Biology subjects

Xu, H.

Publications and source records attributed to Xu, H..

At least 469 records · Page 26Linked to original sources

bioRxiv2019

Intraspecific root and leaf trait variation with tropical forest successional status: consequences for community-weighted patterns

O_LIQuantifying the dimensions and magnitude of intraspecific root trait variation is key to understanding the functional trade-offs in the belowground plant strategies of tropical forest trees. Additionally, accurately measuring how belowground functional trait variation relates to soil environment and forest age is crucial to tropical forest modeling efforts.\nC_LIO_LIWe sampled leaf and root morphologies from 423 juvenile trees of 72 species from 14 Angiosperm families along a 6.6 km transect that corresponded to an environmental gradient in decreasing soil fertility and texture with increasing forest age.\nC_LIO_LIWe observed within-lineage conservative functional trait-shifts in root and leaf morphological traits along the transect. From secondary to primary forest, average leaf area increased 7 cm2 and average root system diameter increased 0.4 mm. Mean specific leaf area decreased by 0.8 m2 kg-1, specific root length decreased by 3.5 m kg-1, and root branching intensity decreased by 0.3 tips cm-1. Leaf thickness and root tissue density showed no change.\nC_LIO_LIWe coupled trait measurements to a network of 164 1/16th-ha plots across a Chinese tropical forest reserve, to scale individual trait measurements up to the community-level, accounting for forest age.\nC_LIO_LIFor most traits, intraspecific trait variation negatively covaried with species compositional turnover between plots in younger versus older forest to compound and create greater community-weighted differences in trait values than would be observed if intraspecific variation in traits with forest age was not accounted for.\nC_LIO_LISummary Root morphologies are variable with local scale variation in soil fertility and texture. Accurately understanding broader (i.e. forest)-scale patterns in root functional traits, requires attention to underlying environmental variation in soil resources, which interacts with environmental filtering of plant communities.\nC_LI

plant biology↗

bioRxiv2019

Pan-genome analysis reveals the molecular basis of niche adaptation of Staphylococcus epidermidis strains

Staphylococcus epidermidis is the most commonly isolated species from human skin and the second leading cause of bloodstream infections. Here, we performed a large-scale comparative study without any pre-assigned reference to identify genomic determinants associated with their diversity and adaptation as a \"double-side spy\", a skin dominant colonization, and a successful pathogen. The pan-genome of S. epidermidis is open with 435 core proteins and a pan-genome size of 8034 proteins. Genome-wide phylogenetic tree shows that whole genome sequence is a powerful tool to analyze the complex evolutionary process of S. epidermidis and investigate the source of infection. Comparative genome analyses demonstrate the high diversity of antimicrobial resistances, especially mobile genetic elements. The complicated relationships of host-bacterium and bacterium-bacterium help S. epidermidis to play a vital role in balancing the epithelial microflora. The highly variable and dynamic nature of the S. epidermidis genome may be the result of its success in adapting to broad habitats, which is necessary to deal with complex environments. This study gives the general landscape of S. epidermidis pan-genome and provides valuable insights into mechanisms for genome evolution and lifestyle adaptation of this ecologically flexible species.

microbiology↗

bioRxiv2019

Paired design of ATP bioluminescence method and colony counting method: whether it is suitable for evaluating the disinfection effect of environmental surface?

ATP bioluminescence method has been used as a on-site rapid detection method in nosocomial infections control more and more. In this study, a paired design between four methods/detectors were performed to detect the environmental surface after disinfection. Two methods were ATP bioluminescence method and colony counting method (C), and ATP bioluminescence method included three kinds of detectors (B, P and N). Every surface was performed by two methods/detectors. ATP content per surface from ICU had no statistically significant difference with internal medicine wards of B and P, of which p was 0.435 and 0.260. By Spearman rank correlation coefficients, with the exception of better correlation between ATP content detected by B and P, the correlation between the remaining methods/detectors was poor or had no correlation. And the differences between detectors are statistically significant. Therefor, ATP bioluminescence method may not be suitable for the evaluation of disinfection effect, but be more appropriate for evaluating the cleanliness of environmental surface.

microbiology↗

bioRxiv2019

Identification of a basal stem cell subpopulation in the prostate via functional, lineage tracing and single-cell RNA-seq analyses

The basal cell compartment in many epithelial tissues such as the prostate, bladder, and mammary gland are generally believed to serve as an important pool of stem cells. However, basal cells are heterogenous and the stem cell subpopulation within basal cells is not well elucidated. Here we uncover that the core epithelial-to-mesenchymal transition (EMT) inducer Zeb is exclusively expressed in a prostate basal cell subpopulation based on both immunocytochemical and cell lineage tracing analysis. The Zeb1+ prostate epithelial cells are multipotent prostate basal stem cells (PBSCs) that can self-renew and generate functional prostatic glandular structures with all three epithelial cell types at the single-cell level. Genetic ablation studies reveal an indispensable role for Zeb1 in prostate basal cell development. Utilizing unbiased single cell transcriptomic analysis of over 9000 mouse prostate basal cells, we find that Zeb1+ basal cell subset shares gene expression signatures with both epithelial and mesenchymal cells and stands out uniquely among all the basal cell clusters. Moreover, Zeb1+ epithelial cells can be detected in mouse and clinical samples of prostate tumors. Identification of the PBSC and its transcriptome profile is crucial to advance our understanding of prostate development and tumorigenesis.

developmental biology↗

bioRxiv2019

Solving the first novel protein structure by 3D micro-crystal electron diffraction

Micro-crystal electron diffraction (MicroED) has recently shown potential for structural biology. It enables studying biomolecules from micron-sized 3D crystals that are too small to be studied by conventional X-ray crystallography. However, to the best of our knowledge, MicroED has only been applied to re-determine protein structures that had already been solved previously by X-ray diffraction. Here we present the first unknown protein structure - an R2lox enzyme - solved using MicroED. The structure was phased by molecular replacement using a search model of 35% sequence identity. The resulting electrostatic scattering potential map at 3.0 [A] resolution was of sufficient quality to allow accurate model building and refinement. Our results demonstrate that MicroED has the potential to become a widely applicable tool for revealing novel insights into protein structure and function, opening up new opportunities for structural biologists.

molecular biology↗

bioRxiv2019

Whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants

Predicting the functional or pathogenic regulatory variants in the human non-coding genome facilitates the interpretation of disease causation. While numerous prediction methods are available, their performance is inconsistent or restricted to specific tasks, which raises the demand of developing comprehensive integration for those methods. Here, we compile whole genome base-wise aggregations, regBase, that incorporate largest prediction scores. Building on different assumptions of causality, we train three composite models to score functional, pathogenic and cancer driver non-coding regulatory variants respectively. We demonstrate the superior and stable performance of our models using independent benchmarks and show great success to fine-map causal regulatory variants. We believe that regBase database together with three composite models will be useful in different areas of human genetic studies, such as annotation-based casual variant fine-mapping, pathogenic variant discovery as well as cancer driver mutation identification. regBase is freely available at https://github.com/mulinlab/regBase.

genomics↗

bioRxiv2019

De novo Identification of Essential Protein Domains from CRISPR/Cas9 Tiling-sgRNA Knockout Screens

High-throughput CRISPR/Cas9 knockout screens using a tiling-sgRNA design permit in situ evaluation of protein domain function. To facilitate de novo identification of essential protein domains from such screens, we developed ProTiler, a computational method for the robust mapping of CRISPR knockout hyper-sensitive (CKHS) regions, which refers to the protein regions that are associated with strong sgRNA dropout effect in the screens. We used ProTiler to analyze a published CRISPR tiling screen dataset, and identified 175 CKHS regions in 83 proteins. Of these CKHS regions, more than 80% overlapped with annotated Pfam domains, including all of the 15 known drug targets in the dataset. ProTiler also revealed unannotated essential domains, including the N-terminus of the SWI/SNF subunit SMARCB1, which we validated experimentally. Surprisingly, the CKHS regions were negatively correlated with phosphorylation and acetylation sites, suggesting that protein domains and post-translational modification sites have distinct sensitivities to CRISPR/Cas9 mediated amino acids loss.

bioinformatics↗

bioRxiv2019

Multiple Cross Displacement Amplification Coupled with Gold Nanoparticles-Based Lateral Flow Biosensor for Detection of the Mobilized Colistin Resistance Gene mcr-1

Fast dissemination of the mobilized colistin resistance (mcr) gene mcr-1 in Enterobacteriaceae causes a huge threat to the treatment of severe infection. In the current report, a multiple cross displacement amplification (MCDA) coupled with the detection of amplified products by gold nanoparticles-based lateral flow biosensor (LFB) assay (MCDA-LFB) was established to identify the mcr-1 gene with simpleness, rapidity, specificity and sensitivity. The MCDA-LFB assay was performed at a isothermal temperature (63{degrees}C) for only 30 min during the amplification stage, and the reaction products were directly identified by using LFB which obtained the result with 2 min. The entire process of experiments, from templates extraction to result judging, was accomplished less than 60 min. For the analytical specificity of this method, all of the 16 mcr-1-producing strains were positive, and all of the non-mcr-1 isolates got the negative results. The sensitivity of mcr-1-MCDA-LFB assay was as little as 600 fg of plasmid DNA per reaction in pure culture, and approximately 4.5x103 CFU/mL (~4.5 CFU/reaction) in fecal samples spiked with 100 l of strains. Therefore, this technique established in the present study is suitable for the surveillance of mcr-1 gene in clinic and livestock industry.

molecular biology↗

bioRxiv2019

Bioelectrical signaling via domain wall migration

AbstractElectrical signaling in biology is typically associated with action potentials, transient spikes in membrane voltage that return to baseline. The Hodgkin-Huxley equations of electrophysiology belong to a more general class of reaction-diffusion equations which could, in principle, support patterns of membrane voltage which are stable in time but structured in space. Here we show theoretically and experimentally that homogeneous or nearly homogeneous tissues can undergo spontaneous spatial symmetry breaking into domains with different resting potentials, separated by stable bioelectrical domain walls. Transitions from one resting potential to another can occur through long-range migration of these domain walls. We map bioelectrical domain wall motion using all-optical electrophysiology in an engineered stable cell line and in human iPSC-derived myoblasts. Bioelectrical domain wall migration may occur during embryonic development and during physiological signaling processes in polarized tissues. These results demonstrate a novel form of bioelectrical pattern formation and long-range signaling.

biophysics↗

bioRxiv2019

Shotgun metagenomics reveals an enrichment of potentially cross-reactive bacterial epitopes in ankylosing spondylitis patients, as well as the effects of TNFi therapy and the host’s genotype upon microbiome composition

Diverse evidence including clinical, genetic and microbiome studies support a major role of the gut microbiome in the common immune-mediated arthropathy, ankylosing spondylitis (AS). To further investigate this we performed metagenomic analysis of a case-control cohort of 250 Han-Chinese subjects. Previous reports of gut dysbiosis in AS were re-confirmed and several notable bacterial species and functional categories were differentially abundant. TNF-inhibitor (TNFi) therapy at least partially restored the perturbed microbiome observed in untreated AS cases to that of healthy controls, including several important bacterial species that have been previously associated with AS and other related diseases. Enrichment of bacterial peptides homologous to HLA-B27-presented epitopes was observed in the stools of AS patients, suggesting that either HLA-B27 fails to clear these or that they are involved in driving HLA-B27-associated immune reactions. TNFi therapy of AS patients was also associated with a reduction of potentially arthritogenic bacterial peptides, relative to untreated patients. An AS-associated SNP in RUNX3 significantly influenced the microbiome in two independent cohorts, highlighting a host genotype (other than HLA-B27) potentially influencing AS via the microbiome. These findings emphasise the key role that the gut microbiome plays in driving the pathogenesis of AS.

microbiology↗

bioRxiv2019

Functional arrays of human pluripotent stem cell-derived cardiac microtissues

To accelerate the cardiac drug discovery pipeline, we set out to develop a platform that would be amenable to standard multiwell-plate manipulations and be capable of quantifying tissue-level functions such as contractile force. We report a 96-well-based array of 3D human pluripotent stem cell (hPSC)-derived cardiac microtissues - termed Cardiac MicroRings (CaMiRi) - in custom printed multiwell plates capable of contractile force measurement. Within each well, two elastomeric microcantilevers are situated above a ramp. The wells are seeded with cell-laden collagen which, in response to the slope of the ramp, self-organizes around tip-gated microcantilevers to form contracting CaMiRi. The contractile force exerted by the CaMiRi is measured and calculated using the deflection of the cantilevers. Platform responses were robust and comparable across wells and we used it to determine an optimal tissue formulation. We validated contractile force response of CaMiRi using selected cardiotropic compounds with known effects. Additionally, we developed automated protocols for CaMiRi seeding, image acquisition, and analysis to enable measurement of contractile force with increased throughput. The unique tissue fabrication properties of the platform, and the consequent effects on tissue function, were demonstrated upon adding hPSC-derived epicardial cells to the system. This platform represents an open-source contractile force screening system useful for drug screening and tissue engineering applications.

bioengineering↗

bioRxiv2019

Streamlined low-input transcriptomics through EASY-RNAseq

High-throughput sequencing for transcriptome profiling is an increasingly accessible and important tool for biological research. However, accurate profiling of small cell populations remains challenging due to issues with gene detection sensitivity and experimental complexity. Here we describe a streamlined RNAseq protocol (EASY RNAseq) for sensitive transcriptome assessment starting from low amount of input materials. EASY RNAseq is technically robust enough for sequencing small pools of homogenous and heterogeneous cells, recovering higher numbers of genes and with a more even expression distribution pattern than other commonly used methods. Application of EASY RNAseq to single human embryos at the 8-cell stage was able to achieve detection of 70% protein-coding genes. This workflow may thus serve as a useful tool for sensitive interrogation of rare cell populations.

genomics↗

bioRxiv2019

Using transfer learning on whole slide images to predict tumor mutational burden in bladder cancer patients

High-TMB (TMB-H) could result in an increased number of neoepitopes from somatic mutations expressed by a patients own tumor cell which can be recognized and targeted by neighboring tumor-infiltrating lymphocytes (TILs). Deeper understanding of spatial heterogeneity and organization of tumor cells and their neighboring immune infiltrates within tumors could provide new insights into tumor progression and treatment response. Here we developed and applied computational approaches using digital whole slide images (WSIs) to investigate spatial heterogeneity and organization of regions harboring TMB-H tumor cells and TILs within tumors, and its prognostic utility. In experiments using WSIs from The Cancer Genome Atlas bladder cancer (BLCA), our findings show that WSI-based approaches can reliably predict patient-level TMB status and delineate spatial TMB heterogeneity and co-organization with TILs. TMB-H patients with low spatial heterogeneity enriched with high TILs show improved overall survival indicating a prognostic role of spatial TMB and TILs information in BLCA.

bioinformatics↗

bioRxiv2019

White Matter Hyperintensity Quantification in Large-Scale Clinical Acute Ischemic Stroke Cohorts - The MRI-GENIE Study

White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94-0.95) and Pearson correlation of total brain volume (r=0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N=2783) and identify a decrease in total brain volume of -2.4cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2,533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited.

neuroscience↗

bioRxiv2019

Gaussian Process Based Heteroscedastic Noise Modeling for Tumor Mutation Burden Prediction from Whole Slide Images

Tumor mutation burden (TMB) is a quantitative measurement of how many mutations present in tumor cells from a patient tumor as assessed by next-generation sequencing (NGS) technology. High TMB is used as a predictive biomarker to select patients that likely respond to immunotherapy in many cancer types, thus it is critical to accurately measure TMB for cancer patients who need to receive the immunotherapy. Recent studies showed that image features from histopathology whole slide images can be used to predict genetic features (e.g., mutation status) or clinical outcome of cancer patients. In this study, we develop a computational method to predict the TMB level from cancer patients histopathology whole slide images. The prediction problem is formulated as multiple instance learning (MIL) because a whole slide image (a bag) has to be divided into multiple image blocks (instances) due to computational reasons but a single label is available only to an entire whole slide image not to each image block. In particular, we propose a novel heteroscedastic noise model for MIL based on the framework of Gaussian process (GP), where the noise variance is assumed to be a latent function of image level features. This noise variance can encode the confidence in predicting the TMB level from each training image and make the method to put different levels of effort to classify images according to how difficult each image can be correctly classified. The method tries to fit an easier image well while it does not put much effort in classifying a harder (ambiguous) image correctly. Expectation and propagation (EP) is employed to efficiently infer our model and to find the optimal hyper-parameters. We have demonstrated from synthetic and real-world data sets that our method outperforms on TMB prediction from whole slide images base-line methods, including a special case of our method that does not include the heteroscedastic noise modeling and multiple instance ordinal regression (MIOR) that is one of few algorithms to solve ordinal regression in the MIL setting.

bioinformatics↗

bioRxiv2019

96 Eyes: Parallel Fourier Ptychographic Microscopy for high throughput screening

We report the implementation of a parallel microscopy system (96 Eyes) that is capable of simultaneous imaging of all wells on a 96-well plate. The optical system consists of 96 microscopy units, where each unit is made out of a four element objective, made through a molded injection process, and a low cost CMOS camera chip. By illuminating the sample with angle varying light and applying Fourier Ptychography, we can improve the effective brightfield imaging numerical apertuure of the objectives from 0.23 to 0.3, and extend the depth of field from {+/-}5 m to {+/-}15 m. The use of Fourier Ptychography additionally allows us to computationally correct the objectives aberrations out of the rendered images, and provides us with the ability to render phase images. The 96 Eyes acquires raw data at a rate of 0.7 frame per second (all wells) and the data are processed with 4 cores of graphical processing units (GPUs; GK210, Nvidia Tesla K80, USA). The system is also capable of fluorescence imaging (excitation = 465 nm, emission = 510 nm) at the native resolution of the objectives. We demonstrate the capability of this system by imaging S1P1-eGFP-Human bone osteosarcoma epithelial (U2OS) cells.

bioinformatics↗

bioRxiv2019

Association between premorbid body mass index and amyotrophic lateral sclerosis: causal inference through genetic approaches

Background: Inverse association between premorbid body mass index (BMI) and amyotrophic lateral sclerosis (ALS) has been discovered in observational studies; however, whether this association is causal remains largely unknown. Methods: We employed a two-sample Mendelian randomization approach to evaluate the causal relationship of genetically increased BMI with the risk of ALS. The analyses were implemented using summary statistics obtained for the independent instruments identified from large-scale genome-wide association studies of BMI (up to ~770,000 individuals) and ALS (up to ~81,000 individuals). The causal relationship between BMI and ALS was estimated using inverse-variance weighted methods and was further validated through extensive complementary and sensitivity analyses. Findings: Using 1,031 instruments strongly related to BMI, the causal effect of per one standard deviation increase of BMI was estimated to be 1.04 (95% CI 0.97~1.11, p=0.275) in the European population. The null association between BMI and ALS discovered in the European population also held in the East Asian population and was robust against various modeling assumptions and outlier biases. Additionally, the Egger-regression and MR-PRESSO ruled out the possibility of horizontal pleiotropic effects of instruments. Interpretation: Our results do not support the causal role of genetically increased or decreased BMI on the risk of ALS.

neuroscience↗

bioRxiv2019

Semantic Integration of Clinical Laboratory Tests from Electronic Health Records for Deep Phenotyping and Biomarker Discovery

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to the Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2421 commonly used laboratory tests with HPO terms. Using these annotations, a software assesses laboratory test results and converts each into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows reusing readily available laboratory tests in EHR for deep phenotyping and using the hierarchical structure of HPO for association studies with medical outcomes and genomics.\n\nOne Sentence SummaryWe present an approach to semantically integrating LOINC-encoded laboratory data with the Human Phenotype Ontology and show that the integrated LOINC data can be used to identify biomarkers for asthma from electronic health record data.

bioinformatics↗