to dynamic network. Hopefully, its application can benefit the research community in the fields of molecular function and evolution.

A novel algorithm for alignment of multiple PPI networks based on simulated annealing.

Proteins play essential roles in almost all life processes. The prediction of protein function is of significance for the understanding of molecular function and evolution. Network alignment provides a fast and effective framework to automatically identify functionally conserved proteins in a systematic way. However, due to the fast growing genomic data, interactions and annotation data, there is an increasing demand for more accurate and efficient tools to deal with multiple PPI networks. Here, we present a novel global alignment algorithm NetCoffee2 based on graph feature vectors to discover functionally conserved proteins and predict function for unknown proteins. To test the algorithm performance, NetCoffee2 and three other notable algorithms were applied on eight real biological datasets. Functional analyses were performed to evaluate the biological quality of these alignments. Results show that NetCoffee2 is superior to existing algorithms IsoRankN, NetCoffee and multiMAGNA++ in terms of both coverage and consistency. The binary and source code are freely available under the GNU GPL v3 license at https://github.com/screamer/NetCoffee2.

A novel algorithm based on bi-random walks to identify disease-related lncRNAs.

BACKGROUNDS: There is evidence to suggest that lncRNAs are associated with distinct and diverse biological processes. The dysfunction or mutation of lncRNAs are implicated in a wide range of diseases. An accurate computational model can benefit the diagnosis of diseases and help us to gain a better understanding of the molecular mechanism. Although many related algorithms have been proposed, there is still much room to improve the accuracy of the algorithm. RESULTS: We developed a novel algorithm, BiWalkLDA, to predict disease-related lncRNAs in three real datasets, which have 528 lncRNAs, 545 diseases and 1216 interactions in total. To compare performance with other algorithms, the leave-one-out validation test was performed for BiWalkLDA and three other existing algorithms, SIMCLDA, LDAP and LRLSLDA. Additional tests were carefully designed to analyze the parameter effects such as α, ß, l and r, which could help user to select the best choice of these parameters in their own application. In a case study of prostate cancer, eight out of the top-ten disease-related lncRNAs reported by BiWalkLDA were previously confirmed in literatures. CONCLUSIONS: In this paper, we develop an algorithm, BiWalkLDA, to predict lncRNA-disease association by using bi-random walks. It constructs a lncRNA-disease network by integrating interaction profile and gene ontology information. Solving cold-start problem by using neighbors' interaction profile information. Then, bi-random walks was applied to three real biological datasets. Results show that our method outperforms other algorithms in predicting lncRNA-disease association in terms of both accuracy and specificity. AVAILABILITY: https://github.com/screamer/BiwalkLDA.

Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease.

Many studies have suggested that lncRNAs are involved in distinct and diverse biological processes. The mutation of lncRNAs plays a major role in a wide range of diseases. A comprehensive information of lncRNA-disease associations would improve our understanding of the underlying molecular mechanism that can explain the development of disease. However, the discovery of the relationship between lncRNA and disease in biological experiment is costly and time-consuming. Although many computational algorithms have been proposed in the last decade, there still exists much room to improve because of diverse computational limitations. In this paper, we proposed a deep-learning framework, NNLDA, to predict potential lncRNA-disease associations. We compared it with other two widely-used algorithms on a network with 205,959 interactions between 19,166 lncRNAs and 529 diseases. Results show that NNLDA outperforms other existing algorithm in the prediction of lncRNA-disease association. Additionally, NNLDA can be easily applied to large-scale datasets using the technique of mini-batch stochastic gradient descent. To our best knowledge, NNLDA is the first algorithm that uses deep neural networks to predict lncRNA-disease association. The source code of NNLDA can be freely accessed at https://github.com/gao793583308/NNLDA.

MD-SVM: a novel SVM-based algorithm for the motif discovery of transcription factor binding sites.

BACKGROUND: Transcription factors (TFs) play important roles in the regulation of gene expression. They can activate or block transcription of downstream genes in a manner of binding to specific genomic sequences. Therefore, motif discovery of these binding preference patterns is of central significance in the understanding of molecular regulation mechanism. Many algorithms have been proposed for the identification of transcription factor binding sites. However, it remains a challengeable problem. RESULTS: Here, we proposed a novel motif discovery algorithm based on support vector machine (MD-SVM) to learn a discriminative model for TF binding sites. MD-SVM firstly obtains position weight matrix (PWM) from a set of training datasets. Then it translates the MD problem into a computational framework of multiple instance learning (MIL). It was applied to several real biological datasets. Results show that our algorithm outperforms MI-SVM in terms of both accuracy and specificity. CONCLUSIONS: In this paper, we modeled the TF motif discovery problem as a MIL optimization problem. The SVM algorithm was adapted to discriminate positive and negative bags of instances. Compared to other svm-based algorithms, MD-SVM show its superiority over its competitors in term of ROC AUC. Hopefully, it could be of benefit to the research community in the understanding of molecular functions of DNA functional elements and transcription factors.

WebNetCoffee: a web-based application to identify functionally conserved proteins from Multiple PPI networks.

BACKGROUND: The discovery of functionally conserved proteins is a tough and important task in system biology. Global network alignment provides a systematic framework to search for these proteins from multiple protein-protein interaction (PPI) networks. Although there exist many web servers for network alignment, no one allows to perform global multiple network alignment tasks on users' test datasets. RESULTS: Here, we developed a web server WebNetcoffee based on the algorithm of NetCoffee to search for a global network alignment from multiple networks. To build a series of online test datasets, we manually collected 218,339 proteins, 4,009,541 interactions and many other associated protein annotations from several public databases. All these datasets and alignment results are available for download, which can support users to perform algorithm comparison and downstream analyses. CONCLUSION: WebNetCoffee provides a versatile, interactive and user-friendly interface for easily running alignment tasks on both online datasets and users' test datasets, managing submitted jobs and visualizing the alignment results through a web browser. Additionally, our web server also facilitates graphical visualization of induced subnetworks for a given protein and its neighborhood. To the best of our knowledge, it is the first web server that facilitates the performing of global alignment for multiple PPI networks. AVAILABILITY: http://www.nwpu-bioinformatics.com/WebNetCoffee.

KF-finder: identification of key factors from host-microbial networks in cervical cancer.

BACKGROUND: The human body is colonized by a vast number of microbes. Microbiota can benefit many normal life processes, but can also cause many diseases by interfering the regular metabolism and immune system. Recent studies have demonstrated that the microbial community is closely associated with various types of cell carcinoma. The search for key factors, which also refer to cancer causing agents, can provide an important clue in understanding the regulatory mechanism of microbiota in uterine cervix cancer. RESULTS: In this paper, we investigated microbiota composition and gene expression data for 58 squamous and adenosquamous cell carcinoma. A host-microbial covariance network was constructed based on the 16s rRNA and gene expression data of the samples, which consists of 259 abundant microbes and 738 differentially expressed genes (DEGs). To search for risk factors from host-microbial networks, the method of bi-partite betweenness centrality (BpBC) was used to measure the risk of a given node to a certain biological process in hosts. A web-based tool KF-finder was developed, which can efficiently query and visualize the knowledge of microbiota and differentially expressed genes (DEGs) in the network. CONCLUSIONS: Our results suggest that prevotellaceade, tissierellaceae and fusobacteriaceae are the most abundant microbes in cervical carcinoma, and the microbial community in cervical cancer is less diverse than that of any other boy sites in health. A set of key risk factors anaerococcus, hydrogenophilaceae, eubacterium, PSMB10, KCNIP1 and KRT13 have been identified, which are thought to be involved in the regulation of viral response, cell cycle and epithelial cell differentiation in cervical cancer. It can be concluded that permanent changes of microbiota composition could be a major force for chromosomal instability, which subsequently enables the effect of key risk factors in cancer. All our results described in this paper can be freely accessed from our website at http://www.nwpu-bioinformatics.com/KF-finder/ .