Cluster Domain Research Articles

Leveraging Higher Education in Our Age-Friendly World

ABSTRACT There is increasing interest in better understanding the connection between higher education and age-friendly community efforts. The global age-friendly community (AFC) movement calls for multi-sectoral engagement in a multi-year model encompassing four core phases (engage, plan, act, measure) to improve livability in domains of community life pertaining to the built, social, and service environment. However, there is limited empirical knowledge regarding the involvement of higher education and how it supports AFC efforts. We used qualitative inquiry to assess the engagement of U.S. institutions as reported by 80 AFCs that completed a five-year cycle of participation. We conducted directed content analysis using paired AFC action plans and progress reports (n = 56) and classified engagement using a priori indicators by higher educational core activities (teaching, research, and service), core phases (e.g. measure), and clustered domain areas (e.g. built environment). Results reveal engagement across all core activities of higher education with most efforts in research, in all areas of the AFC model with most reported in the action phase, and across all clustered domains of practice with the greatest amount identified in the social environment. We identify opportunities for greater engagement and leadership through higher education in our age-friendly world.

Onset of a conceptual outline map to get a hold on the jungle of cluster analysis

AbstractThe domain of cluster analysis is a meeting point for a very rich multidisciplinary encounter, with cluster‐analytic methods being studied and developed in discrete mathematics, numerical analysis, statistics, data analysis, data science, and computer science (including machine learning, data mining, and knowledge discovery), to name but a few. The other side of the coin, however, is that the domain suffers from a major accessibility problem as well as from the fact that it is rife with division across many pretty isolated islands. As a way out, the present paper offers a thorough and in‐depth review of the clustering domain as a whole under the form of an outline map based on an overarching conceptual framework and a common language. With this framework we wish to contribute to structuring the clustering domain, to characterizing methods that have often been developed and studied in quite different contexts, to identifying links between methods, and to introducing a frame of reference for optimally setting up cluster analyses in data‐analytic practice.This article is categorized under: Technologies > Structure Discovery and Clustering

A dual RNA-seq analyses revealed dynamic arms race during the invasion of walnut by Colletotrichum gloeosporioides

BackgroundWalnut anthracnose caused by Colletotrichum gloeosporioides seriously endangers the yield and quality of walnut, and has now become a catastrophic disease in the walnut industry. Therefore, understanding both pathogen invasion mechanisms and host response processes is crucial to defense against C. gloeosporioides infection.ResultsHere, we investigated the mechanisms of interaction between walnut fruits (anthracnose-resistant F26 fruit bracts and anthracnose-susceptible F423 fruit bracts) and C. gloeosporioides at three infection time points (24hpi, 48hpi, and 72hpi) using a high-resolution time series dual transcriptomic analysis, characterizing the arms race between walnut and C. gloeosporioides. A total of 20,780 and 6670 differentially expressed genes (DEGs) were identified in walnut and C. gloeosporioides against 24hpi, respectively. Generous DEGs in walnut exhibited opposite expression patterns between F26 and F423, which indicated that different resistant materials exhibited different transcriptional responses to C. gloeosporioides during the infection process. KEGG functional enrichment analysis indicated that F26 displayed a broader response to C. gloeosporioides than F423. Meanwhile, the functional analysis of the C. gloeosporioides transcriptome was conducted and found that PHI, SignalP, CAZy, TCDB genes, the Fungal Zn (2)-Cys (6) binuclear cluster domain (PF00172.19) and the Cytochrome P450 (PF00067.23) were largely prominent in F26 fruit. These results suggested that C. gloeosporioides secreted some type of effector proteins in walnut fruit and appeared a different behavior based on the developmental stage of the walnut.ConclusionsOur present results shed light on the arms race process by which C. gloeosporioides attacked host and walnut against pathogen infection, laying the foundation for the green prevention of walnut anthracnose.

C2H2 proteins: Evolutionary aspects of domain architecture and diversification.

The largest group of transcription factors in higher eukaryotes are C2H2 proteins, which contain C2H2-type zinc finger domains that specifically bind to DNA. Few well-studied C2H2 proteins, however, demonstrate their key role in the control of gene expression and chromosome architecture. Here we review the features of the domain architecture of C2H2 proteins and the likely origin of C2H2 zinc fingers. A comprehensive investigation of proteomes for the presence of proteins with multiple clustered C2H2 domains has revealed a key difference between groups of organisms. Unlike plants, transcription factors in metazoans contain clusters of C2H2 domains typically separated by a linker with the TGEKP consensus sequence. The average size of C2H2 clusters varies substantially, even between genomes of higher metazoans, and with a tendency to increase in combination with SCAN, and especially KRAB domains, reflecting the increasing complexity of gene regulatory networks.

Connectivity modelling identifies sources and sinks of coral recruitment within reef clusters

Connectivity aids the recovery of populations following disturbances, such as coral bleaching and tropical cyclones. Coral larval connectivity is a function of physical connectivity and larval behaviour. In this study, we used OceanParcels, a particle tracking simulator, with 2D and 3D velocity outputs from a high resolution hydrodynamic-biogeochemical marine model (RECOM) to simulate the dispersal and settlement of larvae from broadcast spawning Acropora corals in the Moore Reef cluster, northern Great Barrier Reef, following the annual spawning events in 2015, 2016 and 2017. 3D velocity simulations showed 19.40–68.80% more links and sinks than those of 2D simulations. Although the patterns of connectivity among sites vary over days and years, coral larvae consistently dispersed from east to west in the cluster domain, with some sites consistently acting as sources or sinks for local larval recruitment. Results can inform coral reef intervention plans for climate change, such as the design of marine protected areas and the deployment of proposed interventions within reef clusters. For example, the wider benefits of interventions (e.g., deployment of heat adapted corals) may be optimised when deployed at locations that are a source of larvae to others within comparable habitats across the reef cluster.

A Novel Clustering Algorithm Integrating Gershgorin Circle Theorem and Nonmaximum Suppression for Neural Spike Data Analysis

(1) Problem Statement: The development of clustering algorithms for neural recordings has significantly evolved, reaching a mature stage with predominant approaches including partitional, hierarchical, probabilistic, fuzzy logic, density-based, and learning-based clustering. Despite this evolution, there remains a need for innovative clustering algorithms that can efficiently analyze neural spike data, particularly in handling diverse and noise-contaminated neural recordings. (2) Methodology: This paper introduces a novel clustering algorithm named Gershgorin—nonmaximum suppression (G–NMS), which incorporates the principles of the Gershgorin circle theorem, and a deep learning post-processing method known as nonmaximum suppression. The performance of G–NMS was thoroughly evaluated through extensive testing on two publicly available, synthetic neural datasets. The evaluation involved five distinct groups of experiments, totaling eleven individual experiments, to compare G–NMS against six established clustering algorithms. (3) Results: The results highlight the superior performance of G–NMS in three out of five group experiments, achieving high average accuracy with minimal standard deviation (SD). Specifically, in Dataset 1, experiment S1 (various SNRs) recorded an accuracy of 99.94 ± 0.01, while Dataset 2 showed accuracies of 99.68 ± 0.15 in experiment E1 (Easy 1) and 99.27 ± 0.35 in experiment E2 (Easy 2). Despite a slight decrease in average accuracy in the remaining two experiments, D1 (Difficult 1) and D2 (Difficult 2) from Dataset 2, compared to the top-performing clustering algorithms in these categories, G–NMS maintained lower SD, indicating consistent performance. Additionally, G–NMS demonstrated robustness and efficiency across various noise-contaminated neural recordings, ranging from low to high signal-to-noise ratios. (4) Conclusions: G–NMS’s integration of deep learning techniques and eigenvalue inclusion theorems has proven highly effective, marking a significant advancement in the clustering domain. Its superior performance, characterized by high accuracy and low variability, opens new avenues for the development of high-performing clustering algorithms, contributing significantly to the body of research in this field.

A New Approach In Metaheuristic Clustering: Coot Clustering

As a result of technological advancements, the increase in vast amounts of data in today's world has made artificial intelligence and data mining significantly crucial. In this context, the clustering process, which aims to explore hidden patterns and meaningful relationships within complex datasets by grouping similar features to conduct more effective analyses, holds vital importance. As an alternative to classical clustering methods that face challenges such as large volumes of data and computational complexities, a metaheuristic clustering method utilizing Coot Optimization (COOT), a swarm intelligence-based algorithm, has been proposed. COOT, inspired by the hunting stages of eagles and recently introduced into the literature, is a metaheuristic method. Through the proposed COOT metaheuristic clustering method, the aim is to contribute to the literature by leveraging COOT's robust exploration and exploitation processes, utilizing its dynamic and flexible structure. Comprehensive experimental clustering studies were conducted to evaluate the consistency and effectiveness of the COOT-based algorithm using randomly generated synthetic data and the widely used Iris dataset in the literature. The same datasets underwent analysis using the traditional clustering algorithm K-Means, renowned for its simplicity and computational speed, for comparative purposes. The performance of the algorithms was assessed using cluster validity measures such as Silhouette Global, Davies-Bouldin, Krznowski-Lai, and Calinski-Harabasz indices, along with the Total Squared Error (SSE) objective function. Experimental results indicate that the proposed algorithm performs clustering at a competitive level with K-Means and shows potential, especially in multidimensional datasets and real-world problems. Despite not being previously used for clustering purposes, the impressive performance of COOT in some tests compared to the K-Means algorithm showcases its success and potential to pioneer different studies aimed at expanding its usage in the clustering domain.

Comparative secretome analysis of Striga and Cuscuta species identifies candidate virulence factors for two evolutionarily independent parasitic plant lineages

BackgroundMany parasitic plants of the genera Striga and Cuscuta inflict huge agricultural damage worldwide. To form and maintain a connection with a host plant, parasitic plants deploy virulence factors (VFs) that interact with host biology. They possess a secretome that represents the complement of proteins secreted from cells and like other plant parasites such as fungi, bacteria or nematodes, some secreted proteins represent VFs crucial to successful host colonisation. Understanding the genome-wide complement of putative secreted proteins from parasitic plants, and their expression during host invasion, will advance understanding of virulence mechanisms used by parasitic plants to suppress/evade host immune responses and to establish and maintain a parasite-host interaction.ResultsWe conducted a comparative analysis of the secretomes of root (Striga spp.) and shoot (Cuscuta spp.) parasitic plants, to enable prediction of candidate VFs. Using orthogroup clustering and protein domain analyses we identified gene families/functional annotations common to both Striga and Cuscuta species that were not present in their closest non-parasitic relatives (e.g. strictosidine synthase like enzymes), or specific to either the Striga or Cuscuta secretomes. For example, Striga secretomes were strongly associated with ‘PAR1’ protein domains. These were rare in the Cuscuta secretomes but an abundance of ‘GMC oxidoreductase’ domains were found, that were not present in the Striga secretomes. We then conducted transcriptional profiling of genes encoding putatively secreted proteins for the most agriculturally damaging root parasitic weed of cereals, S. hermonthica. A significant portion of the Striga-specific secretome set was differentially expressed during parasitism, which we probed further to identify genes following a ‘wave-like’ expression pattern peaking in the early penetration stage of infection. We identified 39 genes encoding putative VFs with functions such as cell wall modification, immune suppression, protease, kinase, or peroxidase activities, that are excellent candidates for future functional studies.ConclusionsOur study represents a comprehensive secretome analysis among parasitic plants and revealed both similarities and differences in candidate VFs between Striga and Cuscuta species. This knowledge is crucial for the development of new management strategies and delaying the evolution of virulence in parasitic weeds.

An oligopeptide permease, OppABCD, requires an iron-sulfur cluster domain for functionality.

Oligopeptide permease, OppABCD, belongs to the type I ABC transporter family. Its role is to import oligopeptides into bacteria for nutrient uptake and to modulate the host immune response. OppABCD consists of a cluster C substrate-binding protein (SBP), OppA, membrane-spanning OppB and OppC subunits, and an ATPase, OppD, that contains two nucleotide-binding domains (NBDs). Here, using cryo-electron microscopy, we determined the high-resolution structures of Mycobacterium tuberculosis OppABCD in the resting state, oligopeptide-bound pre-translocation state, AMPPNP-bound pre-catalytic intermediate state and ATP-bound catalytic intermediate state. The structures show an assembly of a cluster C SBP with its ABC translocator and a functionally required [4Fe-4S] cluster-binding domain in OppD. Moreover, the ATP-bound OppABCD structure has an outward-occluded conformation, although no substrate was observed in the transmembrane cavity. Here, we reveal an oligopeptide recognition and translocation mechanism of OppABCD, which provides a perspective on how this and other type I ABC importers facilitate bulk substrate transfer across the lipid bilayer.

Noncanonical usage of stop codons in ciliates expands proteins with structurally flexible Q-rich motifs.

Serine(S)/threonine(T)-glutamine(Q) cluster domains (SCDs), polyglutamine (polyQ) tracts and polyglutamine/asparagine (polyQ/N) tracts are Q-rich motifs found in many proteins. SCDs often are intrinsically disordered regions that mediate protein phosphorylation and protein-protein interactions. PolyQ and polyQ/N tracts are structurally flexible sequences that trigger protein aggregation. We report that due to their high percentages of STQ or STQN amino acid content, four SCDs and three prion-causing Q/N-rich motifs of yeast proteins possess autonomous protein expression-enhancing activities. Since these Q-rich motifs can endow proteins with structural and functional plasticity, we suggest that they represent useful toolkits for evolutionary novelty. Comparative Gene Ontology (GO) analyses of the near-complete proteomes of 26 representative model eukaryotes reveal that Q-rich motifs prevail in proteins involved in specialized biological processes, including Saccharomyces cerevisiae RNA-mediated transposition and pseudohyphal growth, Candida albicans filamentous growth, ciliate peptidyl-glutamic acid modification and microtubule-based movement, Tetrahymena thermophila xylan catabolism and meiosis, Dictyostelium discoideum development and sexual cycles, Plasmodium falciparum infection, and the nervous systems of Drosophila melanogaster, Mus musculus and Homo sapiens. We also show that Q-rich-motif proteins are expanded massively in 10 ciliates with reassigned TAAQ and TAGQ codons. Notably, the usage frequency of CAGQ is much lower in ciliates with reassigned TAAQ and TAGQ codons than in organisms with expanded and unstable Q runs (e.g. D. melanogaster and H. sapiens), indicating that the use of noncanonical stop codons in ciliates may have coevolved with codon usage biases to avoid triplet repeat disorders mediated by CAG/GTC replication slippage.

Noncanonical usage of stop codons in ciliates expands proteins with structurally flexible Q-rich motifs

Serine(S)/threonine(T)-glutamine(Q) cluster domains (SCDs), polyglutamine (polyQ) tracts and polyglutamine/asparagine (polyQ/N) tracts are Q-rich motifs found in many proteins. SCDs often are intrinsically disordered regions that mediate protein phosphorylation and protein-protein interactions. PolyQ and polyQ/N tracts are structurally flexible sequences that trigger protein aggregation. We report that due to their high percentages of STQ or STQN amino acid content, four SCDs and three prion-causing Q/N-rich motifs of yeast proteins possess autonomous protein expression-enhancing activities. Since these Q-rich motifs can endow proteins with structural and functional plasticity, we suggest that they represent useful toolkits for evolutionary novelty. Comparative Gene Ontology (GO) analyses of the near-complete proteomes of 26 representative model eukaryotes reveal that Q-rich motifs prevail in proteins involved in specialized biological processes, including Saccharomyces cerevisiae RNA-mediated transposition and pseudohyphal growth, Candida albicans filamentous growth, ciliate peptidyl-glutamic acid modification and microtubule-based movement, Tetrahymena thermophila xylan catabolism and meiosis, Dictyostelium discoideum development and sexual cycles, Plasmodium falciparum infection, and the nervous systems of Drosophila melanogaster, Mus musculus and Homo sapiens. We also show that Q-rich-motif proteins are expanded massively in 10 ciliates with reassigned TAAQ and TAGQ codons. Notably, the usage frequency of CAGQ is much lower in ciliates with reassigned TAAQ and TAGQ codons than in organisms with expanded and unstable Q runs (e.g. D. melanogaster and H. sapiens), indicating that the use of noncanonical stop codons in ciliates may have coevolved with codon usage biases to avoid triplet repeat disorders mediated by CAG/GTC replication slippage.

A novel generalized similarity measure under intuitionistic fuzzy environment and its applications to criminal investigation

In our contemporary world, where crime prevails, the expeditious conduct of criminal investigations stands as an essential pillar of law and order. However, these inquiries often grapple with intricate complexities, particularly uncertainties stemming from the scarcity of reliable evidence, which can significantly hinder progress. To surmount these challenges, the invaluable tools of crime linkage and psychological profiling of offenders have come to the forefront. The advent of Intuitionistic Fuzzy Sets (IFS) has proven pivotal in navigating these uncertain terrains of decision-making, and at the heart of this lies the concept of similarity measure-an indispensable tool for unraveling intricate problems of choice. While a multitude of similarity measures exists for gauging the likeness between IFSs, our study introduces a novel generalized similarity measure firmly rooted in the IFS framework, poised to surpass existing methods with enhanced accuracy and applicability. We then extend the horizon of practicality by employing this pioneering similarity measure in the domain of clustering for crime prediction-a paramount application within the realm of law enforcement. Furthermore, we venture into the domain of psychological profiling, a potent avenue that has the potential to significantly fortify the arsenal of crime investigations. Through the application of our proposed similarity measure, we usher in a new era of efficacy and insight in the pursuit of justice. In sum, this study not only unveils a groundbreaking similarity measure within the context of an Intuitionistic fuzzy environment but also showcases its compelling applications in the arena of criminal investigation, marking a significant stride toward swifter and more informed decisions in the realm of law and order.

Fast Local Rules Based Switching and Routing Within Multidimensional Torus Interconnect

Multidimensional torus networking topology has become widespread recently in the domain of high-performance computers, clusters, and grids, as well as in the domain of networks on chip. Torus represents an ideal communication structure with the shortest distance and multitude of alternative shortest paths between a pair of nodes. We study simple and powerful local packet forwarding (switching) rules that provide packet delivery with quasi-optimal load balancing and do not use tables of addresses (routes). Implementation of these rules in the form of micro-program code within switching nodes increases considerably network performance, security, and QoS. We use infinite Petri nets and reenterable models in the form of colored Petri nets for prototyping the multi-dimensional torus interconnect simulator to study and compare the packet forwarding rules. Then, an ad-hoc simulator of torus interconnect ts is implemented in the C language to provide high performance and the possibility of simulation over prolonged intervals of time. The simulation results acknowledge the advantages of local packet forwarding rules.

Nanoscale Ion Transport Enhances Conductivity in Solid Polymer-Ceramic Lithium Electrolytes.

The predictive design of flexible and solvent-free polymer electrolytes for solid-state batteries requires an understanding of the fundamental principles governing the ion transport. In this work, we establish a correlation among the composite structures, polymer segmental dynamics, and lithium ion (Li+) transport in a ceramic-polymer composite. Elucidating this structure-property relationship will allow tailoring of the Li+ conductivity by optimizing the macroscopic electrochemical stability of the electrolyte. The ion dissociation from the slow polymer segmental dynamics was found to be enhanced by controlling the morphology and functionality of the polymer/ceramic interface. The chemical structure of the Li+ salt in the composite electrolyte was correlated with the size of the ionic cluster domains, the conductivity mechanism, and the electrochemical stability of the electrolyte. Polyethylene oxide (PEO) filled with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) or lithium bis(fluorosulfonyl) imide (LiFSI) salts was used as a matrix. A garnet electrolyte, aluminum substituted lithium lanthanum zirconium oxide (Al-LLZO) with a planar geometry, was used for the ceramic nanoparticle moieties. The dynamics of the strongly bound and highly mobile Li+ were investigated using dielectric relaxation spectroscopy. The incorporation of the Al-LLZO platelets increased the number density of more mobile Li+. The structure of the nanoscale ion-agglomeration was investigated by small-angle X-ray scattering, while molecular dynamics (MD) simulation studies were conducted to obtain the fundamental mechanism of the decorrelation of the Li+ in the LiTFSI and LiFSI salts from the long PEO chain.

Big data clustering through fusion of FCM, optimized encoder-decoder CNN, and BiLSTM

Clustering Big Data, as a fundamental component in the processing and analysis of massive datasets, holds crucial importance in addressing complex challenges inherent in handling extensive data sets. Falling within the realm of unsupervised learning methods, the primary objective of clustering is to efficiently organize substantial datasets into homogeneous clusters without relying on pre-existing labels. Our innovative approach seeks to optimize this process by synergistically combining three techniques: the fuzzy C-Means (FCM) methodology, the optimized encoder–decoder CNN model, and the bidirectional recurrent neural network (BiLSTM). This synergy represents a strategic convergence between supervised and unsupervised paradigms. The introduction of BiLSTM is of significant importance, leveraging its capability to sequentially process data from both sides using LSTM cells. This bidirectional approach enhances the understanding of data sequences, a crucial feature in the demanding context of Big Data clustering. Simultaneously, FCM benefits from substantial improvement through the introduction of a function that calculates the separation between the cluster center and the instance, thereby reinforcing the precision of clustering. To optimize performance and reduce computation time, our methodology advocates for the use of the Optimized Encoder–Decoder CNN model. This refined architecture promotes more efficient extraction of data features, thereby enhancing the intrinsic quality of clustering. The rigorous evaluation of our approach revolves around specific data sources, namely fashion MNIST. Performance criteria such as accuracy, adjusted rand index (ARI), and normalized mutual information (NMI) convincingly attest to the remarkable capability of our methodology. In comparative analyses, our approach significantly outperforms existing models, demonstrating its effectiveness and relevance in the complex domain of Big Data clustering.

EXPLORING SECTORAL REACH IN AGE-FRIENDLY COMMUNITIES

Abstract There is increasing interest in better understanding the sectoral reach of age-friendly community practice. Action across a wide variety of actors is central to achieving age-friendly societal change according to the World Health Organization and an explicit goal for governmental participation in its Global Network of Age-Friendly Cities and Communities. However, there is limited knowledge regarding the scope of sectoral reach by age-friendly communities. We used qualitative inquiry to assess sectoral efforts reported by American age-friendly communities that completed a five-year cycle of participation (n=40). We employed directed content analysis using a priori indicators by sectoral actors: public (i.e., government), private (i.e., business), and civil society including nonprofit organizations and volunteers. We classify sectoral actions by type (i.e., intersectoral, multisectoral) and by clustered domain community foci (i.e., built, social, service). Our study identifies the extent and types of sectoral actors and actions reported by age-friendly communities with the greatest efforts reported in the public sector, and while similarly distributed across all the domains, slightly more efforts were noted in the built environment. We also found greater intersectoral efforts (i.e., explicitly working towards shared goals) than multisectoral (i.e., not necessarily in collaboration on shared goals), particularly across government. While our study substantiates the breadth of actions towards age-friendly change, additional research is needed to examine the ways in which the public and other sectoral actions are further linked to outcomes in communities in the United States and in other countries.

A small step to discover candidate biological control agents from preexisting bioresources by using novel nonribosomal peptide synthetases hidden in activated sludge metagenomes.

Biological control agents (BCAs), beneficial organisms that reduce the incidence or severity of plant disease, have been expected to be alternatives to replace chemical pesticides worldwide. To date, BCAs have been screened by culture-dependent methods from various environments. However, previously unknown BCA candidates may be buried and overlooked because this approach preferentially selects only easy-to-culture microbial lineages. To overcome this limitation, as a small-scale test case, we attempted to explore novel BCA candidates by employing the shotgun metagenomic information of the activated sludge (AS) microbiome, which is thought to contain unutilized biological resources. We first performed genome-resolved metagenomics for AS taken from a municipal sewage treatment plant and obtained 97 nonribosomal peptide synthetase (NRPS)/polyketide synthase (PKS)-related gene sequences from 43 metagenomic assembled bins, most of which were assigned to the phyla Proteobacteria and Myxococcota. Furthermore, these NRPS/PKS-related genes are predicted to be novel because they were genetically dissimilar to known NRPS/PKS gene clusters. Of these, the condensation domain of the syringomycin-related NRPS gene cluster was detected in Rhodoferax- and Rhodocyclaceae-related bins, and its homolog was found in previously reported AS metagenomes as well as the genomes of three strains available from the microbial culture collections, implying their potential BCA ability. Then, we tested the antimicrobial activity of these strains against phytopathogenic fungi to investigate the potential ability of BCA by in vitro cultivation and successfully confirmed the actual antifungal activity of three strains harboring a possibly novel NRPS gene cluster. Our findings provide a possible strategy for discovering novel BCAs buried in the environment using genome-resolved metagenomics.

DPH1 Gene Mutations Identify a Candidate SAM Pocket in Radical Enzyme Dph1•Dph2 for Diphthamide Synthesis on EF2.

In eukaryotes, the Dph1•Dph2 dimer is a non-canonical radical SAM enzyme. Using iron-sulfur (FeS) clusters, it cleaves the cosubstrate S-adenosyl-methionine (SAM) to form a 3-amino-3-carboxy-propyl (ACP) radical for the synthesis of diphthamide. The latter decorates a histidine residue on elongation factor 2 (EF2) conserved from archaea to yeast and humans and is important for accurate mRNA translation and protein synthesis. Guided by evidence from archaeal orthologues, we searched for a putative SAM-binding pocket in Dph1•Dph2 from Saccharomyces cerevisiae. We predict an SAM-binding pocket near the FeS cluster domain that is conserved across eukaryotes in Dph1 but not Dph2. Site-directed DPH1 mutagenesis and functional characterization through assay diagnostics for the loss of diphthamide reveal that the SAM pocket is essential for synthesis of the décor on EF2 in vivo. Further evidence from structural modeling suggests particularly critical residues close to the methionine moiety of SAM. Presumably, they facilitate a geometry specific for SAM cleavage and ACP radical formation that distinguishes Dph1•Dph2 from classical radical SAM enzymes, which generate canonical 5'-deoxyadenosyl (dAdo) radicals.

Multiplexed assay of variant effect reveals residues of functional importance in the BRCA1 coiled-coil and serine cluster domains.

Delineating functionally normal variants from functionally abnormal variants in tumor suppressor proteins is critical for cancer surveillance, prognosis, and treatment options. BRCA1 is a protein that has many variants of uncertain significance which are not yet classified as functionally normal or abnormal. In vitro functional assays can be used to identify the functional impact of a variant when the variant has not yet been categorized through clinical observation. Here we employ a homology-directed repair (HDR) reporter assay to evaluate over 300 missense and nonsense BRCA1 variants between amino acid residues 1280 and 1576, which encompasses the coiled-coil and serine cluster domains. Functionally abnormal variants tended to cluster in residues known to interact with PALB2, which is critical for homology-directed repair. Multiplexed results were confirmed by singleton assay and by ClinVar database variant interpretations. Comparison of multiplexed results to designated benign or likely benign or pathogenic or likely pathogenic variants in the ClinVar database yielded 100% specificity and 100% sensitivity of the multiplexed assay. Clinicians can reference the results of this functional assay for help in guiding cancer treatment and surveillance options. These results are the first to evaluate this domain of BRCA1 using a multiplexed approach and indicate the importance of this domain in the DNA repair process.

Endothelial deletion of EPH receptor A4 alters single-cell profile and Tie2/Akap12 signaling to preserve blood–brain barrier integrity

Neurobiological consequences of traumatic brain injury (TBI) result from a complex interplay of secondary injury responses and sequela that mediates chronic disability. Endothelial cells are important regulators of the cerebrovascular response to TBI. Our work demonstrates that genetic deletion of endothelial cell (EC)-specific EPH receptor A4 (EphA4) using conditional EphA4f/f/Tie2-Cre and EphA4f/f/VE-Cadherin-CreERT2 knockout (KO) mice promotes blood-brain barrier (BBB) integrity and tissue protection, which correlates with improved motor function and cerebral blood flow recovery following controlled cortical impact (CCI) injury. scRNAseq of capillary-derived KO ECs showed increased differential gene expression of BBB-related junctional and actin cytoskeletal regulators, namely, A-kinase anchor protein 12, Akap12, whose presence at Tie2 clustering domains is enhanced in KO microvessels. Transcript and protein analysis of CCI-injured whole cortical tissue or cortical-derived ECs suggests that EphA4 limits the expression of Cldn5, Akt, and Akap12 and promotes Ang2. Blocking Tie2 using sTie2-Fc attenuated protection and reversed Akap12 mRNA and protein levels cortical-derived ECs. Direct stimulation of Tie2 using Vasculotide, angiopoietin-1 memetic peptide, phenocopied the neuroprotection. Finally, we report a noteworthy rise in soluble Ang2 in the sera of individuals with acute TBI, highlighting its promising role as a vascular biomarker for early detection of BBB disruption. These findings describe a contribution of the axon guidance molecule, EphA4, in mediating TBI microvascular dysfunction through negative regulation of Tie2/Akap12 signaling.