Biology subjects

Hu, Y.

Publications and source records attributed to Hu, Y..

At least 487 records · Page 27Linked to original sources

bioRxiv2021

eQTL mapping using allele-specific gene expression

Using information from allele-specific gene expression (ASE) can sub-stantially improve the power to map gene expression quantitative trait loci (eQTLs). However, such practice has been limited, partly due to high computational cost and the requirement to access raw data that can take a large amount of storage space. To address these computational challenges, we have developed a computational framework that uses a statistical method named TReCASE as its computational engine, and it is computationally feasible for large scale analysis. We applied it to map eQTLs in 28 human tissues using the data from the Genotype-Tissue Expression (GTEx) project. Compared with a popular linear regression method that does not use ASE data, TReCASE can double the number of eGenes (i.e., genes with at least one significant eQTL) when sample size is relatively small, e.g., n = 200. We also demonstrated how to use the ASE data that we have collected to study dynamic eQTLs whose effect sizes vary with respect to another variable, such as age. We find the majority of such dynamic eQTLs are due to some underlying latent factors, such as cell type proportions. We further compare TReCASE versus another method RASQUAL. TReCASE is ten times or more faster than RASQUAL and it provides more robust type I error control.

genomics↗

bioRxiv2021

FlyPhoneDB: An integrated web-based resource for cell-cell communication prediction in Drosophila

Multicellular organisms rely on cell-cell communication to exchange information necessary for developmental processes and metabolic homeostasis. Cell-cell communication pathways can be inferred from transcriptomic datasets based on ligand-receptor (L-R) expression. Recently, data generated from single cell RNA sequencing (scRNA-seq) have enabled L-R interaction predictions at an unprecedented resolution. While computational methods are available to infer cell-cell communication in vertebrates such a tool does not yet exist for Drosophila. Here, we generated a high confidence list of L-R pairs for the major fly signaling pathways and developed FlyPhoneDB, a quantification algorithm that calculates interaction scores to predict L-R interactions between cells. At the FlyPhoneDB user interface, results are presented in a variety of tabular and graphical formats to facilitate biological interpretation. To demonstrate that FlyPhoneDB can effectively identify active ligands and receptors to uncover cell-cell communication events, we applied FlyPhoneDB to Drosophila scRNA-seq data sets from adult midgut, abdomen, and blood, and demonstrate that FlyPhoneDB can readily identify previously characterized cell-cell communication pathways. Altogether, FlyPhoneDB is an easy-to-use framework that can be used to predict cell-cell communication between cell types from scRNA-seq data in Drosophila.

bioinformatics↗

bioRxiv2021

Daily transient coating of the intestine leads to weight loss and improved glucose tolerance

The worsening obesity epidemic has driven a parallel increase in incidence of type-2 diabetes (T2D) found in 13% of the adult US population. Roux-en-Y gastric bypass surgery (RYGB) is the golden standard surgical treatment for obesity-related T2D. However, over 70% of T2D patient do not meet the NIH criteria for surgery, and many that do, are reluctant to consider such an invasive intervention. Mechanisms behind the antidiabetic effect of RYGB are not clearly understood, but isolation of proximal bowel from nutrient exposure plays a critical role. In this study, we tested the impact of daily administration of an oral gut coating agent, LuCI (Luminal Coating of the Intestine), in controlling weight and insulin resistance. LuCI is not systemically absorbed and provides a transient coating of the proximal intestines and excludes the coated bowel from nutrient contact, thus mimicking the effects of RYGB. We demonstrate that LuCI results in weight loss and improved insulin sensitivity in a rat DIO model. LuCI also appears to replicate hormonal changes seen after RYGB, by increasing post-prandial GLP-1 and GIP levels. LuCI has the potential to replicate the metabolic benefit of surgery and be a novel therapeutic strategy for obesity associated T2D.

bioengineering↗

bioRxiv2021

Immunological profiling of COVID-19 patients with pulmonary sequelae

Cellular immunity may be involved in organ damage and rehabilitation in patients with coronavirus disease 2019 (COVID-19). We aimed to delineate immunological features of COVID-19 patients with pulmonary sequelae (PS) one year after discharge. 50 COVID-19 survivors were recruited and classified according to radiological characteristics: 24 patients with PS and 26 patients without PS. Phenotypic and functional characteristics of immune cells were evaluated by multiparametric flow cytometry. Patients with PS had an increased proportion of natural killer (NK) cells and lower percentage of B cells compared to patients without PS. Phenotypic and functional features of T cells in patients with PS were predominated by the accumulation of CD4+ T cells secreting IL-17A, short-lived effector-like CD8+ T cells (CD27-CD62L-) and senescent T cells with excessive secretion of granzyme-B/perforin/IFN-{gamma}. NK cells were characterized by the excessive secretion of granzyme-B and perforin and the downregulation of NKP30 and NKP46; highly activated NKT and {gamma}{delta} T cells exhibited NKP30 and TIM-3 upregulation and NKB1 downregulation in patients with PS. However, immunosuppressive cells were comparable between the two groups. The interrelation of immune cells in COVID-19 was intrinsically identified, whereby T cells secreting IL-2, IL-4 and IL-17A were enriched among CD28+ and CD57-cells and cells secreting perforin/granzyme-B/IFN-{gamma}/TNF- expressed markers of terminal differentiation. CD57+NK cells, CD4+perforin+ T cells and CD8+CD27+CD62L+ T cells were identified as the independent predictors for residual lesions. Overall, our findings unveil the profound imbalance of immune landscape that may correlate with organ damage and rehabilitation in COVID-19. IMPORTANCEA considerable proportion of COVID-19 survivors have residual lung lesions, such as ground glass opacity and fiber streak shadow. To determine the relationship between host immunity and residual lung lesions, we performed an extensive analysis of immune responses in convalescent patients with COVID-19 one year after discharge. We found significant differences in immunological characteristics between patients with pulmonary sequelae and patients without pulmonary sequelae one year after discharge. Our study highlights the profound imbalance of immune landscape in the COVID-19 patients with pulmonary sequelae, characterized by the robust activation of cytotoxic T cells, NK cells and {gamma}{delta} T cells as well as the deficiencies of immunosuppressive cells. Importantly, CD57+NK cells, CD4+perforin+ T cells and CD8+CD27+CD62L+ T cells were identified as the independent predictors for residual lesions.

immunology↗

bioRxiv2021

Protein phosphatase 1 regulatory inhibitor subunit 14C promotes triple-negative breast cancer progression via sustaining inactive glycogen synthase kinase 3 beta

The majority of clinical deaths among patients with triple-negative breast cancer (TNBC) are caused by uncontrolled cell proliferation and aggressive metastases, which is regulated by hyperactive glycogen synthase kinase 3 beta (GSK3{beta}); however, the underlying mechanisms remain largely unknown. In the present study, we found that protein phosphatase 1 regulatory inhibitor subunit 14C (PPP1R14C) was specifically upregulated in TNBC, compared with that in normal tissues and non-TNBC. High PPP1R14C expression correlated significantly with shorter 5-year relapse-free survival and overall survival in patients with TNBC. Overexpressing PPP1R14C promoted, while suppression of PPP1R14C decreased the cell proliferation and the aggressive phenotype of TNBC cells, in vitro and in vivo. Importantly, we revealed that PPP1R14C interacted with and inactivated type 1 Ser/Thr protein phosphatase (PP1) to sustain GSK3{beta} phosphorylation at S9, and induced the ubiquitylation and degradation of non-phosphorylated GSK3{beta} (S9A) via recruiting E3 ligase, TRIM25. Furthermore, treating with C2 ceramide (C2), which recovered kinase activity of GSK3{beta}, resulted in tumor growth inhibition in PPP1R14C-overexpressing TNBC cells. Our findings reveal a novel mechanism whereby PPP1R14C sustains inactive GSK3{beta}, which might lead to targeted therapy for TNBC.

cancer biology↗

bioRxiv2021

Inferring species compositions of complex fungal communities from long- and short-read sequence data

BackgroundThe kingdom fungi is crucial for life on earth and is highly diverse. Yet fungi are challenging to characterize. They can be difficult to culture and may be morphologically indistinct in culture. They can have complex genomes of over 1 Gb in size and are still underrepresented in whole genome sequence databases. Overall their description and analysis lags far behind other microbes such as bacteria. At the same time, classification of species via high throughput sequencing without prior purification is increasingly becoming the norm for pathogen detection, microbiome studies, and environmental monitoring. However, standardized procedures for characterizing unknown fungi from complex sequencing data have not yet been established. ResultsWe compared different metagenomics sequencing and analysis strategies for the identification of fungal species. Using two fungal mock communities of 44 phylogenetically diverse species, we compared species classification and community composition analysis pipelines using shotgun metagenomics and amplicon sequencing data generated from both short and long read sequencing technologies. We show that regardless of the sequencing methodology used, the highest accuracy of species identification was achieved by sequence alignment against a fungi-specific database. During the assessment of classification algorithms, we found that applying cut-offs to the query coverage of each read or contig significantly improved the classification accuracy and community composition analysis without significant data loss. ConclusionOverall, our study expands the toolkit for identifying fungi by improving sequence-based fungal classification, and provides a practical guide for the design of metagenomics analyses.

bioinformatics↗

bioRxiv2021

Linked machine learning classifiers improve species classification of fungi when using error-prone long-reads on extendedmetabarcodes

BackgroundThe increased usage of error-prone long-read sequencing for metabarcoding of fungi has not been matched with adequate public databases and concomitant analysis approaches. We address this gap and present a proof-of-concept study for classifying fungal taxa using linked machine learning classifiers. We demonstrate the capability of linked machine learning classifiers to accurately classify species and strains using real-world and simulated fungal ribosomal DNA datasets, including plant and human pathogens. We benchmark our new approach in comparison to current alignment and k-mer based methods based on synthetic mock communities. We also assess real world applications of species identification in complex unlabelled datasets. ResultsOur machine learning approach assigned individual nanopore long-read amplicon sequences to fungal species with high recall rates and low false positive rates. Importantly, our approach successfully distinguished between closely-related species and strains when individual read errors were higher than the genetic distance between individual taxa, which the alignment and k-mer methods could not do. The machine learning approach showed an ability to identify key species with high recall rates, even in complex samples of unknown species composition. ConclusionsA proof of concept machine learning approach using a tree-descent approach on a decision tree of classifiers can identify known taxa with high accuracy, and precisely detect known target species from complex samples with high recall rates. We propose this approach is suitable for detecting the known knowns of pathogens or invasive species in any environment of mostly unknown composition, including agriculture and wild ecosystems.

bioinformatics↗

bioRxiv2021

Identification and correction of phase switches with Hi-C data in the Nanopore and HiFi chromosome-scale assemblies of the dikaryotic leaf rust fungus Puccinia triticina

BackgroundMost animals and plants have more than one set of chromosomes and package these haplotypes into a single nucleus within each cell. In contrast, many fungal species carry multiple haploid nuclei per cell. Rust fungi are such species with two nuclei (karyons) that contain a full set of haploid chromosomes each. The physical separation of haplotypes in dikaryons means that, unlike in diploids, Hi-C chromatin contacts between haplotypes are false positive signals. ResultsWe generate the first chromosome-scale, fully-phased assembly for the dikaryotic leaf rust fungus Puccinia triticina and compare Nanopore MinION and PacBio HiFi sequence-based assemblies. We show that false positive Hi-C contacts between haplotypes are predominantly caused by phase switches rather than by collapsed regions or Hi-C read mis-mappings. We introduce a method for phasing of dikaryotic genomes into the two haplotypes using Hi-C contact graphs, including a phase switch correction step. In the HiFi assembly, relatively few phase switches occur, and these are predominantly located at haplotig boundaries and can be readily corrected. In contrast, phase switches are widespread throughout the Nanopore assembly. We show that haploid genome read coverage of 30-40 times using HiFi sequencing is required for phasing of the leaf rust genome (~0.7% heterozygosity) and that HiFi sequencing resolves genomic regions with low heterozygosity that are otherwise collapsed in the Nanopore assembly. ConclusionsThis first Hi-C based phasing pipeline for dikaryons and comparison of long-read sequencing technologies will inform future genome assembly and haplotype phasing projects in other non-haploid organisms.

genomics↗

bioRxiv2021

Targeting circPTPN12/1 miR-21-5p/ΔNp63α pathway as a therapeutic strategy for human endometrial fibrosis

Emerging evidence demonstrates the important role of circular RNAs (circRNAs) in regulating pathological processes in various diseases including organ fibrosis. Endometrium fibrosis is the leading cause of uterine infertility, but the role of circRNAs in its pathogenesis is largely unknown. Here, we provide the evidence that upregulation of circPTPN12 in endometrial epithelial cells (EECs) of fibrotic endometrium functions as endogenous sponge of miR-21-5p to inhibit miR-21-5p expression and activity, which in turn results in upregulation of {Delta}Np63 to induce the epithelial mesenchymal transition (EMT) of EECs (EEC-EMT). In a mouse model of endometrium fibrosis, circPTPN12 appears to be a cofactor of driving EEC-EMT. Our findings reveal the novel mechanism in the pathogenesis of endometrium fibrosis and the potential therapeutic strategy for endometrium fibrosis via targeting circPTPN12/miR-21-5p/{Delta}Np63 pathway.

molecular biology↗

bioRxiv2021

Bistable host-pathogen interaction explains varied infection outcomes

Death or survival are the two outcomes of an infection. The development of an infection depends on multiple interplaying factors, including the temporal dynamic of host nutrition, immune response and pathogen proliferation, but how these factors collectively determine the outcome of an infection is less understood. To understand the underlying principle of infection, here, we took a systems approach by developing a simple mathematical model to understand multi-factored host-pathogen interaction. We find that nonlinear interactions feedback between host and pathogens resulting in two distinct trajectories of disease progression. We show that disease progression, measured by the pathogen load, responds in a bistable manner, by either proliferating or eventually being eliminated. We identified key processes accounting for bistability, including hosts resource assimilation, immune response and intrinsic noise. In addition, resource availability, immunity, and pathogen load at the onset of infection are also critical in determining the bistability. We then highlight, by discussing in the light of previous theoretical and experimental work, how our framework provides testable theories for host-pathogen interaction inside the host.

ecology↗

bioRxiv2021

Class II LBD genes ZmLBD5 and ZmLBD33 regulate gibberellin and abscisic acid biosynthesis

Lateral organ boundaries domain (LBD) proteins are plant-specific transcription factors. Class I LBD members are widely reported to be pivotal for organ development, however, the role of class II members is unknown in cereal crops. Class II LBD proteins are distinguished from class I by the lack of a Gly-Ala-Ser (GAS) peptide and leucine-zipper-like coiled-coil domain, which is thought to be essential for protein dimerization. In this study, ZmLBD5 and ZmLBD33 form homo- and hetero-dimers, like class I members. At seedling stage, ZmLBD5 promoted biomass accumulation (shoot dry weight and root dry weight), root development (root length, root number, and root volume), and organ expansion (leaf area), while ZmLBD33 repressed these processes and display a dwarf phenotype. Both ZmLBD5 and ZmLBD33 displayed negative roles in drought tolerance mainly by increasing stomatal density and stomatal aperture. RNA sequencing, gene ontology enrichment analysis, and transient luciferase expression assays indicated that ZmLBD5 and ZmLBD33 are mainly involved in the regulation of the TPS-KS-GA2ox gene module, which comprises key enzymatic genes upstream of GA and ABA biosynthesis. GA1 content increased in ZmLBD5-overexpressing seedlings, while GA3 and abscisic acid content decreased in both transgenic seedlings. Consequently, exogenous GA1 or GA3 undoubtedly rescued the dwarf phenotype of ZmLBD33-overexpressing plants, with GA1 performing better. The study of ZmLBD5 and ZmLBD33 sheds light on the function of the class II LBD gene family in maize.

plant biology↗

bioRxiv2021

A rarefaction-without-resampling extension ofPERMANOVA for testing presence-absence associations in the microbiome

BackgroundPERMANOVA [1] is currently the most commonly used method for testing community-level hypotheses about microbiome associations with covariates of interest. PERMANOVA can test for associations that result from changes in which taxa are present or absent by using the Jaccard or unweighted UniFrac distance. However, such presence-absence analyses face a unique challenge: confounding by library size (total sample read count), which occurs when library size is associated with covariates in the analysis. It is known that rarefaction (subsampling to a common library size) controls this bias, but at the potential costs of information loss and the introduction of a stochastic component into the analysis. MethodsHere we develop a non-stochastic approach to PERMANOVA presence-absence analyses that aggregates information over all potential rarefaction replicates without actual resampling, when the Jaccard or unweighted UniFrac distance is used. We compare this new approach to three possible ways of aggregating PERMANOVA over multiple rarefactions obtained from resampling: averaging the distance matrix, averaging the (element-wise) squared distance matrix, and averaging the F-statistic. ResultsOur simulations indicate that our non-stochastic approach is robust to confounding by library size and outperforms each of the stochastic resampling approaches. We also show that, when overdispersion is low, averaging the (element-wise) squared distance outperforms averaging the unsquared distance, currently implemented in the R package vegan. We illustrate our methods using an analysis of data on inflammatory bowel disease (IBD) in which samples from case participants have systematically smaller library sizes than samples from control participants.

bioinformatics↗

bioRxiv2021

Twelve-month specific IgG response to SARS-CoV-2 receptor-binding domain among COVID-19 convalescent plasma donors in Wuhan

To investigate the duration of humoral immune response in convalescent coronavirus disease 2019 (COVID-19) patients, we conduct a 12-month longitudinal study through collecting a total of 1,782 plasma samples from 869 convalescent plasma donors in Wuhan, China and test specific antibody responses. The results show that positive rate of IgG antibody against receptor-binding domain of spike protein (RBD-IgG) to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the COVID-19 convalescent plasma donors exceeded 70% for 12 months post diagnosis. The level of RBD-IgG decreases with time, with the titer stabilizing at 64.3% of the initial level by the 9th month. Moreover, male plasma donors produce more RBD-IgG than female, and age of the patients positively correlates with the RBD-IgG titer. A strong positive correlation between RBD-IgG and neutralizing antibody titers is also identified. These results facilitate our understanding of SARS-CoV-2-induced immune memory to promote vaccine and therapy development.

immunology↗

bioRxiv2021

Emerging SARS-CoV-2 mutation hotspots associated with clinical outcomes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Understanding the influence of mutations in the SARS-CoV-2 gene on clinical outcomes and related factors is critical for treatment and prevention. Here, we analyzed 209,551 high-coverage complete virus sequences and 321 RNA-seq samples to mine the mutations associated with clinical outcome in the SARS-CoV-2 genome. Several important hotspot variants were found to be associated with severe clinical outcomes. Q57H variant in ORF3a protein were found to be associated with higher mortality rate, and was high proportion in severe cases (39.36%) and 501Y.V2 strains (100%) but poorly proportional to asymptomatic cases (10.04%). T265I could change nsp2 structure and mitochondrial permeability, and evidently higher in severe cases (20.12%) and 501Y.V2 strains (100%) but lower in asymptomatic cases (1.43%). Additionally, R203K and G204R could decrease the flexibility and immunogenic property of N protein with high frequency among severe cases, VUI 202012/01 and 484K.V2 strains. Interestingly, the SARS-CoV-2 genome was more susceptible to mutation because of the high frequency of nt14408 mutation (which located in RNA polymerase) and the high expression levels of ADAR and APOBEC in severe clinical outcomes. In conclusion, several important mutation hotspots in the SARS-CoV-2 genome associated with clinical outcomes was found in our study, and that might correlate with different SARS-CoV-2 mortality rates.

bioinformatics↗

bioRxiv2021

SARS-CoV-2 variant B.1.1.7 caused HLA-A2+ CD8+ T cell epitope mutations for impaired cellular immune response

The rapid spreading of the newly emerged SARS-CoV-2 variant, B.1.1.7, highlighted the requirements to better understand adaptive immune responses to this virus. Since CD8+ T cell responses play an important role in disease resolution and modulation in COVID-19 patients, it is essential to address whether these newly emerged mutations would result in altered immune responses. Here we evaluated the immune properties of the HLA-A2 restricted CD8+ T cell epitopes containing mutations from B.1.1.7, and furthermore performed a comprehensive analysis of the SARS-CoV-2 specific CD8+ T cell responses from COVID-19 convalescent patients and SARS-CoV-2 vaccinees recognizing the ancestral Wuhan strain compared to B.1.1.7. First, most of the predicted CD8+ T cell epitopes showed proper binding with HLA-A2, while epitopes from B.1.1.7 had lower binding capability than those from the ancestral strain. In addition, these peptides could effectively induced the activation and cytotoxicity of CD8+ T cells. Our results further showed that at least two site mutations in B.1.1.7 resulted in a decrease in CD8+ T cell activation and a possible immune evasion, namely A1708D mutation in ORF1ab1707-1716 and I2230T mutation in ORF1ab2230-2238. Our current analysis provides information that contributes to the understanding of SARS-CoV-2-specific CD8+ T cell responses elicited by infection of mutated strains or vaccination. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=172 SRC="FIGDIR/small/437363v2_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@b9f2cdorg.highwire.dtl.DTLVardef@1f39e3dorg.highwire.dtl.DTLVardef@119c313org.highwire.dtl.DTLVardef@565388_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

Bioinformatic and cell-based tools for pooled CRISPR knockout screening in mosquitos

Mosquito-borne diseases present a worldwide public health burden. Genome-scale screening tools that could inform our understanding of mosquitos and their control are lacking. Here, we adapt a recombination-mediated cassette exchange system for delivery of CRISPR sgRNA libraries into cell lines from several mosquito species and perform pooled CRISPR screens in an Anopheles cell line. To implement this method, we engineered modified mosquito cell lines, validated promoters and developed bioinformatics tools for multiple mosquito species.

genetics↗

bioRxiv2021

Autism_genepheno: Text mining of gene-phenotypeassociations reveals new phenotypic profiles ofautism-associated genes

Autism is a spectrum disorder with wide variation in type and severity of symptoms. Understanding gene-phenotype associations is vital to unravel the disease mechanisms and advance its diagnosis and treatment. To date, several databases have stored a large portion of gene-phenotype associations which are mainly obtained from genetic experiments. However, a large proportion of gene-phenotype associations are still buried in the autism-related literature and there are limited resources to investigate autism-associated gene-phenotype associations. Given the abundance of the autism-related literature, we were thus motivated to develop Autism_genepheno, a text mining pipeline to identify sentence-level mentions of autism-associated genes and phenotypes in literature through natural language processing methods. We have generated a comprehensive database of gene-phenotype associations in the last five years autism-related literature that can be easily updated as new literature becomes available. We have evaluated our pipeline through several different approaches, and we are able to rank and select top autism-associated genes through their unique and wide spectrum of phenotypic profiles, which could provide a unique resource for the diagnosis and treatment of autism. The data resources and the Autism_genpheno pipeline are available at: https://github.com/maiziezhoulab/Autism_genepheno.

bioinformatics↗

bioRxiv2021

Replicative senescence dictates the emergence of disease-associated microglia and contributes to Abeta pathology

The sustained proliferation of microglia is a key hallmark of Alzheimers disease (AD), accelerating its progression. Here, we sought to understand the long-term impact of the early and prolonged microglial proliferation observed in AD, hypothesising that extensive and repeated cycling would engender a distinct transcriptional and phenotypic trajectory. We found that the early and sustained microglial proliferation seen in an AD-like model promotes replicative senescence, characterised by increased {beta}gal activity, a senescence-associated transcriptional signature and telomere shortening, correlating with the appearance of disease-associated microglia (DAM) and senescent microglial profiles in human post-mortem AD cases. Prevention of early microglial proliferation hindered the development of senescence and DAM, impairing the accumulation of A{beta} and associated neuritic damage. Overall, our results support that excessive microglial proliferation leads to the generation of senescent DAM, which contribute to early A{beta} pathology in AD.

neuroscience↗