Reciprocal conversion between annual and polycarpic perennial flowering behavior in the Brassicaceae.

The development of perennial crops holds great promise for sustainable agriculture and food security. However, the evolution of the transition between perenniality and annuality is poorly understood. Here, using two Brassicaceae species, Crucihimalaya himalaica and Erysimum nevadense, as polycarpic perennial models, we reveal that the transition from polycarpic perennial to biennial and annual flowering behavior is a continuum determined by the dosage of three closely related MADS-box genes. Diversification of the expression patterns, functional strengths, and combinations of these genes endows species with the potential to adopt various life-history strategies. Remarkably, we find that a single gene among these three is sufficient to convert winter-annual or annual Brassicaceae plants into polycarpic perennial flowering plants. Our work delineates a genetic basis for the evolution of diverse life-history strategies in plants and lays the groundwork for the generation of diverse perennial Brassicaceae crops in the future.

Extracellular pectin-RALF phase separation mediates FERONIA global signaling function.

The FERONIA (FER)-LLG1 co-receptor and its peptide ligand RALF regulate myriad processes for plant growth and survival. Focusing on signal-induced cell surface responses, we discovered that intrinsically disordered RALF triggers clustering and endocytosis of its cognate receptors and FER- and LLG1-dependent endocytosis of non-cognate regulators of diverse processes, thus capable of broadly impacting downstream responses. RALF, however, remains extracellular. We demonstrate that RALF binds the cell wall polysaccharide pectin. They phase separate and recruit FER and LLG1 into pectin-RALF-FER-LLG1 condensates to initiate RALF-triggered cell surface responses. We show further that two frequently encountered environmental challenges, elevated salt and temperature, trigger RALF-pectin phase separation, promiscuous receptor clustering and massive endocytosis, and that this process is crucial for recovery from stress-induced growth attenuation. Our results support that RALF-pectin phase separation mediates an exoskeletal mechanism to broadly activate FER-LLG1-dependent cell surface responses to mediate the global role of FER in plant growth and survival.

Blue light promotes zero-valent sulfur production in a deep-sea bacterium.

Increasing evidence has shown that light exists in a diverse range of deep-sea environments. We unexpectedly found that blue light is necessary to produce excess zero-valent sulfur (ZVS) in Erythrobacter flavus 21-3, a bacterium that has been recently isolated from a deep-sea cold seep. E. flavus 21-3 is able to convert thiosulfate to ZVS using a novel thiosulfate oxidation pathway comprising a thiosulfate dehydrogenase (TsdA) and a thiosulfohydrolase (SoxB). Using proteomic, bacterial two-hybrid and heterologous expression assays, we found that the light-oxygen-voltage histidine kinase LOV-1477 responds to blue light and activates the diguanylate cyclase DGC-2902 to produce c-di-GMP. Subsequently, the PilZ domain-containing protein mPilZ-1753 binds to c-di-GMP and activates TsdA through direct interaction. Finally, Raman spectroscopy and gene knockout results verified that TsdA and two SoxB homologs cooperate to regulate ZVS production. As ZVS is an energy source for E. flavus 21-3, we propose that deep-sea blue light provides E. flavus 21-3 with a selective advantage in the cold seep, suggesting a previously unappreciated relationship between light-sensing pathways and sulfur metabolism in a deep-sea microorganism.

Inference of gene regulatory networks based on directed graph convolutional networks.

Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose Directed Graph Convolutional neural network-based method for GRN inference (DGCGRN). To better understand and process the directed graph structure data of GRN, a directed graph convolutional neural network is conducted which retains the structural information of the directed graph while also making full use of neighbor node features. The local augmentation strategy is adopted in graph neural network to solve the problem of poor prediction accuracy caused by a large number of low-degree nodes in GRN. In addition, for real data such as E.coli, sequence features are obtained by extracting hidden features using Bi-GRU and calculating the statistical physicochemical characteristics of gene sequence. At the training stage, a dynamic update strategy is used to convert the obtained edge prediction scores into edge weights to guide the subsequent training process of the model. The results on synthetic benchmark datasets and real datasets show that the prediction performance of DGCGRN is significantly better than existing models. Furthermore, the case studies on bladder uroepithelial carcinoma and lung cancer cells also illustrate the performance of the proposed model.

A hybrid demultiplexing strategy that improves performance and robustness of cell hashing.

Cell hashing, a nucleotide barcode-based method that allows users to pool multiple samples and demultiplex in downstream analysis, has gained widespread popularity in single-cell sequencing due to its compatibility, simplicity, and cost-effectiveness. Despite these advantages, the performance of this method remains unsatisfactory under certain circumstances, especially in experiments that have imbalanced sample sizes or use many hashtag antibodies. Here, we introduce a hybrid demultiplexing strategy that increases accuracy and cell recovery in multi-sample single-cell experiments. This approach correlates the results of cell hashing and genetic variant clustering, enabling precise and efficient cell identity determination without additional experimental costs or efforts. In addition, we developed HTOreader, a demultiplexing tool for cell hashing that improves the accuracy of cut-off calling by avoiding the dominance of negative signals in experiments with many hashtags or imbalanced sample sizes. When compared to existing methods using real-world datasets, this hybrid approach and HTOreader consistently generate reliable results with increased accuracy and cell recovery.

A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy.

Antiviral peptides (AVPs) have shown potential in inhibiting viral attachment, preventing viral fusion with host cells and disrupting viral replication due to their unique action mechanisms. They have now become a broad-spectrum, promising antiviral therapy. However, identifying effective AVPs is traditionally slow and costly. This study proposed a new two-stage computational framework for AVP identification. The first stage identifies AVPs from a wide range of peptides, and the second stage recognizes AVPs targeting specific families or viruses. This method integrates contrastive learning and multi-feature fusion strategy, focusing on sequence information and peptide characteristics, significantly enhancing predictive ability and interpretability. The evaluation results of the model show excellent performance, with accuracy of 0.9240 and Matthews correlation coefficient (MCC) score of 0.8482 on the non-AVP independent dataset, and accuracy of 0.9934 and MCC score of 0.9869 on the non-AMP independent dataset. Furthermore, our model can predict antiviral activities of AVPs against six key viral families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight viruses (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). Finally, to facilitate user accessibility, we built a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼dbAMP/AVP/.

PB-LKS: a python package for predicting phage-bacteria interaction through local K-mer strategy.

Bacteriophages can help the treatment of bacterial infections yet require in-silico models to deal with the great genetic diversity between phages and bacteria. Despite the tolerable prediction performance, the application scope of current approaches is limited to the prediction at the species level, which cannot accurately predict the relationship of phages across strain mutants. This has hindered the development of phage therapeutics based on the prediction of phage-bacteria relationships. In this paper, we present, PB-LKS, to predict the phage-bacteria interaction based on local K-mer strategy with higher performance and wider applicability. The utility of PB-LKS is rigorously validated through (i) large-scale historical screening, (ii) case study at the class level and (iii) in vitro simulation of bacterial antiphage resistance at the strain mutant level. The PB-LKS approach could outperform the current state-of-the-art methods and illustrate potential clinical utility in pre-optimized phage therapy design.

The miR159a-PeMYB33 module regulates poplar adventitious rooting through the abscisic acid signal pathway.

As sessile organisms, plants experience variable environments and encounter diverse stresses during their growth and development. Adventitious rooting, orchestrated by multiple coordinated signaling pathways, represents an adaptive strategy evolved by plants to adapt to cope with changing environmental conditions. This study uncovered the role of the miR159a-PeMYB33 module in the formation of adventitious roots (ARs) synergistically with abscisic acid (ABA) signaling in poplar. Overexpression of miR159a increased the number of ARs and plant height while reducing sensitivity to ABA in transgenic plants. In contrast, inhibition of miR159a (using Short Tandem Target Mimic) or overexpression of PeMYB33 decreased the number of ARs in transgenic plants. Additionally, miR159a targets and cleaves transcripts of PeMYB33 using degradome analysis, which was further confirmed by a transient expression experiment of poplar protoplast. We show the miR159a-PeMYB33 module controls ARs development in poplar through ABA signaling. In particular, we demonstrated that miR159a promotes the expression of genes in the ABA signaling pathway. The findings from this study shed light on the intricate regulatory mechanisms governing the development of ARs in poplar plants. The miR159a-PeMYB33 module, in conjunction with ABA signaling, plays a crucial role in modulating AR formation and subsequent plant growth.

LncRNA-disease association identification using graph auto-encoder and learning to rank.

Discovering the relationships between long non-coding RNAs (lncRNAs) and diseases is significant in the treatment, diagnosis and prevention of diseases. However, current identified lncRNA-disease associations are not enough because of the expensive and heavy workload of wet laboratory experiments. Therefore, it is greatly important to develop an efficient computational method for predicting potential lncRNA-disease associations. Previous methods showed that combining the prediction results of the lncRNA-disease associations predicted by different classification methods via Learning to Rank (LTR) algorithm can be effective for predicting potential lncRNA-disease associations. However, when the classification results are incorrect, the ranking results will inevitably be affected. We propose the GraLTR-LDA predictor based on biological knowledge graphs and ranking framework for predicting potential lncRNA-disease associations. Firstly, homogeneous graph and heterogeneous graph are constructed by integrating multi-source biological information. Then, GraLTR-LDA integrates graph auto-encoder and attention mechanism to extract embedded features from the constructed graphs. Finally, GraLTR-LDA incorporates the embedded features into the LTR via feature crossing statistical strategies to predict priority order of diseases associated with query lncRNAs. Experimental results demonstrate that GraLTR-LDA outperforms the other state-of-the-art predictors and can effectively detect potential lncRNA-disease associations. Availability and implementation: Datasets and source codes are available at http://bliulab.net/GraLTR-LDA.

Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy.

Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.

SGCAST: symmetric graph convolutional auto-encoder for scalable and accurate study of spatial transcriptomics.

Recent advances in spatial transcriptomics (ST) have enabled comprehensive profiling of gene expression with spatial information in the context of the tissue microenvironment. However, with the improvements in the resolution and scale of ST data, deciphering spatial domains precisely while ensuring efficiency and scalability is still challenging. Here, we develop SGCAST, an efficient auto-encoder framework to identify spatial domains. SGCAST adopts a symmetric graph convolutional auto-encoder to learn aggregated latent embeddings via integrating the gene expression similarity and the proximity of the spatial spots. This framework in SGCAST enables a mini-batch training strategy, which makes SGCAST memory-efficient and scalable to high-resolution spatial transcriptomic data with a large number of spots. SGCAST improves the overall accuracy of spatial domain identification on benchmarking data. We also validated the performance of SGCAST on ST datasets at various scales across multiple platforms. Our study illustrates the superior capacity of SGCAST on analyzing spatial transcriptomic data.

Pericyte in retinal vascular diseases: A multifunctional regulator and potential therapeutic target.

Retinal vascular diseases (RVDs), in particular diabetic retinopathy, retinal vein occlusion, and retinopathy of prematurity, are leading contributors to blindness. The pathogenesis of RVD involves vessel dilatation, leakage, and occlusion; however, the specific underlying mechanisms remain unclear. Recent findings have indicated that pericytes (PCs), as critical members of the vascular mural cells, significantly contribute to the progression of RVDs, including detachment from microvessels, alteration of contractile and secretory properties, and excessive production of the extracellular matrix. Moreover, PCs are believed to have mesenchymal stem properties and, therefore, might contribute to regenerative therapy. Here, we review novel ideas concerning PC characteristics and functions in RVDs and discuss potential therapeutic strategies based on PCs, including the targeting of pathological signals and cell-based regenerative treatments.

Transmission dynamics, complications and mitigation strategies of the current mpox outbreak: A comprehensive review with bibliometric study.

As the mankind counters the ongoing COVID-19 pandemic by the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), it simultaneously witnesses the emergence of mpox virus (MPXV) that signals at global spread and could potentially lead to another pandemic. Although MPXV has existed for more than 50 years now with most of the human cases being reported from the endemic West and Central African regions, the disease is recently being reported in non-endemic regions too that affect more than 50 countries. Controlling the spread of MPXV is important due to its potential danger of a global spread, causing severe morbidity and mortality. The article highlights the transmission dynamics, zoonosis potential, complication and mitigation strategies for MPXV infection, and concludes with suggested 'one health' approach for better management, control and prevention. Bibliometric analyses of the data extend the understanding and provide leads on the research trends, the global spread, and the need to revamp the critical research and healthcare interventions. Globally published mpox-related literature does not align well with endemic areas/regions of occurrence which should ideally have been the scenario. Such demographic and geographic gaps between the location of the research work and the endemic epicentres of the disease need to be bridged for greater and effective translation of the research outputs to pubic healthcare systems, it is suggested.

Distributed multi-objective optimization for SNP-SNP interaction detection.

The detection of complex interactions between single nucleotide polymorphisms (SNPs) plays a vital role in genome-wide association analysis (GWAS). The multi-objective evolutionary algorithm is a promising technique for SNP-SNP interaction detection. However, as the scale of SNP data further increases, the exponentially growing search space gradually becomes the dominant factor, causing evolutionary algorithm (EA)-based approaches to fall into local optima. In addition, multi-objective genetic operations consume significant amounts of time and computational resources. To this end, this study proposes a distributed multi-objective evolutionary framework (DM-EF) to identify SNP-SNP interactions on large-scale datasets. DM-EF first partitions the entire search space into several subspaces based on a space-partitioning strategy, which is nondestructive because it guarantees that each feasible solution is assigned to a specific subspace. Thereafter, each subspace is optimized using a multi-objective EA optimizer, and all subspaces are optimized in parallel. A decomposition-based multi-objective firework optimizer (DCFWA) with several problem-guided operators was designed. Finally, the final output is selected from the Pareto-optimal solutions in the historical search of each subspace. DM-EF avoids the preference for a single objective function, handles the heavy computational burden, and enhances the diversity of the population to avoid local optima. Notably, DM-EF is load-balanced and scalable because it can flexibly partition the space according to the number of available computational nodes and problem size. Experiments on both artificial and real-world datasets demonstrate that the proposed method significantly improves the search speed and accuracy.

MS-Based Proteomics of Body Fluids: The End of the Beginning.

Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the clinic. Mass spectrometry (MS)-based proteomics excels by its untargeted nature, specificity of identification, and quantification, making it an ideal technology for biomarker discovery and routine measurement. It has unique attributes compared to affinity binder technologies, such as OLINK Proximity Extension Assay and SOMAscan. In in a previous review in 2017, we described technological and conceptual limitations that had held back success. We proposed a 'rectangular strategy' to better separate true biomarkers by minimizing cohort-specific effects. Today, this has converged with advances in MS-based proteomics technology, such as increased sample throughput, depth of identification, and quantification. As a result, biomarker discovery studies have become more successful, producing biomarker candidates that withstand independent verification and, in some cases, already outperform state-of-the-art clinical assays. We summarize developments over the last years, including the benefits of large and independent cohorts, which are necessary for clinical acceptance. Shorter gradients, new scan modes, and multiplexing are about to drastically increase throughput, cross-study integration, and quantification, including proxies for absolute levels. We have found that multiprotein panels are inherently more robust than current single analyte tests and better capture the complexity of human phenotypes. Routine MS measurement in the clinic is fast becoming a viable option. The full set of proteins in a body fluid (global proteome) is the most important reference and the best process control. Additionally, it increasingly has all the information that could be obtained from targeted analysis although the latter may be the most straightforward way to enter regular use. Many challenges remain, not least of a regulatory and ethical nature, but the outlook for MS-based clinical applications has never been brighter.

Cefiderocol: Clinical application and emergence of resistance.

Antibacterial drug resistance of gram-negative bacteria (GNB) results in high morbidity and mortality of GNB infection, seriously threaten human health globally. Developing new antibiotics has become the critical need for dealing with drug-resistant bacterial infections. Cefiderocol is an iron carrier cephalosporin that achieves drug accumulation through a unique "Trojan horse" strategy into the bacterial periplasm. It shows high antibacterial activity against multidrug-resistant (MDR) Enterobacteriaceae and MDR non-fermentative bacteria. The application of cefiderocol offers new hope for treating clinical drug-resistant bacterial infections. However, limited clinical data and uncertainties about its resistance mechanisms constrain the choice of its therapeutic use. This review aimed to summarize the clinical applications, drug resistance mechanisms, and co-administration of cefiderocol.

The mud-dwelling clam Meretrix petechialis secretes endogenously synthesized erythromycin.

Although lacking an adaptive immune system and often living in habitats with dense and diverse bacterial populations, marine invertebrates thrive in the presence of potentially challenging microbial pathogens. However, the mechanisms underlying this resistance remain largely unexplored and promise to reveal novel strategies of microbial resistance. Here, we provide evidence that a mud-dwelling clam, Meretrix petechialis, synthesizes, stores, and secretes the antibiotic erythromycin. Liquid chromatography coupled with mass spectrometry, immunocytochemistry, fluorescence in situ hybridization, RNA interference, and enzyme-linked immunosorbent assay revealed that this potent macrolide antimicrobial, thought to be synthesized only by microorganisms, is produced by specific mucus-rich cells beneath the clam's mantle epithelium, which interfaces directly with the bacteria-rich environment. The antibacterial activity was confirmed by bacteriostatic assay. Genetic, ontogenetic, phylogenetic and genomic evidence, including genotypic segregation ratios in a family of full siblings, gene expression in clam larvae, phylogenetic tree, and synteny conservation in the related genome region further revealed that the genes responsible for erythromycin production are of animal origin. The detection of this antibiotic in another clam species showed that the production of this macrolide is not exclusive to M. petechialis and may be a common strategy among marine invertebrates. The finding of erythromycin production by a marine invertebrate offers a striking example of convergent evolution in secondary metabolite synthesis between the animal and bacterial domains. These findings open the possibility of engineering-animal tissues for the localized production of an antibacterial secondary metabolite.

A Biphasic Strategy to Synergistically Accelerate Activation and CO Spillover in Formic Acid Oxidation Catalysis.

Developing highly efficient and carbon monoxide (CO)-tolerant platinum (Pt) catalysts for the formic acid oxidation reaction (FAOR) is vital for direct formic acid fuel cells (DFAFCs), yet it is challenging due to the high energy barrier of direct intermediates (HCOO* and COOH*) as well as the CO poisoning issues associated with Pt alloy catalysts. Here we present a versatile biphasic strategy by creating a hexagonal/cubic crystalline-phase-synergistic PtPb/C (h/c-PtPb/C) catalyst to tackle the aforementioned issues. Detailed investigations reveal that h/c-PtPb/C can simultaneously facilitate the adsorption of direct intermediates while inhibiting CO adsorption, thereby significantly improving the activation and CO spillover. As a result, h/c-PtPb/C showcases an outstanding FAOR activity of 8.1 A mgPt-1, which is 64.5 times higher than that of commercial Pt/C and significantly surpasses monophasic PtPb. Moreover, the h/c-PtPb/C-based membrane electrode assembly exhibits an exceptional peak power density of 258.7 mW cm-2 for practical DFAFC applications.

Electron-Withdrawing Substituents Enhance the Type I PDT and NIR-II Fluorescence of BODIPY J Aggregates for Bioimaging and Cancer Therapy.

Organic dyes with simultaneously boosted near-infrared-II (NIR-II) fluorescence, type I photodynamic therapy (PDT), and photothermal therapy (PTT) in the aggregate state are still elusive due to the unclear structure-function relationship. Herein, electron-withdrawing substituents are introduced at the 5-indolyl positions of BODIPY dyes to form tight J-aggregates for enhanced NIR-II fluorescence and type I PDT/PTT. The introduction of an electron-rich julolidine group at the meso position and an electron-withdrawing substituent (-F) at the indolyl moiety can enhance intermolecular charge transfer and the hydrogen bonding effect, contributing to the efficient generation of superoxide radicals in the aggregate state. The nanoparticles of BDP-F exhibit NIR-II fluorescence at 1000 nm, good superoxide radical generation ability, and a high photothermal conversion efficiency (50.9%), which enabled NIR-II fluorescence-guided vasculature/tumor imaging and additive PDT/PTT. This work provides a strategy for constructing phototheranostic agents with enhanced NIR-II fluorescence and type I PDT/PTT for broad biomedical applications.

"All in One" Strategy for Achieving Superprotonic Conductivity by Incorporating Strong Acids into a Robust Imidazole-Linked Covalent Organic Framework.

The fabrication of solid-state proton-conducting electrolytes possessing both high performance and long-life reusability is significant but challenging. An "all-in-one" composite, H3PO4@PyTFB-1-SO3H, including imidazole, sulfonic acid, and phosphoric acid, which are essential for proton conduction, was successfully prepared by chemical post-modification and physical loading in the rationally pre-synthesized imidazole-based nanoporous covalent organic framework (COF), PyTFB-1. The resultant H3PO4@PyTFB-1-SO3H exhibits superhigh proton conductivity with its value even highly up to 1.15 × 10-1 S cm-1 at 353 K and 98% relative humidity (RH), making it one of the highest COF-based composites reported so far under the same conditions. Experimental studies and theoretical calculations further confirmed that the imidazole and sulfonic acid groups have strong interactions with the H3PO4 molecules and the synergistic effect of these three groups dramatically improves the proton conductivity properties of H3PO4@PyTFB-1-SO3H. This work demonstrated that by aggregating multiple proton carriers into one composite, effective proton-conducting electrolyte can be feasibly achieved.