PEA-m6A: an ensemble learning framework for accurately predicting N6-methyladenosine modifications in plants.

N 6-methyladenosine (m6A), which is the mostly prevalent modification in eukaryotic mRNAs, is involved in gene expression regulation and many RNA metabolism processes. Accurate prediction of m6A modification is important for understanding its molecular mechanisms in different biological contexts. However, most existing models have limited range of application and are species-centric. Here we present PEA-m6A, a unified, modularized and parameterized framework that can streamline m6A-Seq data analysis for predicting m6A-modified regions in plant genomes. The PEA-m6A framework builds ensemble learning-based m6A prediction models with statistic-based and deep learning-driven features, achieving superior performance with an improvement of 6.7% to 23.3% in the area under precision-recall curve compared with state-of-the-art regional-scale m6A predictor WeakRM in 12 plant species. Especially, PEA-m6A is capable of leveraging knowledge from pretrained models via transfer learning, representing an innovation in that it can improve prediction accuracy of m6A modifications under small-sample training tasks. PEA-m6A also has a strong capability for generalization, making it suitable for application in within- and cross-species m6A prediction. Overall, this study presents a promising m6A prediction tool, PEA-m6A, with outstanding performance in terms of its accuracy, flexibility, transferability, and generalization ability. PEA-m6A has been packaged using Galaxy and Docker technologies for ease of use and is publicly available at https://github.com/cma2015/PEA-m6A.

Global hypermethylation of the N6-methyladenosine RNA modification associated with apple heterografting.

Grafting can facilitate better scion performance and is widely used in plants. Numerous studies have studied the involvement of mRNAs, small RNAs, and epigenetic regulations in the grafting process. However, it remains unclear whether the mRNA N6-methyladenosine (m6A) modification participates in the apple (Malus x domestica Borkh.) grafting process. Here, we decoded the landscape of m6A modification profiles in 'Golden delicious' (a cultivar, Gd) and Malus prunifolia 'Fupingqiuzi' (a unique rootstock with resistance to environmental stresses, Mp), as well as their heterografted and self-grafted plants. Interestingly, global hypermethylation of m6A occurred in both heterografted scion and rootstock compared with their self-grafting controls. Gene Ontology (GO) term enrichment analysis showed that grafting-induced differentially m6A-modified genes were mainly involved in RNA processing, epigenetic regulation, stress response, and development. Differentially m6A-modified genes harboring expression alterations were mainly involved in various stress responses and fatty acid metabolism. Furthermore, grafting-induced mobile mRNAs with m6A and gene expression alterations mainly participated in ABA synthesis and transport (e.g. carotenoid cleavage dioxygenase 1 [CCD1] and ATP-binding cassette G22 [ABCG22]) and abiotic and biotic stress responses, which might contribute to the better performance of heterografted plants. Additionally, the DNA methylome analysis also demonstrated the DNA methylation alterations during grafting. Downregulated expression of m6A methyltransferase gene MdMTA (ortholog of METTL3) in apples induced the global m6A hypomethylation and distinctly activated the expression level of DNA demethylase gene MdROS1 (REPRESSOR OF SILENCING 1) showing the possible association between m6A and 5mC methylation in apples. Our results reveal the m6A modification profiles in the apple grafting process and enhance our understanding of the m6A regulatory mechanism in plant biological processes.

Synthesis of Chiral Diarylmethylamides via Catalytic Asymmetric Aza-Michael Addition of Amides to ortho-Quinomethanes.

Chiral diarylmethylamides are a privileged skeleton in many bioactive molecules. However, the enantioselective synthesis of such molecules remains a long-standing challenge in organic synthesis. Herein, we report a chiral bifunctional squaramide catalyzed asymmetric aza-Michael addition of amides to in situ generated ortho-quinomethanes, affording enantioenriched diarylmethylamides in good yields with excellent enantioselectivities. This work not only provides a new strategy for the construction of the diarylmethylamides but also represents the practicability of amides as nitrogen-nucleophiles in asymmetric organocatalysis.

Diastereo- and Enantioselective Synthesis of Eight-Membered N-Heterocycles from Benzofuran-Derived Azadienes and Ynones.

Enantioselective synthesis of eight-membered N-heterocycles represents a long-standing challenge in organic synthesis. Here, by combining the squaramide and DBU catalysis, a sequential asymmetric conjugate addition/cyclization reaction between benzofuran-derived azadienes and ynones has been well-developed, providing straightforward access to chiral eight-membered N-heterocycles in high yields with stereoselectivities. This protocol features the use of a bifunctional squaramide catalyst for controlling the enantioselectivity of products, while the DBU is utilized to achieve intramolecular cyclization and improve the diastereoselectivity of products.

Exploring transcriptional switches from pairwise, temporal and population RNA-Seq data using deepTS.

Transcriptional switch (TS) is a widely observed phenomenon caused by changes in the relative expression of transcripts from the same gene, in spatial, temporal or other dimensions. TS has been associated with human diseases, plant development and stress responses. Its investigation is often hampered by a lack of suitable tools allowing comprehensive and flexible TS analysis for high-throughput RNA sequencing (RNA-Seq) data. Here, we present deepTS, a user-friendly web-based implementation that enables a fully interactive, multifunctional identification, visualization and analysis of TS events for large-scale RNA-Seq datasets from pairwise, temporal and population experiments. deepTS offers rich functionality to streamline RNA-Seq-based TS analysis for both model and non-model organisms and for those with or without reference transcriptome. The presented case studies highlight the capabilities of deepTS and demonstrate its potential for the transcriptome-wide TS analysis of pairwise, temporal and population RNA-Seq data. We believe deepTS will help research groups, regardless of their informatics expertise, perform accessible, reproducible and collaborative TS analyses of large-scale RNA-Seq data.

CAFU: a Galaxy framework for exploring unmapped RNA-Seq data.

A widely used approach in transcriptome analysis is the alignment of short reads to a reference genome. However, owing to the deficiencies of specially designed analytical systems, short reads unmapped to the genome sequence are usually ignored, resulting in the loss of significant biological information and insights. To fill this gap, we present Comprehensive Assembly and Functional annotation of Unmapped RNA-Seq data (CAFU), a Galaxy-based framework that can facilitate the large-scale analysis of unmapped RNA sequencing (RNA-Seq) reads from single- and mixed-species samples. By taking advantage of machine learning techniques, CAFU addresses the issue of accurately identifying the species origin of transcripts assembled using unmapped reads from mixed-species samples. CAFU also represents an innovation in that it provides a comprehensive collection of functions required for transcript confidence evaluation, coding potential calculation, sequence and expression characterization and function annotation. These functions and their dependencies have been integrated into a Galaxy framework that provides access to CAFU via a user-friendly interface, dramatically simplifying complex exploration tasks involving unmapped RNA-Seq reads. CAFU has been validated with RNA-Seq data sets from wheat and Zea mays (maize) samples. CAFU is freely available via GitHub: https://github.com/cma2015/CAFU.

Access to Derivatizable Octahydrofluorenyl Ligand: Half-Sandwich Rare-Earth Metal Complexes for Highly Syndiospecific (Co)Polymerization of Styrene.

A simple and facile synthetic pathway for accessing new derivatizable bulky-demanding octahydrofluorenyl (OHF) ligands has been developed, and a series of half-sandwich rare-earth metal (Sc, Y, Lu) complexes bearing the OHF ancillary ligands have been synthesized. In conjunction with a borate, the OHF-ligated Sc complexes exhibited high catalytic activity for styrene (co)polymerization to afford polymers with highly syndiotactic polystyrene sequence (>99% rrrr).

Rare-Earth-Metal Complexes Bearing an Iminodibenzyl-PNP Pincer Ligand: Synthesis and Reactivity toward 3,4-Selective Polymerization of 1,3-Dienes.

A PNP-pincer ligand provides a versatile ligation framework, which is highly useful in organometallic chemistry and catalytic chemistry. In this work, by a de novo strategy, a simple and efficient synthetic pathway, has been developed to prepare the new iminodibenzyl-based PNP pincer proligand imin-RPNP(Li or H) (R = isopropyl, phenyl). By employing salt metathesis or direct alkyl elimination, we successfully synthesized a series of iminodibenzyl-PNP rare-earth-metal (Ln = Sc, Y, Dy, Ho, Er, Tm, Lu) complexes and characterized them by NMR and X-ray diffraction analyses. Upon addition of a borate and triisobutylaluminum (TIBA), the rare-earth-metal complexes 2-Y, 2-Dy, 2-Ho, 2-Er, and 2-Tm bearing the imin-PhPNP ligand exhibited unexpectedly high 3,4-selectivity (up to 95%) for the polymerization of 1,3-dienes (isoprene and myrcene); in particular, the chosen yttrium complex 2-Y promoted the 1,3-diene polymerization in a living manner. A computational study suggested that the sterically congested configuration around the metal center imposed by the imin-RPNP ligand might be the main reason for this unusual selectivity.

Rigid Acridane-Based Pincer Supported Rare-Earth Complexes for cis-1,4-Polymerization of 1,3-Conjugated Dienes.

A convenient synthetic route has been developed for preparing the novel rigid 4,5-(PR2)2-2,7,9,9-tetramethylacridane-based pincer ligands (acri-RPNP; R = iPr and Ph), and the first rare-earth (Ln = Y, Lu) alkyl complexes bearing the acri-RPNP ligands were synthesized by a salt metathesis reaction (for the isopropyl-substituent acri-iPrPNP complexes, 1-Ln) or direct alkylation (for the phenyl-substituent acri-PhPNP complexes, 2-Ln). For both 1-Ln and 2-Ln, the NMR spectroscopy and X-ray diffraction study confirmed the successful coordination of the acri-RPNP ligand to the central metal ion in a tridentate manner via the two phosphine and the nitrogen donors. In contrast to 1-Ln that are solvent-free complexes, the metal centers in 2-Ln are each coordinated with one tetrahydrofuran molecule. Upon activation by [Ph3C][B(C6F5)4], 1-Y and 2-Lu could catalyze the living polymerization of isoprene and ß-myrcene with high catalytic activity and high cis-1,4-selectivity (up to 92.3% for isoprene and 98.5% for ß-myrcene). Moreover, the 1-Y/[Ph3C][B(C6F5)4] catalytic system also could promote the polymerization of butadiene and its copolymerization with isoprene to produce copolymers with high cis-1,4-selectivity and narrow polydispersity.

PEA: an integrated R toolkit for plant epitranscriptome analysis.

Motivation: The epitranscriptome, also known as chemical modifications of RNA (CMRs), is a newly discovered layer of gene regulation, the biological importance of which emerged through analysis of only a small fraction of CMRs detected by high-throughput sequencing technologies. Understanding of the epitranscriptome is hampered by the absence of computational tools for the systematic analysis of epitranscriptome sequencing data. In addition, no tools have yet been designed for accurate prediction of CMRs in plants, or to extend epitranscriptome analysis from a fraction of the transcriptome to its entirety. Results: Here, we introduce PEA, an integrated R toolkit to facilitate the analysis of plant epitranscriptome data. The PEA toolkit contains a comprehensive collection of functions required for read mapping, CMR calling, motif scanning and discovery and gene functional enrichment analysis. PEA also takes advantage of machine learning (ML) technologies for transcriptome-scale CMR prediction, with high prediction accuracy, using the Positive Samples Only Learning algorithm, which addresses the two-class classification problem by using only positive samples (CMRs), in the absence of negative samples (non-CMRs). Hence PEA is a versatile epitranscriptome analysis pipeline covering CMR calling, prediction and annotation and we describe its application to predict N6-methyladenosine (m6A) modifications in Arabidopsis thaliana. Experimental results demonstrate that the toolkit achieved 71.6% sensitivity and 73.7% specificity, which is superior to existing m6A predictors. PEA is potentially broadly applicable to the in-depth study of epitranscriptomics. Availability and implementation: PEA Docker image is available at https://hub.docker.com/r/malab/pea, source codes and user manual are available at https://github.com/cma2015/PEA. Supplementary information: Supplementary data are available at Bioinformatics online.

Mannan-Binding Lectin Suppresses Peptidoglycan-Induced TLR2 Activation and Inflammatory Responses.

Peptidoglycan (PGN), as the major components of the bacterial cell wall, is known to cause excessive proinflammatory cytokine production. Toll-like receptor 2 (TLR2) is abundantly expressed on immune cells and has been shown to be involved in PGN-induced signaling. Although more and more evidences have indicated that PGN is recognized by TLR2, the role of TLR2 PGN recognition is controversial. Mannan-binding lectin (MBL), a plasma C-type lectin, plays a key role in innate immunity. More and more evidences show that MBL could suppress the amplification of inflammatory signals. Whether MBL can alter PGN-elicited cellular responses through TLR2 in macrophages is still unknown, and possible mechanism underlying it should be investigated. In this study, we found that MBL significantly attenuated PGN-induced inflammatory cytokine production, including TNF-α and IL-6, in PMA-stimulated THP-1 cells at both mRNA and protein levels. The expression of TLR2 was strongly induced by PGN stimulation. Furthermore, the administration of TLR2-neutralized antibody effectively suppressed PGN-induced TNF-α and IL-6 expression. These results supplied the evidence that PGN from Saccharomyces cerevisiae could be recognized by TLR2. In addition, we also found that MBL decreased PGN-induced TLR2 expression and suppressed TLR2-mediated downstream signaling, including the phosphorylation of IκBα, nuclear translocation of NF-κBp65, and phosphorylation of MAPK p38 and ERK1/2. Administration of MBL alone did not have an effect on the expression of TLR2. Finally, our data showed that PGN-mediated immune responses were more severely suppressed by preincubation with MBL and indicated that MBL can combine with both TLR2 and PGN to block the inflammation cytokine expression induced by PGN. All these data suggest that MBL could downregulate inflammation by modulating PGN/TLR2 signaling pathways. This study supports an important role for MBL in immune regulation and signaling pathways involved in inflammatory responses.

A deep convolutional neural network approach for predicting phenotypes from genotypes.

MAIN CONCLUSION: Deep learning is a promising technology to accurately select individuals with high phenotypic values based on genotypic data. Genomic selection (GS) is a promising breeding strategy by which the phenotypes of plant individuals are usually predicted based on genome-wide markers of genotypes. In this study, we present a deep learning method, named DeepGS, to predict phenotypes from genotypes. Using a deep convolutional neural network, DeepGS uses hidden variables that jointly represent features in genotypes when making predictions; it also employs convolution, sampling and dropout strategies to reduce the complexity of high-dimensional genotypic data. We used a large GS dataset to train DeepGS and compared its performance with other methods. The experimental results indicate that DeepGS can be used as a complement to the commonly used RR-BLUP in the prediction of phenotypes from genotypes. The complementarity between DeepGS and RR-BLUP can be utilized using an ensemble learning approach for more accurately selecting individuals with high phenotypic values, even for the absence of outlier individuals and subsets of genotypic markers. The source codes of DeepGS and the ensemble learning approach have been packaged into Docker images for facilitating their applications in different GS programs.

Cross-species modeling of plant genomes at single nucleotide resolution using a pre-trained DNA language model.

Understanding the function and fitness effects of diverse plant genomes requires transferable models. Language models (LMs) pre-trained on large-scale biological sequences can learn evolutionary conservation, thus expected to offer better cross-species prediction through fine-tuning on limited labeled data compared to supervised deep learning models. We introduce PlantCaduceus, a plant DNA LM based on the Caduceus and Mamba architectures, pre-trained on a carefully curated dataset consisting of 16 diverse Angiosperm genomes. Fine-tuning PlantCaduceus on limited labeled Arabidopsis data for four tasks involving transcription and translation modeling demonstrated high transferability to maize that diverged 160 million years ago, outperforming the best baseline model by 1.45-fold to 7.23-fold. PlantCaduceus also enables genome-wide deleterious mutation identification without multiple sequence alignment (MSA). PlantCaduceus demonstrated a threefold enrichment of rare alleles in prioritized deleterious mutations compared to MSA-based methods and matched state-of-the-art protein LMs. PlantCaduceus is a versatile pre-trained DNA LM expected to accelerate plant genomics and crop breeding applications.

Metal-free and enantioselective synthesis of 1,4-benzoxazepines from para-quinone methide derivatives and α-bromohydroxamates.

A sequential asymmetric conjugate addition/cyclisation of α-bromohydroxamates with para-quinone methide derivatives has been developed, which provides enantioenriched 1,4-benzoxazepines in generally high yields (up to 95%) and good enantioselectivities (up to 97 : 3 er). This protocol not only offers a novel and straightforward strategy for constructing chiral 1,4-benzoxazepines, but also demonstrates the potential of α-bromohydroxamates as three-atom synthons in asymmetric cyclisation reactions.

Interactive Web-based Annotation of Plant MicroRNAs with iwa-miRNA.

MicroRNAs (miRNAs) are important regulators of gene expression. The large-scale detection and profiling of miRNAs have been accelerated with the development of high-throughput small RNA sequencing (sRNA-Seq) techniques and bioinformatics tools. However, generating high-quality comprehensive miRNA annotations remains challenging due to the intrinsic complexity of sRNA-Seq data and inherent limitations of existing miRNA prediction tools. Here, we present iwa-miRNA, a Galaxy-based framework that can facilitate miRNA annotation in plant species by combining computational analysis and manual curation. iwa-miRNA is specifically designed to generate a comprehensive list of miRNA candidates, bridging the gap between already annotated miRNAs provided by public miRNA databases and new predictions from sRNA-Seq datasets. It can also assist users in selecting promising miRNA candidates in an interactive mode, contributing to the accessibility and reproducibility of genome-wide miRNA annotation. iwa-miRNA is user-friendly and can be easily deployed as a web application for researchers without programming experience. With flexible, interactive, and easy-to-use features, iwa-miRNA is a valuable tool for the annotation of miRNAs in plant species with reference genomes. We also illustrate the application of iwa-miRNA for miRNA annotation using data from plant species with varying genomic complexity. The source codes and web server of iwa-miRNA are freely accessible at http://iwa-miRNA.omicstudio.cloud/.

Correction: Detection of Neisseria gonorrhoeae and Chlamydia trachomatis infections in pregnant women by multiplex recombinase polymerase amplification.

[This corrects the article DOI: 10.1371/journal.pone.0251119.].

easyMF: A Web Platform for Matrix Factorization-Based Gene Discovery from Large-scale Transcriptome Data.

With the development of high-throughput experimental technologies, large-scale RNA sequencing (RNA-Seq) data have been and continue to be produced, but have led to challenges in extracting relevant biological knowledge hidden in the produced high-dimensional gene expression matrices. Here, we develop easyMF ( https://github.com/cma2015/easyMF ), a web platform that can facilitate functional gene discovery from large-scale transcriptome data using matrix factorization (MF) algorithms. Compared with existing MF-based software packages, easyMF exhibits several promising features, such as greater functionality, flexibility and ease of use. The easyMF platform is equipped using the Big-Data-supported Galaxy system with user-friendly graphic user interfaces, allowing users with little programming experience to streamline transcriptome analysis from raw reads to gene expression, carry out multiple-scenario MF analysis, and perform multiple-way MF-based gene discovery. easyMF is also powered with the advanced packing technology to enhance ease of use under different operating systems and computational environments. We illustrated the application of easyMF for seed gene discovery from temporal, spatial, and integrated RNA-Seq datasets of maize (Zea mays L.), resulting in the identification of 3,167 seed stage-specific, 1,849 seed compartment-specific, and 774 seed-specific genes, respectively. The present results also indicated that easyMF can prioritize seed-related genes with superior prediction performance over the state-of-art network-based gene prioritization system MaizeNet. As a modular, containerized and open-source platform, easyMF can be further customized to satisfy users' specific demands of functional gene discovery and deployed as a web service for broad applications.

Copper-catalyzed asymmetric 1,6-conjugate addition of in situ generated para-quinone methides with ß-ketoesters.

A Cu-catalyzed asymmetric 1,6-conjugate addition of in situ generated para-quinone methides (p-QMs) with ß-ketoester has been developed to construct a ketoester skeleton bearing an adjacent tertiary-quaternary carbon stereocenter in good yields and high enantioselectivities. This is the first example of metal-catalyzed asymmetric transformations of the in situ generated p-QMs, avoiding using pre-synthesized p-QMs requiring bulky 2,6-substitutions and highlighting a new dual catalytic activation with the chiral bis(oxazoline)-metal complex acting as a normal Lewis acid to activate the ß-ketoesters and a source of Brønsted acid responsible for generating the p-QMs in situ.

Relationship between the elastic modulus of the cage material and the biomechanical properties of transforaminal lumbar interbody fusion: A logarithmic regression analysis based on parametric finite element simulations.

BACKGROUND AND OBJECTIVE: Conventional method for evaluating the biomechanical effects of a specific elastic modulus of cage (cage-E) on spinal fusions requires establishing a "one-on-one" biomechanical model, which seems laborious and inefficient when dealing with the emergence of numerous cage materials with various cage-Es. We aim to offer a much convenient method to instantly predicting the biomechanical effects of any targeted cage-E on transforaminal lumbar interbody fusion (TLIF) by using a parametric finite element (FE) analysis to determining the regression relationship between cage-E and biomechanical properties of TLIF. MATERIALS AND METHODS: A L4/5 FE TLIF construct was modeled. Cage-E was linearly increased from 0.1 GPa (cancellous bone) to 110 GPa (titanium alloy). The function equations for assessing the influence of cage-E on the biomechanical indexes of TLIF were established using a logarithmic regression analysis. EXPERIMENTAL RESULTS: As cage-E increased from 0.1 GPa to 110 GPa, all the biomechanical indexes initially increased or decayed rapidly, and then slowed over time. Logarithmic regression models and functional equations were successfully established between cage-E and these indexes (P<0.0001). Their determination coefficients ranged from 0.72 to 0.99. The range of motions decreased from 0.37-1.10° to 0.20-1.07°. The mean stresses of the central and peripheral grafts reduced from 0.10-0.41 and 0.25-0.42 MPa to 0.03-0.04 and 0.19-0.27 MPa, respectively. In addition, the maximum stresses of the screw-bone interface and posterior instrumentation reduced from 11.76-25.04 and 8.91-84.68 MPa to 9.71-18.92 and 6.99-70.59 MPa, respectively. Finally, the maximum stresses of the cage and endplate increased from 0.28-1.35 MPa and 3.90-8.63 MPa to 14.86-36.16 MPa and 11.01-36.55 MPa, respectively. CONCLUSIONS: The decrease of cage-E reduces the risks of cage subsidence, cage breakage, and pseudarthrosis, while increasing the risk of instrumentation failure. The logarithmic regression models optimally demonstrate the relationship between cage-E and biomechanical properties of TLIF. The functional equations based on these models can be adopted to predict the biomechanical effects of any targeted cage-Es on TLIF, which effectively simplifies the procedures for the biomechanical assessments of cage materials.