Biology subjects

Wu, T.

Publications and source records attributed to Wu, T..

At least 145 records · Page 8Linked to original sources

bioRxiv2022

Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multi-omics

Gene regulatory networks (GRNs) are key determinants of cell function and identity and are dynamically rewired during development and disease. Despite decades of advancement, challenges remain in GRN inference: dynamic rewiring, causal inference, feedback-loop modeling, and context specificity. To address them, we develop Dictys, a dynamic GRN inference and analysis method which leverages multi-omic single-cell assays of chromatin accessibility and gene expression, context specific transcription factor (TF) footprinting, stochastic process network, and efficient probabilistic modeling of scRNA-seq read counts. Dictys improves GRN reconstruction accuracy and reproducibility and enables the inference and comparative analysis of context specific and dynamic GRNs across developmental contexts. Dictys network analyses recover unique insights in human blood and mouse skin development with cell-type specific and dynamic GRNs. Its dynamic network visualizations enable time-resolved discovery and investigation of developmental driver TFs and their regulated targets. Dictys is available as a free, open source, and user-friendly Python package.

bioinformatics↗

bioRxiv2022

Seq2Neo: a comprehensive pipeline for cancer neoantigen im-munogenicity prediction

Neoantigens derived from somatic DNA alterations are ideal cancer-specific targets. In recent years, the combination therapy of PD-1/PD-L1 blockers and neoantigen vaccines shows clinical efficacy in original PD-1/PD-L1 blocker non-responders. However, not all somatic DNA mutations can result in immunogenicity in cancer cells, and efficient tools for predicting the immunogenicity of neoepitope are still urgently needed. Here we present the Seq2Neo pipeline, which provides a one-stop solution for neoepitope features prediction from raw sequencing data, and neoantigens derived from different types of genome DNA alterations, including point mutations, insertion deletions, and gene fusions are supported. Importantly a convolutional neural networks (CNN) based model has been trained to predict the immunogenicity of neoepitope. And this model shows improved performance compared with currently available tools in immunogenicity prediction in independent datasets. We anticipate that the Seq2Neo pipeline will become a useful tool in prediction of neoantigen immunogenicity and cancer immunotherapy. Seq2Neo is an open-source software under an academic free license (AFL) v3.0 and it is freely available at https://github.com/XSLiuLab/Seq2Neo.

bioinformatics↗

bioRxiv2022

CIPK-B is essential for salt stress signalling in Marchantia polymorpha

O_LICalcium signalling is central to many plant processes, with families of calcium decoder proteins having expanded across the green lineage and redundancy existing between decoders. The liverwort Marchantia polymorpha has fast become a new model plant, but it is unclear what calcium decoders exist in this species. C_LIO_LIWe have performed phylogenetic analyses to identify the Calcineurin B-Like (CBL) and CBL-Interacting Protein Kinase (CIPK) network of M. polymorpha. We analysed CBL-CIPK expression during salt stress, and determined protein-protein interactions using yeast two-hybrid and bimolecular fluorescence complementation. We also created genetic knockouts using CRISPR/Cas9. C_LIO_LIWe confirm that M. polymorpha has two CIPKs and three CBLs. Both CIPKs and only one CBL show salt-responsive transcriptional changes. All M. polymorpha CBL-CIPKs interact with each other in planta. Knocking out CIPK-B causes increased sensitivity to salt suggesting that this CIPK is involved in salt signalling. C_LIO_LIWe have identified CBL-CIPKs that form part of a salt tolerance pathway in M. polymorpha. Phylogeny and interaction studies imply that these CBL-CIPKs form an evolutionarily conserved Salt Overly Sensitive (SOS) pathway. Hence, salt responses may be some of the early functions of CBL-CIPK networks and increased abiotic stress tolerance required for land plant emergence. C_LI

plant biology↗

bioRxiv2022

KAS-pipe2: a flexible toolkit for exploring KAS-seq and spKAS-seq data

Kethoxal-assisted ssDNA sequencing (KAS-seq) is gaining popularity as a robust and effective approach to study the dynamics of transcriptionally engaged RNA polymerases through profiling of genome-wide single-stranded DNA (ssDNA). Its latest variant, spKAS-seq, a strand-specific version of KAS-seq, has been developed to map genome-wide R-loop structures by detecting imbalances of ssDNA on two strands. However, user-friendly, open-source analysis pipelines for KAS-seq data are still lacking. Here we present KAS-pipe2 as a flexible and integrated toolkit to facilitate the analysis and interpretation of KAS-seq data. KAS-pipe2 can perform standard analyses such as quality control, read alignment, and differential RNA polymerase activity analysis. In addition, KAS-pipe2 introduces many novel features, including, but not limited to: calculation of transcriptional indexes, identification of single-stranded transcribing enhancers, and high-resolution mapping of R-loops. We use benchmark datasets to demonstrate that KAS-pipe2 provides a powerful framework to study transient transcriptional regulatory programs. KAS-pipe2 is available at https://github.com/Ruitulyu/KAS-pipe2.

bioinformatics↗

bioRxiv2022

Oncogenic EFNA4 amplification promotes lung adenocarcinoma lymph node metastasis

Lymph nodes metastases are common in patients with lung cancer. And those patients are often at a higher risk for death from lung tumor than those with tumor-free lymph nodes. So-matic DNA alterations are key drivers of cancer, and copy number alterations (CNAs) are a major type of DNA alteration that promotes lung cancer progression. Here, we performed genome-wide DNA copy number analysis, and identified a novel lung cancer metastasis related gene, EFNA4. EFNA4 genome locus was significantly amplified and EFNA4 mRNA expression was significantly up-regulated in lung cancer compared with normal lung tissue, and also in lung cancer with lymph node metastases compared with lung cancer without metastasis. EFNA4 encodes Ephrin A4, which is the ligand for Eph receptors. The function of EFNA4 in human lung cancer remains largely unknown. Through cell line experiments we showed that EFNA4 overexpression contributes to lung tumor cells growth, migration and adhesion. Conversely, EFNA4 knockdown or knockout led to the growth suppression of cells and tumor xenografts in mice. Lung cancer patients with EFNA4 over-expression have poor prognosis. Together, by elucidating a new layer of the role of EFNA4 in tumor proliferation and migration, our study demonstrates a better understanding of the function of the significantly amplified and overexpressed gene EFNA4 in lung tumor metastasis and suggests EFNA4 as a potential target in metastatic lung cancer therapy. Simple SummaryLymph nodes are likely to be the first stop for lung cancer metastasis. To further investigate the mechanism of lung cancer lymph node metastasis, we performed cancer genome analysis and found that EFNA4, a member of the ephrin (EPH) family, is amplified and up-regulated in lung tumor patients, especially in patients with lymph node metastases. In vitro and in vivo experiments show that overexpression of EFNA4 promotes lung tumor cell proliferation and migration, while knockdown or knockout of EFNA4 inhibits cell proliferation and migration. Altogether, our results suggest that the DNA amplification of EFNA4 genome locus could play an oncogenic function in promoting lung cancer lymph node metastasis.

cancer biology↗

bioRxiv2022

Deep-SMOLM: Deep Learning Resolves the 3D Orientations and 2D Positions of Overlapping Single Molecules with Optimal Nanoscale Resolution

Dipole-spread function (DSF) engineering reshapes the images of a microscope to maximize the sensitivity of measuring the 3D orientations of dipole-like emitters. However, severe Poisson shot noise, overlapping images, and simultaneously fitting high-dimensional information-both orientation and position-greatly complicates image analysis in single-molecule orientation-localization microscopy (SMOLM). Here, we report a deep-learning based estimator, termed Deep-SMOLM, that archives superior 3D orientation and 2D position measurement precision within 3% of the theoretical limit (3.8{whitebullet} orientation, 0.32 sr wobble angle, and 8.5 nm lateral position using 1000 detected photons). Deep-SMOLM also achieves state-of-art estimation performance on overlapping images of emitters, e.g., a 0.95 Jaccard index for emitters separated by 139 nm, corresponding to a 43% image overlap. Deep-SMOLM accurately and precisely reconstructs 5D information of both simulated biological fibers and experimental amyloid fibrils from images containing highly overlapped DSFs, at a speed [~]10 times faster than iterative estimators.

molecular biology↗

bioRxiv2022

Functional screening of lysosomal storage disorder genes identifies modifiers of alpha-synuclein mediated neurodegeneration

Heterozygous variants in the glucocerebrosidase (GBA) gene are common and potent risk factors for Parkinsons disease (PD). GBA also causes the autosomal recessive lysosomal storage disorder (LSD), Gaucher disease, and emerging evidence from human genetics implicates many other LSD genes in PD susceptibility. We have systemically tested 88 conserved fly homologs of 38 human LSD genes for requirements in the aging adult Drosophila brain and for potential genetic interactions with neurodegeneration caused by -synuclein (Syn), which forms Lewy body pathology in PD. Our screen identifies 15 genetic enhancers of Syn-induced progressive locomotor dysfunction, following knockdown of fly homologs of GBA and other LSD genes with independent support as PD susceptibility factors from human genetics (SCARB2, SMPD1, CTSD, GNPTAB, SLC17A5). For several genes, results from multiple alleles support dose-sensitivity and context-dependent pleiotropy in the presence or absence of Syn. Homologs of 2 genes causing cholesterol storage disorders, Npc1a / NPC1 and Lip4 / LIPA, were independently confirmed as loss-of-function enhancers of Syn-induced brain structural degeneration. The enzymes encoded by several modifier genes are upregulated in Syn transgenic flies, based on unbiased proteomics, revealing a possible compensatory response. Overall, our results reinforce the important role of lysosomal genes in brain health and PD pathogenesis, and implicate several metabolic pathways, including cholesterol homeostasis, in Syn-mediated neurodegeneration.

genetics↗

bioRxiv2022

The chromatin landscape of the euryarchaeon Haloferax volcanii

Archaea, together with Bacteria, represent the two main divisions of life on Earth, with many of the defining characteristics of the more complex eukaryotes tracing their origin to evolutionary innovations first made in their archaeal ancestors. One of the most notable such features is nucleosomal chromatin, although archaeal histones and chromatin differ significantly from those of eukaryotes. Despite increased interest in archaeal histones in recent years, the properties of archaeal chromatin have been little studied using genomic tools. Here, we adapt the ATAC-seq assay to archaea and use it to map the accessible landscape of the genome of the euryarchaeote Haloferax volcanii. We integrate the resulting datasets with genome-wide maps of active transcription and single-stranded DNA (ssDNA) and find that while H. volcanii promoters exist in a preferentially accessible state, unlike most eukaryotes, modulation of transcriptional activity is not associated with changes in promoter accessibility. Applying orthogonal single-molecule footprinting methods, we quantify the absolute levels of physical protection of H. volcanii, and find that Haloferax chromatin is similarly or only slightly more accessible, in aggregate, than that of eukaryotes. We also evaluate the degree of coordination of transcription within archaeal operons and make the unexpected observation that some CRISPR arrays are associated with highly prevalent ssDNA structures. These results provide a foundation for the future functional studies of archaeal chromatin.

genomics↗

bioRxiv2022

Local adaptation to hosts and parasitoids shape Hamiltonella defensa genotypes across aphid species

Facultative symbionts are common in insects and are known to provide important adaptations that can drive rapid host evolution. Yet we still have a limited understanding of what shapes their distributions, such as why particular symbiont strains are common in some host species yet absent in other. To address this question, we genotyped the defensive symbiont Hamiltonella defensa in 26 aphid species that commonly carry this microbe. We found that Hamiltonella strains were strongly associated with specific aphid species and that strains found in one host species rarely occurred in others. To explain these associations, we reciprocally transferred the Hamiltonella strains of 3 aphid species, Acyrthosiphon pisum, Macrosiphoniella artemisiae and Macrosiphum euphorbiae, in the other host species, and assessed the impact of Hamiltonella strain on: the stability of the symbiosis, aphid fecundity, and parasitoid resistance. We demonstrate that the Hamiltonella associations found in nature are locally adapted both to the aphid host itself, and its ecology, in that aphids tend to carry Hamiltonella strains that provide strong protection against their dominant parasitoid species. Our results suggest that Hamiltonella strains function as a horizontal gene pool that aphids draw from to rapidly adapt to pressures from different natural enemies.

evolutionary biology↗

bioRxiv2022

Six-Dimensional Single-Molecule Imaging with Isotropic Resolution using a Multi-View Reflector Microscope

We report a radially and azimuthally polarized multi-view reflector (raMVR) microscope for precise imaging of the 3D positions and 3D orientations of single molecules (SMs, 10.9 nm and 2.0{degrees} precisions using 5000 photons). These precisions are [~]1.5 times better than those of existing methods for SM orientation-localization microscopy. The raMVR microscope achieves 6D super-resolution imaging of Nile red (NR) molecules transiently bound to 150 nm, 350 nm, and 1 {micro}m-diameter lipid-coated spheres, accurately resolving their spherical morphology despite refractive-index mismatch. Simply by observing the rotational dynamics o raMVR images also resolve the infiltration of lipid membranes by amyloid-beta oligomers without covalent labeling. Finally, we demonstrate 6D imaging of HEK-293T cell membranes, where the orientations of merocyanine 540 molecules reveal heterogeneities in membrane fluidity. With its [~]2 {micro}m depth range, nearly isotropic 3D spatial resolution, and superior orientation measurement precision, we expect the raMVR microscope to enable 6D imaging of molecular dynamics within biological and chemical systems with unprecedented detail.

biophysics↗

bioRxiv2022

Structure-based inhibitor optimization for the Nsp3 Macrodomain of SARS-CoV-2

The nonstructural protein 3 (NSP3) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contains a conserved macrodomain enzyme (Mac1) that is critical for pathogenesis and lethality. While small molecule inhibitors of Mac1 have great therapeutic potential, at the outset of the COVID-19 pandemic there were no well-validated inhibitors for this protein nor, indeed, the macrodomain enzyme family, making this target a pharmacological orphan. Here, we report the structure-based discovery and development of several different chemical scaffolds exhibiting low- to sub-micromolar affinity for Mac1 through iterations of computer-aided design, structural characterization by ultra-high resolution protein crystallography, and binding evaluation. Potent scaffolds were designed with in silico fragment linkage and by ultra-large library docking of over 450 million molecules. Both techniques leverage the computational exploration of tangible chemical space and are applicable to other pharmacological orphans. Overall, 160 ligands in 119 different scaffolds were discovered, and 152 Mac1-ligand complex crystal structures were determined, typically to 1 [A] resolution or better. Our analyses discovered selective and cell-permeable molecules, unexpected ligand-mediated protein dynamics within the active site, and key inhibitor motifs that will template future drug development against Mac1. Significance StatementSARS-CoV-2 encodes a viral macrodomain protein (Mac1) that hydrolyzes ribo-adenylate marks on viral proteins, disrupting the innate immune response to the virus. Catalytic mutations in the enzyme make the related SARS-1 virus less pathogenic and non-lethal in animals, suggesting that Mac1 will be a good antiviral target. However, no potent inhibitors of this protein class have been described, and pharmacologically the enzyme remains an orphan. Here, we computationally designed potent inhibitors of Mac1, determining 150 inhibitor-enzyme structures to ultra-high resolution by crystallography. In silico fragment linking and molecular docking of > 450 million virtual compounds led to inhibitors with submicromolar activity. These molecules may template future drug discovery efforts against this crucial but understudied viral target.

biophysics↗

bioRxiv2022

Regulation of pDC Fate Determination by Histone Deacetylase 3

Dendritic cells (DCs), the key antigen-presenting cells, are primary regulators of immune responses. Transcriptional regulation of DC development had been one of the major research interests in DC biology, however, the epigenetic regulatory mechanisms during DC development remains unclear. Here, we report that Histone deacetylase 3 (Hdac3), an important epigenetic regulator, is highly expressed in pDCs, and its deficiency profoundly impaired the development of pDCs. Significant disturbance of homeostasis of hematopoietic progenitors was also observed in HDAC3-deficient mice, manifested by altered cell numbers of these progenitors and defective differentiation potentials for pDCs. Using the in vitro Flt3L supplemented DC culture system, we further demonstrated that HDAC3 was required for the differentiation of pDCs from progenitors at all developmental stages. Mechanistically, HDAC3 deficiency resulted in enhanced expression of cDC1-associated genes, owing to markedly elevated H3K27 acetylation (H3K27ac) at these gene sites in BM pDCs. In contrast, the expression of pDC-associated genes was significantly downregulated, leading to defective pDC differentiation. SummaryThis work reveals for the first time that HDAC3 is required for the development of pDCs and maintenance of homeostasis of hematopoietic progenitors with DC differentiation potential. Mechanistically, HDAC3 promotes pDC development by repressing the expression of cDC1 associated genes in a deacetylase dependent manner.

immunology↗

bioRxiv2022

Endocytosis at Extremes: Formation and Internalization of Giant Clathrin-coated Pits Under Elevated Membrane Tension

Internalization of clathrin-coated vesicles from the plasma membrane constitutes the major endocytic route for receptors and their ligands. Dynamic and structural properties of endocytic clathrin coats are regulated by the mechanical properties of the plasma membrane. Here, we used conventional fluorescence imaging and multiple modes of structured illumination microscopy (SIM) to image formation of endocytic clathrin coats within live cells and tissues of developing fruit fly embryos. High resolution in both spatial and temporal domains allowed us to detect and characterize distinct classes of clathrin-coated structures. For the first time, we show that membrane tension induces formation of giant coated pits (GCPs) that can be up to two orders of magnitude larger than the canonical clathrin-coated pits. GCPs take longer to form but their mechanism of curvature generation is the same as the canonical pits. We also demonstrate that GCPs can split into smaller fragments during internalization. Considering the supporting roles played by actin filament dynamics in clathrin-mediated endocytosis under mechanically stringent conditions, we suggest that local changes in the coat curvature driven by actin machinery can drive splitting and internalization of GCPs.

biophysics↗

bioRxiv2022

A Genetically Encoded Far-Red Fluorescent Indicator for Imaging Synaptically-Released Zn2+

Synaptic Zn2+ has emerged as a key neuromodulator in the brain. However, the lack of research tools for directly tracking synaptic Zn2+ in the brain in live animals hinders our rigorous understanding of the physiological and pathological roles of synaptic Zn2+. In this study, we developed a genetically encoded far-red fluorescent indicator for monitoring synaptic Zn2+ dynamics in the nervous system. Our engineered far-red fluorescent indicator for synaptic Zn2+ (FRISZ) displayed a substantial Zn2+-specific turn-on response and low micromolar affinity. We genetically anchored FRISZ to the mammalian extracellular membrane via a transmembrane -helix. We further successfully used membrane-tethered FRISZ (FRISZ-TM) to image synaptic Zn2+ dynamics in response to sound in the primary auditory cortex (A1) in awake mice. This study thus establishes a new technology for studying the roles of synaptic Zn2+ in the nervous system.

neuroscience↗

bioRxiv2022

Atomic protein structure refinement using all-atom graph representations and SE(3)-equivariant graph neural networks

Three-dimensional (3D) protein structures reveal the fundamental information about protein function. The state-of-art protein structure prediction methods such as Alphafold are being widely used to predict structures of uncharacterized proteins in biomedical research. There is a significant need to further improve the quality and nativeness of the predicted structures to enhance their usability. Current machine learning methods of refining protein structures focus mostly on improving the backbone quality of predicted structures without effectively leveraging and enhancing the conformation of all atoms including side-chain, while molecular simulation methods are computationally intensive and time-consuming. In this work, we develop ATOMRefine, a deep learning-based, end-to-end, all-atom protein structural model refinement method. It uses a SE(3)-equivariant graph transformer network that is equivariant to the rotation and translation of 3D structures in conjunction with a novel graph representation of all atoms to directly refine protein atomic coordinates of all the atoms in a predicted tertiary structure represented as a molecular graph. The method is first trained and tested on the structural models in AlphafoldDB whose experimental structures are known, and then blindly tested on 69 CASP14 regular targets and 7 CASP14 refinement targets. ATOMRefine improves the quality of both backbone atoms and all-atom conformation of the initial structural models generated by AlphaFold. It also performs better than the state-of-the-art refinement methods in multiple evaluation metrics including an all-atom model quality score - the MolProbity score based on the analysis of all-atom contacts, bond length, atom clashes, torsion angles, and side-chain rotamers. As ATOMRefine can refine a protein structure quickly, it provides a viable, fast solution for improving protein geometry and fixing structural errors of predicted structures through direct coordinate refinement.

bioinformatics↗

bioRxiv2022

3D-equivariant graph neural networks for protein model quality assessment

MotivationQuality assessment of predicted protein tertiary structure models plays an important role in ranking and using them. With the recent development of deep learning end-to-end protein structure prediction techniques of generating highly confident tertiary structures for most proteins, it is important to explore corresponding quality assessment strategies to evaluate and select the structural models predicted by them since these models have better quality and different properties than the models predicted by traditional tertiary structure prediction methods. ResultsWe develop EnQA, a novel graph-based 3D-equivariant neural network method that is equivariant to rotation and translation of 3D objects to estimate the accuracy of protein structural models by leveraging the structural features acquired from the state-of-the-art tertiary structure prediction method - AlphaFold2. We train and test the method on both traditional model datasets (e.g., the datasets of the Critical Assessment of Techniques for Protein Structure Prediction (CASP)) and a new dataset of high-quality structural models predicted only by AlphaFold2 for the proteins whose experimental structures were released recently. Our approach achieves state-of-the-art performance on protein structural models predicted by both traditional protein structure prediction methods and the latest end-to-end deep learning method - AlphaFold2. It performs even better than the model quality assessment scores provided by AlphaFold2 itself. The results illustrate the 3D-equivariant graph neural network is a promising approach to the evaluation of protein structural models. AlphaFold2 features are important for improving protein model quality assessment and are complimentary with the geometric property features extracted from structural models. AvailabilityThe source code is available at https://github.com/BioinfoMachineLearning/EnQA. Contactchengji@missouri.edu Supplementary informationSupplementary data are available.

bioinformatics↗

bioRxiv2022

Pan-cancer quantification of neoantigen-mediated immunoediting in cancer evolution

Immunoediting, which includes three temporally distinct stages, termed elimination, equilibrium, and escape, has been proposed to explain the interactions between cancer cells and the immune system during the evolution of cancer. However the status of immunoediting in cancer remains unclear, and the existence of neoantigen depletion signal in untreated cancer has been debated. Here we developed a distribution pattern based method for quantifying neoantigen mediated negative selection in cancer evolution. Our method provides a robust and reliable quantification for immunoediting signal in an individual cancer patient. The prevalence of immunoediting signal in immunotherapy untreated cancer genome has been demonstrated with this method. Importantly, the elimination and escape stages of immunoediting can be quantified separately, tumor types with strong immunoediting-elimination tend to have weak immunoediting-escape signal, and vice versa. Quantified immunoediting-elimination signal predicts cancer immunotherapy clinical response. Immunoediting quantification provides an evolutional perspective for evaluating the antigenicity of neoantigen, and reveals a potential biomarker for cancer precision immunotherapy.

cancer biology↗

bioRxiv2022

Computational design and construction of an Escherichia coli strain engineered to produce a non-standard amino acid

Introducing heterologous pathways into host cells constitutes a promising strategy for synthesizing nonstandard amino acids (nsAAs) to enable the production of proteins with expanded chemistries. However, this strategy has proven challenging as the expression of heterologous pathways can disrupt cellular homeostasis of the host cell. Here, we sought to optimize the heterologous production of the nsAA para-aminophenylalanine (pAF) in Escherichia coli. First, we incorporated a heterologous pAF biosynthesis pathway into a genome-scale model of E. coli metabolism, and computationally identified metabolic interventions in the hosts native metabolism to improve pAF production. Next, we explored different ways of imposing these flux interventions experimentally and found that the upregulation of flux in chorismate biosynthesis pathway through the elimination of feedback inhibition mechanisms could significantly raise pAF titers ([~]20 fold) while maintaining a reasonable pAF yield-growth rate trade-off. Overall, this study provides a promising strategy for the biosynthesis of nsAAs in engineered cells.

systems biology↗