Biology subjects

Fu, Z.

Publications and source records attributed to Fu, Z..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

SpaDE: Semantic Locality Preserving Biclusteringfor Neuroimaging Data

The most discriminative and revealing patterns in the neuroimaging population are often confined to smaller subdivisions of the samples and features. Especially in neuropsychiatric conditions, symptoms are expressed within micro subgroups of individuals and may only underly a subset of neurological mechanisms. As such, running a whole-population analysis yields suboptimal outcomes leading to reduced specificity and interpretability. Biclustering is a potential solution since subject heterogeneity makes one-dimensional clustering less effective in this realm. Yet, high dimensional sparse input space and semantically incoherent grouping of attributes make post hoc analysis challenging. Therefore, we propose a deep neural network called semantic locality preserving auto decoder (SpaDE), for unsupervised feature learning and biclustering. SpaDE produces coherent subgroups of subjects and neural features preserving semantic locality and enhancing neurobiological interpretability. Also, it regularizes for sparsity to improve representation learning. We employ SpaDE on human brain connectome collected from schizophrenia (SZ) and healthy control (HC) subjects. The model outperforms several state-of-the-art biclustering methods. Our method extracts modular neural communities showing significant (HC/SZ) group differences in distinct brain networks including visual, sensorimotor, and subcortical. Moreover, these biclustered connectivity substructures exhibit substantial relations with various cognitive measures such as attention, working memory, and visual learning.

neuroscience↗

bioRxiv2024

Multi-modal Spatial-modality Attentive Fusion for Studying Neurodegenerative Disorders

Multi-modal learning has emerged as a powerful technique that leverages diverse data sources to enhance learning and decision-making processes. Adapting this approach to analyzing data collected from different biological domains is intuitive, especially for studying neuropsychiatric disorders. A complex neuropsychiatric disorder like schizophrenia (SZ) can affect multiple aspects of the brain and biologies. These biological sources each present distinct yet correlated expressions of subjects underlying physiological processes. Joint learning from these data sources can improve our understanding of the disorder. However, combining these biological sources is challenging for several reasons: (i) observations are domains-specific, leading to data being represented in dissimilar subspaces, and (ii) fused data is often noisy and high-dimensional, making it challenging to identify relevant information. To address these challenges, we propose a multi-modal artificial intelligence (AI) model with a novel fusion module inspired by a bottleneck attention module (BAM). We use deep neural networks (DNN) to learn latent space representations of the input streams. Next, we introduce a two-dimensional (spatio-modality) attention module to regulate the intermediate fusion for SZ classification. We implement spatial attention via a dilated convolutional neural network that creates large receptive fields for extracting significant contextual patterns. The resulting joint learning framework maximizes complementarity allowing us to explore the correspondence among the modalities. We test our model on a multi-modal imaging-genetic dataset and achieve an SZ prediction accuracy of 94.10% (P < 0.0001), outperforming state-of-the-art unimodal and multi-modal models for the task. Moreover, the model provides inherent interpretability that helps identify concepts significant for the neural networks decision and explains the underlying physiopathology of the disorder. Results also show that functional connectivity among subcortical, sensorimotor, and cognitive control domains plays an important role in characterizing SZ. Analysis of the spatio-modality attention scores suggests that structural components like the supplementary motor area, caudate, and insula play a significant role in SZ. Biclustering the attention scores discover a multi-modal cluster that includes genes CSMD1, ATK3, MOB4, and HSPE1, all of which have been identified as relevant to schizophrenia. In summary, feature attribution appears to be especially useful for probing the transient and confined but decisive patterns of complex disorders, and it shows promise for extensive applicability in future studies.

neuroscience↗

bioRxiv2024

Inference of heterogeneous effects in single-cell genetic perturbation screens

Recent single-cell CRISPR screening experiments have combined the advances of genetic editing and single-cell technologies, leading to transcriptome-scale readouts of responses to perturbations at single-cell resolution. An outstanding question is how to efficiently identify heterogeneous causal effects of perturbations using these technologies. Here we present scCAPE, a tool designed to facilitate causal analysis of heterogeneous perturbation effects at the single-cell level. scCAPE disentangles perturbation effects from the inherent cell-state variations and provides nonparametric inferences of perturbation effects at single-cell resolution, permitting a range of downstream tasks including perturbation effect analysis, genetic interaction analysis, perturbation clustering and prioritizing. We benchmarked scCAPE through simulation studies and real datasets to evaluate its performance in characterizing latent confounding factors and accuracy in estimating heterogeneous perturbation effects. The application of scCAPE identified novel heterogeneous genetic interactions among erythroid differentiation drivers. For example, our analysis pinpointed the role of the synergistic interaction between CBL and CNN1 in the S phase.

bioinformatics↗

bioRxiv2024

An epilepsy-associated CILK1 variant compromises KATNIP regulation and impairs primary cilia and Hedgehog signaling

Mutations in human CILK1 (ciliogenesis associated kinase 1) are linked to ciliopathies and epilepsy. Homozygous point and nonsense mutations that extinguish kinase activity impair primary cilia function, whereas mutations outside the kinase domain are not well understood. Here, we produced a knock-in mouse equivalent of the human CILK1 A615T variant identified in juvenile myoclonic epilepsy (JME). This residue is in the C-terminal region of CILK1 separate from the kinase domain. Mouse embryo fibroblasts (MEF) with either heterozygous or homozygous A612T mutant alleles exhibited a higher ciliation rate, shorter individual cilia and up-regulation of ciliary Hedgehog signaling. Thus, a single A612T mutant allele was sufficient to impair primary cilia and ciliary signaling in MEFs. Gene expression profiles of wild type versus mutant MEFs revealed profound changes in cilia-related molecular functions and biological processes. CILK1 A615T mutant protein was not increased to the same level as the wild type protein when co-expressed with scaffold protein KATNIP (katanin-interacting protein). Our data show that KATNIP regulation of a JME-associated single residue variant of CILK1 is compromised and this impairs the maintenance of primary cilia and Hedgehog signaling.

cell biology↗

bioRxiv2024

FAPM: Functional Annotation of Proteins using Multi-Modal Models Beyond Structural Modeling

Assigning accurate property labels to proteins, like functional terms and catalytic activity, is challenging, especially for proteins without homologs and "tail labels" with few known examples. Unlike previous methods that mainly focused on protein sequence features, we use a pretrained large natural language model to understand the semantic meaning of protein labels. Specifically, we introduce FAPM, a contrastive multi-modal model that links natural language with protein sequence language. This model combines a pretrained protein sequence model with a pretrained large language model to generate labels, such as Gene Ontology (GO) functional terms and catalytic activity predictions, in natural language. Our results show that FAPM excels in understanding protein properties, outperforming models based solely on protein sequences or structures. It achieves state-of-the-art performance on public benchmarks and in-house experimentally annotated phage proteins, which often have few known homologs. Additionally, FAPMs flexibility allows it to incorporate extra text prompts, like taxonomy information, enhancing both its predictive performance and explainability. This novel approach offers a promising alternative to current methods that rely on multiple sequence alignment for protein annotation. The online demo is at: https://huggingface.co/spaces/wenkai/FAPM_demo.

bioinformatics↗

bioRxiv2024

Structure of the Flotillin Complex in a Native Membrane Environment

In this study we used cryo-electron microscopy to determine the structures of the Flotillin protein complex, part of the Stomatin, Prohibitin, Flotillin, and HflK/C (SPFH) superfamily, from cell-derived vesicles without detergents. It forms a right-handed helical barrel consisting of 22 pairs of Flotillin1 and Flotillin2 subunits, with a diameter of 32 nm its wider end and 19 nm at its narrower end. Oligomerization is stabilized by the C-terminus, which forms two helical layers linked by a {beta}-strand, and coiled-coil domains that enable strong charge-charge inter-subunit interactions. Flotillin interacts with membranes at both ends; through its SPFH1 domains at the wide end and the C-terminus at the narrow end, facilitated by hydrophobic interactions and lipidation. The inward tilting of the SPFH domain, likely triggered by phosphorylation, suggests its role in membrane curvature induction, which could be connected to its proposed role in clathrin-independent endocytosis. The structure suggests a shared architecture across the family of SPFH proteins and will promote further research into Flotillins roles in cell biology. Significance statementIt is well known that many biochemical processes in cells must occur in localized regions. There are many different ideas about how cells keep processes localized. In this study we demonstrate that Flotillin1 and Flotillin2 co-assemble to form a large, truncated cone shaped cage whose wide end is always attached to a membrane surface and whose narrow end is sometimes attached to a separate membrane. The entire wall of the cage is without holes and is likely impervious even to small molecules, forming a diffusion barrier that can connect membrane systems. The Flotillin cage is thus well suited to isolate biochemical processes. Through membrane attachment, it also alters local membrane curvature, which could influence endocytic and mechanosensory processes.

biochemistry↗

bioRxiv2024

Identify compound-protein interaction with knowledge graph embedding of perturbation transcriptomics

The emergence of perturbation transcriptomics provides a new perspective and opportunity for drug discovery, but existing analysis methods suffer from inadequate performance and limited applicability. In this work, we present PertKGE, a method designed to improve compound-protein interaction with knowledge graph embedding of perturbation transcriptomics. PertKGE incorporates diverse regulatory elements and accounts for multi-level regulatory events within biological systems, leading to significant improvements compared to existing baselines in two critical "cold-start" settings: inferring binding targets for new compounds and conducting virtual ligand screening for new targets. We further demonstrate the pivotal role of incorporating multi- level regulatory events in alleviating dataset bias. Notably, it enables the identification of ectonucleotide pyrophosphatase/phosphodiesterase-1 as the target responsible for the unique anti- tumor immunotherapy effect of tankyrase inhibitor K-756, and the discovery of five novel hits targeting the emerging cancer therapeutic target, aldehyde dehydrogenase 1B1, with a remarkable hit rate of 10.2%. These findings highlight the potential of PertKGE to accelerate drug discovery by elucidating mechanisms of action and identifying novel therapeutic compounds.

bioinformatics↗

bioRxiv2024

Analysis of Longitudinal Change Patterns in Developing Brain Using Functional and Structural Magnetic Resonance Imaging via Multimodal Fusion

Functional and structural magnetic resonance imaging (fMRI and sMRI) are complementary approaches that can be used to study longitudinal brain changes in adolescents. Each individual modality offers distinct insights into the brain. Each individual modality may overlook crucial aspects of brain analysis. By combining them, we can uncover hidden brain connections and gain a more comprehensive understanding. In previous work, we identified multivariate patterns of change in whole-brain function during adolescence. In this work, we focus on linking functional change patterns (FCPs) to brain structure. We introduce two approaches and applied them to data from the Adolescent Brain and Cognitive Development (ABCD) dataset. First, we evaluate voxelwise sMRI-FCP coupling to identify structural patterns linked to our previously identified FCPs. Our approach revealed multiple interesting patterns in functional network connectivity (FNC) and gray matter volume (GMV) data that were linked to subject level variation. FCP components 2 and 4 exhibit extensive associations between their loadings and voxel-wise GMV data. Secondly, we leveraged a symmetric multimodal fusion technique called multiset canonical correlation analysis (mCCA) + joint independent component analysis (jICA). Using this approach, we identify structured FCPs such as one showing increased connectivity between visual and sensorimotor domains and decreased connectivity between sensorimotor and cognitive control domains, linked to structural change patterns (SCPs) including alterations in the bilateral sensorimotor cortex. Interestingly, females exhibit stronger coupling between brain functional and structural changes than males, highlighting sex-related differences. The combined results from both asymmetric and symmetric multimodal fusion methods underscore the intricate sex-specific nuances in neural dynamics. By utilizing two complementary multimodal approaches, our study enhances our understanding of the dynamic nature of brain connectivity and structure during the adolescent period, shedding light on the nuanced processes underlying adolescent brain development.

neuroscience↗

bioRxiv2024

Spatial Transcriptomic Analysis Identifies Epithelium-Macrophage Crosstalk in Endometriotic Lesions

The mechanisms underlying the pathophysiology of endometriosis, characterized by the presence of endometrium-like tissue outside the uterus, remain poorly understood. This study aimed to identify cell type-specific gene expression changes in superficial peritoneal endometriotic lesions and elucidate the crosstalk among the stroma, epithelium, and macrophages compared to patient-matched eutopic endometrium. Surprisingly, comparison between lesions and eutopic endometrium revealed transcriptional similarities, indicating minimal alterations in the sub-epithelial stroma and epithelium of lesions. Spatial transcriptomics highlighted increased signaling between the lesion epithelium and macrophages, emphasizing the role of the epithelium in driving lesion inflammation. We propose that the superficial endometriotic lesion epithelium orchestrates inflammatory signaling and promotes a pro-repair phenotype in macrophages, providing a new role for Complement 3 in lesion pathobiology. This study underscores the significance of considering spatial context and cellular interactions in uncovering mechanisms governing disease in endometriotic lesions.

molecular biology↗

bioRxiv2024

Normative growth modeling of brain morphology reveals neuroanatomical heterogeneity and biological subtypes in children with ADHD

BackgroundNeuroimaging studies suggest substantial individual heterogeneity in brain phenotypes in attention-deficit/hyperactivity disorder (ADHD). However, how these individual-level brain phenotypes contribute to the identification of ADHD biotypes and whether these biotypes have different treatment outcomes and neurobiological underpinnings remain largely unknown. MethodsWe collected multisite, high-quality structural magnetic resonance imaging data from 1,006 children aged 6-14 years, including 351 children with ADHD and 655 typically developing children. Normative growth models of cortical thickness were established for 219 regions in the typically developing children. Individual-level deviations from these normal references were quantified and clustered to identify ADHD biotypes. We validated the replicability and generalizability of the ADHD biotypes using two independent datasets and evaluated the associations of the biotypes with symptomatic, cognitive, and gene expression profiles, as well as follow-up treatment outcomes. FindingsNo more than 10% of children with ADHD had extreme deviations in cortical thickness in a single region, suggesting high heterogeneity among individuals with ADHD. On the basis of the brain deviation maps, we discovered two robust ADHD biotypes, an infra-normal subtype with cortical thinning associated with ADHD symptoms and a supranormal subtype with cortical thickening associated with cognition. Patients with the infra-normal subtype responded better to methylphenidate than to atomoxetine, although both subtypes showed treatment efficacy. Brain deviations in the infra-normal subtype were explained by the expression levels of genes enriched in presynaptic and axonal development and polygenic risk of ADHD. InterpretationWe identified anatomically distinct, clinically valuable, and biologically informed ADHD subtypes, providing insight into the neurobiological basis of clinical heterogeneity and facilitating a personalized medication strategy for ADHD patients. Panel: Research in contextO_ST_ABSEvidence before this studyC_ST_ABSSubstantial individual heterogeneity in brain phenotypes in attention-deficit/hyperactivity disorder (ADHD) motivates the need to discover homogeneous biotypes. We searched PubMed for research articles on ADHD biotypes using brain MRI published before December 1, 2023, using the search terms ((attention deficit hyperactivity disorder [Title/Abstract]) OR (ADHD [Title/Abstract])) AND ((subtypes [Title/Abstract]) OR (subgroups [Title/Abstract]) OR (subtyping [Title/Abstract])) AND ((MRI [Title/Abstract]) OR (neuroimaging [Title/Abstract]) OR (brain [Title/Abstract])) without language restrictions. Of the eight included studies, two identified ADHD biotypes using structural morphology, four used functional activity, and two used multimodal features. However, none of these studies considered the developmental effect of the brain phenotypes, examined treatment response, or investigated the genetic correlates of the biotypes. Added value of this studyThis study is the first to use individualized brain measures extracted from normative models to investigate ADHD biotypes in a large sample of more than 1,000 children. We identified two reproducible ADHD biotypes, characterized by distinct symptomatic, cognitive, and gene expression profiles, as well as differential treatment responses. This study advances our understanding of the neurobiological basis underlying the clinical heterogeneity of ADHD and highlights the critical need to discover ADHD biotypes using an unbiased and individualized approach. Implications of all the available evidenceThis study revealed remarkable neuroanatomical heterogeneity in ADHD patients and identified anatomically distinct, clinically valuable, and biologically informed ADHD biotypes. Our findings have potential value for the investigation of data-driven biotypes to evaluate treatment efficacy and facilitate personalized treatment. We also highlight the need for future studies to move beyond the understanding of ADHD solely based on the "average patient" perspective.

neuroscience↗

bioRxiv2023

Potential clinical value and influence of conductivity in conductive cardiac patches for reducing post-MI arrythmia risks

Conductive cardiac patches can help to restore electric signal conduction of the diseased myocardium after myocardial infarction (MI). However, none of the conductive cardiac patches reported in literature has entered clinical trials. Bench-to-bedside translation of conductive patches has long been hindered by the lack of knowledge of the optimal patch conductivity and deep understanding of the potential clinical benefits and risks in patients. Here, we first prepared conductive cardiac patches with conductivities covering 5 orders of magnitude (10-3-101 S/cm). Disagreeing with the mainstream opinion that patch conductivity close to native myocardium (10-3-10-2 S/cm) is most favorable, our results showed that patches with conductivity two orders of magnitudes higher than native myocardium (10-1-100 S/cm) are most effective in restoring cardiac conduction and lowering inducibility quotient. Conduction velocity (CV) is the essence of the observed results. Rat experiments showed that the low-conductivity patch could increase the CV of infarcted myocardium, but did not fully compensate the mismatch in the CVs of infarct and healthy myocardium. Moderate-conductivity patches could increase myocardial CV to the same level of healthy myocardium, while high-conductivity patches further increased myocardial CV, causing a reversed mismatch. The relationship between patch conductivity and improved CVs in myocardium can be explained by monodomain model theory. Based on the theory, 3D finite element simulation of a MI patient heart predicted that a suitable, patch-improved myocardial CV could reduce the number of reentrants, and stabilize the remaining reentry circuits in the myocardium of the MI patient, which indicated its clinical value.

bioengineering↗

bioRxiv2023

Association between the oral microbiome and brain resting state connectivity in schizophrenia

Recent microbiome-brain axis findings have shown evidence of the modulation of microbiome community as an environmental mediator in brain function and psychiatric illness. This work is focused on the role of the microbiome in understanding a rarely investigated environmental involvement in schizophrenia (SZ), especially in relation to brain circuit dysfunction. We leveraged high throughput microbial 16s rRNA sequencing and functional neuroimaging techniques to enable the delineation of microbiome-brain network links in SZ. N=213 SZ and healthy control (HC) subjects were assessed for the oral microbiome. Among them, 139 subjects were scanned by resting-state functional magnetic resonance imaging (rsfMRI) to derive brain functional connectivity. We found a significant microbiome compositional shift in SZ beta diversity (weighted UniFrac distance, p= 6x10-3; Bray-Curtis distance p = 0.021). Fourteen microbial species involving pro-inflammatory and neurotransmitter signaling and H2S production, showed significant abundance alterations in SZ. Multivariate analysis revealed one pair of microbial and functional connectivity components showing a significant correlation of 0.46. Thirty five percent of microbial species and 87.8% of brain functional network connectivity from each component also showed significant differences between SZ and HC with strong performance in classifying SZ from HC, with an area under curve (AUC) = 0.84 and 0.87, respectively. The results suggest a potential link between oral microbiome dysbiosis and brain functional connectivity alteration in relation to SZ, possibly through immunological and neurotransmitter signaling pathways and the hypothalamic-pituitary-adrenal axis, supporting for future work in characterizing the role of oral microbiome in mediating effects on SZ brain functional activity.

microbiology↗

bioRxiv2023

DNA sensor ABCF1 phase separates with cccDNA to inhibit HBV replication

Objectives: Hepatitis B virus (HBV) covalently closed circular DNA (cccDNA) contributes to viral persistence and recurrence. However, it is still less known how the host immune system responds to cccDNA and suppresses HBV replication. Therefore, it is essential to explore host proteins that interact with cccDNA and efficiently suppress HBV replication. Design: The interaction of cccDNA and ABCF1 was assessed by ChIP and pull-down assay. HBV replication was assessed in different cell models, including cccDNA surrogate-transfected and HBV-infected hepatocytes models. Phase separation of recombinant ABCF1 fusion proteins with cccDNA was observed by fluorescence microscopy. Results: Our data found that ABCF1 interacts with cccDNA to form phase-separated condensates via the poly-glutamine (PolyQ) of N-terminal intrinsically disordered low-complexity domain (LCD). Subsequently, ABCF1-cccDNA phase separation not only activates the type I/III interferon (IFN-I/III) pathway but also prevents Pol II accumulation on cccDNA to inhibit HBV transcription. Conclusions: Taken together, our study showed that ABCF1 acts as an antiviral restriction factor of HBV cccDNA by phase-separation-driven innate immune signaling and transcription inhibition. These findings shed new light on the understanding of host defense against cccDNA and provide a novel promising therapeutic strategy for HBV infection.

microbiology↗

bioRxiv2023

CLAIR: An integrated lipid database across multiple crop species

Oilseed crops, which are rich in plant lipids, provide essential fatty acids for human consumption and serve as major sources of biofuels and essential raw materials for the chemical industry. As a result of population growth and ecological changes, the demand for vegetable oil is increasingly outpacing supply. A comprehensive understanding of the genes involved in lipid metabolism in oilseed crops and the regulatory relationships among these genes is essential for improving oil content. However, current studies on lipid metabolism genes rely heavily on a decade-old database of genes involved in lipid metabolism in Arabidopsis thaliana. To address this issue, we mined the literature, integrated data from various databases and studies, and aligned homologs of lipid metabolism genes from nine oilseed crops to construct a comprehensive set of lipid metabolism genes in plants. Using this approach, we identified 221 additional lipid metabolism genes. In addition, we created a user-friendly lipid database called CLAIR (Crop Lipid-Associated Information Resource) by integrating and mining multi-omics data from nine major oil crops. The database is available at http://www.clipair.cn/. These resources should facilitate further research and exploration of lipid metabolism in oil crops, ultimately contributing to improved oil production.

plant biology↗

bioRxiv2023

Rapid evolution of pesticide resistance via adaptation and interspecific introgression in a major North American crop pest

Insect crop pests threaten global food security. This threat is amplified through the spread of non-native species and the evolution of pesticide resistance, which can be introduced to a population though de novo mutation or gene flow. We investigate these processes in an economically important noctuid crop pest, Helicoverpa zea, which has rapidly evolved resistance to several pesticides. Its sister species Helicoverpa armigera, first detected as an invasive species in Brazil in 2013, introduced the pyrethroid resistance gene CYP337B3 to South American H. zea via introgression. To understand whether this contributes to pesticide resistance in North America, we sequenced 237 H. zea genomes across 10 sample sites in the US. First, we report H. armigera introgression into the North American H. zea population. Two individuals sampled in Texas in 2019 carry H. armigera haplotypes in a 4Mbp region containing CYP337B3. Second, we show that the remarkable dispersal ability of H. zea results in a panmictic North American population. Third, we detect signatures of selection in non-admixed H. zea, identifying a selective sweep at a second pesticide resistance locus with a similar name: CYP333B3. We estimate that its derived allele conferred a [~]4.9% fitness advantage and show that this estimate explains independently observed rare nonsynonymous CYP333B3 mutations approaching fixation over a [~]20-year period. We also detect putative signatures of selection at a kinesin gene associated with Bt resistance. Our results show that pesticide resistance in H. zea evolved rapidly and recently via two independent mechanisms: interspecific introgression and rapid intraspecific adaptation.

evolutionary biology↗

bioRxiv2023

TRIM72 restricts lyssavirus infection by inducing K48-linked ubiquitination and proteasome degradation of the matrix protein

The tripartite motif (TRIM) protein family is the largest subfamily of E3 ubiquitin ligases, playing a crucial role in the antiviral process. In this study, we found that TRIM72, a member of the TRIM protein family, was increased in neuronal cells and mouse brains following rabies lyssavirus (RABV) infection. Over-expression of TRIM72 significantly reduced the viral titer of RABV in neuronal cells and mitigated the pathogenicity of RABV in mice. Furthermore, we found that TRIM72 over-expression effectively prevents the release of RABV. In terms of the mechanism, TRIM72 promotes the K48-linked ubiquitination of RABV Matrix protein (M), leading to the degradation of M through the proteasome pathway. TRIM72 directly interacts with M and the interaction sites were identified and confirmed through TRIM72-M interaction model construction and mutation analysis. Further investigation revealed that the degradation of M induced by TRIM72 was attributed to TRIM72s promotion of ubiquitination at site K195 in M. Importantly, the K195 site was found to be partially conserved among lyssaviruss M proteins, and TRIM72 over-expression induced the degradation of these lyssavirus M proteins. In summary, our study has uncovered a TRIM family protein, TRIM72, that can restrict lyssavirus replication by degrading M, and we have identified a novel ubiquitination site (K195) in lyssavirus M. Author SummaryRabies, caused by lyssaviruses, most often by rabies virus, the most lethal zoonotic disease, is responsible for approximately 59000 human deaths globally each year. Lyssavirus M protein displays an essential role in lyssavirus assembly and budding. Here, we found that TRIM family protein TRIM72 directly interacts with lyssavirus M thereby promoting the K48-linked ubiquitination of M, leading to the degradation of M through the proteasome pathway, restricting lyssavirus release. Moreover, we identified a novel important ubiquitination site (K195) in lyssavirus M. Prior to our study, there had been no reports of a direct interaction between TRIM72 and viral proteins, an antiviral function of TRIM72 was proved in our study. It is possible that TRIM72 interacts and ubiquitinates other viral proteins from various viruses, which warrants further investigation. This research contributes to our understanding of how TRIM72 and other TRIM proteins play a role in defending against viral invasions and may inspire further research in this field.

microbiology↗

bioRxiv2023

4D DYNAMIC SPATIAL BRAIN NETWORKS AT REST LINKED TO COGNITION SHOW ATYPICAL VARIABILITY AND COUPLING IN SCHIZOPHRENIA

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain net-works in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan. We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxelwise changes within and between brain networks.

neuroscience↗

bioRxiv2023

Nucleoporin-binding nanobodies that either track or inhibit nuclear pore complex assembly

Nuclear pore complex (NPC) biogenesis is a still enigmatic example of protein self-assembly. We now introduce several cross-reacting anti-Nup nanobodies for imaging intact NPCs from frog to human. We further report a simplified assay that directly tracks postmitotic NPC assembly by added labeled anti-Nup nanobodies. In interphase, NPCs are inserted into a pre-existing nuclear envelope. This makes it difficult to monitor this process as newly-assembled NPCs must be distinguished from pre-existing ones. We solved this problem by inserting Xenopus NPCs into human nuclear envelopes and using frog-specific anti-Nup nanobodies for detection. We also asked whether anti-nucleoporin (Nup) nanobodies could serve as NPC assembly inhibitors. A first generation, selected from immune libraries against Xenopus Nups, comprised only bright stainers of intact NPCs but no inhibitors, perhaps because the immune response was biased towards non-conserved and, thus, functionally-irrelevant epitopes. To overcome this bias, we selected for crossreactivity between Xenopus and human Nups and obtained anti-Nup93, Nup98, and Nup155 nanobodies that block Nup-Nup interfaces and arrest NPC assembly. We solved structures of nanobody-target complexes and identified roles for the Nup93--solenoid in recruiting Nup358 and the Nup214{middle dot}88{middle dot}62 complex, and for Nup155 and the Nup98 autoproteolytic domain in NPC-scaffold assembly. The latter suggests an assembly checkpoint linking pore formation to permeability barrier assembly.

cell biology↗