Biology subjects

Fang, X.

Publications and source records attributed to Fang, X..

At least 91 records · Page 5Linked to original sources

bioRxiv2022

Test-Retest Reliability of Dynamic Functional Connectivity Parameters for a Two-State Model

Dynamic functional connectivity (DFC) metrics derived from resting-state functional magnetic resonance imaging (rs-fMRI) that measure dynamic features of brain communications that change over time are gaining more and more attention. A key concern is the reliability of DFC parameters, especially given the many methodological options. The DFC parameters mean dwell time (MDT), prevalence (Prev.), inter-transition interval (ITI) and state variability (Var.) were computed in 23 participants using a two-state model, sliding window analysis, and two imaging sessions. Reliability of the connectivity states, test-retest reliability of the parameters as well as correlations between DFC parameters were investigated for different scan lengths (i.e., 16 vs 8 min.), atlases (i.e., 116 vs. 442 regions of interest) and with/without within-subject centering of DFC matrices. The results showed an integrated (I) and a segregated (S) brain state with high intra-class correlation (ICC) values of the states between sessions (0.67 [≥] ICC [≥] 0.99). Reliability of the DFC parameters is, at best, intermediate with an ICC = 0.51 for prevalence for not-centered, 16 min. data and the coarse atlas. Moreover, MDTs of both states were positively correlated for centered data. Prevalence of one state was positively correlated with MDT of the same state and frequently negatively correlated with MDT of the other state. Var was positively correlated with MDT and Prev for the same state and negatively correlated for the other state for not-centered data. This study suggests that prevalence is the most reliable DFC parameter with lower reliability for shorter scans, more atlas regions and centering. Thus, it is advantageous to formulate hypotheses related to brain dynamics in terms of prevalence, especially in small-scale studies.

neuroscience↗

bioRxiv2022

Ratio-based quantitative multiomics profiling using universal reference materials empowers data integration

Multiomics profiling is a powerful tool to characterize the same samples with complementary features orchestrating the genome, epigenome, transcriptome, proteome, and metabolome. However, the lack of ground truth hampers the objective assessment of and subsequent choice from a plethora of measurement and computational methods aiming to integrate diverse and often enigmatically incomparable omics datasets. Here we establish and characterize the first suites of publicly available multiomics reference materials of matched DNA, RNA, proteins, and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters, providing built-in truth defined by family relationship and the central dogma. We demonstrate that the "ratio"-based omics profiling data, i.e., by scaling the absolute feature values of a study sample relative to those of a concurrently measured universal reference sample, were inherently much more reproducible and comparable across batches, labs, platforms, and omics types, thus empower the horizontal (within-omics) and vertical (cross-omics) data integration in multiomics studies. Our study identifies "absolute" feature quantitation as the root cause of irreproducibility in multiomics measurement and data integration, and urges a paradigm shift from "absolute" to "ratio"-based multiomics profiling with universal reference materials.

systems biology↗

bioRxiv2022

Quartet DNA reference materials and datasets for comprehensively evaluating germline variants calling performance

Current methods for evaluating the accuracy of germline variant calls are restricted to easy-to-detect high-confidence regions, thus ignoring a substantial portion of difficult variants beyond the benchmark regions. We established four DNA reference materials from immortalized cell lines derived from a Chinese Quartet including parents and monozygotic twins. We integrated benchmark calls of 4.2 million small variants and 15,000 structural variants from multiple platforms and bioinformatic pipelines for evaluating the reliability of germline variant calls inside the benchmark regions. The genetic built-in-truth of the Quartet family design not only improved sensitivity of benchmark calls by removing additional false positive variants with apparently high quality, but also enabled estimation of the precision of variants calls outside the benchmark regions. Batch effects of variant calling in large-scale DNA sequencing efforts can be effectively identified with the concurrent use of the Quartet DNA reference materials along with study samples, and can be alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Matched RNA and protein reference materials were also established in the Quartet project, thereby enabling benchmark calls constructed from DNA reference materials for evaluation of variants calling performance on RNA and protein data. The Quartet DNA reference materials from this study are a resource for objective and comprehensive assessment of the accuracy of germline variant calls throughout the whole-genome regions.

bioinformatics↗

bioRxiv2022

Quartet RNA reference materials and ratio-based reference datasets for reliable transcriptomic profiling

As an indispensable tool for transcriptome-wide analysis of differential gene expression, RNA sequencing (RNAseq) has demonstrated great potential in clinical applications. However, the lack of multi-group RNA reference materials of biological relevance and the corresponding reference datasets for assessing the reliability of RNAseq hampers its wide clinical applications wherein the underlying biological differences among study groups are often small. As part of the Quartet Project for quality control and data integration of multiomic profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets using multi-batch RNAseq datasets, providing "ground truth" for benchmarking. Moreover, Quartet-sample-based quality metrics were developed for assessing reliability of RNAseq technology in terms of intra-batch proficiency and cross-batch reproducibility. The small intrinsic biological differences among the Quartet samples enable sensitive assessment of performance of transcriptomic measurements. The Quartet RNA reference materials combined with the reference datasets can be served as unique resources for assessing data quality and improving reliability of transcriptomic profiling.

genetics↗

bioRxiv2022

Exploring high-quality microbial genomes by assembly of linked-reads with high barcode specificity using deep learning

Despite long-read sequencing enables to generate complete genomes of unculturable microbes, its high cost hinders its widespread application in large cohorts. An alternative method is to assemble short-reads with long-range connectivity, which can be a cost-effective way to generate high-quality microbial genomes. We developed Pangaea to improve metagenome assembly using short-reads with physical or virtual barcodes. It adopts a deep-learning-based binning algorithm to assemble the co-barcoded reads with similar sequence contexts and abundances to improve assemblies of high- and medium-abundance microbes. Pangaea also leverages a multi-thresholding reassembly strategy to refine assembly for low-abundance microbes. We benchmarked Pangaea with linked-reads and a combination of short- and long-reads from mock communities and human gut metagenomes. Pangaea achieved significantly higher contig continuity as well as more near-complete metagenome-assembled genomes (NCMAGs) than the existing assemblers. Pangaea was also observed to generate three complete and circular NCMAGs on the human gut microbiomes.

genomics↗

bioRxiv2022

The addiction-susceptibility TaqIA/Ankyrin repeat and kinase domain containing 1 kinase (ANKK1) controls reward and metabolism through dopamine receptor type 2 (DR2)-expressing neurons

Significant evidence highlights the importance of genetic variants in the development of psychiatric and metabolic conditions. Among these, the Taq1A polymorphism is one of the most commonly studied in psychiatry. TaqIA is located in the gene that codes for the Ankyrin repeat and kinase domain containing 1 kinase (ANKK1) near the dopamine D2 dopamine receptor (DR2) gene. Depending on race it affects 30 to 80% of the population and its homozygous expression of the A1 allele correlates with a 30 to 40% reduction of striatal DR2, a typical feature of addiction, over-eating and other psychiatric pathologies. The mechanisms by which the variant influences dopamine signaling and behavior is unknown. Here we used transgenic and viral-mediated strategies to reveal the role of ANKK1 in the regulation of activity and functions of the striatum. We found that Ankk1 is preferentially enriched in striatal DR2 expressing neurons and that Ankk1 loss-of-function in dorsal and ventral striatum leads to alteration in learning, impulsive, and flexible behaviors resembling the endophenotypes described in A1 carriers. We also observed an unsuspected role of ANKK1 in striatal DR2-expressing neurons in the ventral striatum in the regulation of energy homeostasis and documented differential nutrient partitioning in humans with versus without the A1 allele. Overall, our data demonstrate that the Ankk1 gene is necessary for the integrity of striatal functions and reveal a new role for ANKK1 in the regulation of body metabolism.

neuroscience↗

bioRxiv2022

NF-κB inhibitor alpha has a cross-variant role during SARS-CoV-2 infection in ACE2-overexpressing human airway organoids

As SARS-CoV-2 continues to spread worldwide, tractable primary airway cell models that accurately recapitulate the cell-intrinsic response to arising viral variants are needed. Here we describe an adult stem cell-derived human airway organoid model overexpressing the ACE2 receptor that supports robust viral replication while maintaining 3D architecture and cellular diversity of the airway epithelium. ACE2-OE organoids were infected with SARS-CoV-2 variants and subjected to single-cell RNA-sequencing. NF-{kappa}B inhibitor alpha was consistently upregulated in infected epithelial cells, and its mRNA expression positively correlated with infection levels. Confocal microscopy showed more I{kappa}B expression in infected than bystander cells, but found concurrent nuclear translocation of NF-{kappa}B that I{kappa}B usually prevents. Overexpressing a nondegradable I{kappa}B mutant reduced NF-{kappa}B translocation and increased viral infection. These data demonstrate the functionality of ACE2-OE organoids in SARS-CoV-2 research and identify an incomplete NF-{kappa}B feedback loop as a rheostat of viral infection that may promote inflammation and severe disease.

microbiology↗

bioRxiv2022

A genetically engineered phage-based nanomaterial for detecting bacteria with magnetic resonance imaging

The ability to noninvasively detect bacteria at any depth inside opaque tissues has important applications ranging from infection diagnostics to tracking therapeutic microbes in their mammalian host. Current examples of probes for detecting bacteria with strain-type specificity are largely based on optical dyes, which cannot be used to examine bacteria in deep tissues due to the physical limitation of light scattering. Here, we describe a new biomolecular probe for visualizing bacteria in a cell-type specific fashion using magnetic resonance imaging (MRI). The probe is based on a peptide that selectively binds manganese and is attached in high numbers to the capsid of filamentous phage. By genetically engineering phage particles to display this peptide, we are able to bring manganese ions to specific bacterial cells targeted by the phage, thereby producing MRI contrast. We show that this approach allows MRI-based detection of targeted E. coli strains while discriminating against non-target bacteria as well as mammalian cells. By engineering the phage coat to display a protein that targets cell surface receptors in V. cholerae, we further show that this approach can be applied to image other bacterial targets with MRI. Finally, as a preliminary example of in vivo applicability, we demonstrate MR imaging of phage-labeled V. cholerae cells implanted subcutaneously in mice. The nanomaterial developed here thus represents a path towards noninvasive detection and tracking of bacteria by combining the programmability of phage architecture with the ability to produce three- dimensional images of biological structures at any arbitrary depth with MRI.

bioengineering↗

bioRxiv2022

Benchmarking de novo assembly methods on metagenomic sequencing data

Metagenome assembly is an efficient approach to deciphering the "microbial dark matter" in the microbiota based on metagenomic sequencing, due to the technical challenges involved in isolating and culturing all microbes in vitro. Although short-read sequencing has been widely used for metagenome assembly, linked- and long-read sequencing have shown their advancements by providing long-range DNA connectedness in assembly. Many metagenome assembly tools use dedicated algorithms to simplify the assembly graphs and resolve the repetitive sequences in microbial genomes. However, there remains no comprehensive evaluation of the pros and cons of various metagenomic sequencing technologies in metagenome assembly, and there is a lack of practical guidance on selecting the appropriate metagenome assembly tools. Therefore, this paper presents a comprehensive benchmark of 15 de novo assembly tools applied to 32 metagenomic sequencing datasets obtained from simulation, mock communities, or human stool samples. These datasets were generated using mainstream sequencing platforms, such as Illumina and BGISEQ short-read sequencing, 10x Genomics linked-read sequencing, and PacBio and Oxford Nanopore long-read sequencing. The assembly tools were extensively evaluated against many criteria, which revealed that compared with the other sequencing technologies, long-read assemblers generated the highest contig continuity but failed to reveal some medium- and high-quality metagenome-assembled genomes (MAGs). In addition, hybrid assemblers using both short- and long-read sequencing were promising tools to both improve contig continuity and increase the number of near-complete MAGs. This paper also discussed the running time and peak memory consumption of these tools and provided practical guidance on selecting them.

genomics↗

bioRxiv2022

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations

Drug combination therapies are superior to monotherapy for cancer treatment in many ways when addressing tumor heterogeneity issue. For wet-lab experiment, screening out novel synergistic drug pairs is challenging due to the enormous searching space of possible drug pairs. Thus, computational methods have been developed to predict drug pairs with potential synergistic function. Notwithstanding the success of current models, the power of generalization to other datasets as wells as understanding of mechanism for chemical-chemical interaction or chemical-sample interaction are lack of study, hindering current algorithms from real application. In this paper, we proposed a deep neural model termed DTSyn (Dual Transformer model for drug pair Synergy prediction) based on multi-head attention mechanism to identify novel drug combinations. We designed a fine-granularity transformer for capturing chemical substructure-gene and gene-gene associations and a coarse-granularity transformer for extracting chemical-chemical and chemical-cell line interactions. DTSyn achieves highest Receiver operating characteristic area under curve (ROC AUC) of 0.73, 0.78. 0.82 and 0.81 on four different cross validation tasks, outperforming all competing methods. Further, DTSyn achieved best True Positive Rate (TPR) over five independent datasets. The ablation study showed that both transformer blocks contributed to the performance of DTSyn. In addition, DTSyn can extract interactions among chemicals and cell lines, which may represent the mechanisms of drug action. Thus, we envision our model a valuable tool to prioritize synergistic drug pairs by utilizing chemicals and transcriptome data.

bioinformatics↗

bioRxiv2022

Exosomally targeting microRNA23a ameliorates microvascular endothelial barrier dysfunction following rickettsial infection

Spotted fever group rickettsioses caused by Rickettsia (R) are devastating human infections, which mainly target microvascular endothelial cells (EC) and can induce lethal EC barrier dysfunction in the brain and lungs. Our previous evidence reveals that exosomes (Exos) derived from rickettsial-infected ECs, namely R-ECExos, can induce disruption of the tight junctional (TJ) protein ZO-1 and barrier dysfunction of human normal recipient brain microvascular endothelial cells (BMECs). However, the underlying mechanism remains elusive. Given that we have observed that microRNA23a (miR23a), a negative regulator of endothelial ZO-1 mRNA, is selectively sorted into R-ECExos, the aim of the present study was to characterize the potential functional role of exosomal miR23a delivered by R-ECExos in normal recipient BMECs. We demonstrated that EC-derived Exos (ECExos) have the capacity to deliver oligonucleotide RNAs to normal recipient BMECs in an RNase-abundant environment. miR23a in ECExos impairs normal recipient BMEC barrier function, directly targeting TJ protein ZO-1 mRNAs. In separate studies using a traditional in vitro model and a novel single living-cell biomechanical assay, our group demonstrated that miR23a anti-sense oligonucleotide-enriched ECExos ameliorate R-ECExo-provoked recipient BMEC dysfunction in association with stabilization of ZO-1 in a dose-dependent manner. These results suggest that Exo-based therapy could potentially prove to be a promising strategy to improve vascular barrier function during bacterial infection and concomitant inflammation.

cell biology↗

bioRxiv2022

AtEH/Pan1 proteins drive phase separation of the TPLATE complex and clathrin polymerisation during plant endocytosis

Clathrin-mediated endocytosis (CME) is an essential cellular internalisation pathway involving the dynamic assembly of clathrin and accessory proteins to form membrane-bound vesicles. In plants, the evolutionarily ancient TSET/TPLATE complex (TPC) plays an essential, but not well-defined role in CME. Here, we show that two highly disordered TPC subunits, AtEH1 and AtEH2 function as scaffolds to drive biomolecular condensation of the complex. These condensates specifically nucleate on the plasma membrane through interactions with anionic phospholipids, and facilitate the dynamic recruitment and assembly of clathrin, early-, and late-stage endocytic accessory proteins. Importantly, clathrin forms ordered assemblies within the condensate environment. Biomolecular condensation therefore acts to promote dynamic protein assemblies throughout clathrin-mediated endocytosis. Furthermore, the disordered region sequence properties of AtEH1 regulate the material properties of the endocytic condensates in vivo and alteration of these material properties influences endocytosis dynamics, and consequently plant adaptive growth. HighlightsO_LIAtEH subunits are endocytic scaffolds which drive condensation of the TPC C_LIO_LIAtEH1 condensates nucleate on the plasma membrane via lipid interactions C_LIO_LICondensation of AtEH1/TPC facilitates clathrin re-arrangement and assembly C_LIO_LIAtEH1 IDR1 composition controls condensate properties to regulate endocytosis C_LI

cell biology↗

bioRxiv2022

Oxidative phosphorylation pathway disruption is an alternative pathological mechanism leading to Diamond-Blackfan anemia

Diamond-Blackfan anemia (DBA) is a rare congenital disorder characterized by the failure of erythroid progenitor differentiation; however, the molecular mechanisms leading to erythroid defects remain unclear. By analyzing the transcriptomic profiles of bone marrow from patients with DBA (n = 10), we identified the dysfunction of the oxidative phosphorylation (OXPHOS) pathway as a possible cause of DBA. We established a DBA cell model using differentiating hematopoietic stem progenitor cells in which the OXPHOS pathway was suppressed to completely recapitulate the defects in erythroid progenitor differentiation, ribosome biogenesis, and heme biosynthesis, which are representative characteristics of patients with DBA. Disruption of the OXPHOS pathway led to ribosomal defects and associated erythroid defects via abolishment of the Ran GTPase activating protein RanGAP1, which is pivotal in the RNA transport pathway. The composition of the ribosomal proteins in the established DBA cells was unchanged, but an overall reduction in ribosomal protein levels was observed, leading to an alteration in the translation of a subset of transcripts specific to erythropoiesis. We revealed that the OXPHOS pathway participates in erythropoiesis, particularly at an early stage, and reinforced the relationship between the OXPHOS pathway and erythropoiesis. Coenzyme Q10, an activator of OXPHOS, largely rescued the erythroid defects in DBA cells. Our results reveal that OXPHOS repression is an alternative pathological mechanism leading to DBA, demonstrating its potential as a therapeutic pathway. Key PointsO_LIOxidative phosphorylation (OXPHOS) pathway disruption is an alternative pathological mechanism underlying Diamond-Blackfan anemia (DBA). C_LIO_LISuppression of OXPHOS leads to defects in erythropoiesis and ribosomal biogenesis via the RanGAP1 protein. C_LI

cell biology↗

bioRxiv2022

Gut microbiota-derived tryptamine impairs insulin sensitivity

Gut-microbiota plays a pivotal role in development of type 2 diabetes (T2D), yet the molecular mechanism remains elusive. Here, we show that tryptamine, a microbial metabolite of tryptophan, impairs glucose tolerance and insulin sensitivity. Tryptamine presents a higher level in monkeys with spontaneous diabetes and human with T2D and positively correlated with the glucose tolerance. In parallel, tryptamine level was suppressed by dietary fibers intervention in T2D subjects and negatively correlated with improvement of glucose tolerance. The inhibitory effect of tryptamine on insulin signaling as shown was dependent on a trace amine-associated receptor 1 (TAAR1)-extracellular signal-regulated kinase (ERK) signaling axis. Monoassociation of T2D-associated tryptamine-producing bacteria Ruminococcus gnavus impairs insulin sensitivity in pseudo germ-free mice. Our findings indicate gut microbiota-derived tryptamine contributes to the development of insulin resistance in T2D and may serve as a new target for intervention. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

microbiology↗

bioRxiv2022

Structure of IgG-Fc hexamer reveals a mutual lock-and-key mode of Fc-Fc interaction

IgG exists mainly as monomer, but recent studies suggest that IgG forms hexamer to mediate antibody functions. Although many structures of IgG monomer and its fragments are determined, there is no IgG hexamer structure at atomic level due to the weak Fc-Fc interactions. Here we engineered a hexameric IgG with IgM tailpiece fusion and determined the structure by cryo-EM. IgG-Fc hexamer forms hexagon symmetry with the six Fcs lie in a plane with Fc-Fc interaction in a mutual lock-and-key mode. This structure provides structural insights into Fc-Fc interaction of IgG and reveals molecular basis for its function.

biophysics↗

bioRxiv2021

Characterization of antibiotic resistance in clinical isolates of Klebsiella pneumoniae in Denmark

Klebsiella pneumoniae (KP) is a major global health problem as it leads to hospital outbreaks all over the world and is becoming more difficult to treat due to its increasing antimicrobial resistance (AMR). Optimization and development of new treatments of KP requires understanding of its population structure and AMR properties. Therefore, in this study, we collected and sequenced 491 KP strains from four major Danish microbiology departments covering 51% of the Danish population. The isolates were whole genome sequenced (WGS), phenotypically characterized and compared with 2,124 KP strains from 13 different countries (PATRIC strains). We found that while genomic content varies significantly across the Danish strains, they also differ significantly from strains from other countries, due to the lack of certain AMR sequence types (e.g. ST258 and ST307) in Denmark. Genomic and experimental analysis suggest that Danish strains contain fewer virulence mechanisms and are more susceptible to antimicrobials compared to strains from other countries, likely due to the relatively low antibiotic usage in Denmark where 70% of hospital antibiotic usage is penicillins. We also identified potential novel AMR determinants to tigecycline through statistical analysis of genomic and phenotypic data. To conclude, we obtained a more comprehensive understanding of the KP strains in Denmark and provided valuable insights for future experiments and strategies to combat AMR in KP.

genomics↗

bioRxiv2021

HybridDTA: Hybrid Data Fusion through Pairwise Training for Drug-Target Affinity Prediction

Candidate compounds with high binding affinities toward a target protein are likely to be developed as drugs. Deep neural networks (DNNs) have attracted increasing attention for drug-target affinity (DTA) estimation owning to their efficiency. However, the negative impact of batch effects caused by measure metrics, system technologies, and other assay information is seldom discussed when training a DNN model for DTA. Suffering from the data deviation caused by batch effects, the DNN models can only be trained on a small amount of "clean" data. Thus, it is challenging for them to provide precise and consistent estimations. We design a batch-sensitive training framework, namely BatchDTA, to train the DNN models. BatchDTA implicitly aligns multiple batches toward the same protein, alleviating the impact of the batch effects on the DNN models. Extensive experiments demonstrate that BatchDTA facilitates four mainstream DNN models to enhance the ability and robustness on multiple DTA datasets. The average concordance index (CI) of the DNN models achieves a relative improvement of 4.0%. BatchDTA can also be applied to the fused data collected from multiple sources to achieve further improvement.

bioinformatics↗

bioRxiv2021

Robust and Efficient Assessment of Potency (REAP): A Quantitative Tool for Dose-response Curve Estimation

The median-effect equation has been widely used to describe the dose-response relationship and identify compounds that activate or inhibit specific disease targets in contemporary drug discovery. However, the experimental data often contain extreme responses, which may significantly impair the estimation accuracy and impede valid quantitative assessment in the standard estimation procedure. To improve the quantitative estimation of the dose-response relationship, we introduce a novel approach based on robust beta regression. Substantive simulation studies under various scenarios demonstrate solid evidence that the proposed approach consistently provides robust estimation for the median-effect equation, particularly when there are extreme outcome observations. Moreover, simulation studies illustrate that the proposed approach also provides a narrower confidence interval, suggesting a higher power in statistical testing. Finally, to efficiently and conveniently perform common lab data analyses, we develop a freely accessible web-based analytic tool to facilitate the quantitative implementation of the proposed approach for the scientific community.

pharmacology and toxicology↗