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Abstract Trajectory anomalies serve as early indicators of potential issues and frequently provide valuable insights into event occurrence. Existing methods for detecting abnormal trajectories primarily focus on comparing the spatial characteristics of the trajectories. However, they fail to capture the temporal dimension’s pattern and evolution within the trajectory data, thereby inadequately identifying the behavioral inertia of the target group. A few detection methods that incorporate spatiotemporal features have also failed to adequately analyze the spatiotemporal sequence evolution information; consequently, detection methods that ignore temporal and spatial correlations are too one-sided. Recurrent neural networks (RNNs), especially gate recurrent unit (GRU) that design reset and update gate control units, process nonlinear sequence processing capabilities, enabling effective extraction and analysis of both temporal and spatial characteristics. However, the basic GRU network model has limited expressive power and may not be able to adequately capture complex sequence patterns and semantic information. To address the above issues, an abnormal trajectory detection method based on the improved GRU model is proposed in cloud computing in this paper. To enhance the anomaly detection ability and training efficiency of relevant models, strictly control the input of irrelevant features and improve the model fitting effect, an improved model combining the random forest algorithm and fully connected layer network is designed. The method deconstructs spatiotemporal semantics through reset and update gated units, while effectively capturing feature evolution information and target behavioral inertia by leveraging the integration of features and nonlinear mapping capabilities of the fully connected layer network. The experimental results based on the GeoLife GPS trajectory dataset indicate that the proposed approach improves both generalization ability by 1% and reduces training cost by 31.68%. This success do provides a practical solution for the task of anomaly trajectory detection.

Genetic diversity is a hallmark of RNA viruses and the basis for their evolutionary success. Taking advantage of the uniquely large genomic database of SARS-CoV-2, we examine the impact of mutations across the spectrum of viable amino acid sequences on the biophysical phenotypes of the highly expressed and multifunctional nucleocapsid protein. We find variation in the physicochemical parameters of its extended intrinsically disordered regions (IDRs) sufficient to allow local plasticity, but also observe functional constraints that similarly occur in related coronaviruses. In biophysical experiments with several N-protein species carrying mutations associated with major variants, we find that point mutations in the IDRs can have nonlocal impact and modulate thermodynamic stability, secondary structure, protein oligomeric state, particle formation, and liquid-liquid phase separation. In the Omicron variant, distant mutations in different IDRs have compensatory effects in shifting a delicate balance of interactions controlling protein assembly properties, and include the creation of a new protein-protein interaction interface in the N-terminal IDR through the defining P13L mutation. A picture emerges where genetic diversity is accompanied by significant variation in biophysical characteristics of functional N-protein species, in particular in the IDRs.

ObjectivesTo develop and validate radiomics models utilizing endoscopic ultrasonography (EUS) images to distinguish insulinomas from non-functional pancreatic neuroendocrine tumors (NF-PNETs).MethodsA total of 106 patients, comprising 61 with insulinomas and 45 with NF-PNETs, were included in this study. The patients were randomly assigned to either the training or test cohort. Radiomics features were extracted from both the intratumoral and peritumoral regions, respectively. Six machine learning algorithms were utilized to train intratumoral prediction models, using only the nonzero coefficient features. The researchers identified the most effective intratumoral radiomics model and subsequently employed it to develop peritumoral and combined radiomics models. Finally, a predictive nomogram for insulinomas was constructed and assessed.ResultsA total of 107 radiomics features were extracted based on EUS, and only features with nonzero coefficients were retained. Among the six intratumoral radiomics models, the light gradient boosting machine (LightGBM) model demonstrated superior performance. Furthermore, a peritumoral radiomics model was established and evaluated. The combined model, integrating both the intratumoral and peritumoral radiomics features, exhibited a comparable performance in the training cohort (AUC=0.876) and achieved the highest accuracy in predicting outcomes in the test cohorts (AUC=0.835). The Delong test, calibration curves, and decision curve analysis (DCA) were employed to validate these findings. Insulinomas exhibited a significantly smaller diameter compared to NF-PNETs. Finally, the nomogram, incorporating diameter and radiomics signature, was constructed and assessed, which owned superior performance in both the training (AUC=0.929) and test (AUC=0.913) cohorts.ConclusionA novel and impactful radiomics model and nomogram were developed and validated for the accurate differentiation of NF-PNETs and insulinomas utilizing EUS images.

Abstract The peripheral immune system is important in neurodegenerative diseases, both in protecting and inflaming the brain, but the underlying mechanisms remain elusive. Alzheimer’s Disease is commonly preceded by a prodromal period. Here, we report the presence of large Aβ aggregates in plasma from patients with mild cognitive impairment (n = 38). The aggregates are associated with low level Alzheimer’s Disease-like brain pathology as observed by 11C-PiB PET and 18F-FTP PET and lowered CD18-rich monocytes. We characterize complement receptor 4 as a strong binder of amyloids and show Aβ aggregates are preferentially phagocytosed and stimulate lysosomal activity through this receptor in stem cell-derived microglia. KIM127 integrin activation in monocytes promotes size selective phagocytosis of Aβ. Hydrodynamic calculations suggest Aβ aggregates associate with vessel walls of the cortical capillaries. In turn, we hypothesize aggregates may provide an adhesion substrate for recruiting CD18-rich monocytes into the cortex. Our results support a role for complement receptor 4 in regulating amyloid homeostasis.

ObjectivesTo develop and validate various ultrasomics models based on endoscopic ultrasonography (EUS) for retrospective differentiating pancreatic neuroendocrine tumors (PNET) from pancreatic cancer.MethodsA total of 231 patients, comprising 127 with pancreatic cancer and 104 with PNET, were retrospectively enrolled. These patients were randomly divided into either a training or test cohort at a ratio of 7:3. Ultrasomics features were extracted from conventional EUS images, focusing on delineating the region of interest (ROI) for pancreatic lesions. Subsequently, dimensionality reduction of the ultrasomics features was performed by applying the Mann-Whitney test and least absolute shrinkage and selection operator (LASSO) algorithm. Eight machine learning algorithms, namely logistic regression (LR), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), random forest (RF), extra trees, k nearest neighbors (KNN), support vector machine (SVM), and extreme gradient boosting (XGBoost), were employed to train prediction models using nonzero coefficient features. The optimal ultrasomics model was determined using a ROC curve and utilized for subsequent analysis. Clinical-ultrasonic features were assessed using both univariate and multivariate logistic regression. An ultrasomics nomogram model, integrating both ultrasomics and clinical-ultrasonic features, was developed.ResultsA total of 107 EUS-based ultrasomics features were extracted, and 6 features with nonzero coefficients were ultimately retained. Among the eight ultrasomics models based on machine learning algorithms, the RF model exhibited superior performance with an AUC= 0.999 (95% CI 0.9977 - 1.0000) in the training cohort and an AUC= 0.649 (95% CI 0.5215 - 0.7760) in the test cohort. A clinical-ultrasonic model was established and evaluated, yielding an AUC of 0.999 (95% CI 0.9961 - 1.0000) in the training cohort and 0.847 (95% CI 0.7543 - 0.9391) in the test cohort. Subsequently, the ultrasomics nomogram demonstrated a significant improvement in prediction accuracy in the test cohort, as evidenced by an AUC of 0.884 (95% CI 0.8047 - 0.9635) and confirmed by the Delong test. The calibration curve and decision curve analysis (DCA) depicted this ultrasomics nomogram demonstrated superior accuracy. They also yielded the highest net benefit for clinical decision-making compared to alternative models.ConclusionsA novel ultrasomics nomogram was proposed and validated, that integrated clinical-ultrasonic and ultrasomics features obtained through EUS, aiming to accurately and efficiently identify pancreatic cancer and PNET.

ABSTRACTMolecular mimicry of short linear interaction motifs has emerged as a key mechanism for viral proteins binding host domains and hijacking host cell processes. Here, we examine the role of RNA virus sequence diversity in the dynamics of the virus-host interface by analyzing the uniquely vast sequence record of viable SARS-CoV-2 species with focus on the multi-functional nucleocapsid protein. We observe the abundant presentation of motifs encoding several essential host protein interactions, alongside a majority of possibly non-functional and randomly occurring motif sequences absent in subsets of viable virus species. A large number of motifs emerge ex nihilo through transient mutations relative to the ancestral consensus sequence. The observed mutational landscape implies an accessible motif space that spans at least 25% of known eukaryotic motifs. This reveals motif mimicry as a highly dynamic process with the capacity to broadly explore host motifs, allowing the virus to rapidly evolve the virus-host interface.IMPORTANCEShort linear motifs (SLiMs) are 3–10 amino acid long binding motifs in intrinsically disordered protein regions (IDRs) that serve as ubiquitous protein-protein interaction modules in eukaryotic cells. Through molecular mimicry, viruses hijack these sequence motifs to control host cellular processes. It is thought that the small size of SLiMs and the high mutation frequencies of viral IDRs allow rapid host adaptation. However, a salient characteristic of RNA viruses, due to high replication errors, is their obligate existence as mutant swarms. Taking advantage of the uniquely large genomic database of SARS-CoV-2, here, we analyze the role of sequence diversity in the presentation of SLiMs, focusing on the highly abundant, multi-functional nucleocapsid protein. We find that motif mimicry is a highly dynamic process that produces an abundance of motifs transiently present in subsets of mutant species. This diversity allows the virus to efficiently explore eukaryotic motifs and evolve the host-virus interface.

Sedimentation velocity analytical ultracentrifugation (SV-AUC) is an indispensable tool for the study of particle size distributions in biopharmaceutical industry, for example, to characterize protein therapeutics and vaccine products. In particular, the diffusion-deconvoluted sedimentation coefficient distribution analysis, in the software SEDFIT, has found widespread applications due to its relatively high resolution and sensitivity. However, a lack of suitable software compatible with Good Manufacturing Practices (GMP) has hampered the use of SV-AUC in this regulatory environment. To address this, we have created an interface for SEDFIT so that it can serve as an automatically spawned module with controlled data input through command line parameters and output of key results in files. The interface can be integrated in custom GMP compatible software, and in scripts that provide documentation and meta-analyses for replicate or related samples, for example, to streamline analysis of large families of experimental data, such as binding isotherm analyses in the study of protein interactions. To test and demonstrate this approach we provide a MATLAB script mlSEDFIT.

Background: Insulinoma is the most common functional pancreatic neuroendocrine tumor (PNET) with abnormal insulin hypersecretion. The etiopathogenesis of insulinoma remains indefinable. Based on multiple bioinformatics methods and machine learning algorithms, this study proposed exploring the molecular mechanism from ion channel-related genes to establish a genetic diagnosis model for insulinoma.Methods: The mRNA expression profile dataset of GSE73338 was applied to the analysis, which contains 17 insulinoma samples, 63 nonfunctional PNET (NFPNET) samples, and four normal islet samples. Differently expressed ion channel-related genes (DEICRGs) enrichment analyses were performed. We utilized the protein–protein interaction (PPI) analysis and machine learning of LASSO and support vector machine-recursive feature elimination (SVM-RFE) to identify the target genes. Based on these target genes, a nomogram diagnostic model was constructed and verified by a receiver operating characteristic (ROC) curve. Moreover, immune infiltration analysis, single-gene gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were executed. Finally, a drug–gene interaction network was constructed.Results: We identified 29 DEICRGs, and enrichment analyses indicated they were primarily enriched in ion transport, cellular ion homeostasis, pancreatic secretion, and lysosome. Moreover, the PPI network and machine learning recognized three target genes (MCOLN1, ATP6V0E1, and ATP4A). Based on these target genes, we constructed an efficiently predictable diagnosis model for identifying insulinomas with a nomogram and validated it with the ROC curve (AUC = 0.801, 95% CI 0.674–0.898). Then, single-gene GSEA analysis revealed that these target genes had a significantly positive correlation with insulin secretion and lysosome. In contrast, the TGF-beta signaling pathway was negatively associated with them. Furthermore, statistically significant discrepancies in immune infiltration were revealed.Conclusion: We identified three ion channel-related genes and constructed an efficiently predictable diagnosis model to offer a novel approach for diagnosing insulinoma.

BackgroundAtherosclerosis (AS) is one of the leading causes of the cardio-cerebral vascular incident. The constantly emerging evidence indicates a close association between nonalcoholic fatty liver disease (NAFLD) and AS. However, the exact molecular mechanisms underlying the correlation between these two diseases remain unclear. This study proposed exploring the common signature genes, pathways, and immune cells among AS and NAFLD.MethodsThe common differentially expressed genes (co-DEGs) with a consistent trend were identified via bioinformatic analyses of the Gene Expression Omnibus (GEO) datasets GSE28829 and GSE49541, respectively. Further, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. We utilized machine learning algorithms of lasso and random forest (RF) to identify the common signature genes. Then the diagnostic nomogram models and receiver operator characteristic curve (ROC) analyses were constructed and validated with external verification datasets. The gene interaction network was established via the GeneMANIA database. Additionally, gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed to explore the co-regulated pathways and immune cells.ResultsA total of 11 co-DEGs were identified. GO and KEGG analyses revealed that co-DEGs were mainly enriched in lipid catabolic process, calcium ion transport, and regulation of cytokine. Moreover, three common signature genes (PLCXD3, CCL19, and PKD2) were defined. Based on these genes, we constructed the efficiently predictable diagnostic models for advanced AS and NAFLD with the nomograms, evaluated with the ROC curves (AUC = 0.995 for advanced AS, 95% CI 0.971–1.0; AUC = 0.973 for advanced NAFLD, 95% CI 0.938–0.998). In addition, the AUC of the verification datasets had a similar trend. The NOD-like receptors (NLRs) signaling pathway might be the most crucial co-regulated pathway, and activated CD4 T cells and central memory CD4 T cells were significantly excessive infiltration in advanced NAFLD and AS.ConclusionWe identified three common signature genes (PLCXD3, CCL19, and PKD2), co-regulated pathways, and shared immune features of NAFLD and AS, which might provide novel insights into the molecular mechanism of NAFLD complicated with AS.

Abstract Weak macromolecular interactions assume a dominant role in the behavior of highly concentrated solutions, and are at the center of a variety of fields ranging from colloidal chemistry to cell biology, neurodegenerative diseases, and manufacturing of protein drugs. They are frequently measured in different biophysical techniques in the form of second virial coefficients, and nonideality coefficients of sedimentation and diffusion, which may be related mechanistically to macromolecular distance distributions in solution and interparticle potentials. A problem arises for proteins where reversible self-association often complicates the concentration-dependent behavior, such that grossly inconsistent coefficients are measured in experiments based on different techniques, confounding quantitative conclusions. Here we present a global multi-method analysis that synergistically bridges gaps in resolution and sensitivity of orthogonal techniques. We demonstrate the method with a panel of monoclonal antibodies exhibiting different degrees of self-association. We show how their concentration-dependent behavior, examined by static and dynamic light scattering and sedimentation velocity, can be jointly described in a self-consistent framework that separates nonideality coefficients from self-association properties, and thereby extends the quantitative interpretation of nonideality coefficients to probe dynamics in highly concentrated protein solutions.

Background: Although increasing evidence suggests an association between alterations in peripheral cytokines and autism spectrum disorder (ASD), a consensus is lacking. To determine whether abnormal cytokine profiles in peripheral blood were associated with ASD, we performed this systemic review and meta-analysis.Methods: A systematic literature search was conducted through the Embase, PubMed, Web of Knowledge, PsycINFO, and Cochrane databases up to 4 June 2020. Clinical studies exploring the aberration of peripheral cytokines of autistic patients and controls were included in our meta-analysis. We pooled extracted data using fixed- or random-effects models based on heterogeneity tests with Comprehensive Meta-analysis software. We converted standardized mean differences to Hedges' g statistic to obtain the effect sizes adjusted for sample size. Subgroup analyses, sensitivity analyses, meta-regression, and publication bias tests were also carried out.Results: Sixty-one articles (326 studies) were included to assess the association between 76 cytokines and ASD. We conducted our meta-analysis based on 37 cytokines with 289 studies. Since there were fewer than three studies on any of the other 39 cytokines, we only provided basic information for them. The levels of peripheral IL-6, IL-1β, IL-12p70, macrophage migration inhibitory factor (MIF), eotaxin-1, monocyte chemotactic protein-1 (MCP-1), IL-8, IL-7, IL-2, IL-12, tumor necrosis factor-α (TNF-α), IL-17, and IL-4 were defined as abnormal cytokines in the peripheral blood of ASD patients compared with controls. The other 24 cytokines did not obviously change in ASD patients compared with the controls.Conclusions: The findings of our meta-analysis strengthen the evidence for an abnormal cytokine profile in ASD. These abnormal cytokines may be potential biomarkers for the diagnosis and treatment of ASD in the future.

Summary: Nucleocapsid (N) protein of the SARS-CoV-2 virus packages the viral genome into well-defined ribonucleoprotein particles, but the molecular pathway is still unclear. N-protein is dimeric and consists of two folded domains with nucleic acid (NA) binding sites, surrounded by intrinsically disordered regions that promote liquid-liquid phase separation. Here, we use biophysical tools to study N-protein interactions with oligonucleotides of different lengths, examining the size, composition, secondary structure, and energetics of the resulting states. We observe the formation of supramolecular clusters or nuclei preceding growth into phase-separated droplets. Short hexanucleotide NA forms compact 2:2 N-protein/NA complexes with reduced disorder. Longer oligonucleotides expose additional N-protein interactions and multi-valent protein-NA interactions, which generate higher-order mixed oligomers and simultaneously promote growth of droplets. Phase separation is accompanied by a significant change in protein secondary structure, different from that caused by initial NA binding, which may contribute to the assembly of ribonucleoprotein particles within macromolecular condensates.

N6 methyladenosine (m6A) RNA methylation regulators play an important role in the development of tumors. However, their function in esophageal cancer (EC) has not been fully elucidated. Here, we analyzed the gene expression data of 24 major m6A RNA methylation regulators from 775 patients with EC from TCGA dataset. The present study showed the aberrations of m6A regulators in genome were correlated to prognosis in human ECs. Meanwhile, 17 m6A regulators showed increased expression in EC samples, including YTHDC1, IGF2BP2, FTO, METTL14, YTHDF3, RBM15, WTAP, HNRNPA2B1, HNRNPC, ALKBH5, YTHDF2, METTL16, IGF2BP3, VIRMA, RBM15B, YTHDF1, KIAA1429, HAKAI, and ZC3H13. Among them, we found HNRNPC, YTHDC2, WTAP, VIRMA, IGF2BP3, and HNRNPA2B1 were significantly correlated to worse outcomes and advanced stage in EC. Furthermore, we showed levels of m6A regulators is correlated with the expression of Immuno-regulators (Immunoinhibitors, Immunostimulators, and MHC molecules) and immune infiltration levels in EC. Bioinformatics further confirm m6A regulators were involved in regulating RNA splicing, RNA stability, and cell proliferation. Our study showed m6A regulators are promising targets and biomarkers for cancer immunotherapy in EC.

Transmission to chimpanzees of a precore hepatitis B virus (HBV) mutant implicated in acute liver failure (ALF) in humans did not cause ALF nor the classic form of acute hepatitis B (AHB) seen upon infection with the wild-type HBV strain, but rather a severe AHB with distinct disease features. Here, we investigated the viral and host immunity factors responsible for the unusual severity of AHB associated with the precore HBV mutant in chimpanzees. Archived serial serum and liver specimens from two chimpanzees inoculated with a precore HBV mutant implicated in ALF and two chimpanzees inoculated with wild-type HBV were studied. We used phage-display library and next-generation sequencing (NGS) technologies to characterize the liver antibody response. The results obtained in severe AHB were compared with those in classic AHB and HBV-associated ALF in humans. Severe AHB was characterized by: (i) the highest alanine aminotransferase (ALT) peaks ever seen in HBV transmission studies with a significantly shorter incubation period, compared to classic AHB; (ii) earlier HBsAg clearance and anti-HBs seroconversion with transient or undetectable hepatitis B e antigen (HBeAg); (iii) limited inflammatory reaction relative to hepatocellular damage at the ALT peak with B-cell infiltration, albeit less extensive than in ALF; (iv) detection of intrahepatic germline antibodies against hepatitis B core antigen (HBcAg) by phage-display libraries in the earliest disease phase, as seen in ALF; (v) lack of intrahepatic IgM anti-HBcAg Fab, as seen in classic AHB, but at variance with ALF; and (vi) higher proportion of antibodies in germline configuration detected by NGS in the intrahepatic antibody repertoire compared to classic AHB, but lower than in ALF. This study identifies distinct outcome-specific features associated with severe AHB caused by a precore HBV mutant in chimpanzees, which bear closer resemblance to HBV ALF than to classic AHB. Our data suggest that precore HBV mutants carry an inherently higher pathogenicity that, in addition to specific host factors, may play a critical role in determining the severity of acute HBV disease.

Monoclonal antibodies are a class of biotherapeutics used for an increasing variety of disorders, including cancer, autoimmune, neurodegenerative, and viral diseases. Besides their antigen specificity, therapeutic use also mandates control of their solution interactions and colloidal properties in order to achieve a stable, efficacious, non-immunogenic, and low viscosity antibody solution at concentrations in the range of 50–150 mg/mL. This requires characterization of their reversible self-association, aggregation, and weak attractive and repulsive interactions governing macromolecular distance distributions in solution. Simultaneous measurement of these properties, however, has been hampered by solution nonideality. Based on a recently introduced sedimentation velocity method for measuring macromolecular size distributions in a mean-field approximation for hydrodynamic interactions, we demonstrate simultaneous measurement of polydispersity and weak and strong solution interactions in a panel of antibodies with concentrations up to 45 mg/mL. By allowing approximately an order of magnitude higher concentrations than previously possible in sedimentation velocity size distribution analysis, this approach can substantially improve efficiency and sensitivity for characterizing polydispersity and interactions of therapeutic antibodies at or close to formulation conditions.

Abstract Background The diagnosis of obscure gastrointestinal bleeding (OGIB) which is defined as bleeding of unknown origin of the small bowel by routine evaluation in childhood is a challenge. Case presentation Here we report a one-year-old Chinese girl who was suspected with idiopathic pulmonary haemosiderosis (IPH) and referred to our department for further diagnosis. Finally she was diagnosed with vascular malformations (VM) by exploratory laparoscopy combined with pathological examination. Conclusions Children OGIB could be easily misdiagnosed in the beginning, and OGIB children with active ongoing bleeding may benefit from proceeding directly to exploratory laparoscopy, followed by pathological confirmation of the diagnosis.

Many aspects of concentrated macromolecular solutions, such as encountered in cytosol or in pharmaceutical formulations, are dependent on particle size distributions and weak intermolecular interactions. Here, the authors exploit hydrodynamic separation in the centrifugal field to measure both.

Background/Aims: The aim of this study was to investigate the roles of miR-543 and phospholipase A2 group IVA (PLA2G4A) in cell mobility and the invasiveness cascade in esophageal squamous cell carcinoma (ESCC) and to validate the interactive relationship between miR-543 and PLA2G4A. Methods: Microarray analysis showed the different expression levels of PLA2G4A in two ESCC cell lines (KYSE30 and KYSE180). The expression levels of miR-543 and PLA2G4A in ESCC tissues were confirmed by qRT-PCR and Western blotting. The targeted relationship between miR-543 and PLA2G4A was studied and verified by a luciferase activity assay. Then, the invasion and metastasis ability of ESCC cell lines transfected with miR-543 mimics, miR-543 inhibitor, or PLA2G4A and miR-543 mimics were analyzed separately by Transwell migration and invasion assays. In addition, the roles of miR-543 and PLA2G4A in the expression of E-cadherin and vimentin were also investigated. Results: PLA2G4A up-regulated the level of E-cadherin and down-regulated the level of vimentin, which curbed ESCC cell mobility and invasion. In ESCC cells, the expression of miR-543 was significantly higher, whereas the expression of PLA2G4A was markedly lower. MiR-543 facilitated ESCC cell mobility and invasion by repressing PLA2G4A. Conclusions: MiR-543 enhanced the cell mobility and the invasiveness cascade in ESCC cells via the down-regulation of PLA2G4A expression.

Binding of T cell receptors (TCR) to peptide-loaded major histocompatibility complexes (p/MHC) leads to T-cell activation. Here the authors give structural insights into T-cell signalling and show that p/MHC binding induces conformational changes at the membrane-proximal site of the TCR.

Hearing and balance involve the transduction of mechanical stimuli into electrical signals by deflection of bundles of stereocilia linked together by protocadherin 15 (PCDH15) and cadherin 23 ‘tip links’. PCDH15 transduces tip link tension into opening of a mechano-electrical transduction (MET) ion channel. PCDH15 also interacts with LHFPL5, a candidate subunit of the MET channel. Here we illuminate the PCDH15-LHFPL5 structure, showing how the complex is composed of PCDH15 and LHFPL5 subunit pairs related by a 2-fold axis. The extracellular cadherin domains define a mobile tether coupled to a rigid, 2-fold symmetric ‘collar’ proximal to the membrane bilayer. LHFPL5 forms extensive interactions with the PCDH15 transmembrane helices and stabilizes the overall PCDH15-LHFPL5 assembly. Our studies illuminate the architecture of the PCDH15-LHFPL5 complex, localize mutations associated with deafness, and shed new light on how forces in the PCDH15 tether may be transduced into the stereocilia membrane.

Analytical ultracentrifugation (AUC) is a first-principles based method for studying macromolecules and particles in solution by monitoring the evolution of their radial concentration distribution as a function of time in the presence of a high centrifugal field. In sedimentation velocity experiments, hydrodynamic properties relating to size, shape, density, and solvation of particles can be measured, at a high hydrodynamic resolution, on polydisperse samples. In a recent multilaboratory benchmark study including data from commercial analytical ultracentrifuges in 67 laboratories, the calibration accuracy of the radial dimension was found to be one of the dominant factors limiting the accuracy of AUC. In the present work, we develop an artifact consisting of an accurately calibrated reflective pattern lithographically deposited onto an AUC window. It serves as a reticle when scanned in AUC control experiments for absolute calibration of radial magnification. After analysis of the pitch between landmarks in scans using different optical systems, we estimate that the residual uncertainty in radial magnification after external calibration with the radial scale artifact is ≈0.2 %, of similar magnitude to other important contributions after external calibration such as the uncertainty in temperature and time. The previous multilaboratory study had found many instruments with errors in radial measurements of 1 % to 2 %, and a few instruments with errors in excess of 15 %, meaning that the use of the artifact developed here could reduce errors by 5-to 10-fold or more. Adoption of external radial calibration is thus an important factor for assuring accuracy in studies related to molecular hydrodynamics and particle size measurements by AUC.

Ion conductivity and the gating characteristics of tetrameric glutamate receptor ion channels are determined by their subunit composition. Competitive homo- and hetero-dimerization of their amino-terminal domains (ATDs) is a key step controlling assembly. Here we measured systematically the thermodynamic stabilities of homodimers and heterodimers of kainate and AMPA receptors using fluorescence-detected sedimentation velocity analytical ultracentrifugation. Measured affinities span many orders of magnitude, and complexes show large differences in kinetic stabilities. The association of kainate receptor ATD dimers is generally weaker than the association of AMPA receptor ATD dimers, but both show a general pattern of increased heterodimer stability as compared to the homodimers of their constituents, matching well physiologically observed receptor combinations. The free energy maps of AMPA and kainate receptor ATD dimers provide a framework for the interpretation of observed receptor subtype combinations and possible assembly pathways.

The dynamic assembly of multi-protein complexes underlies fundamental processes in cell biology. A mechanistic understanding of assemblies requires accurate measurement of their stoichiometry, affinity and cooperativity, and frequently consideration of multiple co-existing complexes. Sedimentation velocity analytical ultracentrifugation equipped with fluorescence detection (FDS-SV) allows the characterization of protein complexes free in solution with high size resolution, at concentrations in the nanomolar and picomolar range. Here, we extend the capabilities of FDS-SV with a single excitation wavelength from single-component to multi-component detection using photoswitchable fluorescent proteins (psFPs). We exploit their characteristic quantum yield of photo-switching to imprint spatio-temporal modulations onto the sedimentation signal that reveal different psFP-tagged protein components in the mixture. This novel approach facilitates studies of heterogeneous multi-protein complexes at orders of magnitude lower concentrations and for higher-affinity systems than previously possible. Using this technique we studied high-affinity interactions between the amino-terminal domains of GluA2 and GluA3 AMPA receptors.

Analytical ultracentrifugation (AUC) is a classical technique of physical biochemistry providing information on size, shape, and interactions of macromolecules from the analysis of their migration in centrifugal fields while free in solution. A key mechanical element in AUC is the centerpiece, a component of the sample cell assembly that is mounted between the optical windows to allow imaging and to seal the sample solution column against high vacuum while exposed to gravitational forces in excess of 300,000 g. For sedimentation velocity it needs to be precisely sector-shaped to allow unimpeded radial macromolecular migration. During the history of AUC a great variety of centerpiece designs have been developed for different types of experiments. Here, we report that centerpieces can now be readily fabricated by 3D printing at low cost, from a variety of materials, and with customized designs. The new centerpieces can exhibit sufficient mechanical stability to withstand the gravitational forces at the highest rotor speeds and be sufficiently precise for sedimentation equilibrium and sedimentation velocity experiments. Sedimentation velocity experiments with bovine serum albumin as a reference molecule in 3D printed centerpieces with standard double-sector design result in sedimentation boundaries virtually indistinguishable from those in commercial double-sector epoxy centerpieces, with sedimentation coefficients well within the range of published values. The statistical error of the measurement is slightly above that obtained with commercial epoxy, but still below 1%. Facilitated by modern open-source design and fabrication paradigms, we believe 3D printed centerpieces and AUC accessories can spawn a variety of improvements in AUC experimental design, efficiency and resource allocation.

Analytical ultracentrifugation (AUC) is a first principles based method to determine absolute sedimentation coefficients and buoyant molar masses of macromolecules and their complexes, reporting on their size and shape in free solution. The purpose of this multi-laboratory study was to establish the precision and accuracy of basic data dimensions in AUC and validate previously proposed calibration techniques. Three kits of AUC cell assemblies containing radial and temperature calibration tools and a bovine serum albumin (BSA) reference sample were shared among 67 laboratories, generating 129 comprehensive data sets. These allowed for an assessment of many parameters of instrument performance, including accuracy of the reported scan time after the start of centrifugation, the accuracy of the temperature calibration, and the accuracy of the radial magnification. The range of sedimentation coefficients obtained for BSA monomer in different instruments and using different optical systems was from 3.655 S to 4.949 S, with a mean and standard deviation of (4.304 ± 0.188) S (4.4%). After the combined application of correction factors derived from the external calibration references for elapsed time, scan velocity, temperature, and radial magnification, the range of s-values was reduced 7-fold with a mean of 4.325 S and a 6-fold reduced standard deviation of ± 0.030 S (0.7%). In addition, the large data set provided an opportunity to determine the instrument-to-instrument variation of the absolute radial positions reported in the scan files, the precision of photometric or refractometric signal magnitudes, and the precision of the calculated apparent molar mass of BSA monomer and the fraction of BSA dimers. These results highlight the necessity and effectiveness of independent calibration of basic AUC data dimensions for reliable quantitative studies.

Fluorescence optical detection in sedimentation velocity analytical ultracentrifugation allows the study of macromolecules at nanomolar concentrations and below. This has significant promise, for example, for the study of systems of high-affinity protein interactions. Here we describe adaptations of the direct boundary modeling analysis approach implemented in the software SEDFIT that were developed to accommodate unique characteristics of the confocal fluorescence detection system. These include spatial gradients of signal intensity due to scanner movements out of the plane of rotation, temporal intensity drifts due to instability of the laser and fluorophores, and masking of the finite excitation and detection cone by the sample holder. In an extensive series of experiments with enhanced green fluorescent protein ranging from low nanomolar to low micromolar concentrations, we show that the experimental data provide sufficient information to determine the parameters required for first-order approximation of the impact of these effects on the recorded data. Systematic deviations of fluorescence optical sedimentation velocity data analyzed using conventional sedimentation models developed for absorbance and interference optics are largely removed after these adaptations, resulting in excellent fits that highlight the high precision of fluorescence sedimentation velocity data, thus allowing a more detailed quantitative interpretation of the signal boundaries that is otherwise not possible for this system.

Sedimentation velocity analytical ultracentrifugation (SV) is a powerful first-principle technique for the study of protein interactions, and allows a rigorous characterization of binding stoichiometry and affinities. A recently introduced commercial fluorescence optical detection system (FDS) permits analysis of high-affinity interactions by SV. However, for most proteins the attachment of an extrinsic fluorophore is an essential prerequisite for analysis by FDS-SV. Using the glutamate receptor GluA2 amino terminal domain as a model system for high-affinity homo-dimerization, we demonstrate how the experimental design and choice of fluorescent label can impact both the observed binding constants as well as the derived hydrodynamic parameter estimates for the monomer and dimer species. Specifically, FAM (5,6-carboxyfluorescein) was found to create different populations of artificially high-affinity and low-affinity dimers, as indicated by both FDS-SV and the kinetics of dimer dissociation studied using a bench-top fluorescence spectrometer and Förster Resonance Energy Transfer. By contrast, Dylight488 labeled GluA2, as well as GluA2 expressed as an EGFP fusion protein, yielded results consistent with estimates for unlabeled GluA2. Our study suggests considerations for the choice of labeling strategies, and highlights experimental designs that exploit specific opportunities of FDS-SV for improving the reliability of the binding isotherm analysis of interacting systems.

The partial-specific volume of proteins is an important thermodynamic parameter required for the interpretation of data in several biophysical disciplines. Building on recent advances in the use of density variation sedimentation velocity analytical ultracentrifugation for the determination of macromolecular partial-specific volumes, we have explored a direct global modeling approach describing the sedimentation boundaries in different solvents with a joint differential sedimentation coefficient distribution. This takes full advantage of the influence of different macromolecular buoyancy on both the spread and the velocity of the sedimentation boundary. It should lend itself well to the study of interacting macromolecules and/or heterogeneous samples in microgram quantities. Model applications to three protein samples studied in either H(2)O, or isotopically enriched H(2) (18)O mixtures, indicate that partial-specific volumes can be determined with a statistical precision of better than 0.5%, provided signal/noise ratios of 50-100 can be achieved in the measurement of the macromolecular sedimentation velocity profiles. The approach is implemented in the global modeling software SEDPHAT.

Objectives: To develop and validate radiomics models utilizing endoscopic ultrasonography (EUS) images to distinguish insulinomas from non-functional pancreatic neuroendocrine tumors (NF-PNETs). Methods: A total of 106 patients, comprising 61 with insulinomas and 45 with NFPNETs, were included in this study. The patients were randomly assigned to either the training or test cohort. Radiomics features were extracted from both the intratumoral and peritumoral regions, respectively. Six machine learning algorithms were utilized to train intratumoral prediction models, using only the nonzero coefficient features. The researchers identified the most effective intratumoral radiomics model and subsequently employed it to develop peritumoral and combined radiomics models. Finally, a predictive nomogram for insulinomas was constructed and assessed. Results: A total of 107 radiomics features were extracted based on EUS, and only features with nonzero coefficients were retained. Among the six intratumoral radiomics models, the light gradient boosting machine (LightGBM) model demonstrated superior performance. Furthermore, a peritumoral radiomics model was established and evaluated. The combined model, integrating both the intratumoral and peritumoral radiomics features, exhibited a comparable performance in the training cohort (AUC=0.876) and achieved the highest accuracy in predicting outcomes in the test cohorts (AUC=0.835). The Delong test, calibration curves, and decision curve analysis (DCA) were employed to validate these findings. Insulinomas exhibited a significantly smaller diameter compared to NF-PNETs. Finally, the nomogram, incorporating diameter and radiomics signature, was constructed and assessed, which owned superior performance in both the training (AUC=0.929) and test (AUC=0.913) cohorts. Conclusion: A novel and impactful radiomics model and nomogram were developed and validated for the accurate differentiation of NF-PNETs and insulinomas utilizing EUS images. [ABSTRACT FROM AUTHOR]