Biochemical characterisation of a PL24 ulvan lyase from seaweed-associated Vibrio sp. FNV38.

Ulvan is a green macroalgal cell wall polysaccharide that has tremendous potential for valorisation due to its unique composition of sulphated rhamnose, glucuronic acid, iduronic acid and xylose. Several potential applications such as production of biofuels, bioplastics and other value-added products necessitate the breakdown of the polysaccharide to oligomers or monomers. Research on ulvan saccharifying enzymes has been continually increasing over the last decade, with the increasing focus on valorisation of seaweed biomass for a biobased economy. Lyases are the first of several enzymes that are involved in saccharifying the polysaccharide and several ulvan lyases have been structurally and biochemically characterised to enable their effective use in the valorisation processes. This study investigates the whole genome of Vibrio sp. FNV38, an ulvan metabolising organism and biochemical characteristics of a PL24 ulvan lyase that it possesses. The genome of Vibrio sp. FNV38 has a diverse CAZy profile with several genes involved in the metabolism of ulvan, cellulose, agar, and alginate. The enzyme exhibits optimal activity at pH 8.5 in 100 mM Tris-HCl buffer and 30 °C. However, its thermal stability is poor with significant loss of activity after 2 h of incubation at temperatures above 25 °C. Breakdown product analysis reveals that the enzyme depolymerised the polysaccharide predominantly to disaccharides and tetrasaccharides. Supplementary Information: The online version contains supplementary material available at 10.1007/s10811-023-03136-3.

Evolutionary analysis reveals the role of a non-catalytic domain of peptidyl arginine deiminase 2 in transcriptional regulation.

Peptidyl arginine deiminases (PADIs) catalyze protein citrullination, a post-translational conversion of arginine to citrulline. The most widely expressed member of this family, PADI2, regulates cellular processes that impact several diseases. We hypothesized that we could gain new insights into PADI2 function through a systematic evolutionary and structural analysis. Here, we identify 20 positively selected PADI2 residues, 16 of which are structurally exposed and maintain PADI2 interactions with cognate proteins. Many of these selected residues reside in non-catalytic regions of PADI2. We validate the importance of a prominent loop in the middle domain that encompasses PADI2 L162, a residue under positive selection. This site is essential for interaction with the transcription elongation factor (P-TEFb) and mediates the active transcription of the oncogenes c-MYC, and CCNB1, as well as impacting cellular proliferation. These insights could be key to understanding and addressing the role of the PADI2 c-MYC axis in cancer progression.

Integrative single-cell expression and functional studies unravels a sensitization to cytarabine-based chemotherapy through HIF pathway inhibition in AML leukemia stem cells.

Relapse remains a major challenge in the clinical management of acute myeloid leukemia (AML) and is driven by rare therapy-resistant leukemia stem cells (LSCs) that reside in specific bone marrow niches. Hypoxia signaling maintains cells in a quiescent and metabolically relaxed state, desensitizing them to chemotherapy. This suggests the hypothesis that hypoxia contributes to the chemoresistance of AML-LSCs and may represent a therapeutic target to sensitize AML-LSCs to chemotherapy. Here, we identify HIFhigh and HIFlow specific AML subgroups (inv(16)/t(8;21) and MLLr, respectively) and provide a comprehensive single-cell expression atlas of 119,000 AML cells and AML-LSCs in paired diagnostic-relapse samples from these molecular subgroups. The HIF/hypoxia pathway signature is attenuated in AML-LSCs compared with more differentiated AML cells but is more expressed than in healthy hematopoietic cells. Importantly, chemical inhibition of HIF cooperates with standard-of-care chemotherapy to impair AML growth and to substantially eliminate AML-LSCs in vitro and in vivo. These findings support the HIF pathway in the stem cell-driven drug resistance of AML and unravel avenues for combinatorial targeted and chemotherapy-based approaches to specifically eliminate AML-LSCs.

Chromosomal instability in aneuploid acute lymphoblastic leukemia associates with disease progression.

Chromosomal instability (CIN) lies at the core of cancer development leading to aneuploidy, chromosomal copy-number heterogeneity (chr-CNH) and ultimately, unfavorable clinical outcomes. Despite its ubiquity in cancer, the presence of CIN in childhood B-cell acute lymphoblastic leukemia (cB-ALL), the most frequent pediatric cancer showing high frequencies of aneuploidy, remains unknown. Here, we elucidate the presence of CIN in aneuploid cB-ALL subtypes using single-cell whole-genome sequencing of primary cB-ALL samples and by generating and functionally characterizing patient-derived xenograft models (cB-ALL-PDX). We report higher rates of CIN across aneuploid than in euploid cB-ALL that strongly correlate with intraclonal chr-CNH and overall survival in mice. This association was further supported by in silico mathematical modeling. Moreover, mass-spectrometry analyses of cB-ALL-PDX revealed a "CIN signature" enriched in mitotic-spindle regulatory pathways, which was confirmed by RNA-sequencing of a large cohort of cB-ALL samples. The link between the presence of CIN in aneuploid cB-ALL and disease progression opens new possibilities for patient stratification and offers a promising new avenue as a therapeutic target in cB-ALL treatment.

SBILib: a handle for protein modeling and engineering.

SUMMARY: The SBILib Python library provides an integrated platform for the analysis of macromolecular structures and interactions. It combines simple 3D file parsing and workup methods with more advanced analytical tools. SBILib includes modules for macromolecular interactions, loops, super-secondary structures, and biological sequences, as well as wrappers for external tools with which to integrate their results and facilitate the comparative analysis of protein structures and their complexes. The library can handle macromolecular complexes formed by proteins and/or nucleic acid molecules (i.e. DNA and RNA). It is uniquely capable of parsing and calculating protein super-secondary structure and loop geometry. We have compiled a list of example scenarios which SBILib may be applied to and provided access to these within the library. AVAILABILITY AND IMPLEMENTATION: SBILib is made available on Github at https://github.com/structuralbioinformatics/SBILib.

Theoretical 3D Modeling of NLRP3 Inflammasome Complex.

The NOD-like receptor pyrin domain containing 3 (NLRP3) is a multidomain protein that plays a key role in innate immune response. Structures of NLRP3 in different conformational states and bound to cognate partners are available. In this chapter we present an approach to model the oligomeric structure of NLRP3 by homology modeling using multiple templates, symmetry, and refinement. The overall process presented here represents advanced exercise in structural modeling that provides unique insights into the biological role and activation of NLRP3 oligomer. Finally, the same approach can be easily adapted to the rest of the members of the NLRP family.

CM2D3: Furnishing the Human Interactome with Structural Models of Protein Complexes Derived by Comparative Modeling and Docking.

The human interactome is composed of around half a million interactions according to recent estimations and it is only for a small fraction of those that three-dimensional structural information is available. Indeed, the structural coverage of the human interactome is very low and given the complexity and time-consuming requirements of solving protein structures this problem will remain for the foreseeable future. Structural models, or predictions, of protein complexes can provide valuable information when the experimentally determined 3D structures are not available. Here we present CM2D3, a relational database containing structural models of the whole human interactome derived both from comparative modeling and data-driven docking. Starting from a consensus interactome derived from integrating several interactomics databases, a strategy was devised to derive structural models by computational means. Currently, CM2D3 includes 33338 structural models of which 5121 derived from comparative modeling and the remaining from docking. Of the latter, the structures of 14554 complexes were derived from monomers modeled by M4T while the rest were modeled with structures as predicted by AlphaFold2. Lastly, CM2D3 complements existing resources by focusing on models derived from both free-docking, as opposed to template-based docking, and hence expanding the available structural information on protein complexes to the scientific community. Database URL:http://www.bioinsilico.org/CM2D3.

Prediction of Adverse Drug Reaction Linked to Protein Targets Using Network-Based Information and Machine Learning.

Drug discovery attrition rates, particularly at advanced clinical trial stages, are high because of unexpected adverse drug reactions (ADR) elicited by novel drug candidates. Predicting undesirable ADRs produced by the modulation of certain protein targets would contribute to developing safer drugs, thereby reducing economic losses associated with high attrition rates. As opposed to the more traditional drug-centric approach, we propose a target-centric approach to predict associations between protein targets and ADRs. The implementation of the predictor is based on a machine learning classifier that integrates a set of eight independent network-based features. These include a network diffusion-based score, identification of protein modules based on network clustering algorithms, functional similarity among proteins, network distance to proteins that are part of safety panels used in preclinical drug development, set of network descriptors in the form of degree and betweenness centrality measurements, and conservation. This diverse set of descriptors were used to generate predictors based on different machine learning classifiers ranging from specific models for individual ADR to higher levels of abstraction as per MEDDRA hierarchy such as system organ class. The results obtained from the different machine-learning classifiers, namely, support vector machine, random forest, and neural network were further analyzed as a meta-predictor exploiting three different voting systems, namely, jury vote, consensus vote, and red flag, obtaining different models for each of the ADRs in analysis. The level of accuracy of the predictors justifies the identification of problematic protein targets both at the level of individual ADR as well as a set of related ADRs grouped in common system organ classes. As an example, the prediction of ventricular tachycardia achieved an accuracy and precision of 0.83 and 0.90, respectively, and a Matthew correlation coefficient of 0.70. We believe that this approach is a good complement to the existing methodologies devised to foresee potential liabilities in preclinical drug discovery. The method is available through the DocTOR utility at GitHub (https://github.com/cristian931/DocTOR).

In silico identification of two peptides with antibacterial activity against multidrug-resistant Staphylococcus aureus.

Here we report two antimicrobial peptides (AMPs), HG2 and HG4 identified from a rumen microbiome metagenomic dataset, with activity against multidrug-resistant (MDR) bacteria, especially methicillin-resistant Staphylococcus aureus (MRSA) strains, a major hospital and community-acquired pathogen. We employed the classifier model design to analyse, visualise, and interpret AMP activities. This approach allowed in silico discrimination of promising lead AMP candidates for experimental evaluation. The lead AMPs, HG2 and HG4, are fast-acting and show anti-biofilm and anti-inflammatory activities in vitro and demonstrated little toxicity to human primary cell lines. The peptides were effective in vivo within a Galleria mellonella model of MRSA USA300 infection. In terms of mechanism of action, HG2 and HG4 appear to interact with the cytoplasmic membrane of target cells and may inhibit other cellular processes, whilst preferentially binding to bacterial lipids over human cell lipids. Therefore, these AMPs may offer additional therapeutic templates for MDR bacterial infections.

Novel SIX6 mutations cause recessively inherited congenital cataract, microcornea, and corneal opacification with or without coloboma and microphthalmia.

Purpose: To investigate the molecular basis of recessively inherited congenital cataract, microcornea, and corneal opacification with or without coloboma and microphthalmia in two consanguineous families. Methods: Conventional autozygosity mapping was performed using single nucleotide polymorphism (SNP) microarrays. Whole-exome sequencing was completed on genomic DNA from one affected member of each family. Exome sequence data were also used for homozygosity mapping and copy number variation analysis. PCR and Sanger sequencing were used to confirm the identification of mutations and to screen further patients. Evolutionary conservation of protein sequences was assessed using CLUSTALW, and protein structures were modeled using PyMol. Results: In family MEP68, a novel homozygous nucleotide substitution in SIX6 was found, c.547G>C, that converts the evolutionarily conserved aspartic acid residue at the 183rd amino acid in the protein to a histidine, p.(Asp183His). This residue mapped to the third helix of the DNA-binding homeobox domain in SIX6, which interacts with the major groove of double-stranded DNA. This interaction is likely to be disrupted by the mutation. In family F1332, a novel homozygous 1034 bp deletion that encompasses the first exon of SIX6 was identified, chr14:g.60975890_60976923del. Both mutations segregated with the disease phenotype as expected for a recessive condition and were absent from publicly available variant databases. Conclusions: Our findings expand the mutation spectrum in this form of inherited eye disease and confirm that homozygous human SIX6 mutations cause a developmental spectrum of ocular phenotypes that includes not only the previously described features of microphthalmia, coloboma, and congenital cataract but also corneal abnormalities.

Prediction of Protein-Protein Binding Affinities from Unbound Protein Structures.

Proteins are the workhorses of cells to carry out sophisticated and complex cellular processes. Such processes require a coordinated and regulated interactions between proteins that are both time and location specific. The strength, or binding affinity, of protein-protein interactions ranges between the micro- and the nanomolar association constant, often dictating the molecular mechanisms underlying the interaction and the longevity of the complex, i.e., transient or permanent. In consequence, there is a need to quantify the strength of protein-protein interactions for biological, biomedical, and biotechnological applications. While experimental methods are labor intensive and costly, computational ones are useful tools to predict the affinity of protein-protein interactions. In this chapter, we review the methods developed by us to address this question. We briefly present two methods to comprehend the structure of the protein complex derived by either comparative modeling or docking. Then we introduce BADOCK, a method to predict the binding energy without requiring the structure of the protein complex, thus overcoming one of the major limitations of structure-based methods for the prediction of binding affinity. BADOCK utilizes the structure of unbound proteins and the protein docking sampling space to predict protein-protein binding affinities. We present step-by-step protocols to utilize these methods, describing the inputs and potential pitfalls as well as their respective strengths and limitations.

In silico characterisation of the complete Ly6 protein family in Fasciola gigantica supported through transcriptomics of the newly-excysted juveniles.

Fasciola gigantica is one of the aetiological trematodes associated with fascioliasis, which heavily impacts food-production systems and human and animal welfare on a global scale. In the absence of a vaccine, fascioliasis control and treatment is restricted to pasture management, such as clean grazing, and a limited array of chemotherapies, to which signs of resistance are beginning to appear. Research into novel control strategies is therefore urgently required and the advent of 'omics technologies presents considerable opportunity for novel drug and vaccine target discovery. Here, interrogation of the first available F. gigantica newly excysted juvenile (NEJ) transcriptome revealed several protein families of current interest to parasitic flatworm vaccine research, including orthologues of mammalian complement regulator CD59 of the Ly6 family. Ly6 proteins have previously been identified on the tegument of Schistosoma mansoni and induced protective immunity in vaccination trials. Incorporating the recently available F. gigantica genome, the current work revealed 20 novel Ly6 family members in F. gigantica and, in parallel, significantly extended the F. hepatica complement from 3 to 18 members. Phylogenetic analysis revealed several distinct clades within the family, some of which are unique to Fasciola spp. trematodes. Analysis of available proteomic databases also revealed three of the newly discovered FhLy6s were present in extracellular vesicles, which have previously been prioritised in studying the host-parasite interface. The presentation of this new transcriptomic resource, in addition to the Ly6 family proteins here identified, represents a wealth of opportunity for future vaccine research.

Inhibition of Viral Membrane Fusion by Peptides and Approaches to Peptide Design.

Fusion of lipid-enveloped viruses with the cellular plasma membrane or the endosome membrane is mediated by viral envelope proteins that undergo large conformational changes following binding to receptors. The HIV-1 fusion protein gp41 undergoes a transition into a "six-helix bundle" after binding of the surface protein gp120 to the CD4 receptor and a co-receptor. Synthetic peptides that mimic part of this structure interfere with the formation of the helix structure and inhibit membrane fusion. This approach also works with the S spike protein of SARS-CoV-2. Here we review the peptide inhibitors of membrane fusion involved in infection by influenza virus, HIV-1, MERS and SARS coronaviruses, hepatitis viruses, paramyxoviruses, flaviviruses, herpesviruses and filoviruses. We also describe recent computational methods used for the identification of peptide sequences that can interact strongly with protein interfaces, with special emphasis on SARS-CoV-2, using the PePI-Covid19 database.

A Collection of Designed Peptides to Target SARS-CoV-2 Spike RBD-ACE2 Interaction.

The angiotensin-converting enzyme 2 (ACE2) is the receptor used by SARS-CoV and SARS-CoV-2 coronaviruses to attach to cells via the receptor-binding domain (RBD) of their viral spike protein. Since the start of the COVID-19 pandemic, several structures of protein complexes involving ACE2 and RBD as well as monoclonal antibodies and nanobodies have become available. We have leveraged the structural data to design peptides to target the interaction between the RBD of SARS-CoV-2 and ACE2 and SARS-CoV and ACE2, as contrasting exemplar, as well as the dimerization surface of ACE2 monomers. The peptides were modelled using our original method: PiPreD that uses native elements of the interaction between the targeted protein and cognate partner(s) that are subsequently included in the designed peptides. These peptides recapitulate stretches of residues present in the native interface plus novel and highly diverse conformations surrogating key interactions at the interface. To facilitate the access to this information we have created a freely available and dedicated web-based repository, PepI-Covid19 database, providing convenient access to this wealth of information to the scientific community with the view of maximizing its potential impact in the development of novel therapeutic and diagnostic agents.

Mining drug-target and drug-adverse drug reaction databases to identify target-adverse drug reaction relationships.

The level of attrition on drug discovery, particularly at advanced stages, is very high due to unexpected adverse drug reactions (ADRs) caused by drug candidates, and thus, being able to predict undesirable responses when modulating certain protein targets would contribute to the development of safer drugs and have important economic implications. On the one hand, there are a number of databases that compile information of drug-target interactions. On the other hand, there are a number of public resources that compile information on drugs and ADR. It is therefore possible to link target and ADRs using drug entities as connecting elements. Here, we present T-ARDIS (Target-Adverse Reaction Database Integrated Search) database, a resource that provides comprehensive information on proteins and associated ADRs. By combining the information from drug-protein and drug-ADR databases, we statistically identify significant associations between proteins and ADRs. Besides describing the relationship between proteins and ADRs, T-ARDIS provides detailed description about proteins along with the drug and adverse reaction information. Currently T-ARDIS contains over 3000 ADR and 248 targets for a total of more 17 000 pairwise interactions. Each entry can be retrieved through multiple search terms including target Uniprot ID, gene name, adverse effect and drug name. Ultimately, the T-ARDIS database has been created in response to the increasing interest in identifying early in the drug development pipeline potentially problematic protein targets whose modulation could result in ADRs. Database URL: http://www.bioinsilico.org/T-ARDIS.

On the use of direct-coupling analysis with a reduced alphabet of amino acids combined with super-secondary structure motifs for protein fold prediction.

Direct-coupling analysis (DCA) for studying the coevolution of residues in proteins has been widely used to predict the three-dimensional structure of a protein from its sequence. We present RADI/raDIMod, a variation of the original DCA algorithm that groups chemically equivalent residues combined with super-secondary structure motifs to model protein structures. Interestingly, the simplification produced by grouping amino acids into only two groups (polar and non-polar) is still representative of the physicochemical nature that characterizes the protein structure and it is in line with the role of hydrophobic forces in protein-folding funneling. As a result of a compressed alphabet, the number of sequences required for the multiple sequence alignment is reduced. The number of long-range contacts predicted is limited; therefore, our approach requires the use of neighboring sequence-positions. We use the prediction of secondary structure and motifs of super-secondary structures to predict local contacts. We use RADI and raDIMod, a fragment-based protein structure modelling, achieving near native conformations when the number of super-secondary motifs covers >30-50% of the sequence. Interestingly, although different contacts are predicted with different alphabets, they produce similar structures.

A comparison of shared patterns of differential gene expression and gene ontologies in response to water-stress in roots and leaves of four diverse genotypes of Lolium and Festuca spp. temperate pasture grasses.

Ryegrasses (Lolium spp.) and fescues (Festuca spp.) are closely related and widely cultivated perennial forage grasses. As such, resilience in the face of abiotic stresses is an important component of their traits. We have compared patterns of differentially expressed genes (DEGs) in roots and leaves of two perennial ryegrass genotypes and a single genotype of each of a festulolium (predominantly Italian ryegrass) and meadow fescue with the onset of water stress, focussing on overall patterns of DEGs and gene ontology terms (GOs) shared by all four genotypes. Plants were established in a growing medium of vermiculite watered with nutrient solution. Leaf and root material were sampled at 35% (saturation) and, as the medium dried, at 15%, 5% and 1% estimated water contents (EWCs) and RNA extracted. Differential gene expression was evaluated comparing the EWC sampling points from RNAseq data using a combination of analysis methods. For all genotypes, the greatest numbers of DEGs were identified in the 35/1 and 5/1 comparisons in both leaves and roots. In total, 566 leaf and 643 root DEGs were common to all 4 genotypes, though a third of these leaf DEGs were not regulated in the same up/down direction in all 4 genotypes. For roots, the equivalent figure was 1% of the DEGs. GO terms shared by all four genotypes were often enriched by both up- and down-regulated DEGs in the leaf, whereas generally, only by either up- or down-regulated DEGs in the root. Overall, up-regulated leaf DEGs tended to be more genotype-specific than down-regulated leaf DEGs or root DEGs and were also associated with fewer GOs. On average, only 5-15% of the DEGs enriching common GO terms were shared by all 4 genotypes, suggesting considerable variation in DEGs between related genotypes in enacting similar biological processes.

A Comparison of Differential Gene Expression in Response to the Onset of Water Stress Between Three Hybrid Brachiaria Genotypes.

Brachiaria (Trin.) Griseb. (syn. Urochloa P. Beauv.) is a C4 grass genus belonging to the Panicoideae. Native to Africa, these grasses are now widely grown as forages in tropical areas worldwide and are the subject of intensive breeding, particularly in South America. Tolerance to abiotic stresses such as aluminum and drought are major breeding objectives. In this study, we present the transcriptomic profiling of leaves and roots of three Brachiaria interspecific hybrid genotypes with the onset of water stress, Br12/3659-17 (gt-17), Br12/2360-9 (gt-9), and Br12/3868-18 (gt-18), previously characterized as having good, intermediate and poor tolerance to drought, respectively, in germplasm evaluation programs. RNA was extracted from leaf and root tissue of plants at estimated growing medium water contents (EWC) of 35, 15, and 5%. Differentially expressed genes (DEGs) were compared between different EWCs, 35/15, 15/5, and 35/5 using DESeq2. Overall, the proportions of DEGs enriched in all three genotypes varied in a genotype-dependent manner in relation to EWC comparison, with intermediate and sensitive gt-9 and gt-18 being more similar to each other than to drought tolerant gt-17. More specifically, GO terms relating to carbohydrate and cell wall metabolism in the leaves were enriched by up-regulated DEGs in gt-9 and gt-18, but by down-regulated DEGs in gt-17. Across all genotypes, analysis of DEG enzyme activities indicated an excess of down-regulated putative apoplastic peroxidases in the roots as water stress increased. This suggests that changes in root cell-wall architecture may be an important component of the response to water stress in Brachiaria.

The rumen eukaryotome is a source of novel antimicrobial peptides with therapeutic potential.

BACKGROUND: The rise of microbial antibiotic resistance is a leading threat to the health of the human population. As such, finding new approaches to tackle these microbes, including development of novel antibiotics is vital. RESULTS: In this study, we mined a rumen eukaryotic metatranscriptomic library for novel Antimicrobial peptides (AMPs) using computational approaches and thereafter characterised the therapeutic potential of the AMPs. We identified a total of 208 potentially novel AMPs from the ruminal eukaryotome, and characterised one of those, namely Lubelisin. Lubelisin (GIVAWFWRLAR) is an α-helical peptide, 11 amino acid long with theoretical molecular weight of 1373.76 D. In the presence of Lubelisin, strains of methicillin-resistant Staphylococcus aureus (MRSA) USA300 and EMRSA-15 were killed within 30 min of exposure with ≥103 and 104 CFU/mL reduction in viable cells respectively. Cytotoxicity of Lubelisin against both human and sheep erythrocytes was low resulting in a therapeutic index of 0.43. Membrane permeabilisation assays using propidium iodide alongside transmission electron microscopy revealed that cytoplasmic membrane damage may contribute to the antimicrobial activities of Lubelisin. CONCLUSIONS: We demonstrate that the rumen eukaryotome is a viable source for the discovery of antimicrobial molecules for the treatment of bacterial infections and further development of these may provide part of the potential solution to the ongoing problem of antimicrobial resistance. The role of these AMPs in the ecological warfare within the rumen is also currently unknown.

Stabilisation of half MCM ring by Cdt1 during DNA insertion.

Origin licensing ensures precise once per cell cycle replication in eukaryotic cells. The Origin Recognition Complex, Cdc6 and Cdt1 load Mcm2-7 helicase (MCM) into a double hexamer, bound around duplex DNA. The complex formed by ORC-Cdc6 bound to duplex DNA (OC) recruits the MCM-Cdt1 complex into the replication origins. Through the stacking of both complexes, the duplex DNA is inserted inside the helicase by an unknown mechanism. In this paper we show that the DNA insertion comes with a topological problem in the stacking of OC with MCM-Cdt1. Unless an essential, conserved C terminal winged helix domain (C-WHD) of Cdt1 is present, the MCM splits into two halves. The binding of this domain with the essential C-WHD of Mcm6, allows the latching between the MCM-Cdt1 and OC, through a conserved Orc5 AAA-lid interaction. Our work provides new insights into how DNA is inserted into the eukaryotic replicative helicase, through a series of synchronized events.