PAI-1 Regulates the Cytoskeleton and Intrinsic Stiffness of Vascular Smooth Muscle Cells.

BACKGROUND: Plasma concentration of PAI-1 (plasminogen activator inhibitor-1) correlates with arterial stiffness. Vascular smooth muscle cells (SMCs) express PAI-1, and the intrinsic stiffness of SMCs is a major determinant of total arterial stiffness. We hypothesized that PAI-1 promotes SMC stiffness by regulating the cytoskeleton and that pharmacological inhibition of PAI-1 decreases SMC and aortic stiffness. METHODS: PAI-039, a specific inhibitor of PAI-1, and small interfering RNA were used to inhibit PAI-1 expression in cultured human SMCs. Effects of PAI-1 inhibition on SMC stiffness, F-actin (filamentous actin) content, and cytoskeleton-modulating enzymes were assessed. WT (wild-type) and PAI-1-deficient murine SMCs were used to determine PAI-039 specificity. RNA sequencing was performed to determine the effects of PAI-039 on SMC gene expression. In vivo effects of PAI-039 were assessed by aortic pulse wave velocity. RESULTS: PAI-039 significantly reduced intrinsic stiffness of human SMCs, which was accompanied by a significant decrease in cytoplasmic F-actin content. PAI-1 gene knockdown also decreased cytoplasmic F-actin. PAI-1 inhibition significantly increased the activity of cofilin, an F-actin depolymerase, in WT murine SMCs, but not in PAI-1-deficient SMCs. RNA-sequencing analysis suggested that PAI-039 upregulates AMPK (AMP-activated protein kinase) signaling in SMCs, which was confirmed by Western blotting. Inhibition of AMPK prevented activation of cofilin by PAI-039. In mice, PAI-039 significantly decreased aortic stiffness and tunica media F-actin content without altering the elastin or collagen content. CONCLUSIONS: PAI-039 decreases intrinsic SMC stiffness and cytoplasmic stress fiber content. These effects are mediated by AMPK-dependent activation of cofilin. PAI-039 also decreases aortic stiffness in vivo. These findings suggest that PAI-1 is an important regulator of the SMC cytoskeleton and that pharmacological inhibition of PAI-1 has the potential to prevent and treat cardiovascular diseases involving arterial stiffening.

Landsat observations of total suspended solids concentrations in the Pearl River Estuary, China, over the past 36 years.

Information on long-term trends in total suspended solids (TSS) is critical for assessing aquatic ecosystems. However, the long-term patterns of TSS concentration (CTSS) and its latent drivers have not been well investigated. In this study, we developed and validated three semi-analysis algorithms for deriving CTSS using Landsat images. Subsequently, the long-term trends in CTSS in the Pearl River Estuary (PRE) from 1987 to 2022 and the driving factors were clarified. The developed algorithms yielded excellent performance in estimating CTSS, with mean absolute percentage errors <25% and root mean square errors of <13 mg/L. Long-term Landsat observations showed an overall decreasing trend and significant spatiotemporal dynamics of the CTSS in the PRE from 1987 to 2022. The analysis of driving factors suggested that industrial sewage, cropland, forests and grasslands, and built-up land were the four potential driving forces that explained 87.81% of the long-term variation in CTSS. This study not only provides 36-year recorded datasets of CTSS in estuary water, but also offers new insights into the complex mechanisms that regulate CTSS spatiotemporal dynamics for water resource management.

Unveiling the multicomponent phase separation through molecular dynamics simulation and graph theory.

Biomolecular condensates formed by multicomponent phase separation play crucial roles in diverse cellular processes. Accurate assessment of individual-molecule contributions to condensate formation and precise characterization of their spatial organization within condensates are crucial for understanding the underlying mechanism of phase separation. Using molecular dynamics simulations and graph theoretical analysis, we demonstrated quantitatively the significant roles of cation-π and π-π interactions mediated by aromatic residues and arginine in the formation of condensates in polypeptide systems. Our findings reveal temperature and chain length-dependent alterations in condensate network parameters, such as the number of condensate network layers, and changes in aggregation and connectivity. Notably, we observe a transition between assortativity and disassortativity in the condensate network. Moreover, polypeptides W, Y, F, and R consistently promote condensate formation, while the contributions of other charged and two polar polypeptides (Q and N) to condensate formation depend on temperature and chain length. Furthermore, polyadenosine and polyguanosine can establish stable connections with aromatic and R polypeptides, resulting in the reduced involvement of K, E, D, Q, and N in phase separation. Overall, this study provides a distinctive, precise, and quantitative approach to characterize the multicomponent phase separation.

The value of fibrinogen combined with D-dimer and neonatal weight in predicting postpartum hemorrhage in vaginal delivery.

OBJECTIVES: The purpose of this study was to explore whether fibrinogen (Fib) can be used as a predictor of postpartum hemorrhage (PPH) in parturients with vaginal delivery, and the value of combining Fib with other indexes to predict postpartum hemorrhage in vaginal delivery. METHODS: A total of 207 parturients who delivered via vagina were divided into PPH group (n=102) and non-PPH group (n=105). The PPH group was further divided into mild PPH group and severe PPH group. The differences of Fib, platelet (PLT), mean platelet volume (MPV), platelet distribution width (PDW), D-dimer (D-D), hemoglobin (HGB) and neonatal weight (Nw) between the two groups were compared to explore the significance of these indexes in predicting PPH. RESULTS: Fib, PLT and PDW in PPH group were significantly lower than those in non-PPH group, while D-D and Nw in PPH group were significantly higher than those in non-PPH group. In the binary logistic regression model, we found that Fib, D-D and Nw were independently related to PPH. The risk of PPH increased by 9.87 times for every 1 g/L decrease in Fib. The cut-off value of Fib is 4.395 (sensitivity 0.705, specificity 0.922). The AUC value of PPH predicted by Fib combined with D-D and Nw was significantly higher than that of PPH predicted by Fib (p<0.05, 95 % CI 0.00313-0.0587). CONCLUSIONS: Fib, D-D and Nw have good predictive value for PPH of vaginal delivery, among which Fib is the best. The combination of three indexes of Fib, D-D and Nw can predict PPH more systematically and comprehensively, and provide a basis for clinical prevention and treatment of PPH.

Effect of maleylation and denaturation of human serum albumin on its interaction with scavenger receptors.

The specific recognition of serum proteins by scavenger receptors is critical and fundamental in many biological processes. However, the underlying mechanism of scavenger receptor-serum protein interaction remains elusive. In this work, taking scavenger receptors class A1 (SR-A1) as an example, we systematically investigate its interaction with human serum albumin (HSA) at different states through a combination of molecular docking and all-atom molecular dynamics simulations. It is found that native HSA can moderately bind to collagen-like (CL) region or scavenger receptor cysteine-rich (SRCR) region, with both electrostatic (ELE) and van der Waals (VDW) interactions, playing important roles. After maleylation, the binding energy, particularly the ELE energy, between HSA and CL region is significantly enhanced, while the binding energy between HSA and SRCR region remains nearly unchanged. Additionally, we also observe that unfolding of the secondary structures in HSA leads to a larger contact surface area between denatured HSA and CL region, but has little impact on the HSA-SRCR region interaction. Therefore, similar to maleylated HSA, denatured HSA is also more likely to bind to the CL region of SR-A1.

The Allele Catalog Tool: a web-based interactive tool for allele discovery and analysis.

BACKGROUND: The advancement of sequencing technologies today has made a plethora of whole-genome re-sequenced (WGRS) data publicly available. However, research utilizing the WGRS data without further configuration is nearly impossible. To solve this problem, our research group has developed an interactive Allele Catalog Tool to enable researchers to explore the coding region allelic variation present in over 1,000 re-sequenced accessions each for soybean, Arabidopsis, and maize. RESULTS: The Allele Catalog Tool was designed originally with soybean genomic data and resources. The Allele Catalog datasets were generated using our variant calling pipeline (SnakyVC) and the Allele Catalog pipeline (AlleleCatalog). The variant calling pipeline is developed to parallelly process raw sequencing reads to generate the Variant Call Format (VCF) files, and the Allele Catalog pipeline takes VCF files to perform imputations, functional effect predictions, and assemble alleles for each gene to generate curated Allele Catalog datasets. Both pipelines were utilized to generate the data panels (VCF files and Allele Catalog files) in which the accessions of the WGRS datasets were collected from various sources, currently representing over 1,000 diverse accessions for soybean, Arabidopsis, and maize individually. The main features of the Allele Catalog Tool include data query, visualization of results, categorical filtering, and download functions. Queries are performed from user input, and results are a tabular format of summary results by categorical description and genotype results of the alleles for each gene. The categorical information is specific to each species; additionally, available detailed meta-information is provided in modal popups. The genotypic information contains the variant positions, reference or alternate genotypes, the functional effect classes, and the amino-acid changes of each accession. Besides that, the results can also be downloaded for other research purposes. CONCLUSIONS: The Allele Catalog Tool is a web-based tool that currently supports three species: soybean, Arabidopsis, and maize. The Soybean Allele Catalog Tool is hosted on the SoyKB website ( https://soykb.org/SoybeanAlleleCatalogTool/ ), while the Allele Catalog Tool for Arabidopsis and maize is hosted on the KBCommons website ( https://kbcommons.org/system/tools/AlleleCatalogTool/Zmays and https://kbcommons.org/system/tools/AlleleCatalogTool/Athaliana ). Researchers can use this tool to connect variant alleles of genes with meta-information of species.

Multiple miRNA Detection through a Suspended Microbead Array Encoded by Triple-Color Upconversion Luminescent Nanotags via Bi-Beam Splitter Hybrid-Multitrap Optical Tweezers.

In recent years, optical tweezers have become a novel tool for biodetection, and to improve the inefficiency of a single trap, the development of multitraps is required. Herein, we constructed a set of hybrid multitrap optical tweezers with the balance of stability and flexibility by the combination of two different beam splitters, a diffraction optical element (DOE) and galvano mirrors (GMs), to capture polystyrene (PS) microbeads in aqueous solutions to create an 18-trap suspended array. A sandwich hybridization strategy of DNA-miRNA-DNA was adopted to detect three kinds of target miRNAs associated with triple negative breast cancer (TNBC), in which different upconversion nanoparticles (UCNPs) with red, green, and blue emissions were applied as luminescent tags to encode the carrier PS microbeads to further indicate the levels of the targets. With encoded luminescent microbeads imaged by a three-channel microscopic system, the biodetection displayed high sensitivity with low limits of detection (LODs) of 0.27, 0.32, and 0.33 fM and exceptional linear ranges of 0.5 fM to 1 nM, 0.7 fM to 1 nM, and 1 fM to 1 nM for miR-343-3p, miR-155, and miR-199a-5p, respectively. In addition, this bead-based assay method was demonstrated to have the potential for being applied in patients' serum by satisfactory standard addition recovery experiment results.

Long-term dynamics and drivers of particulate phosphorus concentration in eutrophic lake Chaohu, China.

Particulate phosphorus (PP) plays an important biological role in the eutrophication process, and is thus an important water quality parameter for assessing climatic change and anthropogenic activity factors that affect aquatic ecosystems. Here, we used 20-year Moderate Resolution Imaging Spectroradiometer (MODIS) data to explore the patterns and trends of PP concentration (CPP) in eutrophic Lake Chaohu based on a new empirical model. The validation results indicated that the developed model performed satisfactorily in estimating CPP, with a mean absolute percentage error of 31.89% and root mean square error of 0.022 mg/L. Long-term MODIS observations (2000-2019) revealed that the CPP of Lake Chaohu has experienced an overall increasing trend and distinct spatiotemporal heterogeneity. The driving factor analysis revealed that the chemical fertilizer consumption, municipal wastewater, industrial sewage, precipitation, and air temperature were the five potential driving factors and collectively explained more than 81% of the long-term variation in CPP. This study provides the long-term datasets of CPP in inland waters and new insights for future water eutrophication control and restoration efforts.

G2PDeep: a web-based deep-learning framework for quantitative phenotype prediction and discovery of genomic markers.

G2PDeep is an open-access web server, which provides a deep-learning framework for quantitative phenotype prediction and discovery of genomics markers. It uses zygosity or single nucleotide polymorphism (SNP) information from plants and animals as the input to predict quantitative phenotype of interest and genomic markers associated with phenotype. It provides a one-stop-shop platform for researchers to create deep-learning models through an interactive web interface and train these models with uploaded data, using high-performance computing resources plugged at the backend. G2PDeep also provides a series of informative interfaces to monitor the training process and compare the performance among the trained models. The trained models can then be deployed automatically. The quantitative phenotype and genomic markers are predicted using a user-selected trained model and the results are visualized. Our state-of-the-art model has been benchmarked and demonstrated competitive performance in quantitative phenotype predictions by other researchers. In addition, the server integrates the soybean nested association mapping (SoyNAM) dataset with five phenotypes, including grain yield, height, moisture, oil, and protein. A publicly available dataset for seed protein and oil content has also been integrated into the server. The G2PDeep server is publicly available at http://g2pdeep.org. The Python-based deep-learning model is available at https://github.com/shuaizengMU/G2PDeep_model.

Structural transition of parenthood among Chinese nulliparous couples with planned pregnancies, 2013-2019.

BACKGROUND: The postponement of parenthood is a global public health issue that has received attention of many public health experts. However, few studies have investigated the postponement in marriage age, marriage and conception interval, and pregnancy age in terms of demographic and regional heterogenicities. METHODS: This is a cross-sectional, registry-based study, and a total of 13 894 601 nulliparous couples who participated in the National Free Pre-Pregnancy Check-ups Project and became pregnant during 2013-2019 were included. We calculated annual percentage change and forest plots for marriage age, marriage and conception interval, and pregnancy age. RESULTS: Late marriage (marriage age ≥ 35 years), long marriage and conception interval (marriage and conception interval ≥ 2 years), and advanced pregnancy (pregnancy age ≥ 35 years) increased from 1.20%, 22.01%, and 1.88% in 2013 to 1.69%, 32.75%, and 2.79% in 2019, respectively. The corresponding annual percentage changes were 6.55%, 8.44%, and 8.17%. Participants without higher education had a higher annual percentage change, but comparable prevalence for long marriage and conception interval with participants with higher education. Participants residing in second- or new first-tier cities, and the northeast of China who had a higher prevalence of parenthood postponement also had higher corresponding annual percentage changes. CONCLUSIONS: Structural postponement of parenthood with demographic and regional heterogenicities was observed among Chinese nulliparous couples with planned pregnancies during 2013-2019. Inclusive and comprehensive parenting support should be developed and implemented in mainland China to minimize the negative health effects arising from the postponement, especially for couples without higher education and living in new first/second-tier cities or the northeast China.

Circ-LRP1B functions as a competing endogenous RNA to regulate proliferation, apoptosis and oxidative stress of LPS-induced human C28/I2 chondrocytes.

Circular RNAs (circRNAs) are crucial for the pathogenesis of human diseases, including osteoarthritis (OA). Here, we set to elucidate the biological action of circ-LRP1B in OA pathogenesis. Human C28/I2 chondrocytes were stimulated by lipopolysaccharide (LPS). Circ-LRP1B, nuclear factor, erythroid 2 like 1 (NRF1) and microRNA (miR)-34a-5p were quantified by quantitative real-time PCR (qRT-PCR) or immunoblotting. Cell viability, proliferation, and apoptosis abilities were gauged by MTT, 5-Ethynyl-2'-Deoxyuridine (EdU) staining, and flow cytometry assays, respectively. Direct relationship between miR-34a-5p and circ-LRP1B or NRF1 was validated by RNA pull-down and dual-luciferase reporter assays. Circ-LRP1B was found to be underexpressed in OA cartilage and LPS-stimulated C28/I2 chondrocytes. Enforced expression of circ-LRP1B promoted cell proliferation, and repressed apoptosis and oxidative stress, as well as impacted OA-specific hallmarks expression of LPS-stimulated C28/I2 cells. NRF1 was identified as a downstream effector of circ-LRP1B function. Moreover, NRF1 was identified as a miR-34a-5p target in LPS-stimulated C28/I2 cells. Circ-LRP1B acted as a competing endogenous RNA (ceRNA) for miR-34a-5p to involve the post-transcriptional regulation of NRF1 expression. Furthermore, the effects of circ-LRP1B overexpression partly depended on the reduction of available miR-34a-5p. These findings demonstrate that circ-LRP1B functions as a ceRNA to regulate the proliferation, apoptosis and oxidative stress of LPS-stimulated human C28/I2 chondrocytes by miR-34a-5p/NRF1 network.

Differential regulation of flower transpiration during abiotic stress in annual plants.

Heat waves occurring during droughts can have a devastating impact on yield, especially if they happen during the flowering and seed set stages of the crop cycle. Global warming and climate change are driving an alarming increase in the frequency and intensity of combined drought and heat stress episodes, critically threatening global food security. Because high temperature is detrimental to reproductive processes, essential for plant yield, we measured the inner temperature, transpiration, sepal stomatal aperture, hormone concentrations and transcriptomic response of closed soybean flowers developing on plants subjected to a combination of drought and heat stress. Here, we report that, during a combination of drought and heat stress, soybean plants prioritize transpiration through flowers over transpiration through leaves by opening their flower stomata, while keeping their leaf stomata closed. This acclimation strategy, termed 'differential transpiration', lowers flower inner temperature by about 2-3°C, protecting reproductive processes at the expense of vegetative tissues. Manipulating stomatal regulation, stomatal size and/or stomatal density of flowers could serve as a viable strategy to enhance the yield of different crops and mitigate some of the current and future impacts of global warming and climate change on agriculture.

Effect of the Graphene Nanosheet on Functions of the Spike Protein in Open and Closed States: Comparison between SARS-CoV-2 Wild Type and the Omicron Variant.

The spread of coronavirus disease 2019 caused by SARS-CoV-2 and its variants has become a global health crisis. Although there were many attempts to use nanomaterials-based devices to fight against SARS-CoV-2, it still remains elusive as to how the nanomaterials interact with SARS-CoV-2 and affect its biofunctions. Here, taking the graphene nanosheet (GN) as the model nanomaterial, we investigate its interaction with the spike protein in both WT and Omicron by molecular simulations. In the closed state, the GN can insert into the region between the receptor binding domain (RBD) and the N-terminal domain (NTD) in both wild type (WT) and Omicron, which keeps the RBD in the down conformation. In the open state, the GN can hamper the binding of up RBD to ACE2 in WT, but it has little impact on up RBD and, even worse, stimulates the down-to-up transition of down RBDs in Omicron. Moreover, the GN can insert in the vicinity of the fusion peptide in both WT and Omicron and prevents the detachment of S1 from the whole spike protein. The present study reveals the effect of the SARS-CoV-2 variant on the nanomaterial-spike protein interaction, which informs prospective efforts to design functional nanomaterials against SARS-CoV-2.

Bioinformatics for plant and agricultural discoveries in the age of multiomics: A review and case study of maize nodal root growth under water deficit.

Advances in next-generation sequencing and other high-throughput technologies have facilitated multiomics research, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and phenomics. The resultant emerging multiomics data have brought new challenges as well as opportunities, as seen in the plant and agriculture science domains. We reviewed several bioinformatic and computational methods, models, and platforms, and we have highlighted some of our in-house developed efforts aimed at multiomics data analysis, integration, and management issues faced by the research community. A case study using multiomics datasets generated from our studies of maize nodal root growth under water deficit stress demonstrates the power of these datasets and some other publicly available tools. This analysis also sheds light on the landscape of such applied bioinformatic tools currently available for plant and crop science studies and introduces emerging trends and how they may affect the future.

Soybean transporter database: A comprehensive database for identification and exploration of natural variants in soybean transporter genes.

Transporters, a class of membrane proteins that facilitate exchange of solutes including diverse molecules and ions across the cellular membrane, are vital component for the survival of all organisms. Understanding plant transporters is important to get insight of the basic cellular processes, physiology, and molecular mechanisms including nutrient uptake, signaling, response to external stress, and many more. In this regard, extensive analysis of transporters predicted in soybean and other plant species was performed. In addition, an integrated database for soybean transporter protein, SoyTD, was developed that will facilitate the identification, classification, and extensive characterization of transporter proteins by integrating expression, gene ontology, conserved domain and motifs, gene structure organization, and chromosomal distribution features. A comprehensive analysis was performed to identify highly confident transporters by integrating various prediction tools. Initially, 7541 transmembrane (TM) proteins were predicted in the soybean genome; out of these, 3306 non-redundant transporter genes carrying two or more transmembrane domains were selected for further analysis. The identified transporter genes were classified according to a standard transporter classification (TC) system. Comparative analysis of transporter genes among 47 plant genomes provided insights into expansion and duplication of transporter genes in land plants. The whole genome resequencing (WGRS) and tissue-specific transcriptome datasets of soybean were integrated to investigate the natural variants and expression profile associated with transporter(s) of interest. Overall, SoyTD provides a comprehensive interface to study genetic and molecular function of soybean transporters. SoyTD is publicly available at http://artemis.cyverse.org/soykb_dev/SoyTD/.

Functional analysis of PGI1 and ZWF1 in thermotolerant yeast Kluyveromyces marxianus.

Glycolysis and the pentose phosphate pathway (PPP) are two basic metabolic pathways that are simultaneously present in yeasts. As the main pathway in most species, the glycolysis provides ATP and NADH for cell metabolism while PPP, as a complementary pathway, supplies NADPH. In this study, the performance of Kluyveromyces marxianus using glycolysis or PPP were studied through the disruption of PGI1 or ZWF1 gene, respectively. K. marxianus using glycolysis as the only pathway showed higher ethanol production ability than that of the Kluyveromyces lactis zwf1Δ mutant; K. marxianus using only PPP showed more robustness than that of Saccharomyces cerevisiae pgi1Δ mutant. Additionally, K. marxianus pgi1Δ strain accumulated much more intracellular NADPH than the wild type strain and co-utilized glucose and xylose more effectively. These findings suggest that phosphoglucose isomerase participates in the regulation of the repression of glucose on xylose utilization in K. marxianus. The NADPH/NADP+ ratio, dependent on the activity of the PPP, regulated the expression of multiple genes related to NADPH metabolism in K. marxianus (including NDE1, NDE2, GLR1, and GDP1). Since K. marxianus is considered a promising host in industrial biotechnology to produce renewable chemicals from plant biomass feedstocks, our research showed the potential of the thermotolerant K. marxianus to produce NADP(H)-dependent chemical synthesis from multiple feedstocks. KEY POINTS: • The function of PGI1 and ZWF1 in K. marxianus has been analyzed in this study. • K. marxianus zwf1Δ strain produced ethanol but with decreased productivity. • K. marxianus pgi1Δ strain grew with glucose and accumulated NADPH. • K. marxianus pgi1Δ strain released glucose repression on xylose utilization.

Morphology-dependent interfacial interactions of Fe2O3 with Ag nanoparticles for determining the catalytic reduction of p-nitrophenol.

In this work, we fabricated three kinds of Ag/Fe2O3 model catalysts with different morphologies to study the interfacial interactions between Ag and Fe2O3, and how they affected the catalytic activity in hydrogenation of p-nitrophenol was explored. The hydrothermal method was used to synthesize the metal oxide supported silver catalyst, with various morphologies including nanoplates (NPs), nanospheres (NSs), and nanocubes (NCs). The crystal structure, morphology and surface elements of the composite were investigated by various measurements, such as X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). The catalytic activity was also evaluated by the reduction of p-nitrophenol to p-aminophenol. It was found that the activities of the above catalysts varied with the morphology of the support. Among them, Ag/Fe2O3 NPs promoted the highest performance, Ag/Fe2O3 NSs were slightly inferior, and Ag/Fe2O3 NCs were the worst. At last, we ascribed the remarkable activity of Ag/Fe2O3 NPs to the strong metal-support interactions between Ag and Fe2O3.

Knowledge Base Commons (KBCommons) v1.1: a universal framework for multi-omics data integration and biological discoveries.

BACKGROUND: Knowledge Base Commons (KBCommons) v1.1 is a universal and all-inclusive web-based framework providing generic functionalities for storing, sharing, analyzing, exploring, integrating and visualizing multiple organisms' genomics and integrative omics data. KBCommons is designed and developed to integrate diverse multi-level omics data and to support biological discoveries for all species via a common platform. METHODS: KBCommons has four modules including data storage, data processing, data accessing, and web interface for data management and retrieval. It provides a comprehensive framework for new plant-specific, animal-specific, virus-specific, bacteria-specific or human disease-specific knowledge base (KB) creation, for adding new genome versions and additional multi-omics data to existing KBs, and for exploring existing datasets within current KBs. RESULTS: KBCommons has an array of tools for data visualization and data analytics such as multiple gene/metabolite search, gene family/Pfam/Panther function annotation search, miRNA/metabolite/trait/SNP search, differential gene expression analysis, and bulk data download capacity. It contains a highly reliable data privilege management system to make users' data publicly available easily and to share private or pre-publication data with members in their collaborative groups safely and securely. It allows users to conduct data analysis using our in-house developed workflow functionalities that are linked to XSEDE high performance computing resources. Using KBCommons' intuitive web interface, users can easily retrieve genomic data, multi-omics data and analysis results from workflow according to their requirements and interests. CONCLUSIONS: KBCommons addresses the needs of many diverse research communities to have a comprehensive multi-level OMICS web resource for data retrieval, sharing, analysis and visualization. KBCommons can be publicly accessed through a dedicated link for all organisms at http://kbcommons.org/.

12-Hydroxy-Jasmonoyl-l-Isoleucine Is an Active Jasmonate That Signals through CORONATINE INSENSITIVE 1 and Contributes to the Wound Response in Arabidopsis.

12-hydroxy-jasmonoyl-isoleucine (12OH-JA-Ile) is a metabolite in the catabolic pathway of the plant hormone jasmonate, and is synthesized by the cytochrome P450 subclade 94 enzymes. Contrary to the well-established function of jasmonoyl-isoleucine (JA-Ile) as the endogenous bioactive form of jasmonate, the function of 12OH-JA-Ile is unclear. Here, the potential role of 12OH-JA-Ile in jasmonate signaling and wound response was investigated. Exogenous application of 12OH-JA-Ile mimicked several JA-Ile effects including marker gene expression, anthocyanin accumulation and trichome induction in Arabidopsis thaliana. Genome-wide transcriptomics and untargeted metabolite analyses showed large overlaps between those affected by 12OH-JA-Ile and JA-Ile. 12OH-JA-Ile signaling was blocked by mutation in CORONATINE INSENSITIVE 1. Increased anthocyanin accumulation by 12OH-JA-Ile was additionally observed in tomato and sorghum, and was disrupted by the COI1 defect in tomato jai1 mutant. In silico ligand docking predicted that 12OH-JA-Ile can maintain many of the key interactions with COI1-JAZ1 residues identified earlier by crystal structure studies using JA-Ile as ligand. Genetic alternation of jasmonate metabolic pathways in Arabidopsis to deplete both JA-Ile and 12OH-JA-Ile displayed enhanced jasmonate deficient wound phenotypes and was more susceptible to insect herbivory than that depleted in only JA-Ile. Conversely, mutants overaccumulating 12OH-JA-Ile showed intensified wound responses compared with wild type with similar JA-Ile content. These data are indicative of 12OH-JA-Ile functioning as an active jasmonate signal and contributing to wound and defense response in higher plants.

MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction.

MOTIVATION: Computational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. Deep learning as the cutting-edge machine learning method has the ability to automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of phosphorylation site prediction. RESULTS: We present MusiteDeep, the first deep-learning framework for predicting general and kinase-specific phosphorylation sites. MusiteDeep takes raw sequence data as input and uses convolutional neural networks with a novel two-dimensional attention mechanism. It achieves over a 50% relative improvement in the area under the precision-recall curve in general phosphorylation site prediction and obtains competitive results in kinase-specific prediction compared to other well-known tools on the benchmark data. AVAILABILITY AND IMPLEMENTATION: MusiteDeep is provided as an open-source tool available at https://github.com/duolinwang/MusiteDeep. CONTACT: xudong@missouri.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.