Biology subjects

Li, B.

Publications and source records attributed to Li, B..

At least 631 records · Page 35Linked to original sources

bioRxiv2018

Single-cell Transcriptomic Landscape of Nucleated Cells in Umbilical Cord Blood

Umbilical cord blood (UCB) transplant is a therapeutic option for both pediatric and adult patients with a variety of hematologic diseases such as several types of blood cancers, myeloproliferative disorders, genetic diseases, and metabolic disorders. However, the level of cellular heterogeneity and diversity of nucleated cells in the UCB has not yet been assessed in an unbiased and systemic fashion. In the current study, nucleated cells from UCB were subjected to single-cell RNA sequencing, a technology enabled simultaneous profiling of the gene expression signatures of thousands of cells, generating rich resources for further functional studies. Here, we report the transcriptomic maps of 19,052 UCB cells, covering 11 major cell types. Many of these cell types are comprised of distinct subpopulations, including distinct signatures in NK and NKT cell types in the UCB. Pseudotime ordering of nucleated red blood cells (NRBC) identifies wave-like activation and suppression of transcription regulators, leading to a polarized cellular state, which may reflect the NRBC maturation. Progenitor cells in the UBC also consist two subpopulations with divergent transcription programs activated, leading to specific cell-fate commitment. Collectively, we provide this comprehensive single-cell transcriptomic landscape and show that it can uncover previously unrecognized cell types, pathways and gene expression regulations that may contribute to the efficacy and outcome of UCB transplant, broadening the scope of research and clinical innovations.

genomics↗

bioRxiv2018

Characterization and identification of long non-coding RNAs based on feature relationship

The significance of long non-coding RNAs (lncRNAs) in many biological processes and diseases has gained intense interests over the past several years. However, computational identification of lncRNAs in a wide range of species remains challenging; it requires prior knowledge of well-established sequences and annotations or species-specific training data, but the reality is that only a limited number of species have high-quality sequences and annotations. Here we first characterize lncRNAs by contrast to protein-coding RNAs based on feature relationship and find that the feature relationship between ORF (open reading frame) length and GC content presents universally substantial divergence in lncRNAs and protein-coding RNAs, as observed in a broad variety of species. Based on the feature relationship, accordingly, we further present LGC, a novel algorithm for identifying lncRNAs that is able to accurately distinguish lncRNAs from protein-coding RNAs in a cross-species manner without any prior knowledge. As validated on large-scale empirical datasets, comparative results show that LGC outperforms existing algorithms by achieving higher accuracy, well-balanced sensitivity and specificity, and is robustly effective (>90% accuracy) in discriminating lncRNAs from protein-coding RNAs across diverse species that range from plants to mammals. To our knowledge, this study, for the first time, differentially characterizes lncRNAs and protein-coding RNAs based on feature relationship, which is further applied in computational identification of lncRNAs. Taken together, our study represents a significant advance in characterization and identification of lncRNAs and LGC thus bears broad potential utility for computational analysis of lncRNAs in a wide range of species.

bioinformatics↗

bioRxiv2018

A particle-filter framework for robust cryoEM 3D reconstruction

Electron cryo-microscopy (cryoEM) is now a powerful tool in determining atomic structures of biological macromolecules under nearly natural conditions. The major task of single-particle cryoEM is to estimate a set of parameters for each input particle image to reconstruct the three-dimensional structure of the macromolecules. As future large-scale applications require increasingly higher resolution and automation, robust high-dimensional parameter estimation algorithms need to be developed in the presence of various image qualities. In this paper, we introduced a particle-filter algorithm for cryoEM, which was a sequential Monte Carlo method for robust and fast high-dimensional parameter estimation. The cryoEM parameter estimation problem was described by a probability density function of the estimated parameters. The particle filter uses a set of random and weighted support points to represent such a probability density function. The statistical properties of the support points not only enhance the parameter estimation with self-adaptive accuracy but also provide the belief of estimated parameters, which is essential for the reconstruction phase. The implementation of these features showed strong tolerance to bad particles and enabled robust defocus refinement, demonstrated by the remarkable resolution improvement at the atomic level.

biophysics↗

bioRxiv2018

Population coding of valence in the basolateral amygdala

The basolateral amygdala (BLA) plays an important role in associative learning, by representing both conditioned stimuli (CSs) and unconditioned stimuli (USs) of positive and negative valences, and by forming associations between CSs and USs. However, how such associations are formed and updated during learning remains unclear. Here we show that associative learning driven by reward and punishment profoundly alters BLA neuronal responses at population levels, reducing noise correlations and transforming the representations of CSs to resemble the distinctive valence-specific representations of USs. This transformation is accompanied by the emergence of prevalent inhibitory CS and US responses, and by the plasticity of CS responses in individual BLA neurons. During reversal learning wherein the expected valences are reversed, BLA population CS representations are remapped onto ensembles representing the opposite valences and track the switching in valence-specific behavioral actions. Our results reveal how signals predictive of opposing valences in the BLA evolve during reward and punishment learning, and how these signals might be updated and used to guide flexible behaviors.

neuroscience↗

bioRxiv2018

TRAF6 directs Foxp3 localization and facilitates Treg function through K63-type ubiquitination

Regulatory T cells (Treg) are crucial mediators of immune control. The characteristic gene expression and suppressive function of Treg depend considerably on the stable expression and activity of the transcription factor Foxp3. While transcriptional regulation of the Foxp3 gene has been studied in depth, both the expression and function of Foxp3 are also modulated at the protein level. However, the molecular players involved in posttranslational Foxp3 regulation are just beginning to be elucidated. Here we found TRAF6-deficient Tregs were dysfunctional in vivo; mice with Treg-restricted deletion of TRAF6 were resistant to B16 melanomas and displayed enhanced anti-tumor immunity. We further determined that Foxp3 undergoes lysine-63 chain (K63) ubiquitination at lysine 262 mediated by the E3 ligase TRAF6. When deprived of TRAF6 activity or rendered insensitive to K63 ubiquitination, Foxp3 displayed aberrant, perinuclear accumulation, disrupted function. Thus, Foxp3 ubiquitination by TRAF6 ensures proper localization of Foxp3 and facilitates Foxp3s gene-regulating activity in Tregs. These results implicate TRAF6 as a key posttranslational, Treg-stabilizing force that may be targeted in novel tolerance-breaking therapies.

immunology↗

bioRxiv2018

Rhizosphere microbes and host plant genotype influence the plant metabolome and reduce insect herbivory

O_LIRhizosphere microbes affect plant performance, including plant resistance against insect herbivores; yet, the relative influence of rhizosphere microbes vs. plant genotype on herbivory levels and on metabolites related to defense remains unclear.\nC_LIO_LIIn Boechera stricta, we tested the effects of rhizosphere microbes and plant genotype on herbivore resistance, the primary metabolome, and select secondary metabolites.\nC_LIO_LIPlant populations differed significantly in the concentrations of 6 glucosinolates (GLS), secondary metabolites known to provide herbivore resistance in the Brassicaceae, and the population with lower GLS levels experienced ~60% higher levels of aphid (Aphis spp.) attack; no effect was observed of GLS on damage by a second herbivore, flea beetles (Altica spp.). Rhizosphere microbiome (intact vs. disrupted) had no effect on plant GLS concentrations. However, aphid number and flea beetle damage were respectively ~3-fold and 7-fold higher among plants grown in the disrupted rhizosphere microbiome treatment, and distinct (as estimated from 16s rRNA amplicon sequencing) intact native microbiomes also differed in their effects on herbivore damage. These differences may be attributable to shifts in primary metabolic pathways.\nC_LIO_LIThe findings suggest that rhizosphere microbes can play a greater role than plant genotype in defense against insect herbivores, and act through mechanisms independent of plant genotype.\nC_LI

plant biology↗

bioRxiv2018

CagY-dependent regulation of type IV secretion in Helicobacter pylori is associated with alterations in integrin binding

Strains of Helicobacter pylori that cause ulcer or gastric cancer typically express a type IV secretion system (T4SS) encoded by the cag pathogenicity island (PAI). CagY is an ortholog of VirB10 that, unlike other VirB10 orthologs, has a large middle repeat region (MRR) with extensive repetitive sequence motifs, which undergo CD4+ T cell-dependent recombination during infection of mice. Recombination in the CagY MRR reduces T4SS function, diminishes the host inflammatory response, and enables the bacteria to colonize at a higher density. Since CagY is known to bind human 5{beta}1 integrin, we tested the hypothesis that recombination in the CagY MRR regulates T4SS function by modulating binding to 5{beta}1 integrin. Using a cell-free microfluidic assay, we found that H. pylori binding to 5{beta}1 integrin under shear flow is dependent on the CagY MRR, but independent of the presence of the T4SS pili, which are only formed when H. pylori is in contact with host cells. Similarly, expression of CagY in the absence of other T4SS genes was necessary and sufficient for whole bacterial cell binding to 5{beta}1 integrin. Bacteria with variant cagY alleles that reduced T4SS function showed comparable reduction in binding to 5{beta}1 integrin, though CagY was still expressed on the bacterial surface. We speculate that cagY-dependent modulation of H. pylori T4SS function is mediated by alterations in binding to 5{beta}1 integrin, which in turn regulates the host inflammatory response so as to maximize persistent infection.\n\nIMPORTANCEInfection with H. pylori can cause peptic ulcers, and is the most important risk factor for gastric cancer, the third most common cause of cancer death worldwide. The major H. pylori virulence factor that determines whether infection causes disease or asymptomatic colonization is the type IV secretion system (T4SS), a sort of molecular syringe that injects bacterial products into gastric epithelial cells and alters host cell physiology. We previously showed that recombination in CagY, an essential T4SS component, modulates the function of the T4SS. Here we found that these recombination events produce parallel changes in specific binding to 5{beta}1 integrin, a host cell receptor that is essential for T4SS-dependent translocation of bacterial effectors. We propose that CagY-dependent binding to 5{beta}1 integrin acts like a molecular rheostat that alters T4SS function and modulates the host immune response to promote persistent infection.

microbiology↗

bioRxiv2018

Collective feature selection to identify crucial epistatic variants

BackgroundMachine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called \"short fat data\" problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach.\n\nResultsThrough our simulation study we propose a collective feature selection approach to select features that are in the \"union\" of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~44,000 samples obtained from Geisingers MyCode Community Health Initiative (on behalf of DiscovEHR collaboration).\n\nConclusionsIn this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.

bioinformatics↗

bioRxiv2018

A statistical framework for cross-tissue transcriptome-wide association analysis

Transcriptome-wide association analysis is a powerful approach to studying the genetic architecture of complex traits. A key component of this approach is to build a model to predict (impute) gene expression levels from genotypes from samples with matched genotypes and expression levels in a specific tissue. However, it is challenging to develop robust and accurate imputation models with limited sample sizes for any single tissue. Here, we first introduce a multi-task learning approach to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average 39% improvement in imputation accuracy and generated effective imputation models for an average 120% (range 13%-339%) more genes in each tissue. We then describe a summary statistic-based testing framework that combines multiple single-tissue associations into a single powerful metric to quantify overall gene-trait association at the organism level. When our method, called UTMOST, was applied to analyze genome wide association results for 50 complex traits (Ntotal=4.5 million), we were able to identify considerably more genes in tissues enriched for trait heritability, and cross-tissue analysis significantly outperformed single-tissue strategies (p=1.7e-8). Finally, we performed a cross-tissue genome-wide association study for late-onset Alzheimers disease (LOAD) and replicated our findings in two independent datasets (Ntotal=175,776). In total, we identified 69 significant genes, many of which are novel, leading to novel insights on LOAD etiologies.

genetics↗

bioRxiv2018

Phosphorothioate-modified DNA oligonucleotides inactivate CRISPR-Cpf1 mediated genome editing

CRISPR-Cpf1, a microbial adaptive immune system discovered from Prevotella and Francisella 1, employs a single-stranded CRISPR RNA (crRNA) to induce double stranded DNA breaks1. To modulate genome editing activity of Cpf1 in human cells, we designed a series of crRNA variants including DNA-crRNA and RNA-crRNA duplexes, and identified that phosphorothioate (PS)-modified DNA-crRNA duplex completely blocked the function of Cpf1 mediated gene editing. More importantly, without prehybridization, this PS-modified DNA was able to regulate Cpf1 activity in a time-and dose-dependent manner. Mechanistic studies indicate that PS-modified DNA oligonucleotides hinder the binding between Cpf1-crRNA complex and target DNA substrate. Consequently, phosphorothioate-modified DNA oligonucleotides provide a tunable platform to inactivate Cpf1 mediated genome editing.

bioengineering↗

bioRxiv2018

Electron transfer dictates metabolic reprogramming in proliferating cells under hypoxia

Metabolic reprogramming extensively occurs in proliferating cancer cells. This phenomenon occurs highly heterogeneously, but its origin has remained unclear. Here we use a physicochemical concept of free electron potential (FEP) and its equation of state to profile metabolites. We demonstrate that FEP change between substrates and products exactly reflects electrons dissipated in a metabolic transformation. Based on the law of conservation of electron in chemical reactions, a function of FEP change for central metabolism in proliferating cells are further derived, and it can accurately predict metabolic behaviors under hypoxia by maximizing the cellular FEP change to consume electrons. Therefore, enabling electron transfer dictates metabolic reprogramming in hypoxic cells, which underlies the major findings in cancer metabolism and is supported by our experiments. Importantly, our model established on FEP helps to reveal a combination of promising targets to inhibit tumor growth under hypoxia by blocking electron consumption, and could also guide future studies on cancer metabolism under hypoxia.

biochemistry↗

bioRxiv2017

Scalable volumetric imaging for ultrahigh-speed brain mapping at synaptic resolution

We describe a new light-sheet microscopy method for fast, large-scale volumetric imaging. Combining synchronized scanning illumination and oblique imaging over cleared, thick tissue sections in smooth motion, our approach achieves high-speed 3D image acquisition of an entire mouse brain within 2 hours, at a resolution capable of resolving synaptic spines. It is compatible with immunofluorescence labeling, enabling flexible cell-type specific brain mapping, and is readily scalable for large biological samples such as primate brain.

neuroscience↗

bioRxiv2017

Collective cell migration without proliferation: density determines cell velocity and wave velocity

Collective cell migration contributes to morphogenesis, wound healing or tumor metastasis. Culturing epithelial monolayers on a substrate enables to quantify such tissue migration. By using narrow strips, we stabilise the front shape; by inhibiting cell division, we limit density increase and favor steady migration; by using long strips, we observe a confined cell monolayer migrating over days. A coherent collective movement propagates over millimeters; cells spread and density decreases from the monolayer bulk toward the front. Cell velocity ([~]micrometer per minute) increases linearly with cell radius, and does not depend explicitly on the distance to the front. Over ten periods of backwards propagating velocity waves, with wavelength [~]millimeter, are detected with a signal-to-noise ratio enabling for quantitative spatio-temporal analysis. Their velocity ([~]ten micrometers per minute) is ten times the cell velocity; it increases linearly with the cell radius. Their period ([~]two hours) is spatially homogeneous, and increases with the front density. When we inhibit the formation of lamellipodia, cell velocity drops while waves either disappear, or have a smaller amplitude and slower period. Our phenomenological model assumes that both cell and wave velocities are related with the activity of lamellipodia, and that the local stretching in the monolayer bulk modulates traction stresses. We find that parameter values close to the instability limit where waves appear yield qualitative and quantitative predictions compatible with experiments, including the facts that: waves propagate backwards; wave velocity increases with cell radius; lamellipodia inhibition attenuates, slows down or even suppresses the waves. Together, our experiments and modelling evidence the importance of lamellipodia in collective cell migration and waves.

biophysics↗

bioRxiv2017

A systems approach to refine disease taxonomy by integrating phenotypic and molecular networks

The International Classification of Diseases (ICD) relies on clinical features and lags behind the current understanding of the molecular specificity of disease pathobiology, necessitating approaches that incorporate growing biomedical data for classifying diseases to meet the needs of precision medicine. Our analysis revealed that the heterogeneous molecular diversity of disease chapters and the blurred boundary between disease categories in ICD should be further investigated. Here, we propose a new classification of diseases (NCD) by developing an algorithm that predicts the additional categories of a disease by integrating multiple networks consisting of disease phenotypes and their molecular profiles. With statistical validations from phenotype-genotype associations and interactome networks, we demonstrate that NCD improves disease specificity owing to its overlapping categories and polyhierarchical structure. Furthermore, NCD captures the molecular diversity of diseases and defines clearer boundaries in terms of both phenotypic similarity and molecular associations, establishing a rational strategy to reform disease taxonomy.

bioinformatics↗

bioRxiv2017

Generation of Mouse-Zebrafish Hematopoietic Tissue Chimeric Embryos for Hematopoiesis and Host-Pathogen Interaction Studies

Xenografts of the hematopoietic system are extremely useful as disease models and for translational research. Zebrafish xenografts have been widely used to monitor blood cancer cell dissemination and homing due to the optical clarity of embryos and larvae, which allow unrestricted in vivo visualization of migratory events. To broaden the scope of xenotransplantation studies in zebrafish, we have developed a technique that transiently generates hematopoietic tissue chimeras by transplanting murine bone marrow cells into zebrafish blastulae. This procedure leads to mammalian cell integration into the fish developmental hematopoietic program. Monitoring zebrafish chimeras at different time points post fertilization using in vivo time-lapse and confocal imaging showed murine cell co-localization with developing primitive and definitive hematopoietic tissues, intravasation into fish circulation, and dynamic hematopoietic cell-vascular endothelial and hematopoietic cell-niche interactions. Immunohistochemistry assays performed in chimeric animals showed that, after engraftment, murine cells expressed antigens related to i) hematopoietic stem and progenitor cells, ii) active cell proliferation, and iii) myeloid cell lineages. Lastly, xenografted zebrafish larvae infected with Klebsiella pneumoniae showed murine immune cells trafficking to bacterial foci and interacting with bacterial cells. Overall, these results show that mammalian bone marrow cells xenografted in zebrafish integrate into the host hematopoietic system revealing highly conserved molecular mechanisms of hematopoiesis between zebrafish and mammals. In addition, this procedure introduces a useful and simple method that improves and broadens the scope of hematopoietic tissue xenotransplantation studies in zebrafish.

developmental biology↗

bioRxiv2017

The DEAD box RNA helicase Ddx39a is essential for myocyte and lens development in zebrafish

RNA helicases from the DEAD-box family are found in almost all organisms and have important roles in RNA metabolism including RNA synthesis, processing and degradation. The function and mechanism of action of most of these helicases in animal development and human disease are largely unexplored. In a zebrafish mutagenesis screen to identify genes essential for heart development we identified a zebrafish mutant, which disrupts the gene encoding the RNA helicase DEAD-box 39a (ddx39a).Homozygous ddx39a mutant embryos exhibit profound cardiac and trunk muscle dystrophy, along with lens abnormalities caused by abrupt terminal differentiation of cardiomyocyte, myoblast and lens fiber cells. Further investigation indicated that loss of ddx39a hindered mRNA splicing of members of the kmt2 gene family, leading to mis-regulation of structural gene expression in cardiomyocyte, myoblast and lens fiber cells. Taken together, these results show that Ddx39a plays an essential role in establishment of proper epigenetic status during cell differentiation.

developmental biology↗

bioRxiv2017

Identification of Cancer-associated Metabolic Vulnerabilities by Modeling Multi-objective Optimality in Metabolism

Computational modeling of the genome-wide metabolic network is essential for designing new therapeutics targeting cancer-associated metabolic disorder, which is a hallmark of human malignancies. However, previous studies generally assumed that metabolic fluxes of cancer cells are subjected to the maximization of biomass production, despite the wide existence of trade-offs among multiple metabolic objectives. To address this issue, we developed a multi-objective model of cancer metabolism with algorithms depicting approximate Pareto surfaces and incorporating multiple omics datasets. To validate this approach, we built individualized models for NCI-60 cancer cell lines, and accurately predicted cell growth rates and other biological consequences of metabolic perturbations in these cells. By analyzing the landscape of approximate Pareto surface, we identified a list of metabolic targets essential for cancer cell proliferation and the Warburg effect, and further demonstrated their close association with cancer patient survival. Finally, metabolic targets predicted to be essential for tumor progression were validated by cell-based experiments, confirming this multi-objective modelling method as a novel and effective strategy to identify cancer-associated metabolic vulnerabilities.

systems biology↗

bioRxiv2017

Spatiotemporal DNA Methylome Dynamics of the Developing Mammalian Fetus

Genetic studies have revealed an essential role for cytosine DNA methylation in mammalian development. However, its spatiotemporal distribution in the developing embryo remains obscure. Here, we profiled the methylome landscapes of 12 mouse tissues/organs at 8 developmental stages spanning from early embryogenesis to birth. Indepth analysis of these spatiotemporal epigenome maps systematically delineated ~2 million methylation variant regions and uncovered widespread methylation dynamics at nearly one-half million tissue-specific enhancers, whose human counterparts were enriched for variants involved in genetic diseases. Strikingly, these predicted regulatory elements predominantly lose CG methylation during fetal development, whereas the trend is reversed after birth. Accumulation of non-CG methylation within gene bodies of key developmental transcription factors coincided with their transcriptional repression during later stages of fetal development. These spatiotemporal epigenomic maps provide a valuable resource for studying gene regulation during mammalian tissue/organ progression and for pinpointing regulatory elements involved in human developmental diseases.

genomics↗