Biology subjects

Chen, S.

Publications and source records attributed to Chen, S..

At least 685 records · Page 38Linked to original sources

bioRxiv2021

Essential functions of MLL1 and MLL2 in retinal development and cone cell maintenance

MLL1 (KMT2A) and MLL2 (KMT2B) are homologous members of the mixed-lineage leukemia (MLL) family of histone methyltransferases involved in epigenomic transcriptional regulation. Their sequence variants have been associated with neurological and psychological disorders, but little is known about their roles and mechanism of action in CNS development. Using mouse retina as a model, we previously reported MLL1s role in retinal neurogenesis and horizontal cell maintenance. Here we determine roles of MLL2 and MLL1/MLL2 together in retinal development using conditional knockout (CKO) mice. Deleting Mll2 from Chx10+ retinal progenitors resulted in a similar phenotype as Mll1 CKO, but removal of both alleles produced much more severe deficits than each single CKO: 1-month double CKO mutants displayed null light responses in electroretinogram; thin retinal layers, including shorter photoreceptor outer segments with impaired phototransduction gene expression; and reduced numbers of M-cones, horizontal and amacrine neurons, followed by fast retinal degeneration. Despite moderately reduced progenitor cell proliferation at P0, the neurogenic capacity was largely maintained in double CKO mutants. However, upregulated apoptosis and reactive gliosis were detected during postnatal retinal development. Finally, the removal of both MLLs in fated rods produced a normal phenotype, but the CKO in M-cones impaired M-cone function and survival, indicating both cell non-autonomous and autonomous mechanisms. Altogether, our results suggest that MLL1/MLL2 play redundant roles in maintaining specific retinal neurons after cell fate specification and are essential for establishing functional neural networks.

neuroscience↗

bioRxiv2021

Three-dimensional Shear Wave Elastography Using a 2D Row Column Addressing (RCA) Array

ObjectiveTo develop a 3D shear wave elastography (SWE) technique using a 2D row column addressing (RCA) array, with either external vibration or acoustic radiation force (ARF) as the shear wave source. Impact StatementThe proposed method paves the way for clinical translation of 3D-SWE based on the 2D RCA, providing a low-cost and high volume-rate solution that is compatible with existing clinical systems. IntroductionSWE is an established ultrasound imaging modality that provides a direct and quantitative assessment of tissue stiffness, which is significant for a wide range of clinical applications including cancer and liver fibrosis. SWE requires high frame-rate imaging for robust shear wave tracking. Due to the technical challenges associated with high volume-rate imaging in 3D, current SWE techniques are typically confined to 2D. Advancing SWE from 2D to 3D is significant because of the heterogeneous nature of tissue, which demands 3D imaging for accurate and comprehensive evaluation. MethodsA 3D SWE method using a 2D RCA array was developed with a volume-rate up to 2000 Hz. The performance of the proposed method was systematically evaluated on tissue-mimicking elasticity phantoms. Results3D shear wave motion induced by either external vibration or ARF was successfully detected with the proposed method. Robust 3D shear wave speed maps were reconstructed for both homogeneous and heterogeneous phantoms with inclusions. ConclusionThe high volume-rate 3D imaging provided by the 2D RCA array provides a robust and practical solution for 3D SWE with a clear pathway for future clinical translation.

bioengineering↗

bioRxiv2021

USPNet: unbiased organism-agnostic signal peptidepredictor with deep protein language model

Signal peptide is a short peptide located in the N-terminus of proteins. It plays an important role in targeting and transferring transmembrane proteins and secreted proteins to correct positions. Compared with traditional experimental methods to identify and discover signal peptides, the computational methods are faster and more efficient, which are more practical for the analysis of thousands or even millions of protein sequences in reality, especially for the metagenomic data. Therefore, computational tools are recently proposed to classify signal peptides and predict cleavage site positions, but most of them disregard the extreme data imbalance problem in these tasks. In addition, almost all these methods rely on additional group information of proteins to boost their performances, which, however, may not always be available. To deal with these issues, in this paper, we present Unbiased Organism-agnostic Signal Peptide Network (USPNet), a signal peptide prediction and cleavage site prediction model based on deep protein language model. We propose to use label distribution-aware margin (LDAM) loss and evolutionary scale modeling (ESM) embedding to handle data imbalance and object-dependence problems. Extensive experimental results demonstrate that the proposed method significantly outperforms all the previous methods on the classification performance. Additional study on the simulated metagenomic data further indicates that our model is a more universal and robust tool without dependency on additional group information of proteins, with the Matthews correlation coefficient improved by up to 17.5%. The proposed method will be potentially useful to discover new signal peptides from the abundant metagenomic data.

bioinformatics↗

bioRxiv2021

Montage electron tomography of vitrified specimens

Cryo-electron tomography provides detailed views of macromolecules in situ. However, imaging a large field of view to provide more cellular context requires reducing magnification during data collection, which in turn restricts the resolution. To circumvent this trade-off between field of view and resolution, we have developed a montage data collection scheme that uniformly distributes the dose throughout the specimen. In this approach, sets of slightly overlapping circular tiles are collected at high magnification and stitched to form a composite projection image at each tilt angle. These montage tilt-series are then reconstructed into massive tomograms with a small pixel size but a large field of view. For proof-of-principle, we applied this method to the thin edge of HeLa cells. Thon rings to better than 15 [A] were observed in the montaged tilt-series, and diverse cellular features were evident in the resulting tomograms. These results indicate that the additional dose required by this technique is not prohibitive to performing structural analysis to intermediate resolution across a large field of view. We anticipate that montage tomography will prove particularly useful for lamellae, increase the likelihood of imaging rare cellular events, and facilitate visual proteomics.

biophysics↗

bioRxiv2021

A multivariate to multivariate approach for voxel-wise genome-wide association analysis

The joint analysis of imaging-genetics data facilitates the systematic investigation of genetic effects on brain structures and functions with spatial specificity. We focus on voxel-wise genome-wide association analysis, which may involve trillions of single nucleotide polymorphism (SNP)-voxel pairs. We attempt to identify underlying organized association patterns of SNP-voxel pairs and understand the polygenic and pleiotropic networks on brain imaging traits. We propose a bi-clique graph structure (i.e., a set of SNPs highly correlated with a cluster of voxels) for the systematic association pattern. Next, we develop computational strategies to detect latent SNP-voxel bi-cliques and inference model for statistical testing. We further provide theoretical results to guarantee the accuracy of our computational algorithms and statistical inference. We validate our method by extensive simulation studies, and then apply it to the whole genome genetic and voxel-level white matter integrity data collected from 1052 participants of the human connectome project (HCP). The results demonstrate multiple genetic loci influencing white matter integrity measures on splenium and genu of the corpus callosum.

neuroscience↗

bioRxiv2021

Mecp2 deletion results in profound alterations of developmental and adult functional connectivity

As a regressive neurodevelopmental disorder with a well-established genetic cause, Rett Syndrome and its Mecp2 loss-of-function mouse model provide an excellent opportunity to define potentially translatable functional signatures of disease progression, as well as offer insight into Mecp2s role in functional circuit development. Thus, we applied optical fluorescence imaging to assess mesoscale calcium functional connectivity (FC) in the Mecp2 cortex prior to symptom onset as well as during decline. We found that FC was profoundly disrupted in Mecp2 males both in juvenile development and early adulthood. Female Mecp2 mice displayed a subtle homotopic contralateral increase in motor cortex as juveniles but not in adulthood, where instead parietal regions were implicated. Additionally, conditional rescue studies indicated FC phenotypes are driven by excitatory neurons. Altogether, the female results identify subtle candidate translatable biomarkers of disease progression, while the male results indicate MeCP2 protein is needed in a circuit-specific manner for FC.

neuroscience↗

bioRxiv2021

Architecture of the cytoplasmic face of the nuclear pore

The nuclear pore complex (NPC) is the sole bidirectional gateway for nucleocytoplasmic transport. Despite recent progress in elucidating the NPC symmetric core architecture, the asymmetrically decorated cytoplasmic face, essential for mRNA export and a hotspot for nucleoporin-associated diseases, has remained elusive. Here, we report a composite structure of the entire human cytoplasmic face obtained by combining biochemical reconstitution, crystal structure determination, docking into cryo-electron tomographic reconstructions, and physiological validation, accounting for a third of the NPCs mass. Whereas an evolutionarily conserved [~]540 kDa hetero-hexameric cytoplasmic filament nucleoporin complex is anchored by species-specific motifs above the central transport channel, attachment of the pentameric NUP358 bundles depends on the double-ring arrangement of the coat nucleoporin complex. Our results and the predictive power of our composite structure provide a rich foundation for elucidating the molecular basis of mRNA export and nucleoporin diseases. One sentence summaryAn interdisciplinary analysis established the near-atomic molecular architecture of the cytoplasmic face of the human nuclear pore complex.

biochemistry↗

bioRxiv2021

Palaeogenomics reveal a hybrid origin of the world's largest Camelus species

The extinct Camelus knoblochi is known as the largest camel in genus Camelus, but its relationship to modern Camelus species remains unclear. In this study, we reported the first mitochondrial and nuclear analyses of seven Late Pleistocene C. knoblochi samples from Northeastern China. We found that they are inseparable to wild Bactrian camel on the matrilineal side, but belong to a distinct cluster on the biparental nuclear side. Further admixture proportion analyses suggested hybrid ancestry between both the ancestors of the modern wild and domesticated Bactrian camels, with ~65% contribution from the former and ~35% from the later. By calculating the coalescence time for three Camelus species above, we estimated the hybridization event occurred between approximately 0.8 and 0.33 Ma. We also used Bayesian skyline to reconstruct the maternal demographic trajectories for different Camelus to better compare their evolutionary histories. Our results provide molecular insights into C. knoblochi and fill in a vital piece in understanding the genus Camelus.

molecular biology↗

bioRxiv2021

A Novel Mice Model of Catecholaminergic Polymorphic Ventricular Tachycardia Generated by CRISPR/Cas9

Catecholaminergic polymorphic ventricular tachycardia (CPVT) has been considered as one of the most important causes of childrens sudden cardiac death. Mutations in the genes for RyR2 and CASQ2, two mainly subtypes of CPVT, have been identified. However, the mutation in the gene of TECRL was rarely reported, which could be another genetic cause of CPVT. We evaluated myocardial contractility, electrophysiology, calcium handling in Tecrl knockout (Tecrl KO) mice and human induced pluripotent stem cell-derived cardiomyocytes. Immediately after epinephrine plus caffeine injection, Tecrl KO mice showed much more multiple premature ventricular beats and ventricular tachycardia. The Tecrl KO mice demonstrate CPVT phenotypes. Mechanistically, intracellular calcium amplitude was reduced, while time to baseline of 50 was increased in acute isolated cardiomyocytes. RyR2 protein levels decreased significantly upon cycloheximide treatment in TECRL deficiency cardiomyocytes. Overexpression of TECRL and KN93 can partially reverse cardiomyocytes calcium dysfunction, and this is p-CaMKII/CaMKII dependent. Therefore, a new CPVT mouse model was constructed. We propose a previously unrecognized mechanism of TECRL and provide support for the therapeutic targeting of TECRL in treating CPVT.

molecular biology↗

bioRxiv2021

A new Mendelian Randomization method to estimate causal effects of multivariable brain imaging exposures

The advent of simultaneously collected imaging-genetics data in large study cohorts provides an unprecedented opportunity to assess the causal effect of brain imaging traits on externally measured experimental results (e.g., cognitive tests) by treating genetic variants as instrumental variables. However, classic Mendelian Randomization methods are limited when handling high-throughput imaging traits as exposures to identify causal effects. We propose a new Mendelian Randomization framework to jointly select instrumental variables and imaging exposures, and then estimate the causal effect of multivariable imaging data on the outcome. We validate the proposed method with extensive data analyses and compare it with existing methods. We further apply our method to evaluate the causal effect of white matter microstructure integrity on cognitive function. The findings suggest that our method achieved better performance regarding sensitivity, bias, and false discovery rate compared to individually assessing the causal effect of a single exposure and jointly assessing the causal effect of multiple exposures without dimension reduction. Our application results indicated that WM measures across different tracts have a joint causal effect that significantly impacts the cognitive function among the participants from the UK Biobank.

bioinformatics↗

bioRxiv2021

Characterization of morpho-functional traits in mesophotic corals reveals optimized light capture and photosynthesis

The morphology and skeleton architecture of photosynthetic corals modulates the light capture and functioning of the coral-algal symbiosis on shallow-water corals. Since corals can thrive on mesophotic reefs under extreme light-limited conditions, we hypothesized that microskeletal coral features optimize light capture under low-light environments. Using micro-computed tomography scanning, we conducted a comprehensive three-dimensional (3D) assessment of small-scale skeleton morphology of the depth-generalist coral Stylophora pistillata collected from shallow (5 m) and mesophotic (45 m) depths. We detected a high phenotypic diversity between depths, resulting in two distinct morphotypes, with calyx diameter, theca height, and corallite marginal spacing contributing to most of the variation between depths. To determine whether such depth-specific morphotypes affect coral light capture and photosynthesis on the corallite-scale, we developed 3D simulations of light propagation based on photosynthesis-irradiance parameters. We found that corals associated with shallow morphotypes dissipated excess light through self-shading microskeletal features; while mesophotic morphotypes facilitated enhanced light absorption and photosynthesis under low-light conditions. We conclude that the mesophotic coral architecture provides a greater ability to trap solar energy and efficiently exploit the limited light conditions, and suggest that morphological modifications play a key role in the photoadaptation response to low-light.

ecology↗

bioRxiv2021

Localization free super-resolution microbubble velocimetry using a long short-term memory neural network

Ultrasound localization microscopy is a super-resolution imaging technique that exploits the unique characteristics of contrast microbubbles to side-step the fundamental trade-off between imaging resolution and penetration depth. However, the conventional reconstruction technique is confined to low microbubble concentrations to avoid localization and tracking errors. Several research groups have introduced sparsity- and deep learning-based approaches to overcome this constraint to extract useful vascular structural information from overlapping microbubble signals, but these solutions have not been demonstrated to produce blood flow velocity maps of the microcirculation. Here, we introduce Deep-SMV, a localization free super-resolution microbubble velocimetry technique, based on a long short-term memory neural network, that provides high imaging speed and robustness to high microbubble concentrations, and directly outputs blood velocity measurements at a super-resolution. Deep-SMV is trained efficiently using microbubble flow simulation on real in vivo vascular data and demonstrates real-time velocity map reconstruction suitable for functional vascular imaging and pulsatility mapping at super-resolution. The technique is successfully applied to a wide variety of imaging scenarios, include flow channel phantoms, chicken embryo chorioallantoic membranes, and mouse kidney, tumor, and brain imaging.

bioengineering↗

bioRxiv2021

Structural architecture of a dimeric paramyxovirus polymerase complex

Human parainfluenza virus type 3 (hPIV3), a member of non-segmented, negative-strand RNA viruses (nsNSVs), is the second most common cause of severe respiratory disease in pediatrics. The transcription and replication processes of nsNSVs are catalyzed by a multi-functional RNA-dependent RNA polymerase (RdRp) complex composed of the large protein (L) and the phosphoprotein (P). Previous studies have shown that the polymerase can adopt a dimeric form, however, the structure of the dimer and how it functions are not understood. Here we determined the cryo-EM structure of hPIV3 L-P complex at 2.7 [A] with substantial structural details. A putative catalytic magnesium ion could be built in our structure, and structural comparison revealed atomic features conserved with other RNA viruses. Interactions identified between the two priming and intrusion loops and the connector domain potentially trigger the spatial movement of three C-terminal L domains for different steps of transcription and replication. Structural comparison with other nsNSV RdRps suggests common features of L-P binding. Furthermore, we report for the first time the structural basis of the L-L interaction in the partially modelled dimeric L-P structure, in which the connector domain of one L is positioned at the putative RNA template entry of the other L. Based on these findings, we propose a model by which L dimerization promotes efficient conversion of nascent RNA into a template.

molecular biology↗

bioRxiv2021

A General Framework for Representing and Annotating Multifaceted Cell Heterogeneity in Human Cell Atlas

A universal coordinate system that can ensemble the huge number of cells and capture their heterogeneities is of vital importance for constructing large-scale cell atlases as references for future molecular and cellular studies. Studies have shown that cells in complex organs exhibit multifaceted heterogeneities in their transcriptomic features at multiple resolutions. This nature of complexity makes it hard to design a fixed coordinate system through a combination of known features. It is desirable to build a learnable universal coordinate model that can capture major heterogeneities and serve as a controlled generative model for data argumentation. We developed UniCoord, a specially tuned joint-VAE model to represent single-cell transcriptomic data in a lower-dimensional latent space with high interpretability. Each latent dimension can represent either discrete or continuous feature, and either supervised by prior knowledge or unsupervised. The original transcriptomic profiles can be regenerated from the latent dimensions. The latent dimensions can be easily reconfigured to generate transcriptomic profiles of pseudo cells with desired properties. UniCoord can also be used as a pre-trained model to analyze new data with unseen cell types and thus can serve as a feasible framework for cell annotation and comparison. UniCoord provides a prototype for a learnable universal coordinate framework to enable better analysis and generation of cells with highly orchestrated functions and heterogeneities.

bioinformatics↗

bioRxiv2021

JAMMIT Analysis Defines 2 Semi-Independent Immune Processes Common to 29 Solid Tumors

Immunophenotype of solid tumors has relevance to cancer immunotherapy, as not all patients respond optimally to treatment utilizing monoclonal antibodies. Bioinformatic studies have failed to clearly identify tumor immunophenotype in a way that encompasses a wide variety of tumor types and highlights fundamental differences among them, complicating prediction of patient clinical response. The novel JAMMIT algorithm was used to analyze mRNA data for 33 cancer types in The Cancer Genome Atlas (TCGA). We found that B cells and T cells constitute the principal source of variation in most patient cohorts, and that virtually all solid malignancies formed three hierarchical clustering patterns with similar molecular features. The second main source of variability in transcriptomic studies we attribute to monocytes. We identified the three tumor types as TC1-mediated, TC17-mediated and non-immunogenic immunophenotypes and used a 3-gene signature to approximate infiltration by agranulocytes. Methods of in silico validation such as pathway analysis, Cibersort and published data from treated cohorts were used to substantiate these findings. Monocytic infiltrate is found to be related to patient survival according to immunophenotype, important differences in some solid tumors are identified and deficiencies of common bioinformatic approaches relevant to diagnosis are detailed by this work.

cancer biology↗

bioRxiv2021

Spatio-temporal feature based deep neural network for cell lineage analysis in microscopy images

BackgroundTime-lapse microscopy has been widely used in biomedical experiments because it can visualize the molecular activities of living cells in real time. However, biomedical researchers are still conducting cell lineage analysis manually. Developing automatic lineage tracing algorithms is a challenging task. In the past two decades, deep neural networks (DNNs) became have shown outstanding performance on computer vision tasks. They can learn complex visual features, capture long-range temporal dependencies, and have the potential to be used for automatic cell lineage analysis. MethodsIn this study, we propose a multi-task spatio-temporal feature based deep neural network for cell lineages analysis (Cell-STN). The Cell-STN extracts spatio-temporal features from microscopy image sequences by leveraging our convolutional long short-term memory based core block. And the proposed Cell-STN utilized a task specific network to predict the cell location, the mitosis event, and the apoptosis event in a multi-task manner. ResultsWe evaluated the Cell-STN on three in-house datasets (MCF7, U2OS, and HCT116) and one public dataset (Fluo-N2DL-HeLa). For cell tracking, we used peak-wise precision, track-wise precision, end-peak precision, and spatial distance as metrics. The overall results showed the Cell-STN models outperform other state-of-the-art cell trackers. For mitosis and apoptosis tasks, we used accuracy, F1-score, temporal distance, and spatial distance as metrics. The Cell-STN models achieved the highest performance on all datasets. ConclusionThis study presented a novel DNNs approach for cell lineage analysis in microscopy images. The Cell-STN showed outstanding performance on the four datasets. Additionally, the Cell-STN required minimal training data and can be adapted to new biological event detection tasks by appending task-specific layers. This algorithm has the potential to be used in real-world biomedical research.

bioinformatics↗

bioRxiv2021

Binary outcomes of enhancer activity underlie stable random monoallelic expression

Mitotically stable random monoallelic gene expression (RME) is documented for a small percentage of autosomal genes. Here we investigated the role of enhancers in the RME of natural killer (NK) cell receptor genes. Enhancers were accessible and enriched in H3K27ac on silent and active alleles alike, decoupling enhancer activation and expression. Enhancers controlled gene expression frequency, as predicted by the binary model of enhancer action, and enhancer deletion converted the broadly expressed Nkg2d into an RME gene, recapitulating natural variegation. The results suggested that RME is a consequence of general enhancer properties and therefore many genes may be subject to some degree of RME, which was borne out by analysis of a panel of genes previously thought to be universally expressed within defined hematopoietic lineages: Nkg2d, Cd45, Cd8a and Thy1. We propose that previously documented RME is an extreme on a continuum of intrinsically probabilistic gene expression.

molecular biology↗

bioRxiv2021

AlphaFold-aware prediction of protein-DNA binding sites using graph transformer

Protein-DNA interactions play crucial roles in the biological systems, and identifying protein-DNA binding sites is the first step for mechanistic understanding of various biological activities (such as transcription and repair) and designing novel drugs. How to accurately identify DNA-binding residues from only protein sequence remains a challenging task. Currently, most existing sequence-based methods only consider contextual features of the sequential neighbors, which are limited to capture spatial information. Based on the recent breakthrough in protein structure prediction by AlphaFold2, we propose an accurate predictor, GraphSite, for identifying DNA-binding residues based on the structural models predicted by AlphaFold2. Here, we convert the binding site prediction problem into a graph node classification task and employ a transformer-based variant model to take the protein structural information into account. By leveraging predicted protein structures and graph transformer, GraphSite substantially improves over the latest sequence-based and structure-based methods. The algorithm is further confirmed on the independent test set of 181 proteins, where GraphSite surpasses the state-of-the-art structure-based method by 16.4% in AUPR and 11.2% in MCC, respectively. We provide the datasets, the predicted structures, and the source codes along with the pre-trained models of GraphSite at https://github.com/biomed-AI/GraphSite. The GraphSite web server is freely available at https://biomed.nscc-gz.cn/apps/GraphSite.

bioinformatics↗