Science Bulletin

The axon initial segment (AIS) undergoes structural plasticity and refines neuronal excitability, yet the underlying mechanisms remain unclear. We here developed an in vivo CRISPR/Cas9 knockout platform using an all-in-one triple-guide RNA vector introduced via electroporation and employed this approach to seek molecules that regulate the developmental shortening of AIS in the chicken nucleus magnocellularis. We have targeted fourteen molecules associated with microtubules and found that knockouts of glycogen synthase kinase 3{beta} (GSK3{beta}) and Tau disabled the AIS shortening. Conversely, overexpression of constitutively active form of GSK3{beta} facilitated the AIS shortening in vivo. This extensive shortening was replicated in slice cultures, which was occluded by stabilization of microtubules. These results suggested that microtubule remodeling by GSK3{beta} activity contributed to the AIS shortening. This study thus provides a genetic approach suitable for genetic screening that allows identifying regulators of the AIS plasticity in the chicken brain.

Heterogeneous nuclear ribonucleoprotein U (HNRNPU) deficiency is a rare genetic cause of neurodevelopmental disorders (NDDs) lacking targeted therapies. Here, we developed a transcriptomic-guided compound prioritization pipeline using Connectivity Map (CMap) analysis on multi-model transcriptomic signatures from HNRNPU-deficient human cells and mouse models. Ten compounds were selected through manual curation and functionally screened in patient-derived HNRNPU-deficient neuroepithelial stem (NES) cells with earlier observed cellular phenotypes. Two of the compounds, AS601245 and Lenalidomide, significantly reduced the elevated neural progenitor population during differentiation, and their combination further decreased primary cilia incidence, indicating partial rescue of the patient-specific cellular phenotypes. To understand the mechanisms underlying the partial rescue, we employed proteome integral solubility alteration (PISA) and expression proteomics. PISA assay identified TMEM150C and GSK3A as proximal targets of combined treatment. Additionally, we observed reversal of multiple biological pathways including downregulation of Wnt signalling and upregulation of mitochondrial pathways and transmembrane proteins. Altogether, we established a computational-experimental pipeline for transcriptomic-guided drug repurposing for a monogenic NDD, and demonstrated that the network-level modulation partially rescues the delayed neural differentiation in HNRNPU-deficient neural cells.

Orthohantaviruses cause severe human diseases including hemorrhagic fever with renal syndrome (HFRS) and hantavirus cardiopulmonary syndrome (HCPS), with case fatality rates up to 40%. No FDA-approved therapeutics are currently available, highlighting urgent need for drug development following recent outbreak events. We systematically examined host protease dependencies in hantavirus replication, focusing on Signal Peptidase (SP) and Signal Peptide Peptidase (SPP) essential for viral glycoprotein maturation. Through comprehensive database mining and molecular docking analysis, we identified six potential protease inhibitors, with Compound E achieving the highest binding confidence score (-0.28) against SPP. Our analysis reveals that targeting host ER proteases represents a viable antiviral strategy, providing a systematic framework for protease-targeted antihantavirus drug development and identifying specific lead compounds for experimental validation.

Accurate prediction of drug synergy is paramount for developing effective combination therapies and advancing personalized medicine. Although methods based on graph neural networks (GNNs) have become a prevalent approach, they often treat molecules as flat graphs of connected atoms, thus overlooking their inherent hierarchical structure (i.e., atoms forming functional groups) and the critical topological information that governs molecular interactions. To address this limitation, we introduce TopoFuseNet, a novel hierarchical graph representation learning framework that integrates multi-scale topological features. The core innovations of TopoFuseNet include: 1) The first-ever application of "Group Centrality" from network science to cheminformatics, enabling the identification and quantification of functional groups crucial to drug activity; 2) A systematic, multi- path strategy to seamlessly integrate node-level (atom) and group-level (functional group) topological features into a Graph Attention Network (GAT) via feature augmentation, attention biasing, and hierarchical pooling; 3) A Differential Transformer module to deeply fuse multi-modal features learned from sequences, fingerprints, and our proposed hierarchical graph representations. Extensive experiments on two large-scale benchmark datasets, DrugComb and DrugCombDB, demonstrate that TopoFuseNet significantly outperforms state-of-the-art methods across multiple key metrics, including AUC, AUPRC, and F1-score, while exhibiting exceptional generalization robustness under various stringent cold-start scenarios. In-depth ablation studies further confirm the effectiveness and necessity of each proposed innovative module. Furthermore, multi-scale interpretability analysis and zero-shot cross-domain transfer experiments reveal that the model successfully captures molecular interaction rules with clear pharmacological significance, demonstrating immense practical potential for discovering novel combination therapies through large-scale virtual screening. Our work not only delivers a superior model for drug synergy prediction, but more importantly, it establishes a novel and scalable paradigm for effectively integrating hierarchical molecular structures and topological information into GNNs.

Background: Pediatric dilated cardiomyopathy (DCM) is a leading cause of heart failure and transplantation, with variable prognosis and high early mortality. This study developed and validated a nomogram predicting short-term mortality risk to guide clinical decisions. Methods: The data were sourced from the Pediatric Cardiomyopathy Database at Shandong Provincial Hospital. Cox regression analysis was conducted to determine outcome-associated factors, and a nomogram was developed to estimate 1, 3, and 5year mortality risks for children with DCM. Model effectiveness was assessed through the concordance index (C-index) and area under the receiver operating characteristic curve (AUC). Additionally, calibration curves and decision curve analysis (DCA) were employed to evaluate the model's predictive accuracy and clinical relevance. Results: A cohort of 106 children diagnosed with primary DCM and who underwent genetic analysis was studied, with a median diagnostic age of 10 months (ranging from 5 to 84 months), comprising 50 girls (47.2%). The rate of detecting genetic mutations was 28.3%, uncovering 14 gene variants linked to DCM, with TTN mutations being the most common. Both univariate and multivariate Cox regression analyses indicated that both sex and NT-proBNP levels had a significant impact on survival rates among pediatric DCM patients.The model exhibited strong discriminative performance, calibration, and clinical net benefit, as assessed by the C-index, calibration plots, and decision curve analysis (DCA). Conclusions: The prediction model created in this research shows strong accuracy in forecasting survival rates at 1, 3, and 5 years for children with DCM, highlighting its significant relevance in clinical settings.

Batten disease, also known as neuronal ceroid lipofuscinoses, is one of the most common causes of childhood dementia. It is characterized by the accumulation of lipofuscin in lysosomes, leading to loss of brain cell function, onset of dementia-like symptoms, vision loss and seizures and has extremely limited treatment options. Here, we performed computational drug repurposing analysis to identify existing compounds that may target Batten disease risk genes. A total of 81 candidate compounds were identified, 6 of which were selected based on clinical tractability for downstream testing in Batten disease (CLN3) iPSC-derived models. After confirming disease phenotype and drug candidate safety, CLN3 brain cell cultures treated with and without drug candidates underwent bulk RNA-seq to identify drug responses. One of the candidate drugs N-acetylglucosamine (GlcNAc) significantly upregulated Batten disease risk gene CLN5 expression and several other lysosomal markers within CLN3 brain cells, and modulated several pathways implicated in lysosomal storage disorders. Importantly, GlcNAc significantly reduced lipofuscin burden in both CLN3 iPSC-derived neurons and astrocytes, supporting its investigation as an additional therapy for Batten disease.

The transcription factor FOXP2 is the most well-known language-related gene in humans, yet its role in primate vocalization remains poorly understood. Here we report that knockdown of FOXP2 in the striatum markedly disrupts vocalization stability in the marmoset monkey, a valuable non-human primate model for studying vocal behavior. FOXP2 exhibited high expression in the marmoset striatum, especially during early development. Using the CRISPR-Cas12 system, we achieved specific in vivo editing of the FOXP2 gene and effective knockdown of FOXP2 protein expression in the marmoset striatum. Two neonatal marmosets received bilateral striatal injections of the gene-editing and control virus, respectively, and were raised together in the same family. In three such marmoset pairs, analysis of vocalizations recorded during 6-15 weeks post-injection revealed that striatal FOXP2 knockdown significantly altered vocal features and increased intra-individual variability in phee syllables--the most common marmoset vocalization, often produced repetitively as multi-syllable phee calls. Notably, in FOXP2-edited marmosets, acoustic alterations were minimal in the first syllable of phee calls but became progressively more pronounced in subsequent syllables, which exhibited a marked upward shift in the frequency spectrum over time with progressively steeper slopes. These temporal dynamics in vocal features reflect a reduction in the stability of continuous vocal production. In line with the known striatal functions in motor control, our findings provide the first evidence of FOXP2 in controlling vocalization in non-human primates, thereby opening new avenues for investigating the neural mechanisms underlying FOXP2 function.

Clot resistance to pharmacological thrombolysis remains a critical challenge in ischemic stroke (IS) management. Thrombus heterogeneity, particularly the presence of thrombolysis-resistant domains composed of dense fibrin and non-fibrin components, including neutrophil extracellular traps (NETs), significantly limits the efficacy of recombinant tissue-type plasminogen activator (r-tPA) and its variant, Tenecteplase (TNK). Consequently, novel therapeutic strategies are urgently required. Emerging evidence suggests that co-administration of deoxyribonuclease I (DNase I) with r-tPA can degrade DNA fibers and enhance clot lysis. In this study, we optimized a previously developed theranostic agent--iron oxide microparticles coated with polydopamine--by dual-grafting both r-tPA and DNase to target resistant thrombi. Using functional ultrasound imaging (fUS) during the acute phase of IS, we demonstrated accelerated reperfusion with this dual-functionalized platform in a r-tPA resistant IS model. Furthermore, MRI analysis confirmed a significant reduction in lesion volume at 24 hours, correlating with improved functional recovery five days post-ischemia.

Fucoidans have been widely reported to show SARS-CoV-2 antiviral activity. In this study, we observed a striking difference in the inhibitory potency between two commercially available fucoidans: Fucus vesiculosus crude (Fvc) and pure (Fvp). SEC-MALS analysis revealed two molecular weight populations for Fvc (1098 kDa, 58.58 kDa) and one for Fvp (40.48 kDa). At micromolar concentrations of fucoidans, the binding affinities (KDs) of Fvc_1098 (223 nM) and Fvc_58 (4.27 {micro}M) for the amine-biotinylated SARS-CoV-2 receptor binding domain (RBD) were higher than that of Fvp (76.5 {micro}M). At nanomolar concentrations, binding was observed only to the Avi-tag-, but not amine-biotinylated RBDs, suggesting better accessibility of their binding sites. The association rates (kon) were faster for Fvc than for Fvp. Similarly, affinities of Fvc_1098 (23.4 nM) and Fvc_58 (4.48 M) for ACE2 were greater than that of Fvp (66.8 M), indicating that Fvc can bind directly to both RBD and ACE2. Fvc demonstrated enhanced inhibitory potency (IC50 = 58 g/mL) compared to Fvp (IC50 > 239 g/mL) in the pseudovirus entry assay and did not induce cytotoxicity in HEK293T cells. In conclusion, crude fucoidan with high fucose content and high molecular weight shows promising antiviral activity.

Current treatment of IDH-wildtype glioblastoma (GBM) relies on the first-line chemotherapy-temozolomide. Although MGMT methylation is routinely conducted to predict chemosensitivity, its efficacy is often compromised. Thus, there is an urgent need to discover more accurate prognostic biomarkers. Cholesteryl ester (CE) has been recently recognized as a key feature of GBM, however, its role in GBM prognosis remains poorly understood. We first employed label-free stimulated Raman scattering (SRS) imaging to quantitatively analyze CE level in intact tumor tissues obtained from IDH-wildtype GBM patients. Our result revealed significantly prolonged 2-year overall survival (OS) in patients with CE level [&ge;] 40% compared to those with CE level < 40%. CE outperformed MGMT methylation for 2-year OS prognosis (AUC: 0.836 vs. 0.763). Importantly, CE also achieved superior prognostic performance over MGMT methylation on an independent cohort, with higher sensitivity (0.856 vs. 0.667), specificity (0.833 vs. 0.583), NPV (1.00 vs. 0.667), PPV (0.833 vs. 0.583). Given synergistic effects between CE and MGMT methylation, we developed a prognostic model combining these two biomarkers. Specially, machine learning (XGBoost) model exhibited optimal performance in the training cohort (AUC: 0.920), and maintained its superior performance on the independent cohort (sensitivity: 0.946, specificity: 0.873, NPV: 1.00; PPV: 0.917). Mechanistically, integrative analysis of TCGA database linked poor prognosis to the coordinated upregulation of genes involved in cholesterol efflux, hydrolysis, transport, and inhibition of de novo synthesis, unraveling a possible underlying mechanism between poor prognosis and cholesterol metabolism. This work identified CE as a prognostic biomarker for IDH-wildtype GBM.

Transmembrane (TM) proteins play essential roles in biology as transporters, ion channels, chaperones, enzymes, and mediators of signal transduction. However, membrane proteins often suffer from inefficient folding and intrinsic instability. Misfolding in cells can cause numerous loss-of-function pathologies. Likewise, denaturation upon purification in the laboratory is a critical barrier to structure determination and characterization of key biochemical mechanisms. Generalizable strategies to stabilize membrane proteins remain limited. Here, we developed an informatics-based de novo design strategy to create synthetic auxiliary subunits that interact with the TM helices of a model pentameric ion channel, thereby bolstering folding while maintaining channel function. Biochemical and structural characterization reveal the synthetic TM subunits can also be used to create larger multi-spanning designer proteins of custom topology. This proof-of-concept motivates the feasibility of computationally designed accessory TM helices as potential pharmacological chaperone "folding correctors" of membrane proteins in disease and as tools in structural biology.

The influenza virus poses a significant global health threat due to its continuous evolution, immune evasion, and zoonotic spillover. The rise of drug resistance, reduced susceptibility to existing antiviral medications, and the limited effectiveness of annual vaccines underscore the need for new antiviral strategies. To infect, the influenza virus binds to sialic acid (SA)-containing molecules on host cell membranes through hemagglutinin (HA). Blocking this interaction represents a promising antiviral approach. Herein, we report that SA containing plasma membrane-derived vesicles (PMV) efficiently inhibits in vitro Influenza A virus (IAV) infection. Using orthogonal methods, we demonstrate that PMV derived from A549, MDCK, and HEK cells competitively bind to H1N1 (WSN) and H3N2 (X-31) IAV strains, block entry and infection in human respiratory epithelial cells in a dose-dependent manner, without causing significant toxicity. When the size of the vesicles was reduced through extrusion, the antiviral activity was enhanced, and this was found to be correlated with a size-dependent increase in hemagglutination inhibition and reduced IAV internalisation. Plasma membrane-derived vesicles may serve as a novel antiviral strategy against influenza virus infections due to their simple production method and conserved SA binding site on HA.

Virtual screening has become an indispensable tool in modern structure-based drug discovery, enabling the identification of candidate molecules by computationally evaluating their potential to bind target proteins. The accuracy of such screenings critically depends on the quality of the target structures employed. Recent advances in protein structure prediction, particularly AlphaFold2, have revolutionized this field with unprecedented accuracy. However, AlphaFold2 models often exhibit limitations in local structural details, especially within binding pockets, which limit their utility for small molecule docking. In contrast, molecular dynamics simulations with accurate atomistic force fields can refine protein structures, but lack the ability to leverage the structural information provided by deep learning approaches. Here, we introduce bAIes, an integrative method that bridges this gap by combining physics-based force fields with data-driven predictions through Bayesian inference. Crucially, bAIes demonstrates a superior ability to discriminate between binders and non-binders in virtual screening campaigns, outperforming both AlphaFold2 and molecular dynamics-refined models. By enhancing the usability of AlphaFold2 models without requiring extensive experimental or computational resources, bAIes offers a convenient solution to a longstanding challenge in structure-based drug design, potentially accelerating the early phases of drug discovery.

The cerebellar nuclei form the main output structures of the cerebellum and are composed of a deeply conserved set of cell types. Two excitatory cell classes, Class-A and -B, are present in each cerebellar nucleus and mediate all excitatory output of the cerebellum. To provide genetic access to these cell types, here we identified Acan as a marker gene for Class-B cells and generated a knock-in Acan-P2A-Cre mouse line. We demonstrate that this Acan-Cre line selectively labels Class-B neurons in the cerebellar nuclei and validate its use in viral projection tracing. This new mouse line provides a valuable genetic tool to study cerebellar nuclei organization and function.

WD40 domains share a widespread {beta}-propeller fold, and often act as versatile scaffold proteins. Despite their central role in organizing dynamic cellular complexes, the molecular and structural mechanisms of many WD40 proteins remain poorly understood. Among them, DCAF7, an ubiquitously expressed and essential gene in human, also encodes a highly conserved WD40 protein in eukaryotic organisms. It is known to interact with multiple and functionnally diverse partners to coordinates cellular activity of several protein kinases as well as transcriptional regulators, thereby modulating key cellular processes such as cell growth, differentiation, and transcriptional regulation. However, the precise mode of action of DCAF7 is unknown and its important divergence in sequence from better characterize WD40 prevent information transfer by similarity. Structural interactomic can reveal how protein-protein interactions (PPIs) occur within an organism and are essential for understanding biological functions and developing new therapeutic strategies. Using SLiMAn2, AlphaFold2/3 and PSSMsearch, we identified a conserved -helical short linear motif (SLiM) in several well known DCAF7 partners that binds to the top surface of its {beta}-propeller. This motif was subsequently used to generate a regular expression, to identify potential new direct binders across the DCAF7 meta-interactome and the human proteome. Domain-domain interactions were also predicted for some other partners. Finally, modeling of oligomeric complexes with such new hits reveals the structural basis of DCAF7 scaffolding, with links to neurodevelopmental disorders such as autism.

Oncolytic virotherapy is an innovative approach to cancer treatment that uses replication-competent viruses to selectively target and destroy cancer cells while leaving healthy tissues largely unaffected. Zika virus (ZIKV), a neurotropic orthoflavivirus, has recently gained attention as a potential oncolytic agent due to its ability to infect neural-derived cells and suppress tumor growth in preclinical models. Although existing studies have examined ZIKVs oncolytic effects, the mechanisms underlying these effects remain largely unexplored. Additionally, the roles of individual ZIKV proteins and their interactions with host factors remain incompletely understood. Here, we used RNA sequencing, affinity purification-mass spectrometry, and functional assays to uncover previously unidentified mechanisms underlying ZIKVs oncolytic activity in pediatric neural tumors. We found that the ZIKV non-structural proteins NS4A and NS5 exert oncolytic effects, reducing tumorsphere size. ZIKV-host protein-protein interaction networks were characterized and showed that integrin 3 (gene: ITGA3), a mediator of cell-matrix adhesion, interacts with ZIKV NS2B and NS4A. Integrin 3 was further shown to be involved in ZIKV- and NS4A-induced tumorsphere size reduction, while ITGA3 knockdown and ZIKV infection additively inhibited 3D invasion. These findings provide critical mechanistic insights that could inform the rational design of ZIKV-based virotherapies and highlight opportunities for combination treatment strategies.

The Caenorhabditis elegans AWC olfactory neuron pair specifies asymmetric subtypes, AWCOFF and AWCON, through stochastic and coordinated cell signaling events. UNC-104/kinesin-3 (KIF1A) and UNC-116/kinesin-1 motor proteins act in the AWCON cell to regulate the synaptic localization of the TIR-1/SARM1-assembled calcium signaling complex in the AWCOFF cell to promote AWCOFF. However, the molecular mechanism in the AWCON cell that acts non-cell autonomously to control synaptic TIR-1 calcium signaling to promote AWCOFF remains unclear. Here, we show that JIP-1, a conserved c-Jun N-terminal kinase (JNK)-interacting protein 1, mediates the synaptic localization of TIR-1 in the AWC axon to specify the AWCOFF subtype. A jip-1 loss-of-function mutant, identified from an unbiased forward genetic screen, has reduced localization of TIR-1 at synapses in the AWC axon and accumulation of TIR-1 in the AWC cell body. jip-1 mutants significantly enhance the 2AWCON phenotype of a hypomorphic tir-1 mutant. JIP-1, like UNC-104 and UNC-116, mainly acts non-cell autonomously in AWCON to specify the AWCOFF subtype. Our findings provide mechanistic insights into how cell-specific Ca2+ signaling proteins, such as TIR-1, target synaptic regions via intercellular signaling to promote neuronal diversification.

Light microscopy imaging with histological stains is central to disease diagnosis and research. It is enhanced with immunostaining to reveal cellular composition and complexity linked to clinical utility and biological mechanisms. Emerging multiplex imaging technologies like Phenocycler markedly increase the coverage to capture the cellular diversity but are costly, technically demanding, and inaccessible to most clinical laboratories. We developed DigitAb, a deep learning framework that classifies cell types directly from hematoxylin and eosin (H&E) stained slides, eliminating the need for specialized assays. Using Phenocycler imaging, we generated highlZlresolution ground truths for [~]3.5 million cells from 29 human kidney samples across four multi-institutional datasets to train a semantic segmentation model for 10 cell types, achieving a balanced accuracy of 0.78. By employing an integrated adversarial domain adaptation module, we tested DigitAb on unlabeled and untested biopsy samples from kidney transplant and diabetic samples. We were able to predict several cell types just from histology images, without using any special technology or immunostains, and demonstrate high concordance with clinical gold-standard Banff schema in kidney transplant rejection, and clinical characteristics of diabetic nephropathy. Our cloudlZlbased tool, DigitAb, provides scalable, accessible, labellZlfree cellular segmentation for research and clinical pathology.

Despite antiretroviral therapy, human immunodeficiency virus type 1 (HIV-1) persists in latently-infected cells through epigenetic and transcriptional mechanisms. Latency-reversing agents have failed clinically, partly due to incomplete understanding of HIV-1 latency reversal. Here, using DNA-affinity capture and mass spectrometry on the HIV-1 5 long terminal repeat (5LTR) enhancer-core promoter, we identify KLF16 (Kruppel-like Factor 16) as a novel regulator of HIV-1 gene expression. KLF16 binds to the HIV-1 5LTR in vivo at Sp1 binding sites, and KLF16 depletion reactivates latent HIV-1 in T-lymphoid and monocytic cell models. Mechanistically, KLF16 represses HIV-1 transcription by competing with Sp1 for promoter binding and by recruiting the Sin3A/HDAC1 and HP1/Suv39H1 repressive epigenetic complexes. KLF16 is also upregulated in CD4+ T cells from ART-treated people with HIV-1 upon T-cell activation. Additionally, All-Trans retinoic acid (ATRA) reactivates latent HIV-1 in myeloid cells, partly by downregulating KLF16. These findings establish KLF16 as a novel transcriptional repressor of HIV-1, identifying it as a potential promising therapeutic target for cure strategies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/722432v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1611026org.highwire.dtl.DTLVardef@16b5eaforg.highwire.dtl.DTLVardef@153b11org.highwire.dtl.DTLVardef@1d90330_HPS_FORMAT_FIGEXP M_FIG C_FIG

HrpZ2, a harpin protein produced by Pseudomonas syringae, a gram-negative plant pathogenic bacterium, elicits hypersensitive response and pathogen defense in non-host plants. Harpins from various bacterial sources elicit varying responses in different non-host plants, due to its structural variations, their precise mechanisms of action are not yet completely understood. As per previous reports, harpins from diverse bacterial sources interact with distinctive members of integral membrane proteins, known as aquaporins. For example, harpin (Hpa1Xoo) interacts with OsPIP1;3 in rice, whereas, in Arabidopsis the harpins Hpa1 and HrpZ interacts with AtPIP1;4 and AtPIP1;3 respectively. Here, we conducted the first genome-wide computational screening of protein-protein interactions between HrpZ2 and all 47 members of tomato aquaporins. Molecular docking identified nine interactors across five subfamilies of aquaporins, with HrpZ2 N-terminal residues mediating these interactions. We validated these via molecular dynamics (MD) simulations, principal component analysis, and free energy landscape analysis, assessing the stability (RMSD, RMSF, radius of gyration), dynamics, and affinity (MM-GMSA). PIP complexes, especially PIP2;1 (-460.46 kcal/mol) and PIP1;7 (-303.82 kcal/mol), exhibited superior stability, compactness, and defined energy minima, confirming PIPs as primary sensors of harpins. Non-PIP aquaporins like TIP1;1 and NIP4;1 showed moderate stability, outperforming weaker interactors (SIP2;1, XIP1;5, XIP1;3). These findings provide robust evidence that HrpZ2 preferentially targets PIPs in tomato, while engaging TIPs and NIPs as auxiliary partners. This multifaceted interaction profile of harpins suggests complex plant-pathogen recognition, modulating aquaporin-mediated cellular responses like growth and stress management in plants.