Biology subjects

Xu, J.

Publications and source records attributed to Xu, J..

At least 685 records · Page 38Linked to original sources

bioRxiv2021

Predicting Epigenomic Functions of Genetic Variants in the Context of Neurodevelopment via Deep Transfer Learning

Decoding the regulatory effects of non-coding variants is a key challenge in understanding the mechanisms of gene regulation as well as the genetics of common diseases. Recently, deep learning models have been introduced to predict genome-wide epigenomic profiles and effects of DNA variants, in various cellular contexts, but they were often trained in cell lines or bulk tissues that may not be related to phenotypes of interest. This is particularly a challenge for neuropsychiatric disorders, since the most relevant cell and tissue types are often missing in the training data of such models. To address this issue, we introduce a deep transfer learning framework termed MetaChrom that takes advantage of both a reference dataset - an extensive compendium of publicly available epigenomic data, and epigenomic profiles of cell types related to specific phenotypes of interest. We trained and evaluated our model on a comprehensive set of epigenomic profiles from fetal and adult brain, and cellular models representing early neurodevelopment. MetaChrom predicts these epigenomic features with much higher accuracy than previous methods, and than models without the use of reference epigenomic data for transfer learning. Using experimentally determined regulatory variants from iPS cell-derived neurons, we show that MetaChrom predicts functional variants more accurately than existing non-coding variant scoring tools. By combining genome-wide association study (GWAS) data with MetaChrom predictions, we prioritized 31 SNPs for Schizophrenia (SCZ). These candidate SNPs suggest potential risk genes of SCZ and the biological contexts where they act. In summary, MetaChrom is a general transfer learning framework that can be applied to the study of regulatory functions of DNA sequences and variants in any disease-related cell or tissue types. The software tool is available at https://github.com/bl-2633/MetaChrom and a prediction web server is accessible at https://metachrom.ttic.edu/.

bioinformatics↗

bioRxiv2021

Larval zebrafish display dynamic learning of aversive stimuli in a constant visual surrounding

Balancing exploration and anti-predation are fundamental to the fitness and survival of all animal species from early life stages. How these basic survival instincts drive learning remains poorly understood. Here, employing a light/dark preference paradigm with well-controlled luminance history and constant visual surrounding in larval zebrafish, we analyzed intra- and inter-trial dynamics for two behavioral components, dark avoidance and center avoidance. We uncover that larval zebrafish display short-term learning of dark avoidance with initial sensitization followed by habituation; they also exhibit long-term learning that is sensitive to trial interval length. We further show that such stereotyped learning patterns is stimulus specific, as they are not observed for center avoidance. Finally, we demonstrate at individual levels that long-term learning is under homeostatic control. Together, our work has established a novel paradigm to understand learning, uncovered sequential sensitization and habituation, and demonstrated stimulus specificity, individuality, as well as dynamicity in learning.

animal behavior and cognition↗

bioRxiv2021

mRNA vaccine-elicited antibodies to SARS-CoV-2 and circulating variants

To date severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has infected over 100 million individuals resulting in over two million deaths. Many vaccines are being deployed to prevent coronavirus disease 2019 (COVID-19) including two novel mRNA-based vaccines1,2. These vaccines elicit neutralizing antibodies and appear to be safe and effective, but the precise nature of the elicited antibodies is not known3-6. Here we report on the antibody and memory B cell responses in a cohort of 20 volunteers who received either the Moderna (mRNA-1273) or Pfizer-BioNTech (BNT162b2) vaccines. Consistent with prior reports, 8 weeks after the second vaccine injection volunteers showed high levels of IgM, and IgG anti-SARS-CoV-2 spike protein (S) and receptor binding domain (RBD) binding titers3,5,6. Moreover, the plasma neutralizing activity, and the relative numbers of RBD-specific memory B cells were equivalent to individuals who recovered from natural infection7,8. However, activity against SARS-CoV-2 variants encoding E484K or N501Y or the K417N:E484K:N501Y combination was reduced by a small but significant margin. Consistent with these findings, vaccine-elicited monoclonal antibodies (mAbs) potently neutralize SARS-CoV-2, targeting a number of different RBD epitopes in common with mAbs isolated from infected donors. Structural analyses of mAbs complexed with S trimer suggest that vaccine- and virus-encoded S adopts similar conformations to induce equivalent anti-RBD antibodies. However, neutralization by 14 of the 17 most potent mAbs tested was reduced or abolished by either K417N, or E484K, or N501Y mutations. Notably, the same mutations were selected when recombinant vesicular stomatitis virus (rVSV)/SARS-CoV-2 S was cultured in the presence of the vaccine elicited mAbs. Taken together the results suggest that the monoclonal antibodies in clinical use should be tested against newly arising variants, and that mRNA vaccines may need to be updated periodically to avoid potential loss of clinical efficacy.

immunology↗

bioRxiv2021

Large field of view-spatially resolved transcriptomics at nanoscale resolution

Spatially resolved transcriptomic technologies are promising tools to study cell fate decisions in a physical microenvironment, which is fundamental for enhancing our knowledge of mammalian development. However, the imbalance between resolution, transcript capture and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation mammalian embryos. Here, we combined DNA nanoball (DNB) patterned arrays and tissue RNA capture to create SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq). This approach allows transcriptomic profiling of large histological sections with high resolution and sensitivity. We have applied Stereo-seq to study the kinetics and directionality of transcriptional variation in a time course of mouse organogenesis. We used this information to gain insight into the molecular basis of regional specification, neuronal migration and differentiation in the developing brain. Furthermore, we mapped the expression of a panel of developmental disease-related loci on our global transcriptomic maps to define the spatiotemporal windows of tissue vulnerability. Our panoramic atlas constitutes an essential resource to investigate longstanding questions concerning normal and abnormal mammalian development.

cell biology↗

bioRxiv2021

Resolving the graft ischemia-reperfusion injury during liver transplantation at the single cell resolution

Ischemia-reperfusion injury (IRI) remains the major reason for impaired donor graft function and increased mortality post-liver transplantation. The mechanism of IRI involves multiple pathophysiological processes and numerous types of cells. However, a systematic and comprehensive single-cell transcriptional profile of intrahepatic cells during liver transplantation is still unclear. We performed a single-cell transcriptome analysis of 14,313 cells from liver tissues collected from pre-procurement, at the end of preservation and 2 h post-reperfusion. We made detailed annotations of mononuclear phagocyte, endothelial cell, NK/T, B and plasma cell clusters, and we described the dynamic changes of the transcriptome of these clusters during IRI and the interaction between mononuclear phagocyte clusters and other cell clusters. In addition, we found that TNFAIP3 interacting protein 3 (TNIP3), specifically and highly expressed in Kupffer cell clusters post-reperfusion, may have a protective effect on IRI. In summary, our study provides the first dynamic transcriptome map of intrahepatic cell clusters during liver transplantation at single-cell resolution.

cell biology↗

bioRxiv2021

Network Analysis Reveals Synergistic Genetic Dependencies for Rational Combination Therapy in Philadelphia Chromosome-like Acute Lymphoblastic Leukemia

Systems biology approaches can identify critical targets in complex cancer signaling networks to inform therapy combinations and overcome conventional treatment resistance. Herein, we developed a data-driven, network controllability-based approach to identify synergistic key regulator targets in Philadelphia chromosome-like B-acute lymphoblastic leukemia (Ph-like B-ALL), a high-risk leukemia subtype associated with hyperactive signal transduction and chemoresistance. Integrated analysis of 1,046 childhood B-ALL cases identified 14 dysregulated network nodes in Ph-like ALL involved in aberrant JAK/STAT, Ras/MAPK, and apoptosis pathways and other critical processes. Consistent with network controllability theory, combination small molecule inhibitor therapy targeting a pair of key nodes shifted the transcriptomic state of Ph-like ALL cells to become less like kinase-activated BCR-ABL1-rearranged (Ph+) B-ALL and more similar to prognostically-favorable childhood B-ALL subtypes. Functional validation experiments further demonstrated enhanced anti-leukemia efficacy of combining the BCL-2 inhibitor venetoclax with tyrosine kinase inhibitors ruxolitinib or dasatinib in vitro in human Ph-like ALL cell lines and in vivo in multiple patient-derived xenograft models. Our study represents a broadly-applicable conceptual framework for combinatorial drug discovery, based on systematic interrogation of synergistic vulnerability pathways with pharmacologic targeted validation in sophisticated preclinical human leukemia models.

cancer biology↗

bioRxiv2021

FGF21 Normalizes Plasma Glucose in Mouse Models of Type 1 Diabetes and Insulin Receptor Dysfunction

Fibroblast growth factor 21 (FGF21) is a member of the fibroblast growth factor (FGF) family of proteins. The biological activity of FGF21 was first shown to induce insulin independent glucose uptake in adipocytes through the GLUT1 transporter. Subsequently, it was shown to have effects on the liver to increase fatty acid oxidation. FGF21 treatment provides beneficial metabolic effects in both animal models and patients with obesity, type 2 diabetes mellitus (T2D) and/or fatty liver disease. In this paper, we revisited the original finding and found that insulin independent glucose uptake in adipocytes is preserved in the presence of an insulin receptor antagonist. Using a 40 kDa PEGylated (PEG) and half-life extended form of FGF21 (FGF21-PEG), we extended these in vitro results to two different mouse models of diabetes. FGF21-PEG normalized plasma glucose in streptozotocin-treated mice, a model of type 1 diabetes (T1D), without restoring pancreatic {beta}-cell function. FGF21-PEG also normalized plasma glucose levels and improved glucose tolerance in mice chronically treated with an insulin competitive insulin receptor antagonist, a model of autoimmune/Type-B insulin resistance. These data extend the pharmacological potential of FGF21 beyond the settings of T2D, fatty liver and obesity.

pharmacology and toxicology↗

bioRxiv2021

High-throughput tandem-microwell assay for ammonia repositions FDA-Approved drugs to Helicobacter pylori infection

To date, little attempt has been made to develop new treatments for Helicobacter pylori (H. pylori), although the community is aware of the shortage of treatments for H. pylori. In this study, we developed a 192-tandem-microwell-based high-throughput-assay for ammonia that is a known virulence factor of H. pylori and a product of urease. We could identify few drugs, i.e. panobinostat, dacinostat, ebselen, captan and disulfiram, to potently inhibit the activity of ureases from bacterial or plant species. These inhibitors suppress the activity of urease via substrate-competitive or covalent-allosteric mechanism, but all except captan prevent the antibiotic-resistant H. pylori strain from infecting human gastric cells, with a more pronounced effect than acetohydroxamic acid, a well-known urease inhibitor and clinically used drug for the treatment of bacterial infection. This study offers several bases for the development of new treatments for urease-containing pathogens and to study the mechanism responsible for the regulation of urease activity.

biochemistry↗

bioRxiv2020

ADP-ribosylation of mitochondrial proteins is mediated by Neuralized-like protein 4 (NEURL4)

ADP-ribosylation is a reversible post-translational modification where an ADP-ribose moiety is covalently attached to amino acid side-chains of target proteins either as mono-ADP-ribose (MARylation or MAR) or poly-ADP-ribose chains (PARylation or PAR) by a class of enzymes called ADP-ribosyltransferases (ARTs). Although ADP-ribosylation is best known for its nuclear roles, ADP-ribosylation of extra nuclear proteins is increasingly recognized as a key regulatory strategy across cellular compartments. ADP-ribosylation of mitochondrial proteins, in particular, has been widely reported, even though the extent to which ADP-ribosylation of specific proteins regulates mitochondrial functions is unclear and the exact nature of mitochondrial ART enzymes is debated. Here, we have identified Neuralized-like protein 4 (NEURL4) as a mitochondrial ART enzyme and profiled the NEURL4-dependent ADP-ribosylome in mitochondrial extracts from Hela cells by LC-MS/MS, using isobaric tandem mass tag (TMT) labeling for relative quantification. Comparison of WT and NEURL4-KO cells generated by CRISPR/Cas9 genome editing revealed that most ART activity associated with mitochondria is lost in absence of NEURL4. Putative NEURL4 targets include numerous mitochondrial proteins previously shown to be ADP-ribosylated. In particular, we show that NEURL4 enzymatic activity is required for the regulation of mtDNA integrity via poly-ADP-ribosylation of mitochondrial specific Ligase III (mtLIG3), the rate-limiting enzyme for mitochondrial DNA (mtDNA) Base Excision Repair (BER). Collectively, our studies reveal that NEURL4 acts as the main mitochondrial ART enzyme under physiological conditions and provide novel insights in the regulation of mitochondria homeostasis through ADP-ribosylation.

cell biology↗

bioRxiv2020

ProALIGN: Directly learning alignments for protein structure prediction via exploiting context-specific alignment motifs

Template-based modeling (TBM), including homology modeling and protein threading, is one of the most reliable techniques for protein structure prediction. It predicts protein structure by building an alignment between the query sequence under prediction and the templates with solved structures. However, it is still very challenging to build the optimal sequence-template alignment, especially when only distantly-related templates are available. Here we report a novel deep learning approach ProALIGN that can predict much more accurate sequence-template alignment. Like protein sequences consisting of sequence motifs, protein alignments are also composed of frequently-occurring alignment motifs with characteristic patterns. Alignment motifs are context-specific as their characteristic patterns are tightly related to sequence contexts of the aligned regions. Inspired by this observation, we represent a protein alignment as a binary matrix (in which 1 denotes an aligned residue pair) and then use a deep convolutional neural network to predict the optimal alignment from the query protein and its template. The trained neural network implicitly but effectively encodes an alignment scoring function, which reduces inaccuracies in the handcrafted scoring functions widely used by the current threading approaches. For a query protein and a template, we apply the neural network to directly infer likelihoods of all possible residue pairs in their entirety, which could effectively consider the correlations among multiple residues. We further construct the alignment with maximum likelihood, and finally build structure model according to the alignment. Tested on three independent datasets with in total 6,688 protein alignment targets and 80 CASP13 TBM targets, our method achieved much better alignments and 3D structure models than the existing methods including HHpred, CNFpred, CEthreader and DeepThreader. These results clearly demonstrate the effectiveness of exploiting the context-specific alignment motifs by deep learning for protein threading.

bioinformatics↗

bioRxiv2020

Deep Template-based Protein Structure Prediction

MotivationTBM (template-based modeling) is a popular method for protein structure prediction. When very good templates are not available, it is challenging to identify the best templates, build accurate sequence-template alignments and construct 3D models from alignments. ResultsThis paper presents a new method NDThreader (New Deep-learning Threader) to address the challenges of TBM. DNThreader first employs DRNF (deep convolutional residual neural fields), which is an integration of deep ResNet (convolutional residue neural networks) and CRF (conditional random fields), to align a query protein to templates without using any distance information. Then NDThreader uses ADMM (alternating direction method of multipliers) and DRNF to further improve sequence-template alignments by making use of predicted distance potential. Finally NDThreader builds 3D models from a sequence-template alignment by feeding it and sequence co-evolution information into a deep ResNet to predict inter-atom distance distribution, which is then fed into PyRosetta for 3D model construction. Our experimental results on the CASP13 and CAMEO data show that our methods outperform existing ones such as CNFpred, HHpred, DeepThreader and CEthreader. NDThreader was blindly tested in CASP14 as a part of RaptorX server, which obtained the best GDT score among all CASP14 servers on the 58 TBM targets. Availability and Implementationavailable as a part of web server at http://raptorx.uchicago.edu Contactjinboxu@gmail.com Supplementary InformationSupplementary data are available online.

bioinformatics↗

bioRxiv2020

Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Cellular Population

Revealing dynamic features of cellular systems, such as links among metabolic phenotypes, typically requires a time- or condition-series set of samples. Here Intra-Ramanome Correlation analysis (IRCA) was proposed to achieve this goal from just one snapshot of an isogenic population, by pairwise correlating among cells all the thousands of Raman bands from Single-cell Raman Spectra (SCRS), i.e., based on the intrinsic inter-cellular metabolic heterogeneity. IRCA of Chlamydomonas reinhardtii under nitrogen depletion revealed a metabolite conversion network at each time point and its temporal dynamics that feature protein-to-starch conversion followed by starch-to-TAG conversion (plus conversion of membrane lipids to TAG). Such correlation patterns in IRCA were abrased by knocking out the starch-biosynthesis pathway yet fully restored by genetic complementation. Extension to 64 ramanomes from microalgae, fungi and bacteria under various conditions suggests IRCA-derived metabolite conversion network as an intrinsic, reliable, species-resolved and state-sensitive metabolic signature of isogenic cellular population. The high throughput, low cost, excellent scalability and broad extendibility of IRCA suggest its broad application in cellular systems.

microbiology↗

bioRxiv2020

Sterilizing immunity against SARS-CoV-2 in hamsters conferred by a novel recombinant subunit vaccine

A safe and effective SARS-CoV-2 vaccine is essential to avert the on-going COVID-19 pandemic. Here, we developed a subunit vaccine, which is comprised of CHO-expressed spike ectodomain protein (StriFK) and nitrogen bisphosphonates-modified zinc-aluminum hybrid adjuvant (FH002C). This vaccine candidate rapidly elicited the robust humoral response, Th1/Th2 balanced helper CD4 T cell and CD8 T cell immune response in animal models. In mice, hamsters, and non-human primates, 2-shot and 3-shot immunization of StriFK-FH002C generated 28- to 38-fold and 47- to 269-fold higher neutralizing antibody titers than the human COVID-19 convalescent plasmas, respectively. More importantly, the StriFK-FH002C immunization conferred sterilizing immunity to prevent SARS-CoV-2 infection and transmission, which also protected animals from virus-induced weight loss, COVID-19-like symptoms, and pneumonia in hamsters. Vaccine-induced neutralizing and cell-based receptor-blocking antibody titers correlated well with protective efficacy in hamsters, suggesting vaccine-elicited protection is immune-associated. The StriFK-FH002C provided a promising SARS-CoV-2 vaccine candidate for further clinical evaluation.

microbiology↗

bioRxiv2020

The 3'tsRNAs are aminoacylated: Implications for their biogenesis

Emerging evidence indicates that tRNA-derived small RNAs (tsRNAs) are involved in fine-tuning gene expression and become dysregulated in various cancers. We recently showed that the 22nt LeuCAG tsRNA from 3 end of tRNALeu is required for efficient translation of a ribosomal protein mRNA and ribosome biogenesis. Inactivation of this tsRNA induced apoptosis in rapidly dividing cells and suppressed the growth of a patient derived orthotopic hepatocellular carcinoma in mice. The mechanism involved in the generation of the 3-tsRNAs remains elusive and it is unclear if the 3-ends of 3-tsRNAs are aminoacylated. Here we report an enzymatic method utilizing exonuclease T to determine the 3charging status of tRNAs and tsRNAs. Our results showed that the LeuCAG 3-tsRNA is fully charged and originated solely from charged mature tRNA. When the leucyl-tRNA synthetase was knocked down, less tsRNA was generated while the mature tRNA was not reduced further supporting that tsRNA generation is regulated. The fact that the 3-tsRNA is aminoacylated has implications for their biogenesis and provides additional insights into their biological role in post-transcriptional gene regulation.

molecular biology↗

bioRxiv2020

Fast and effective protein model refinement by deep graph neural networks

Protein structure prediction has been greatly improved, but there are still a good portion of predicted models that do not have very high quality. Protein model refinement is one of the methods that may further improve model quality. Nevertheless, it is very challenging to refine a protein model towards better quality. Currently the most successful refinement methods rely on extensive conformation sampling and thus, take hours or days to refine even a single protein model. Here we propose a fast and effective method that may refine protein models with very limited conformation sampling. Our method applies GNN (graph neural networks) to predict refined inter-atom distance probability distribution from an initial model and then rebuilds the model using the predicted distance as restraints. On the CASP13 refinement targets our method may refine models with comparable quality as the two leading human groups (Feig and Baker) and greatly outperforms the others. On the CASP14 refinement targets our method is only second to Feigs method, comparable to Bakers method and much better than the others (who worsened instead of improved model quality). Our method achieves this result by generating only 5 refined models for an initial model, which can be done in [~]15 minutes. Our study also shows that GNN performs much better than convolutional residual neural networks for protein model refinement when conformation sampling is limited. AvailabilityThe code will be released once the manuscript is published and available at http://raptorx.uchicago.edu Contactjinboxu@gmail.com

bioinformatics↗

bioRxiv2020

In-House Forecasting of Crop Latitude Adaptation through a Daylength-sensing-based Environment Adaptation Simulator (DEAS)

Global climate change necessitates the accelerated breeding of new crop varieties that can sustain yields in new environments. As a proxy for environmental adaptation, the selection of crops that can adapt to different latitudes is an appealing strategy. However, such selection currently involves a lengthy procedure that severely restricts the rapid breeding of varieties. Here, we aimed to combine molecular technologies with an in-house streamlined screening method to facilitate rapid selection for latitude adaptation. We established the Daylength-sensing-based Environment Adaptation Simulator (DEAS) to measure crop latitude adaptation via the transcriptional dynamics of florigen genes at different latitudes. We used different statistical approaches to demonstrate that DEAS predicts the florigen expression profiles in rice with high accuracy. Furthermore, we demonstrated the potential for application of DEAS in different crops. Incorporating DEAS into the breeding programs of conventional and underutilized crops could help meet the future needs for crop adaptation and promote sustainable agriculture.

plant biology↗

bioRxiv2020

Circular RNA profiling reveals abundant and diverse circRNAs of SARS-CoV-2, SARS-CoV and MERS-CoV origin

Circular RNAs (circRNAs) encoded by DNA genomes have been identified across host and pathogen species as parts of the transcriptome. Accumulating evidences indicate that circRNAs play critical roles in autoimmune diseases and viral pathogenesis. Here we report that RNA viruses of the Betacoronavirus genus of Coronaviridae, SARS-CoV-2, SARS-CoV and MERS-CoV, encode a novel type of circRNAs. Through de novo circRNA analyses of publicly available coronavirus-infection related deep RNA-Sequencing data, we identified 351, 224 and 2,764 circRNAs derived from SARS-CoV-2, SARS-CoV and MERS-CoV, respectively, and characterized two major back-splice events shared by these viruses. Coronavirus-derived circRNAs are more abundant and longer compared to host genome-derived circRNAs. Using a systematic strategy to amplify and identify back-splice junction sequences, we experimentally identified over 100 viral circRNAs from SARS-CoV-2 infected Vero E6 cells. This collection of circRNAs provided the first line of evidence for the abundance and diversity of coronavirus-derived circRNAs and suggested possible mechanisms driving circRNA biogenesis from RNA genomes. Our findings highlight circRNAs as an important component of the coronavirus transcriptome. SummaryWe report for the first time that abundant and diverse circRNAs are generated by SARS-CoV-2, SARS-CoV and MERS-CoV and represent a novel type of circRNAs that differ from circRNAs encoded by DNA genomes.

microbiology↗

bioRxiv2020

Species-resolved sequencing of low-biomass microbiomes by 2bRAD-M

Microbiome samples with low microbial biomass or severe DNA degradation remain challenging for amplicon-based (e.g., 16S/18S-rRNA) or whole-metagenome sequencing (WMS) approaches. Here, we introduce 2bRAD-M, a highly reduced and cost-effective metagenome-sequencing strategy which only sequences ~1% of metagenome and can simultaneously produce species-level bacterial, archaeal, and fungal profiles for low-biomass and highly degraded samples. For mock communities, 2bRAD-M can accurately generate species-level taxonomic profiles for otherwise hard-to-sequence samples with (i) low biomass of merely 1 pg of total DNA, (ii) high host DNA contamination (99%), and (iii) severely fragmented DNA (50-bp) from degraded samples. Tests of 2bRAD-M on stool, skin and environment-surface samples deliver successful reconstruction of comprehensive, high-resolution microbial profiles with agreement across 16S-rRNA, WMS and existing literature. In addition, it enables microbial profiling in formalin-fixed paraffin-embedded (FFPE) cervical tissue samples which were recalcitrant to conventional approaches due to the low amount and heavy degradation of microbial DNA, and discriminated healthy tissue, pre-invasive cancer and invasive cancer via species-level microbial profiles with 91.1% accuracy. Therefore, 2bRAD-M greatly expands the reach of microbiome sequencing.

microbiology↗