The characteristic patterns of individual brain susceptibility networks underlie Alzheimer's disease and white matter hyperintensity-related cognitive impairment.

Excessive iron accumulation in the brain cortex increases the risk of cognitive deterioration. However, interregional relationships (defined as susceptibility connectivity) of local brain iron have not been explored, which could provide new insights into the underlying mechanisms of cognitive decline. Seventy-six healthy controls (HC), 58 participants with mild cognitive impairment due to probable Alzheimer's disease (MCI-AD) and 66 participants with white matter hyperintensity (WMH) were included. We proposed a novel approach to construct a brain susceptibility network by using Kullback‒Leibler divergence similarity estimation from quantitative susceptibility mapping and further evaluated its topological organization. Moreover, sparse logistic regression (SLR) was applied to classify MCI-AD from HC and WMH with normal cognition (WMH-NC) from WMH with MCI (WMH-MCI).The altered susceptibility connectivity in the MCI-AD patients indicated that relatively more connectivity was involved in the default mode network (DMN)-related and visual network (VN)-related connectivity, while more altered DMN-related and subcortical network (SN)-related connectivity was found in the WMH-MCI patients. For the HC vs. MCI-AD classification, the features selected by the SLR were primarily distributed throughout the DMN-related and VN-related connectivity (accuracy = 76.12%). For the WMH-NC vs. WMH-MCI classification, the features with high appearance frequency were involved in SN-related and DMN-related connectivity (accuracy = 84.85%). The shared and specific patterns of the susceptibility network identified in both MCI-AD and WMH-MCI may provide a potential diagnostic biomarker for cognitive impairment, which could enhance the understanding of the relationships between brain iron burden and cognitive decline from a network perspective.

Machine learning models using symptoms and clinical variables to predict coronary artery disease on coronary angiography.

Introduction: Coronary angiography (CAG) is invasive and expensive, while numbers of patients suspected of coronary artery disease (CAD) undergoing CAG results have no coronary lesions. Aim: To develop machine learning algorithms using symptoms and clinical variables to predict CAD. Material and methods: This study was conducted as a cross-sectional study of patients undergoing CAG. We randomly chose 2082 patients from 2602 patients suspected of CAD as the training set, and 520 patients as the test set. We utilized LASSO regression to do feature selection. The area under the receiver operating characteristic curve (AUC), confusion matrix of different thresholds, positive predictive value (PPV) and negative predictive value (NPV) were shown. Support vector machine algorithm performances in 10 folds were conducted in the training set for detecting severe CAD, while XGBoost algorithm performances were conducted in the test set for detecting severe CAD. Results: The algorithm of logistic regression achieved an average AUC of 0.77 in the training set during 10-fold validation and an AUC of 0.75 in the test set. When probability predicted by the model was less than 0.1, 11 patients in the test set (520 patients) were screened out, and NPV reached 90.9%. When probability predicted by the model was less than 0.2, 110 patients in the test set were screened out, and reached 83.6%. Meanwhile, when threshold was set to 0.9, PPV reached 97.4%. When the threshold was set to 0.8, PPV reached 91.5%. Conclusions: Machine learning algorithm using data from hospital information systems could assist in severe CAD exclusion and confirmation, and thus help patients avoid unnecessary CAG.

Multipattern graph convolutional network-based autism spectrum disorder identification.

The early diagnosis of autism spectrum disorder (ASD) has been extensively facilitated through the utilization of resting-state fMRI (rs-fMRI). With rs-fMRI, the functional brain network (FBN) has gained much attention in diagnosing ASD. As a promising strategy, graph convolutional networks (GCN) provide an attractive approach to simultaneously extract FBN features and facilitate ASD identification, thus replacing the manual feature extraction from FBN. Previous GCN studies primarily emphasized the exploration of topological simultaneously connection weights of the estimated FBNs while only focusing on the single connection pattern. However, this approach fails to exploit the potential complementary information offered by different connection patterns of FBNs, thereby inherently limiting the performance. To enhance the diagnostic performance, we propose a multipattern graph convolution network (MPGCN) that integrates multiple connection patterns to improve the accuracy of ASD diagnosis. As an initial endeavor, we endeavored to integrate information from multiple connection patterns by incorporating multiple graph convolution modules. The effectiveness of the MPGCN approach is evaluated by analyzing rs-fMRI scans from a cohort of 92 subjects sourced from the publicly accessible Autism Brain Imaging Data Exchange database. Notably, the experiment demonstrates that our model achieves an accuracy of 91.1% and an area under ROC curve score of 0.9742. The implementation codes are available at https://github.com/immutableJackz/MPGCN.

Structural insights into vesicular monoamine storage and drug interactions.

Biogenic monoamines-vital transmitters orchestrating neurological, endocrinal and immunological functions1-5-are stored in secretory vesicles by vesicular monoamine transporters (VMATs) for controlled quantal release6,7. Harnessing proton antiport, VMATs enrich monoamines around 10,000-fold and sequester neurotoxicants to protect neurons8-10. VMATs are targeted by an arsenal of therapeutic drugs and imaging agents to treat and monitor neurodegenerative disorders, hypertension and drug addiction1,8,11-16. However, the structural mechanisms underlying these actions remain unclear. Here we report eight cryo-electron microscopy structures of human VMAT1 in unbound form and in complex with four monoamines (dopamine, noradrenaline, serotonin and histamine), the Parkinsonism-inducing MPP+, the psychostimulant amphetamine and the antihypertensive drug reserpine. Reserpine binding captures a cytoplasmic-open conformation, whereas the other structures show a lumenal-open conformation stabilized by extensive gating interactions. The favoured transition to this lumenal-open state contributes to monoamine accumulation, while protonation facilitates the cytoplasmic-open transition and concurrently prevents monoamine binding to avoid unintended depletion. Monoamines and neurotoxicants share a binding pocket that possesses polar sites for specificity and a wrist-and-fist shape for versatility. Variations in this pocket explain substrate preferences across the SLC18 family. Overall, these structural insights and supporting functional studies elucidate the mechanism of vesicular monoamine transport and provide the basis to develop therapeutics for neurodegenerative diseases and substance abuse.

Cutting Edge: The Tetraspanin CD53 Promotes CXCR4 Signaling and Bone Marrow Homing in B Cells.

B cell trafficking involves the coordinated activity of multiple adhesive and cytokine-receptor interactions, and the players in this process are not fully understood. In this study, we identified the tetraspanin CD53 as a critical regulator of both normal and malignant B cell trafficking. CXCL12 is a key chemokine in B cell homing to the bone marrow and secondary lymphoid organs, and both normal and malignant B cells from Cd53-/- mice have reduced migration toward CXCL12 in vitro, as well as impaired marrow homing in vivo. Using proximity ligation studies, we identified the CXCL12 receptor, CXCR4, as a novel, to our knowledge, CD53 binding partner. This interaction promotes receptor function, because Cd53-/- B cells display reduced signaling and internalization of CXCR4 in response to CXCL12. Together, our data suggest that CD53 interacts with CXCR4 on both normal and malignant B cells to promote CXCL12 signaling, receptor internalization, and marrow homing.

Topological properties analysis and identification of mild cognitive impairment based on individual morphological brain network connectome.

Mild cognitive impairment is considered the prodromal stage of Alzheimer's disease. Accurate diagnosis and the exploration of the pathological mechanism of mild cognitive impairment are extremely valuable for targeted Alzheimer's disease prevention and early intervention. In all, 100 mild cognitive impairment patients and 86 normal controls were recruited in this study. We innovatively constructed the individual morphological brain networks and derived multiple brain connectome features based on 3D-T1 structural magnetic resonance imaging with the Jensen-Shannon divergence similarity estimation method. Our results showed that the most distinguishing morphological brain connectome features in mild cognitive impairment patients were consensus connections and nodal graph metrics, mainly located in the frontal, occipital, limbic lobes, and subcortical gray matter nuclei, corresponding to the default mode network. Topological properties analysis revealed that mild cognitive impairment patients exhibited compensatory changes in the frontal lobe, while abnormal cortical-subcortical circuits associated with cognition were present. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarkers for early detection of Alzheimer's disease and understanding the neuroimaging mechanisms of the disease.

Effects of individual metabolic brain network changes co-affected by T2DM and aging on the probabilities of T2DM: protective and risk factors.

Neuroimaging markers for risk and protective factors related to type 2 diabetes mellitus are critical for clinical prevention and intervention. In this work, the individual metabolic brain networks were constructed with Jensen-Shannon divergence for 4 groups (elderly type 2 diabetes mellitus and healthy controls, and middle-aged type 2 diabetes mellitus and healthy controls). Regional network properties were used to identify hub regions. Rich-club, feeder, and local connections were subsequently obtained, intergroup differences in connections and correlations between them and age (or fasting plasma glucose) were analyzed. Multinomial logistic regression was performed to explore effects of network changes on the probability of type 2 diabetes mellitus. The elderly had increased rich-club and feeder connections, and decreased local connection than the middle-aged among type 2 diabetes mellitus; type 2 diabetes mellitus had decreased rich-club and feeder connections than healthy controls. Protective factors including glucose metabolism in triangle part of inferior frontal gyrus, metabolic connectivity between triangle of the inferior frontal gyrus and anterior cingulate cortex, degree centrality of putamen, and risk factors including metabolic connectivities between triangle of the inferior frontal gyrus and Heschl's gyri were identified for the probability of type 2 diabetes mellitus. Metabolic interactions among critical brain regions increased in type 2 diabetes mellitus with aging. Individual metabolic network changes co-affected by type 2 diabetes mellitus and aging were identified as protective and risk factors for the likelihood of type 2 diabetes mellitus, providing guiding evidence for clinical interventions.

Towards an accurate autism spectrum disorder diagnosis: multiple connectome views from fMRI data.

Functional connectome has revealed remarkable potential in the diagnosis of neurological disorders, e.g. autism spectrum disorder. However, existing studies have primarily focused on a single connectivity pattern, such as full correlation, partial correlation, or causality. Such an approach fails in discovering the potential complementary topology information of FCNs at different connection patterns, resulting in lower diagnostic performance. Consequently, toward an accurate autism spectrum disorder diagnosis, a straightforward ambition is to combine the multiple connectivity patterns for the diagnosis of neurological disorders. To this end, we conduct functional magnetic resonance imaging data to construct multiple brain networks with different connectivity patterns and employ kernel combination techniques to fuse information from different brain connectivity patterns for autism diagnosis. To verify the effectiveness of our approach, we assess the performance of the proposed method on the Autism Brain Imaging Data Exchange dataset for diagnosing autism spectrum disorder. The experimental findings demonstrate that our method achieves precise autism spectrum disorder diagnosis with exceptional accuracy (91.30%), sensitivity (91.48%), and specificity (91.11%).

Precursor Reagent Hydrophobicity Affects Membrane Protein Footprinting.

Membrane proteins (MPs) play a crucial role in cell signaling, molecular transport, and catalysis and thus are at the heart of designing pharmacological targets. Although structural characterization of MPs at the molecular level is essential to elucidate their biological function, it poses a significant challenge for structural biology. Although mass spectrometry-based protein footprinting may be developed into a powerful approach for studying MPs, the hydrophobic character of membrane regions makes structural characterization difficult using water-soluble footprinting reagents. Herein, we evaluated a small series of MS-based photoactivated iodine reagents with different hydrophobicities. We used tip sonication to facilitate diffusion into micelles, thus enhancing reagent access to the hydrophobic core of MPs. Quantification of the modification extent in hydrophilic extracellular and hydrophobic transmembrane domains provides structurally sensitive information at the residue-level as measured by proteolysis and LC-MS/MS for a model MP, vitamin K epoxide reductase (VKOR). It also reveals a relationship between the reagent hydrophobicity and its preferential labeling sites in the local environment. The outcome should guide the future development of chemical probes for MPs and promote a direction for relatively high-throughput information-rich characterization of MPs in biochemistry and drug discovery.

Wavelet transform-based frequency self-adaptive model for functional brain network.

The accurate estimation of functional brain networks is essential for comprehending the intricate relationships between different brain regions. Conventional methods such as Pearson Correlation and Sparse Representation often fail to uncover concealed information within diverse frequency bands. To address this limitation, we introduce a novel frequency-adaptive model based on wavelet transform, enabling selective capture of highly correlated frequency band sequences. Our approach involves decomposing the original time-domain signal from resting-state functional magnetic resonance imaging into distinct frequency domains, thus constructing an adjacency matrix that offers enhanced separation of features across brain regions. Comparative analysis demonstrates the superior performance of our proposed model over conventional techniques, showcasing improved clarity and distinctiveness. Notably, we achieved the highest accuracy rate of 89.01% using Sparse Representation based on Wavelet Transform, outperforming Pearson Correlation based on Wavelet Transform with an accuracy of 81.32%. Importantly, our method optimizes raw data without significantly altering feature topology, rendering it adaptable to various functional brain network estimation approaches. Overall, this innovation holds the potential to advance the understanding of brain function and furnish more accurate samples for future research and clinical applications.

Molecular basis of inherited protein C deficiency results from genetic variations in the signal peptide and propeptide regions.

BACKGROUND: Inherited protein C deficiency (PCD) caused by mutations in protein C (PC) gene (PROC) increases the risk of thrombosis. Missense mutations in PC's signal peptide and propeptide have been reported in patients with PCD, but their pathogenic mechanisms, except mutations in R42 residue, remain unclear. OBJECTIVES: To investigate the pathogenic mechanisms of inherited PCD caused by 11 naturally occurring missense mutations in PC's signal peptide and propeptide. METHODS: Using cell-based assays, we evaluated the impact of these mutations on various aspects such as activities and antigens of secreted PC, intracellular PC expression, subcellular localization of a reporter protein, and propeptide cleavage. Additionally, we investigated their effect on pre-messenger RNA (pre-mRNA) splicing using a minigene splicing assay. RESULTS: Our data revealed that certain missense mutations (L9P, R32C, R40C, R38W, and R42C) disrupted PC secretion by impeding cotranslational translocation to the endoplasmic reticulum or causing endoplasmic reticulum retention. Additionally, some mutations (R38W and R42L/H/S) resulted in abnormal propeptide cleavage. However, a few missense mutations (Q3P, W14G, and V26M) did not account for PCD. Using a minigene splicing assay, we observed that several variations (c.8A>C, c.76G>A, c.94C>T, and c.112C>T) increased the incidence of aberrant pre-mRNA splicing. CONCLUSION: Our findings suggest that variations in PC's signal peptide and propeptide have varying effects on the biological process of PC, including posttranscriptional pre-mRNA splicing, translation, and posttranslational processing. Additionally, a variation could affect the biological process of PC at multiple levels. Except for W14G, our results provide a clear understanding of the relationship between PROC genotype and inherited PCD.

The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification.

18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely employed to reveal metabolic abnormalities linked to Parkinson's disease (PD) at a systemic level. However, the individual metabolic connectome details with PD based on 18F-FDG PET remain largely unknown. To alleviate this issue, we derived a novel brain network estimation method for individual metabolic connectome, that is, Jensen-Shannon Divergence Similarity Estimation (JSSE). Further, intergroup difference between the individual's metabolic brain network and its global/local graph metrics was analyzed to investigate the metabolic connectome's alterations. To further improve the PD diagnosis performance, multiple kernel support vector machine (MKSVM) is conducted for identifying PD from normal control (NC), which combines both topological metrics and connection. Resultantly, PD individuals showed higher nodal topological properties (including assortativity, modularity score, and characteristic path length) than NC individuals, whereas global efficiency and synchronization were lower. Moreover, 45 most significant connections were affected. Further, consensus connections in occipital, parietal, and frontal regions were decrease in PD while increase in subcortical, temporal, and prefrontal regions. The abnormal metabolic network measurements depicted an ideal classification in identifying PD of NC with an accuracy up to 91.84%. The JSSE method identified the individual-level metabolic connectome of 18F-FDG PET, providing more dimensional and systematic mechanism insights for PD.

TIToK: A solution for bi-imbalanced unsupervised domain adaptation.

Unsupervised domain adaptation (UDA) aims to transfer knowledge via domain alignment, and typically assumes balanced data distribution. When deployed in real tasks, however, (i) each domain usually suffers from class imbalance, and (ii) different domains may have different class imbalance ratios. In such bi-imbalanced cases with both within-domain and across-domain imbalance, source knowledge transfer may degenerate the target performance. Some recent efforts have adopted source re-weighting to this issue, in order to align label distributions across domains. However, since target label distribution is unknown, the alignment might be incorrect or even risky. In this paper, we propose an alternative solution named TIToK for bi-imbalanced UDA, by directly Transferring Imbalance-Tolerant Knowledge across domains. In TIToK, a class contrastive loss is presented for classification, in order to alleviate the sensitivity to imbalance in knowledge transfer. Meanwhile, knowledge of class correlation is transferred as a supplementary, which is commonly invariant to imbalance. Finally, discriminative feature alignment is developed for a more robust classifier boundary. Experiments over benchmark datasets show that TIToK achieves competitive performance with the state-of-the-arts, and its performance is less sensitive to imbalance.

Advances in Mass Spectrometry on Membrane Proteins.

Understanding the higher-order structure of membrane proteins (MPs), which are vital for numerous biological processes, is crucial for comprehending their function. Although several biophysical approaches have been used to study the structure of MPs, limitations exist owing to the proteins' dynamic nature and heterogeneity. Mass spectrometry (MS) is emerging as a powerful tool for investigating membrane protein structure and dynamics. Studying MPs using MS, however, must meet several challenges including the lack of stability and solubility of MPs, the complexity of the protein-membrane system, and the difficulty of digestion and detection. To meet these challenges, recent advances in MS have engendered opportunities in resolving the dynamics and structures of MP. This article reviews achievements over the past few years that enable the study of MPs by MS. We first introduce recent advances in hydrogen deuterium exchange and native mass spectrometry for MPs and then focus on those footprinting methods that report on protein structure.

Distinct enzymatic strategies for de novo generation of disulfide bonds in membranes.

Disulfide bond formation is a catalyzed reaction essential for the folding and stability of proteins in the secretory pathway. In prokaryotes, disulfide bonds are generated by DsbB or VKOR homologs that couple the oxidation of a cysteine pair to quinone reduction. Vertebrate VKOR and VKOR-like enzymes have gained the epoxide reductase activity to support blood coagulation. The core structures of DsbB and VKOR variants share the architecture of a four-transmembrane-helix bundle that supports the coupled redox reaction and a flexible region containing another cysteine pair for electron transfer. Despite considerable similarities, recent high-resolution crystal structures of DsbB and VKOR variants reveal significant differences. DsbB activates the cysteine thiolate by a catalytic triad of polar residues, a reminiscent of classical cysteine/serine proteases. In contrast, bacterial VKOR homologs create a hydrophobic pocket to activate the cysteine thiolate. Vertebrate VKOR and VKOR-like maintain this hydrophobic pocket and further evolved two strong hydrogen bonds to stabilize the reaction intermediates and increase the quinone redox potential. These hydrogen bonds are critical to overcome the higher energy barrier required for epoxide reduction. The electron transfer process of DsbB and VKOR variants uses slow and fast pathways, but their relative contribution may be different in prokaryotic and eukaryotic cells. The quinone is a tightly bound cofactor in DsbB and bacterial VKOR homologs, whereas vertebrate VKOR variants use transient substrate binding to trigger the electron transfer in the slow pathway. Overall, the catalytic mechanisms of DsbB and VKOR variants have fundamental differences.

Partial Domain Adaptation Without Domain Alignment.

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a well-labeled source domain to a related and unlabeled target domain with identical label space. The main workhorse in UDA is domain alignment and has proven successful. However, it is practically difficult to find an appropriate source domain with identical label space. A more practical scenario is partial domain adaptation (PDA) where the source label space subsumes the target one. Unfortunately, due to the non-identity between label spaces, it is extremely hard to obtain an ideal alignment, conversely, easier resulting in mode collapse and negative transfer. These motivate us to find a relatively simpler alternative to solve PDA. To achieve this, we first explore a theoretical analysis, which says that the target risk is bounded by both model smoothness and between-domain discrepancy. Then, we instantiate the model smoothness as an intra-domain structure preserving (IDSP) while giving up possibly riskier domain alignment. To our best knowledge, this is the first naive attempt for PDA without alignment. Finally, our empirical results on benchmarks demonstrate that IDSP is not only superior to the PDA SOTAs (e.g.,  âˆ¼ +10% on Cl → Rw and  âˆ¼ +8% on Ar → Rw), but also complementary to domain alignment in the standard UDA.

Missense VKOR mutants exhibit severe warfarin resistance but lack VKCFD via shifting to an aberrantly reduced state.

Missense vitamin K epoxide reductase (VKOR) mutations in patients cause resistance to warfarin treatment but not abnormal bleeding due to defective VKOR activity. The underlying mechanism of these phenotypes remains unknown. Here we show that the redox state of these mutants is essential to their activity and warfarin resistance. Using a mass spectrometry-based footprinting method, we found that severe warfarin-resistant mutations change the VKOR active site to an aberrantly reduced state in cells. Molecular dynamics simulation based on our recent crystal structures of VKOR reveals that these mutations induce an artificial opening of the protein conformation that increases access of small molecules, enabling them to reduce the active site and generating constitutive activity uninhibited by warfarin. Increased activity also compensates for the weakened substrate binding caused by these mutations, thereby maintaining normal VKOR function. The uninhibited nature of severe resistance mutations suggests that patients showing signs of such mutations should be treated by alternative anticoagulation strategies.