Biology subjects

Yang, Y.

Publications and source records attributed to Yang, Y..

At least 1,153 records · Page 64Linked to original sources

bioRxiv2020

Modeling pronoun resolution in the brain

Human language processing involves not only combining word meanings in accordance with semantic and syntactic constraints, but also figuring out who and what is being referred to. Here we present a first study towards a mechanistic understanding of the neural basis for referential processing. Using both functional MRI and magnetoencephalography (MEG), we identified a consistent increase of activity in a network spanning the anterior and posterior left middle temporal gyrus and the angular gyrus for pronoun processing during naturalistic listening for both English and Chinese speakers. We then adopted a "reverse-engineering" approach to examine the cognitive processes underlying pronoun resolution. We evaluated the neural fit of three symbolic models that each formalizes a different strand of explanation for pronoun resolution in the cognitive and linguistic literature, as well as two deep neural network models with an LSTM or a Transformer architecture. Our results favor the memory-based symbolic model, suggesting a domain-general mechanism of pronoun resolution that resembles memory retrieval.

neuroscience↗

bioRxiv2020

Reproducible abnormalities of functional gradient reliably predict clinical and cognitive symptoms in schizophrenia

BackgroundSchizophrenia (SZ) typically manifests heterogeneous phenotypes involving positive, negative and cognitive symptoms. However, the underlying neural mechanisms of these symptoms keep unclear. Functional gradient is a fascinating measure to characterize continuous, hierarchical organization of brain. MethodsWe aimed to investigate whether reproducible disruptions of functional gradient existed in SZ compared to normal controls (NC), and these abnormalities were associated with severity of clinical and cognitive symptoms in SZ. All analyses were implemented in two independent large-sample multi-site datasets (discovery dataset, 400 SZ and 336 NC; replication dataset, 279 SZ and 262 NC). First, functional gradient across cerebral cortex was calculated in each subject. Second, vertex-wise comparisons of cortical gradient between SZ and NC groups were performed to identify abnormalities in SZ. Meanwhile, reproducible and robustness analyses were implemented to validate these abnormalities. Finally, regression analyses were performed using generalized additive models to link these abnormalities to severity of clinical and cognitive symptoms in SZ. ResultsWe found an abnormal gradient map in SZ in the discovery dataset, which was reproducible in the replication dataset. The abnormal gradient pattern was also robust when performing methodological alternatives and control analyses. Further, these reproducible abnormalities can reliably predict symptoms of clinical and cognitive domains across the two independent datasets. ConclusionThese findings demonstrated that alterations in functional gradient can provide a reliable signature of SZ, characterizing the heterogenous symptoms of clinical or cognitive domains, and may be further investigated to understand the neurobiological mechanisms of these symptoms. Impact StatementIn our study, using functional gradient measure and statistical learning technology and two independent multi-site case-control resting-state fMRI datasets (discovery dataset: 736 subjects; replication dataset: 541 subjects), we comprehensively investigated functional hierarchical organization in the cerebral cortex of SZ and its association with interindividual severity of symptoms. We found reproducible and robust abnormalities of functional gradient existed in SZ, which provided a reliable signature to characterize negative and general psychopathology symptoms, as well as cognitive deficits. Our findings can provide new insights to understand the neurobiological mechanisms of clinical and cognitive symptoms in SZ.

neuroscience↗

bioRxiv2020

HPRep: Quantifying reproducibility in HiChIP and PLAC-seq datasets

HiChIP and PLAC-seq are emerging technologies for studying genome-wide long-range chromatin interactions mediated by protein of interest, enabling more sensitive and cost-efficient interrogation of protein-centric chromatin conformation. However, due to the unbalanced read distribution introduced by protein immunoprecipitation, existing reproducibility measures developed for Hi-C data are not appropriate for the analysis of HiChIP and PLAC-seq data. Here, we present HPRep, a stratified and weighted correlation metric derived from normalized contact counts, to quantify reproducibility in HiChIP and PLAC-seq data. We applied HPRep to multiple real datasets and demonstrate that HPRep outperforms existing reproducibility measures developed for Hi-C data. Specifically, we applied HPRep to H3K4me3 PLAC-seq data from mouse embryonic stem cells and mouse brain tissues, as well as H3K27ac HiChIP data from human lymphoblastoid cell line GM12878 and leukemia cell line K562, showing that HPRep can more clearly separate among pseudo-replicates, real replicates, and non-replicates. Furthermore, in an H3K4me3 PLAC-seq dataset consisting of 11 samples from four human brain cell types, HPRep demonstrates expected clustering of data which could not be achieved by existing methods developed for Hi-C data, highlighting the need of a reproducibility metric tailored to HiChIP and PLAC-seq data.

genomics↗

bioRxiv2020

Seasonal variations in soil fungal communities and co-occurrence networks along an altitudinal gradient in the cold temperate zone of China: A case study on Oakley Mountain

The biogeography of soil fungi has attracted much attention in recent years; however, studies on this topic have mainly focused on mid- and low-altitude regions. The seasonal patterns of soil fungal community structure and diversity along altitudinal gradients under the unique climatic conditions at high latitudes remain unclear, which limits our insight into soil microbial interactions and the mechanisms of community assembly. In this study, Illumina MiSeq sequencing was used to investigate the spatiotemporal changes in soil fungal communities along an altitudinal gradient (from 750 m to 1420 m) on Oakley Mountain in the northern Greater Khingan Mountains. Altitude had significant impacts on the relative abundances of the dominant phyla and classes of soil fungi, and the interaction of altitude and season significantly affected the relative abundances of Ascomycota and Basidiomycota. The number of soil fungal taxa and Faiths phylogenetic diversity (PD) index tended to monotonically decline with increasing elevation. Soil moisture (SM), soil temperature (ST) and pH were the main factors affecting fungal community structure in May, July and September, respectively. The soil dissolved organic carbon (DOC) content significantly shaped the soil fungal community composition along the altitudinal gradient throughout the growing season. Compared to that in May and July, the soil fungal network in September had more nodes and links, a higher average degree and a higher average clustering coefficient. The nine module nodes in the co-occurrence network were all Ascomycota taxa, and the identities of the keystone taxa of soil fungi in the network showed obvious seasonality. Our results demonstrated that altitude has stronger effects than season on soil fungal community structure and diversity at high latitudes. In addition, the co-occurrence network of soil fungi exhibited obvious seasonal succession, which indicated that the keystone taxa of soil fungi exhibit niche differentiation among seasons.

ecology↗

bioRxiv2020

Investigating the shared genetic architecture between multiple sclerosis and inflammatory bowel diseases

An epidemiological association between multiple sclerosis (MS) and inflammatory bowel disease (IBD) is well-established, but whether this reflects a shared genetic aetiology, and whether consistent genetic relationships exist between MS and the two predominant subtypes of IBD, ulcerative colitis (UC) and Crohns disease (CD), remains unclear. Here, we used genome-wide association study (GWAS) summary data to estimate genetic correlations (rg) between MS and each of IBD, UC and CD, finding that the rg between MS and UC was approximately twice that between MS and CD. On the basis of these genetic correlations, we performed cross-trait meta-analysis of GWAS summary data for MS and each of IBD, UC and CD, identifying a total of 42 novel SNPs shared between MS and IBD (N=19), UC (N=14), and CD (N=18). We then used multiple Mendelian randomization (MR) methods to investigate causal relationships between these diseases, finding suggestive but inconclusive evidence for a causal effect of MS on UC and IBD, and no or weak and inconsistent evidence for a causal effect of IBD or UC on MS. There was also no evidence for causality in bidirectional analyses of MS and CD. We also investigated tissue- and cell-type-specific enrichment of SNP heritability for each disease using stratified LD score regression. At the tissue level, we observed largely consistent patterns of enrichment for all four diseases in immune system-related tissues, including lung, spleen and whole blood, and in contrast to prior studies, small intestine. At the cell-type level, we identified significant enrichment for all diseases in CD4+ T cells in lung, and for MS, IBD and CD in CD8+ cytotoxic T cells in both lung and spleen, and regulatory T cells in lung. Our study sheds new insights into the biological basis of comorbidity between MS and both UC and CD.

genetics↗

bioRxiv2020

Transcriptome Profiling of different types of human respiratory tract cells infected by SARS-CoV-2 Highlight an unique Role for Inflammatory and Interferon Response

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19) at the end of 2019 has caused a large global outbreak and now become a major public health issue. Lack of data underlying how the human host interacts with SARS-CoV-2 virus. In the current study, We performed Venn-analysis, Gene ontology (GO), KEGG pathway analysis and Protein-protein interaction analysis of whole transcriptome studies with the aim of clarifying the genes and pathways potentially altered during human respiratory tract cells infected with SARS-CoV-2. We selected four studies through a systematic search of the Gene Expression Omnibus (GEO) database or published article about SARS-CoV-2 infection in different types of respiratory tract cells. We found 36 overlapping upregulated genes among different types of cells after viral infection. Further functional enrichment analysis revealed these DEGs are most likely involved in biological processes related to inflammatory response and response to cytokine, cell component related to extracellular space and I-kappaB/NF-kappaB complex, molecular function related to protein binding and cytokine activity. KEGG pathways analysis highlighted altered conical and casual pathways related to TNF, NF-kappa B, Cytokine-cytokine receptor interaction and IL17 signaling pathways during SARS-CoV-2 infection with CXCL1, CXCL2, CXCL3, CXCL8, CXCL10, IL32, CX3CL1, CCL20, IRF1, NFKB2 and NFKB1A up-regulated which may explain the inflammatory cytokine storms associated with severe cases of COVID-19.

immunology↗

bioRxiv2020

MUNIn (Multiple cell-type UNifying long-range chromatin Interaction detector): a statistical framework for identifying long-range chromatin interactions from multiple cell types

Chromatin spatial organization (interactome) plays a critical role in genome function. Deep understanding of chromatin interactome can shed insights into transcriptional regulation mechanisms and human disease pathology. One essential task in the analysis of chromatin interactomic data is to identify long-range chromatin interactions. Existing approaches, such as HiCCUPS, FitHiC/FitHiC2 and FastHiC, are all designed for analyzing individual cell types or samples. None of them accounts for unbalanced sequencing depths and heterogeneity among multiple cell types or samples in a unified statistical framework. To fill in the gap, we have developed a novel statistical framework MUNIn (Multiple sample UNifying long-range chromatin Interaction detector) for identifying long-range chromatin interactions from multiple samples. MUNIn adopts a hierarchical hidden Markov random field (H-HMRF) model, in which the status (peak or background) of each interacting chromatin loci pair depends not only on the status of loci pairs in its neighborhood region, but also on the status of the same loci pair in other samples. To benchmark the performance of MUNIn, we performed comprehensive simulation studies and real data analysis, and showed that MUNIn can achieve much lower false positive rates for detecting sample-specific interactions (33.1 - 36.2%), and much enhanced statistical power for detecting shared peaks (up to 74.3%), compared to uni-sample analysis. Our data demonstrated that MUNIn is a useful tool for the integrative analysis of interactomic data from multiple samples.

genetics↗

bioRxiv2020

Applications of genetic-epigenetic tissue mapping for plasma DNA in prenatal testing, transplantation and oncology

We developed Genetic-Epigenetic Tissue Mapping (GETMap) to determine the tissue composition of plasma DNA carrying genetic variants not present in the constitutional genome through comparing their methylation profiles with relevant tissues. We validated this approach by showing that, in pregnant women, circulating DNA carrying fetal-specific alleles was entirely placenta-derived. In lung-transplant recipients, we showed that, at 72 hours after transplantation, the lung contributed only a median of 17% to the plasma DNA carrying donor-specific alleles and hematopoietic cells contributed a median of 78%. In hepatocellular cancer patients, the liver was identified as the predominant source of plasma DNA carrying tumor-specific mutations. In a pregnant woman with lymphoma, plasma DNA molecules carrying cancer mutations and fetal-specific alleles were accurately shown to be derived from the lymphocytes and placenta, respectively. Analysis of tissue origin for plasma DNA carrying genetic variants is potentially useful for noninvasive prenatal testing, transplantation monitoring and cancer screening.

physiology↗

bioRxiv2020

Germline Selection by Meiosis Defends the Transmission of defective Mitochondria with mtDNA variants

Germline selection of mtDNA is vital in maternal inheritance of mtDNA, as it can eliminate severe mtDNA mutations. However, current evidence concerning germline selection at meiosis level comes from incomplete mtDNA sequencing in human first polar body (PB1), which lacks persuasion. Here, we found various variants, including pathogenic mutation sites, present on whole genome of mtDNA in human PB1 compared with its oocyte. And that PB1 mitochondria with mtDNA variants were defective. Afterwards, to further explore how mitochondria enter PB, the defective mitochondria transfer in mouse germline, including cumulus-oocyte-complexes at germinal vesicle and matured oocytes stage. It confirmed that in the first and second meiosis, active purification selected defective mitochondria into PB1 and PB2. Thus, twice meiosis is the last defense system for purifying selection of mtDNA mutations during oogenesis, which also demonstrated that PB1 and PB2 would be final destination of deleterious mtDNA mutations in germline selection.

cell biology↗

bioRxiv2020

An End-to-end Oxford Nanopore Basecaller Using Convolution-augmented Transformer

Oxford Nanopore sequencing is fastly becoming an active field in genomics, and its critical to basecall nucleotide sequences from the complex electrical signals. Many efforts have been devoted to developing new basecalling tools over the years. However, the basecalled reads still suffer from a high error rate and slow speed. Here, we developed an open-source basecalling method, CATCaller, by simultaneously capturing global context through Attention and modeling local dependencies through dynamic convolution. The method was shown to consistently outper-form the ONT default basecaller Albacore, Guppy, and a recently developed attention-based method SACall in read accuracy. More importantly, our method is fast through a heterogeneously computational model to integrate both CPUs and GPUs. When compared to SACall, the method is nearly 4 times faster on a single GPU, and is highly scalable in parallelization with a further speedup of 3.3 on a four-GPU node.

bioinformatics↗

bioRxiv2020

iSMNN: Batch Effect Correction for Single-cell RNA-seq data via Iterative Supervised Mutual Nearest Neighbor Refinement

Batch effect correction is an essential step in the integrative analysis of multiple single cell RNA-seq (scRNA-seq) data. One state-of-the-art strategy for batch effect correction is via unsupervised or supervised detection of mutual nearest neighbors (MNNs). However, both two kinds of methods only detect MNNs across batches on the top of uncorrected data, where the large batch effect may affect the MNN search. To address this issue, we presented iSMNN, a batch effect correction approach via iterative supervised MNN refinement across data after correction. Our benchmarking on both simulation and real datasets showed the advantages of the iterative refinement of MNNs on the performance of correction. Compared to popular alternative methods, our iSMNN is able to better mix the cells of the same cell type across batches. In addition, iSMNN can also facilitate the identification of differentially expression genes (DEGs) relevant to the biological function of certain cell types. These results indicated that iSMNN will be a valuable method for integrating multiple scRNA-seq datasets that can facilitate biological and medical studies at single-cell level.

bioinformatics↗

bioRxiv2020

HiC-ACT: Improved Detection of Chromatin Interactions from Hi-C Data via Aggregated Cauchy Test

Genome-wide chromatin conformation capture technologies such as Hi-C are commonly employed to study chromatin spatial organization. In particular, to identify statistically significant long-range chromatin interactions from Hi-C data, most existing methods such as Fit-Hi-C/FitHiC2 and HiCCUPS assume that all chromatin interactions are statistically independent. Such an independence assumption is reasonable at low resolution (e.g., 40Kb bin), but is invalid at high resolution (e.g., 5 or 10Kb bins) since spatial dependency of neighboring chromatin interactions is non-negligible at high resolution. Our previous hidden Markov random field based methods accommodate spatial dependency but are computationally intensive. It is urgent to develop approaches that can model spatial dependence, in a computationally efficient and scalable manner. Here, we develop HiC-ACT, an aggregated Cauchy test (ACT) based approach, to improve the detection of chromatin interactions by post-processing results from methods assuming independence. To benchmark the performance of HiC-ACT, we re-analyzed deeply sequenced Hi-C data from a human lymphoblastoid cell line GM12878 and mouse embryonic stem cell line (mESC). Our results demonstrate advantages of HiC-ACT in improving sensitivity with controlled type-I error. By leveraging information from neighboring chromatin interactions, HiC-ACT enhances the power to detect interactions with lower signal to noise ratio and similar (if not stronger) epigenetic signatures that suggest regulatory roles. We further demonstrate that HiC-ACT peaks show higher overlap with known enhancers than Fit-Hi-C/FitHiC2 peaks, in both GM12878 and mESC. HiC-ACT, effectively a summary statistic based approach, is computationally efficient (~6 minutes and ~2GB memory to process 25,000 pairwise interactions).

bioinformatics↗

bioRxiv2020

Large-scale single-cell analysis reveals critical immune characteristics of COVID-19 patients

Dysfunctional immune response in the COVID-19 patients is a recurrent theme impacting symptoms and mortality, yet the detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 205 COVID-19 patients and controls to create a comprehensive immune landscape. Lymphopenia and active T and B cell responses were found to coexist and associated with age, sex and their interactions with COVID-19. Diverse epithelial and immune cell types were observed to be virus-positive and showed dramatic transcriptomic changes. Elevation of ANXA1 and S100A9 in virus-positive squamous epithelial cells may enable the initiation of neutrophil and macrophage responses via the ANXA1-FPR1 and S100A8/9-TLR4 axes. Systemic upregulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis and designing effective therapeutic strategies for COVID-19. HIGHLIGHTSO_LILarge-scale scRNA-seq analysis depicts the immune landscape of COVID-19 C_LIO_LILymphopenia and active T and B cell responses coexist and are shaped by age and sex C_LIO_LISARS-CoV-2 infects diverse epithelial and immune cells, inducing distinct responses C_LIO_LICytokine storms with systemic S100A8/A9 are associated with COVID-19 severity C_LI

genomics↗

bioRxiv2020

Identifying the functions of two biomarkers in human oligodendrocyte progenitor cell development

NG2 and A2B5 are important biological markers of human oligodendrocyte progenitor cells. To study their functional differences during the development of human oligodendrocyte progenitor cells to oligodendrocytes, we used cell sorting technology and obtained a large number of sterile, high-purity NG2+/- and A2B5+/- cells with high viability. Further research was then conducted via in vitro cell proliferation and migration assays, single-cell sequencing, mRNA sequencing, and cell transplantation into shiverer mice. The results showed that the migration ability of the cells was inversely proportional to the myelination ability. NG2 may be a marker of early oligodendrocyte progenitor cells and is conducive to cell migration and proliferation, while A2B5 may be a marker of slightly mature oligodendrocyte progenitor cells and is conducive to cell differentiation. Further, cell migration, proliferation, and myelination capacity of the negative cell population were stronger than those of the positive cell population. In summary, these results suggest that oligodendrocyte progenitor cells in the mid-stage may be more suitable for clinical cell transplantation to treat demyelinating diseases. Summary statementThis research found that oligodendrocyte progenitor cells in the middle developmental stages may be more suitable for cell transplantation to treat demyelinating diseases.

neuroscience↗

bioRxiv2020

selfRL: Two-Level Self-Supervised Transformer Representation Learning for Link Prediction of Heterogeneous Biomedical Networks

Predicting potential links in heterogeneous biomedical networks (HBNs) can greatly benefit various important biomedical problem. However, the self-supervised representation learning for link prediction in HBNs has been slightly explored in previous researches. Therefore, this study proposes a two-level self-supervised representation learning, namely selfRL, for link prediction in heterogeneous biomedical networks. The meta path detection-based self-supervised learning task is proposed to learn representation vectors that can capture the global-level structure and semantic feature in HBNs. The vertex entity mask-based self-supervised learning mechanism is designed to enhance local association of vertices. Finally, the representations from two tasks are concatenated to generate high-quality representation vectors. The results of link prediction on six datasets show selfRL outperforms 25 state-of-the-art methods. In particular, selfRL reveals great performance with results close to 1 in terms of AUC and AUPR on the NeoDTI-net dataset. In addition, the PubMed publications demonstrate that nine out of ten drugs screened by selfRL can inhibit the cytokine storm in COVID-19 patients. In summary, selfRL provides a general frame-work that develops self-supervised learning tasks with unlabeled data to obtain promising representations for improving link prediction.

bioinformatics↗

bioRxiv2020

Dysfunction of the histone demethylase IBM1 in Arabidopsis reshapes the root-associated bacteria community

Root-associated bacteria communities are influential to plant growth and stress tolerance. Epigenetic regulation plays important roles in many plant biological processes, but its potential impacts on the assembly of root microbiota remain unclear. Here we report that dysfunction of the histone demethylase IBM1 in Arabidopsis substantially alters root-associated soil bacteria community. We compared two alleles of ibm1 mutant (ibm1-1 and ibm1-4) with wild type Arabidopsis regarding the root-associated bacteria community by 16S rRNA gene sequencing. The constrained principal coordinate analysis (PCoA) showed that the ibm1 mutants are both clearly separated from Col-0 along the major coordinates. Among the 29 families which have a relative abundance more than 0.5% in at least one sample, 10 and 11 families were commonly affected by ibm1-1 and ibm1-4 alleles in the rhizosphere and the endosphere compartment, respectively. Notably, the ACMs (Abundant Community members) belonging Pseudomonadaceae showed increased relative abundance in the ibm1 mutant alleles compared to Col-0 in both the rhizosphere and the endosphere compartments. The ACMs belonging to Oxalobacteraceae mostly showed decreased relative abundance in ibm1 mutants compared to Col-0 in endosphere compartment. These findings demonstrate an influential function of IBM1-mediated epigenetic regulation in shaping the root-associated microbiota.

microbiology↗

bioRxiv2020

Alisertib exerts KRAS allele specific anticancer effects in colorectal cancer

Colorectal cancer (CRC) is a common cancer causing substantial mortality and morbidity worldwide. Oncogene RAS mutations occur notably in [~]45% of CRCs, associated with a poor prognosis. KRAS is subject to multiple tiers of regulation, including kinase. Aurora kinases has been implicated in many types of tumor onsets and progression, making them as a promising therapeutic targets. Alisertib (ALS), selectively inhibits Aurora kinase A (AURKA) and exerts potent anticancer activities in vitro and in vivo studies, but the latent anticancer effect of ALS on CRC remains unclear in the context of different KRAS mutations. This study aimed to assess the effects of ALS on RAS signaling pathway in a panel of CRC lines expressing different KRAS alleles, including Caco-2 (KRAS WT), Colo-678 (KRAS G12D), SK-CO-1 (KRAS G12V), HCT116 (KRAS G13D), CCCL-18 (KRAS A146T), and HT29 (BRAF V600E). The results showed that ALS modulated the active form of KRAS in a RAS allele specific manner across the panel of CRC lines; ALS differentially regulated RAS signal via PI3K/Akt and MAPK pathways; and ALS induced apoptosis and autophagy in a RAS allele specific manner. Of note, in combination of ALS and MEK inhibitor, selumetinib, enhanced ALS regulatory effects in CRC lines in a RAS allele specific manner on apoptosis, autophagy, and cell growth. Taken together, this study suggests that ALS differentially regulates RAS signaling pathway and manipulates cell apoptosis and autophagy in RAS allele specific manner. The combinatorial approach of ALS and MEK inhibitor may represent a new therapeutic strategy for precision therapy of CRC in a RAS allele manner.

cancer biology↗

bioRxiv2020

exosome-LncPICALM-AU1 regulates endothelial-mesenchymal transition in hepatopulmonary syndrome

As important mediators of intercellular communication, exosome have can modulate various cellular functions by transferring a variety of intracellular components to target cells. However, little is known about the role of exosome-mediated communication between distant organs. Hepatopulmonary syndrome (HPS) is a severe lung injury caused by chronic liver disease. A new long noncoding RNA (lncRNA) PICALM-AU1 was found and upregulated in the liver of HPS. It was located in the cholangiocytes of liver and then, secreted as exosome into the serum. PICALM-AU1 carrying serum exosomes induced endothelial-mesenchymal transition (EndMT) of PMVECs and promoted lung injury in vivo and in vitro. Furthermore, overexpression of PICALM-AU1 significantly suppressed miR144-3p and subsequently induced ZEB1 expression. Taken together, our findings identified cholangiocyte-derived exosomal lncRNA PICALM-AU1 plays a critical role in the EndMT of HPS lung. And PICALM-AU1 represents a noninvasive biomarker and potential therapeutic target for HPS.

pathology↗