bioRxiv : the preprint server for biology最新文献

Many eukaryotic viruses require membrane-bound compartments for replication, but no such organelles are known to be formed by prokaryotic viruses^1-3. Bacteriophages of the Chimalliviridae family sequester their genomes within a phage-generated organelle, the phage nucleus, which is enclosed by a lattice of the viral protein ChmA^4-10. Previously, we observed lipid membrane-bound vesicles in cells infected by Chimalliviridae, but due to the paucity of genetics tools for these viruses it was unknown if these vesicles represented unproductive, abortive infections or a bona fide stage in the phage life cycle. Using the recently-developed dRfxCas13d-based knockdown system CRISPRi-ART¹¹ in combination with fluorescence microscopy and cryo-electron tomography, we show that inhibiting phage nucleus formation arrests infections at an early stage in which the injected phage genome is enclosed within a membrane-bound early phage infection (EPI) vesicle. We demonstrate that early phage genes are transcribed by the virion-associated RNA polymerase from the genome within the compartment, making the EPI vesicle the first known example of a lipid membrane-bound organelle that separates transcription from translation in prokaryotes. Further, we show that the phage nucleus is essential for the phage life cycle, with genome replication only beginning after the injected DNA is transferred from the EPI vesicle to the newly assembled phage nucleus. Our results show that Chimalliviridae require two sophisticated subcellular compartments of distinct compositions and functions that facilitate successive stages of the viral life cycle.

Direct electrical stimulation has been used for decades as a gold standard clinical tool to map cognitive function in neurosurgery patients^1-8. However, the molecular impact of electrical stimulation in the human brain is unknown. Here, using state-of-the-art transcriptomic and epigenomic sequencing techniques, we define the molecular changes in bulk tissue and at the single-cell level in the human cerebral cortex following direct electrical stimulation of the anterior temporal lobe in patients undergoing neurosurgery. Direct electrical stimulation surprisingly had a robust and consistent impact on the expression of genes related to microglia-specific cytokine activity, an effect that was replicated in mice. Using a newly developed deep learning computational tool, we further demonstrate cell type-specific molecular activation, which underscores the effects of electrical stimulation on gene expression in microglia. Taken together, this work challenges the notion that the immediate impact of electrical stimulation commonly used in the clinic has a primary effect on neuronal gene expression and reveals that microglia robustly respond to electrical stimulation, thus enabling these non-neuronal cells to sculpt and shape the activity of neuronal circuits in the human brain.

A key challenge in neuroscience is to understand the structural and functional relationships of the brain from high-dimensional, multimodal neuroimaging data. While conventional multivariate approaches often simplify statistical assumptions and estimate one-dimensional independent sources shared across modalities, the relationships between true latent sources are likely more complex - statistical dependence may exist within and between modalities, and span one or more dimensions. Here we present Multimodal Subspace Independent Vector Analysis (MSIVA), a methodology to capture both joint and unique vector sources from multiple data modalities by defining both cross-modal and unimodal subspaces with variable dimensions. In particular, MSIVA enables flexible estimation of varying-size independent subspaces within modalities and their one-to-one linkage to corresponding subspaces across modalities. As we demonstrate, a main benefit of MSIVA is the ability to capture subject-level variability at the voxel level within independent subspaces, contrasting with the rigidity of traditional methods that share the same independent components across subjects. We compared MSIVA to a unimodal initialization baseline and a multimodal initialization baseline, and evaluated all three approaches with five candidate subspace structures on both synthetic and neuroimaging datasets. We show that MSIVA successfully identified the ground-truth subspace structures in multiple synthetic datasets, while the multimodal baseline failed to detect high-dimensional subspaces. We then demonstrate that MSIVA better detected the latent subspace structure in two large multimodal neuroimaging datasets including structural MRI (sMRI) and functional MRI (fMRI), compared with the unimodal baseline. From subsequent subspace-specific canonical correlation analysis, brain-phenotype prediction, and voxelwise brain-age delta analysis, our findings suggest that the estimated sources from MSIVA with optimal subspace structure are strongly associated with various phenotype variables, including age, sex, schizophrenia, lifestyle factors, and cognitive functions. Further, we identified modality- and group-specific brain regions related to multiple phenotype measures such as age (e.g., cerebellum, precentral gyrus, and cingulate gyrus in sMRI; occipital lobe and superior frontal gyrus in fMRI), sex (e.g., cerebellum in sMRI, frontal lobe in fMRI, and precuneus in both sMRI and fMRI), schizophrenia (e.g., cerebellum, temporal pole, and frontal operculum cortex in sMRI; occipital pole, lingual gyrus, and precuneus in fMRI), shedding light on phenotypic and neuropsychiatric biomarkers of linked brain structure and function.

Current therapeutic strategies for Alzheimer's disease (AD) target amyloid-beta (Aβ) fibrils and high molecular weight protofibrils associated with plaques, but molecular cascades associated with AD may drive neural systems failure before Aβ plaque deposition in AD. Employing hippocampal electrophysiological recordings and dynamic calcium imaging across the sleep-wake cycle in the APP/PS1 mouse model of AD before Aβ plaques accumulated, we detected marked impairments of hippocampal systems function: In a spatial behavioral task, but not REM sleep, phase-amplitude coupling (PAC) of the hippocampal theta and gamma oscillations was impaired and place cell calcium fluctuations were hyper-synchronized with the theta oscillation. In subsequent slow wave sleep (SWS), place cell reactivation was reduced. These degraded neural functions underlying memory encoding and consolidation support targeting pathological processes of the pre-plaque phase of AD to treat and prevent hippocampal impairments.

Tumor initiation represents the first step in tumorigenesis during which normal progenitor cells undergo cell fate transition to cancer. Capturing this process as it occurs in vivo, however, remains elusive. Here we employ spatiotemporally controlled oncogene activation and tumor suppressor inhibition together with multiomics to unveil the processes underlying oral epithelial progenitor cell reprogramming into tumor initiating cells (TIC) at single cell resolution. TIC displayed a distinct stem-like state, defined by aberrant proliferative, hypoxic, squamous differentiation, and partial epithelial to mesenchymal (pEMT) invasive gene programs. YAP-mediated TIC programs included the activation of oncogenic transcriptional networks and mTOR signaling, and the recruitment of myeloid cells to the invasive front contributing to tumor infiltration. TIC transcriptional programs are conserved in human head and neck cancer and associated with poor patient survival. These findings illuminate processes underlying cancer initiation at single cell resolution, and identify candidate targets for early cancer detection and prevention.

Neurons are challenged to maintain proteostasis in neuronal projections, particularly with the physiological stress at synapses to support intercellular communication underlying important functions such as memory and movement control. Proteostasis is maintained through regulated protein synthesis and degradation and chaperone-assisted protein folding. Using high-resolution fluorescent microscopy, we discovered that neurons localize a subset of chaperone mRNAs to their dendrites, particularly more proximal regions, and increase this asymmetric localization following proteotoxic stress through microtubule-based transport from the soma. The most abundant chaperone mRNA in dendrites encodes the constitutive heat shock protein 70, HSPA8. Proteotoxic stress in cultured neurons, induced by inhibiting proteasome activity or inducing oxidative stress, enhanced transport of Hspa8 mRNAs to dendrites and the percentage of mRNAs engaged in translation on mono and polyribosomes. Knocking down the ALS-related protein Fused in Sarcoma (FUS) and a dominant mutation in the heterogenous nuclear ribonucleoprotein A2/B1 (HNRNPA2B1) impaired stress-mediated localization of Hspa8 mRNA to dendrites in cultured murine motor neurons and human iPSC-derived neurons, respectively, revealing the importance of these RNA-binding proteins in maintaining proteostasis. These results reveal the increased dendritic localization and translation of the constitutive HSP70 Hspa8 mRNA as a crucial neuronal stress response to uphold proteostasis and prevent neurodegeneration.

Functional magnetic resonance imaging (fMRI) of the auditory and visual sensory systems of the human brain is an active area of investigation in the study of human health and disease. The medial geniculate nucleus (MGN) and lateral geniculate nucleus (LGN) are key thalamic nuclei involved in the processing and relay of auditory and visual information, respectively, and are the subject of blood-oxygen-level-dependent (BOLD) fMRI studies of neural activation and functional connectivity in human participants. However, localization of BOLD fMRI signal originating from neural activity in MGN and LGN remains a technical challenge, due in part to the poor definition of boundaries of these thalamic nuclei in standard T1-weighted and T2-weighted magnetic resonance imaging sequences. Here, we report the development and evaluation of an auditory and visual sensory thalamic localizer (TL) fMRI task that produces participant-specific functionally-defined regions of interest (fROIs) of both MGN and LGN, using 3 Tesla multiband fMRI and a clustered-sparse temporal acquisition sequence, in less than 16 minutes of scan time. We demonstrate the use of MGN and LGN fROIs obtained from the TL fMRI task in standard resting-state functional connectivity (RSFC) fMRI analyses in the same participants. In RSFC analyses, we validated the specificity of MGN and LGN fROIs for signals obtained from primary auditory and visual cortex, respectively, and benchmark their performance against alternative atlas- and segmentation-based localization methods. The TL fMRI task and analysis code (written in Presentation and MATLAB, respectively) have been made freely available to the wider research community.

Since motion can only be defined relative to a reference frame, which reference frame guides perception? A century of psychophysical studies has produced conflicting evidence: retinotopic, egocentric, world-centric, or even object-centric. We introduce a hierarchical Bayesian model mapping retinal velocities to perceived velocities. Our model mirrors the structure in the world, in which visual elements move within causally connected reference frames. Friction renders velocities in these reference frames mostly stationary, formalized by an additional delta component (at zero) in the prior. Inverting this model automatically segments visual inputs into groups, groups into supergroups, etc. and "perceives" motion in the appropriate reference frame. Critical model predictions are supported by two new experiments, and fitting our model to the data allows us to infer the subjective set of reference frames used by individual observers. Our model provides a quantitative normative justification for key Gestalt principles providing inspiration for building better models of visual processing in general.

Objectives: Brain segmentation of infant magnetic resonance (MR) images is vitally important in studying developmental mental health and disease. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here, we introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source, community-driven model that relies on data augmentation and a large sample size of manually annotated images to facilitate the production of robust and generalizable brain segmentations.

Experimental design: Included in model training and testing were MR brain images on 84 participants with an age range of 0-8 months (median postmenstrual ages of 13.57 months). Using manually annotated real and synthetic segmentation images, the model was trained using a 10-fold cross-validation procedure. Testing occurred on MRI data processed with the DCAN labs infant-ABCD-BIDS processing pipeline using segmentations produced from gold standard manual annotation, joint-label fusion (JLF), and BIBSNet to assess model performance.

Principal observations: Using group analyses, results suggest that cortical metrics produced using BIBSNet segmentations outperforms JLF segmentations. Additionally, when analyzing individual differences, BIBSNet segmentations perform even better.

Conclusions: BIBSNet segmentation shows marked improvement over JLF segmentations across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF and can be easily included in other processing pipelines.