Biology subjects

Wang, D.

Publications and source records attributed to Wang, D..

At least 667 records · Page 37Linked to original sources

bioRxiv2020

High-quality chromosome-level genomes of two tilapia species reveal their evolution of repeat sequences and sex chromosomes

BackgroundTilapias are one of the most farmed fishes that are coined as aquatic chicken by the food industry. Like many other teleosts, Nile tilapia and blue tilapia exhibit very recent transition of sex chromosome systems since their divergence about 5 million years ago, making them a great model for elucidating the molecular and evolutionary mechanisms of sex chromosome turnovers. Studies into their sex-determining pathways are also critical for developing genetic sex control in aquaculture. ResultsWe report here the newly produced genomes of Nile tilapia and blue tilapia that integrate long-read sequencing and chromatin conformation data. The two nearly complete genomes have anchored over 97% of the sequences into linkage groups (LGs), and assembled majorities of complex repetitive regions including telomeres, centromeres and rDNA clusters. In particular, we inferred two episodes of repeat expansion at LG3 respectively in the ancestor of cichlids and that of tilapias. The consequential large heterochromatic region concentrated at one end of LG3 comprises tandem arrays of mRNA and small RNA genes, among which we have identified a candidate female determining gene Paics in blue tilapia. Paics show female-specific patterns of single-nucleotide variants, copy numbers and expression patterns in gonads during early gonadogenesis. ConclusionsOur work provide a very important genomic resource for functional studies of cichlids, and suggested that unequal distribution of repeat content that impacts the local recombination rate might make some chromosomes more likely to become sex chromosomes.

genomics↗

bioRxiv2020

SARS-CoV-2 proteome microarray for mapping COVID-19 antibody interactions at amino acid resolution

COVID-19 has quickly become a worldwide pandemic, which has significantly impacted the economy, education, and social interactions. Understanding the humoral antibody response to SARS-CoV-2 proteins may help identify biomarkers that can be used to detect and treat COVID-19 infection. However, no immuno-proteomics platform exists that can perform such proteome-wide analysis. To address this need, we created a SARS-CoV-2 proteome microarray to analyze antibody interactions at amino acid resolution by spotting peptides 15 amino acids long with 5-amino acid offsets representing full-length SARS-CoV-2 proteins. Moreover, the array processing time is short (1.5 hours), the dynamic range is ~2 orders of magnitude, and the lowest limit of detection is 94 pg/mL. Here, the SARS-CoV-2 proteome array reveals that antibodies commercially available for SARS-CoV-1 proteins can also target SARS-CoV-2 proteins. These readily available reagents could be used immediately in COVID-19 research. Second, IgM and IgG immunogenic epitopes of SARS-CoV-2 proteins were profiled in the serum of ten COVID-19 patients. Such epitope biomarkers provide insight into the immune response to COVID-19 and are potential targets for COVID-19 diagnosis and vaccine development. Finally, serological antibodies that may neutralize viral entry into host cells via the ACE2 receptor were identified. Further investigation into whether these antibodies can inhibit the propagation of SARS-CoV-2 is warranted. Antibody and epitope profiling in response to COVID-19 is possible with our peptide-based SARS-COV-2 proteome microarray. The data gleaned from the array could provide invaluable information to the scientific community to understand, detect, and treat COVID-19.

biochemistry↗

bioRxiv2020

Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high- frequency components in interictal iEEG

Presurgical investigations for categorizing focal patterns are crucial, leading to localization and surgical removal of the epileptic focus. This paper presents a machine learning approach using information theoretic features extracted from high-frequency subbands to detect the epileptic focus from interictal intracranial electroencephalogram (iEEG). It is known that high-frequency subbands (>80 Hz) include important biomarkers such as high-frequency oscillations (HFOs) for identifying epileptic focus commonly referred to as the seizure on-set zone (SOZ). In this analysis, the multi-channel interictal iEEG signals were splitted into segments and each segment was decomposed into multiple high-frequency subbands. The different types of entropy were calculated for each of the subbands and the sparse linear discriminant analysis (sLDA) was applied to select the prominent entropy features. Due to the imbalance of SOZ and non-SOZ channels in iEEG data, the use of machine learning techniques is always tricky. To deal with the imbalanced learning problem, an adaptive synthetic oversampling approach (ADASYN) with radial basis function kernel-based SVM was used to detect the focal segments. Finally, the epileptic focus was identified based on detection of focal segments on SOZ and non-SOZ channels. Eight patients were examined to observe the efficiency of the automatic detector. The experimental results and statistical tests indicate that the proposed automatic detector can identify the epileptic focus accurately and efficiently.

bioengineering↗

bioRxiv2020

Detection and skeletonization of single neurons and tracer injections using topological methods

Neuroscientific data analysis has traditionally relied on linear algebra and stochastic process theory. However, the tree-like shapes of neurons cannot be described easily as points in a vector space (the subtraction of two neuronal shapes is not a meaningful operation), and methods from computational topology are better suited to their analysis. Here we introduce methods from Discrete Morse (DM) Theory to extract the tree-skeletons of individual neurons from volumetric brain image data, and to summarize collections of neurons labelled by tracer injections. Since individual neurons are topologically trees, it is sensible to summarize the collection of neurons using a consensus tree-shape that provides a richer information summary than the traditional regional connectivity matrix approach. The conceptually elegant DM approach lacks hand-tuned parameters and captures global properties of the data as opposed to previous approaches which are inherently local. For individual skeletonization of sparsely labelled neurons we obtain substantial performance gains over state-of-the-art non-topological methods (over 10% improvements in precision and faster proofreading). The consensus-tree summary of tracer injections incorporates the regional connectivity matrix information, but in addition captures the collective collateral branching patterns of the set of neurons connected to the injection site, and provides a bridge between single-neuron morphology and tracer-injection data.

neuroscience↗

bioRxiv2020

Single-cell transcriptome profiling an adult human cell atlas of 15 major organs

BackgroundAs core units of organ tissues, cells of various types play their harmonious rhythms to maintain the homeostasis of the human body. It is essential to identify the characteristics of cells in human organs and their regulatory networks for understanding the biological mechanisms related to health and disease. However, a systematic and comprehensive single-cell transcriptional profile across multiple organs of a normal human adult is missing. ResultsWe perform single-cell transcriptomes of 84,363 cells derived from 15 tissue organs of one adult donor and generate an adult human cell atlas. The adult human cell atlas depicts 252 subtypes of cells, including major cell types such as T, B, myeloid, epithelial, and stromal cells, as well as novel COCH+ fibroblasts and FibSmo cells, each of which is distinguished by multiple marker genes and transcriptional profiles. These collectively contribute to the heterogeneity of major human organs. Moreover, T cell and B cell receptor repertoire comparisons and trajectory analyses reveal direct clonal sharing of T and B cells with various developmental states among different tissues. Furthermore, novel cell markers, transcription factors and ligand-receptor pairs are identified with potential functional regulations in maintaining the homeostasis of human cells among tissues. ConclusionsThe adult human cell atlas reveals the inter- and intra-organ heterogeneity of cell characteristics and provides a useful resource in uncovering key events during the development of human diseases in the context of the heterogeneity of cells and organs.

cell biology↗

bioRxiv2020

Facing small and biased data dilemma in drug discovery with federated learning

Artificial intelligence (AI) models usually require large amounts of high-quality training data, which is in striking contrast to the situation of small and biased data faced by current drug discovery pipelines. The concept of federated learning has been proposed to utilize distributed data from different sources without leaking sensitive information of these data. This emerging decentralized machine learning paradigm is expected to dramatically improve the success of AI-powered drug discovery. We here simulate the federated learning process with 7 aqueous solubility datasets from different sources, among which there are overlapping molecules with high or low biases in the recorded values. Beyond the benefit of gaining more data, we also demonstrate federated training has a regularization effect making it superior than centralized training on the pooled datasets with high biases. Further, two more cases are studied to test the usability of federated learning in drug discovery. Our work demonstrates the application of federated learning in predicting drug related properties, but also highlights its promising role in addressing the small data and biased data dilemma in drug discovery.

pharmacology and toxicology↗

bioRxiv2020

Diffusion property and functional connectivity of superior longitudinal fasciculus underpin human metacognition

Metacognition as the capacity of monitoring ones own cognition operates across domains. Here, we addressed whether metacognition in different cognitive domains rely on common or distinct neural substrates with combined diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) techniques. After acquiring DTI and resting-state fMRI data, we asked participants to perform a temporal-order memory task and a perceptual discrimination task, followed by trial-specific confidence judgments. DTI analysis revealed that the structural integrity (indexed by fractional anisotropy) in the anterior portion of right superior longitudinal fasciculus (SLF) was associated with both perceptual and mnemonic metacognitive abilities. Using perturbed mnemonic metacognitive scores produced by inhibiting the precuneus using TMS, the mnemonic metacognition scores did not correlate with individuals SLF structural integrity anymore, revealing the relevance of this tract in memory metacognition. In order to further verify the involvement of several cortical regions connected by SLF, we took the TMS-targeted precuneus region as a seed in a functional connectivity analysis and found the functional connectivity between precuneus and two SLF-connected regions (inferior parietal cortex and precentral gyrus) differentially mediated mnemonic but not perceptual metacognition performance. These results illustrate the importance of SLF and a putative white-matter grey-matter circuitry that supports human metacognition.

neuroscience↗

bioRxiv2020

Multiple approaches for massively parallel sequencing of HCoV-19 genomes directly from clinical samples

COVID-19 has caused a major epidemic worldwide, however, much is yet to be known about the epidemiology and evolution of the virus. One reason is that the challenges underneath sequencing HCoV-19 directly from clinical samples have not been completely tackled. Here we illustrate the application of amplicon and hybrid capture (capture)-based sequencing, as well as ultra-high-throughput metatranscriptomic (meta) sequencing in retrieving complete genomes, inter-individual and intra-individual variations of HCoV-19 from clinical samples covering a range of sample types and viral load. We also examine and compare the bias, sensitivity, accuracy, and other characteristics of these approaches in a comprehensive manner. This is, to date, the first work systematically implements amplicon and capture approaches in sequencing HCoV-19, as well as the first comparative study across methods. Our work offers practical solutions for genome sequencing and analyses of HCoV-19 and other emerging viruses.

genomics↗

bioRxiv2020

Adaptive evolution of DNA methylation reshaped gene regulation in maize

DNA methylation is a ubiquitous chromatin feature -- in maize, more than 25% of cytosines in the genome are methylated. Recently, major progress has been made in describing the molecular mechanisms driving methylation, yet variation and evolution of the methylation landscape during maize domestication remain largely unknown. Here we leveraged whole-genome sequencing (WGS) and whole genome bisulfite sequencing (WGBS) on populations of modern maize, landrace, and teosinte (Zea mays ssp. parviglumis) to investigate the adaptive and phenotypic consequences of methylation variations in maize. By using a novel estimation approach, we inferred the methylome site frequency spectrum (mSFS) to estimate forward and backward epimutation rates and selection coefficients. We found weak evidence for direct selection on DNA methylation in any context, but thousands of differentially methylated regions (DMRs) were identified in population-wide that are correlated with recent selection. Further investigation revealed that DMRs are enriched in 5 untranslated regions, and that maize hypomethylated DMRs likely helped rewire distal gene regulation. For two trait-associated DMRs, vgt1-DMR and tb1-DMR, our HiChIP data indicated that the interactive loops between DMRs and respective downstream genes were present in B73, a modern maize line, but absent in teosinte. Functional analyses suggested that these DMRs likely served as cis-acting elements that modulated gene regulation after domestication. Our results enable a better understanding of the evolutionary forces acting on patterns of DNA methylation and suggest a role of methylation variation in adaptive evolution.

plant biology↗

bioRxiv2020

Resting-State Connectivity of Auditory and Reward Systems in Alzheimer's Disease and Mild Cognitive Impairment

Music-based interventions have become increasingly widely adopted for dementia and related disorders. Previous research shows that music engages reward-related regions through functional connectivity with the auditory system. Here we characterize intrinsic connectivity of the auditory and reward systems in healthy aging, mild cognitive impairment (MCI) - a predementia phase of cognitive dysfunction, and Alzheimers disease (AD). Using resting-state fMRI data from the Alzheimers Database Neuroimaging Initiative, we tested functional connectivity within and between auditory and reward systems in older adults with MCI, AD, and age-matched healthy controls (N=105). Seed-based correlations were assessed from regions of interest (ROIs) in the auditory network, i.e. anterior superior temporal gyrus (aSTG), posterior superior temporal gyrus (pSTG), Heschls Gyrus, and reward network (i.e., nucleus accumbens, caudate, putamen, and orbitofrontal cortex [OFC]). AD individuals were lower in both within-network and between-network functional connectivity in the auditory network and reward networks compared to MCI and healthy controls. Furthermore, graph theory analyses showed that MCI individuals had higher clustering, local efficiency, degrees, and strengths than both AD individuals and healthy controls. Together, the auditory and reward systems show preserved within- and between-network connectivity in MCI relative to AD. These results suggest that music-based interventions have the potential to make an early difference in individuals with MCI, due to the preservation of functional connectivity in reward-related regions and between auditory and reward networks at that initial stage of neurodegeneration.

neuroscience↗

bioRxiv2020

Rapid Epistatic Mixed Model Association Studies by Controlling Multiple Polygenic Effects

SummaryWe have developed a rapid mixed model algorithm for exhaustive genome-wide epistatic association analysis by controlling multiple polygenic effects. Our model can simultaneously handle additive by additive epistasis, dominance by dominance epistasis and additive by dominance epistasis, and account for intrasubject fluctuations due to individuals with repeated records. Furthermore, we suggest a simple but efficient approximate algorithm, which allows examination of all pairwise interactions in a remarkably fast manner of linear with population size. Application to publicly available yeast and human data has showed that our mixed model-based method has similar performance with simple linear model-based Plink on computational efficiency. It took less than 40 hours for the pairwise analysis of 5,000 individuals genotyped with roughly 350,000 SNPs with five threads on Intel Xeon E5 2.6GHz CPU. Availability and implementationSource codes are freely available at https://github.com/chaoning/GMAT.

bioinformatics↗

bioRxiv2020

Varmole: A biologically drop-connect deep neural network model for prioritizing disease risk variants and genes

Population studies such as GWAS have identified a variety of genomic variants associated with human diseases. To further understand potential mechanisms of disease variants, recent statistical methods associate functional omic data (e.g., gene expression) with genotype and phenotype and link variants to individual genes. However, how to interpret molecular mechanisms from such associations, especially across omics is still challenging. To address this, we develop an interpretable deep learning method, Varmole to simultaneously reveal genomic functions and mechanisms while predicting phenotype from genotype. In particular, Varmole embeds multi-omic networks into a deep neural network architecture and prioritizes variants, genes and regulatory linkages via drop-connect without needing prior feature selections. Varmole is available as a Python package on GitHub at https://github.com/daifengwanglab/Varmole.

bioinformatics↗

bioRxiv2020

Hippo signaling promotes Ets21c-dependent apical cell extrusion in the Drosophila wing disc

Cell extrusion is a crucial regulator of epithelial tissue development and homeostasis. Epithelial cells undergoing apoptosis, bearing pathological mutations, and possessing developmental defects are actively extruded toward elimination. However, the molecular mechanisms of Drosophila epithelial cell extrusion are not fully understood. Here, we report that activation of the conserved Hippo (Hpo) signaling pathway induces both apical and basal cell extrusion in the Drosophila wing disc epithelia. We show that canonical Yorki targets Diap1, and that dMyc and Cyclin E are not required for either apical or basal cell extrusion induced by activation of this pathway. Another target gene, bantam, is only involved in basal cell extrusion, suggesting novel Hpo-regulated apical cell extrusion mechanisms. Using RNA-Seq analysis, we found that JNK signaling is activated in the extruding cells. We provide genetic evidence that JNK signaling activation is both sufficient and necessary for Hpo-regulated cell extrusion. Furthermore, we demonstrate that the ETS-domain transcription factor Ets21c, an ortholog of proto-oncogenes FLI1 and ERG, acts downstream of JNK signaling to mediate apical cell extrusion. Our findings reveal a novel molecular link between Hpo signaling and cell extrusion. SUMMARY STATEMENTActivation of Hippo signaling induces cell extrusion in the Drosophila wing epithelia, in which bantam mediates basal cell extrusion and Ets21c mediates apical cell extrusion.

developmental biology↗

bioRxiv2020

The involvement of HDAC3-mediated inhibition of microRNA cluster 17-92 in hyperoxia-mediated impairment of lung development in neonatal rats

BackgroundThe incidence of bronchopulmonary dysplasia (BPD), a chronic lung disease of newborns, has been paradoxically rising despite medical advances. Histone deacetylase 3 (HDAC3) has been reported to be a crucial regulator in alveologenesis. Hence, this study aims to investigate the mechanism of HDAC3 in the pulmonary angiogenesis and alveolarization of BPD. MethodsA hyperoxia-induced mouse model of BPD was constructed. The mean liner intercept (MLI) and alveolar volume were measured to evaluate the alveolarization in BPD mice. Immunofluorescence assay was performed to detect the microvessel density (MVD) of lung tissues. Next, the expression of HDAC3 and its enrichment in the promoter region of microRNA (miR)-17-92 cluster, as well as the enrichment of p65 in the placental growth factor (Pgf) promoter region were detected by Western blot analysis and chromatin immunoprecipitation (ChIP) assay. The effect of HDAC3 and p65 on the activity of miR-17-92 promoter and Pgf promoter were examined by dual-luciferase reporter gene assay, respectively. Finally, the role of HDAC3 in angiogenesis and alveolarization through miR-17 regulated EZH1-p65-Pgf axis was validated in BPD mouse models. ResultsHDAC3 was involved in the regulation of alveolarization and angiogenesis in BPD. Results demonstrated that the expression of the miR-17-92 cluster in BPD was regulated by HDAC3. miR-17 was related to the regulatory role of HDAC3 in regulating EZH1 expression and in lung fibroblasts of BPD. Besides, results showed that EZH1 could promote Pgf expression by recruiting p65 to regulate BPD. HDAC3 regulated the expression of EZH1 through miR-17 to promote the recruitment of p65 in the Pgf promoter region, thus enhancing the transcription and expression of Pgf. HDAC3 was demonstrated to regulate Pgf through the miR-17-EZH1-p65 axis to mediate angiogenesis and alveolarization of BPD mice. ConclusionAltogether, the present study revealed that HDAC3 could regulate the EZH1-p65-Pgf axis through miR-17 in the miR-17-92 cluster in the pulmonary angiogenesis and alveolarization of BPD mice.

molecular biology↗

bioRxiv2020

Aberrant pathogenic GM-CSF+ T cells and inflammatory CD14+CD16+ monocytes in severe pulmonary syndrome patients of a new coronavirus

Pathogenic human coronavirus infections, such as severe acute respiratory syndrome CoV (SARS-CoV) and Middle East respiratory syndrome CoV (MERS-CoV), cause high morbidity and mortality 1,2. Recently, a severe pneumonia-associated respiratory syndrome caused by a new coronavirus was reported at December 2019 (2019-nCoV) in the city Wuhan, Hubei province, China3-5, which was also named as pneumonia-associated respiratory syndrome (PARS)6. Up to 9th of February 2020, at least 37, 251 cases have been reported with 812 fatal cases according to the report from China CDC. However, the immune mechanism that potential orchestrated acute mortality from patients of 2019-nCoV is still unknown. Here we show that after the 2019-nCoV infection, CD4+T lymphocytes are rapidly activated to become pathogenic T helper (Th) 1 cells and generate GM-CSF etc. The cytokines environment induces inflammatory CD14+CD16+ monocytes with high expression of IL-6 and accelerates the inflammation. These aberrant and excessive immune cells may enter the pulmonary circulation in huge numbers and play an immune damaging role to causing lung functional disability and quick mortality. Our results demonstrate that excessive non-effective host immune responses by pathogenic T cells and inflammatory monocytes may associate with severe lung pathology. Therefore, we suggest that monoclonal antibody that targets the GM-CSF or interleukin 6 receptor may potentially curb immunopathology caused by 2019-nCoV and consequently win more time for virus clearance.

immunology↗

bioRxiv2020

The role of CAPG in molecular communication between the embryo and the uterine endometrium: Is its function conserved in species with different implantation strategies?

During the pre-implantation period of pregnancy in eutherian mammals, changes to the uterine endometrium are required (both at the transcriptional and protein level) to facilitate the endometrium becoming receptive to an implanting embryo. We know that the developing conceptus (embryo and extraembryonic membranes) produces proteins during this developmental stage. We hypothesised that this common process in early pregnancy in eutheria may be facilitated by highly conserved conceptus-derived proteins such as macrophage capping protein CAPG. More specifically, we propose that CAPG may share functionality in modifying the transcriptome of the endometrial epithelial cells to facilitate receptivity to implantation in species with different implantation strategies, such as human and bovine. A recombinant bovine form of CAPG (91% sequence identity between bovine and human) was produced and bovine endometrial epithelial (bEECs) and stromal (bESCs) cells and human endometrial epithelial cells (hEECs) were cultured for 24 h with or without rbCAPG. RNA sequencing and quantitative real-time PCR analysis was used to assess the transcriptional response to rbCAPG (Control, vehicle, CAPG 10, 100, 1000 ng/ml: n=3 biological replicates per treatment per species). Treatment of bEECs with CAPG resulted in changes to 1052 transcripts (629 increased and 423 decreased) compared to vehicle controls, including those previously only identified as regulated by interferon-tau, the pregnancy recognition signal in cattle. Treatment of hEECs with bovine CAPG increased expression of transcripts previously known to interact with CAPG in different systems (CAPZB, CAPZA2, ADD1 and ADK) compared with vehicle controls (P<0.05). In conclusion, we have demonstrated that CAPG, a highly conserved protein in eutherian mammals elicits a transcriptional response in the endometrial epithelium in two species with different implantation strategies that may facilitate uterine receptivity.

physiology↗

bioRxiv2020

Altered patterning of interneuron progenitors in Down syndrome

Individuals with Down syndrome (DS, Ts21), the most common genetic cause of intellectual disability, have smaller brains that reflect fewer neurons at pre- and post-natal stages, implicating impaired neurogenesis during development. Our stereological analysis of adult DS cortex indicates a reduction of calretinin expressing interneurons. Using Ts21 human induced pluripotent stem cells (iPSCs) and isogenic controls, we find Ts21 progenitors generate fewer COUP-TFII+ progenitors with reduced proliferation. Single cell RNA-sequencing of Ts21 progenitors confirms the altered specification of progenitor subpopulations and identifies reduced WNT signaling. Activation of WNT signaling partially restores the COUP-TFII+ progenitor population in Ts21, suggesting that altered WNT signaling contributes to the defective development of cortical interneurons in DS.

developmental biology↗

bioRxiv2020

Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks

Understanding of neuronal circuitry at cellular resolution within the brain has relied on tract tracing methods which involve careful observation and interpretation by experienced neuroscientists. With recent developments in imaging and digitization, this approach is no longer feasible with the large scale (terabyte to petabyte range) images. Machine learning based techniques, using deep networks, provide an efficient alternative to the problem. However, these methods rely on very large volumes of annotated images for training and have error rates that are too high for scientific data analysis, and thus requires a significant volume of human-in-the-loop proofreading. Here we introduce a hybrid architecture combining prior structure in the form of topological data analysis methods, based on discrete Morse theory, with the best-in-class deep-net architectures for the neuronal connectivity analysis. We show significant performance gains using our hybrid architecture on detection of topological structure (e.g. connectivity of neuronal processes and local intensity maxima on axons corresponding to synaptic swellings) with precision/recall close to 90% compared with human observers. We have adapted our architecture to a high performance pipeline capable of semantic segmentation of light microscopic whole-brain image data into a hierarchy of neuronal compartments. We expect that the hybrid architecture incorporating discrete Morse techniques into deep nets will generalize to other data domains.

neuroscience↗