Biology subjects

Liao, X.

Publications and source records attributed to Liao, X..

At least 73 records · Page 4Linked to original sources

bioRxiv2021

Mapping Consistent, Reproducible, and Transcriptionally Relevant Connectome Hubs of the Human Brain

Human brain connectomes include sets of densely connected regions, known as connectome hubs, which play a vital role in understanding global brain communication, cognitive processing, and brain disorders. However, the consistency and reproducibility of functional connectome hubs anatomical localization have not been established to date and the genetic signatures underlying robust connectome hubs remain unknown. Here, we conduct the first worldwide, harmonized meta-connectomic analysis by pooling resting-state functional MRI data of 5,212 healthy young adults across 61 independent cohorts. We identify highly consistent and reproducible functional connectome hubs in heteromodal and unimodal regions both across cohorts and across individuals. These connectome hubs show heterogeneous connectivity profiles and are critical for both intra- and inter-network communications. Using post-mortem gene expression data, we show that these connectome hubs have a spatiotemporally distinctive transcriptomic pattern dominated by genes involved in the neuropeptide signaling pathway, neurodevelopmental processes, and metabolic processes. These results highlight the robustness of macroscopic connectome hubs and their potential cellular and molecular underpinnings.

neuroscience↗

bioRxiv2021

Wound-like tumor periphery in human breast cancer predicts a convergent drug nonresponse

A significant portion of breast cancer patients are nonresponsive to well-established drugs and destined for a poor outcome regardless of molecular subtype. Although several (multiparameter) molecular markers have predicted their resistance to some of these drugs, profound uniparameter markers predictive of a convergent nonresponse to all these drugs remain elusive. We employ co-registered standard-multiphoton histology to representatively sample a few peripheral niches of the primary tumor, so that hundreds of patients can be stratified with either a wound-like or non-wound tumor periphery. With no fitting variable, this simple uniparameter morphological marker is: (a) highly sensitive and specific to predict a multidrug-nonresponsive phenotype that accounts for the majority of recurrence or death, independent of the molecular subtype or related adjuvant drug selection, clinical endpoint (disease-free versus overall survival), and hosting medical center; (b) robust against intratumor heterogeneity and valid at the earliest clinicopathological stage; and (c) dominant in predicting prognosis in the context of routine clinicopathological markers. Considering the mechanistic link between a wound-like extracellular matrix and a microenvironment supporting migratory or mesenchymal tumor cells, we attribute these unusual capabilities to an epithelial-mesenchymal transition nature of the morphological marker long sought after by pathologists.

pathology↗

bioRxiv2021

Transcriptomics analysis reveals intracellular mutual regulation of coral-zooxanthella holobionts

Corals should make excellent models for cross-kingdom regulation research because of their natural animal-photobiont holobiont composition, yet a lack of studies and experimental data restricts their use. Here we integrate new full-length transcriptomes and small RNAs of four common reef-building corals with the published Symbiodinium C1 genome to gain deeper insight into mutual gene regulation in coral-zooxanthella holobionts. We show that zooxanthellae secrete miRNA to downregulate rejection from host coral cells, and that a potential correlation exists between miRNA diversity and physiological activity. Convergence of these holobionts biological functions in different species is also revealed, which implies the low gene impact on bottom ecological niche organisms. This work provides evidence for the early origin of cross-kingdom regulation as a mechanism of self-defense autotrophs can use against heterotrophs, sheds more light on coral-zooxanthella holobionts, and contributes valuable data for further coral research.

ecology↗

bioRxiv2021

The genome of the camphor tree and the genetic and climatic relevance of the top-geoherbalism in this medicinal plant

Camphor tree (Cinnamomum camphora (L.) J. Presl), a species in the magnoliid family Lauraceae, is known for its rich volatile oils and is used as a medical cardiotonic and as a scent in many perfumed hygiene products. Here, we present a high-quality chromosome-scale genome of C. camphora with a scaffold N50 of 64.34 Mb and an assembled genome size of 755.41 Mb. Phylogenetic inference revealed that the magnoliids are a sister group to the clade of eudicots and monocots. Comparative genomic analyses identified two rounds of ancient whole-genome duplication (WGD). Tandem duplicated genes exhibited a higher evolutionary rate, a more recent evolutionary history and a more clustered distribution on chromosomes, contributing to the production of secondary metabolites, especially monoterpenes and sesquiterpenes, which are the principal essential oil components. Three-dimensional analyses of the volatile metabolites, gene expression and climate data of samples with the same genotype grown in different locations showed that low temperature and low precipitation during the cold season modulate the expression of genes in the terpenoid biosynthesis pathways, especially TPS genes, which facilitates the accumulation of volatile compounds. Our study lays a theoretical foundation for policy-making regarding the agroforestry applications of camphor tree.

genomics↗

bioRxiv2021

Serine/arginine-rich splicing factor 7 plays oncogenic roles through specific regulation of m6A RNA modification

Serine/Arginine-Rich Splicing Factor 7 (SRSF7), which is previously recognized as a splicing factor, has been revealed to play oncogenic roles in multiple cancers. However, the mechanisms underlying its oncogenic roles have not been well addressed. Here, based on N6-methyladenosine (m6A) co-methylation network analysis across diverse cell lines, we found SRSF7 positively correlated with glioblastoma cell-specific m6A methylation. We then proved SRSF7 is a novel m6A regulator that specifically facilitates the m6A methylation near its binding sites on the mRNAs involved in cell proliferation and migration through recruiting methyltransferase complex. Moreover, SRSF7 promotes the proliferation and migration of glioblastoma cells largely dependent on the m6A methyltransferase. The two single-nucleotide m6A sites on PBK are regulated by SRSF7 and partially mediate the effects of SRSF7 on glioblastoma cells through recognition by IGF2BP2. Together, our discovery revealed a novel role of SRSF7 in regulating m6A and timely confirmed the existence and functional importance of RNA binding protein (RBP) mediated specific regulation of m6A.

molecular biology↗

bioRxiv2021

Alterations in connectome dynamics in autism spectrum disorder: A harmonized mega- and meta-analysis study using the ABIDE dataset

BACKGROUNDNeuroimaging studies have reported functional connectome aberrancies in autism spectrum disorder (ASD). However, the time-varying patterns of connectome topology in ASD individuals and the connection between these patterns and gene expression profiles remain unknown. METHODSTo investigate case-control differences in dynamic connectome topology, we conducted mega- and meta-analyses of resting-state functional magnetic resonance imaging data of 939 participants (440 ASD patients and 499 healthy controls, all males) from 18 independent sites, selected from the ABIDE (Autism Brain Imaging Data Exchange) dataset. Functional data was preprocessed and analyzed using harmonized protocols, and brain module dynamics was assessed using a multilayer network model. We further leveraged postmortem brain-wide gene expression data to identify transcriptomic signatures associated with ASD-related alterations in brain dynamics. RESULTSCompared to healthy controls, ASD individuals exhibited a higher global mean and lower standard deviation of whole-brain module dynamics, indicating an unstable and less regionally differentiated pattern. More specifically, ASD individuals showed higher module switching, primarily in the medial prefrontal cortex, posterior cingulate gyrus, and angular gyrus, and lower switching in the visual regions. These alterations in brain dynamics were predictive of social impairments in ASD individuals and were linked with expression profiles of genes primarily involved in the regulation of neurotransmitter transport and secretion, as well as with previously identified autism-related genes. CONCLUSIONSThis study is the first to identify consistent alterations in brain network dynamics in ASD and the transcriptomic signatures related to those alterations, furthering insights into the biological basis behind this disorder.

neuroscience↗

bioRxiv2021

Development of Functional Connectome Gradients during Childhood and Adolescence

Connectome mapping studies have documented a principal primary-to-transmodal gradient in the adult brain network, capturing a functional spectrum which ranges from perception and action to abstract cognition. However, how this gradient pattern develops and whether its development is linked to cognitive growth, topological reorganization, and gene expression profiles remain largely unknown. Using longitudinal resting-state functional magnetic resonance imaging data from 305 children (ages 6-14), we describe substantial changes in the primary-to-transmodal gradient between childhood and adolescence, including emergence as the principal gradient, expansion of global topography, and focal tuning in primary and default-mode regions. These gradient changes are mediated by developmental changes in network integration and segregation, and are associated with abstract processing functions such as working memory and expression levels of calcium ion regulated exocytosis, synaptic transmission, and axon and synapse part related genes. Our findings have implications for understanding connectome maturation principles in normal development and developmental disorders. TeaserOur study reported the maturation of the core connectome gradient and its association with cognitions and genes expression.

neuroscience↗

bioRxiv2021

Computational mechanisms for neural representation of words

A critical way for humans to acquire, represent and communicate information is through language, yet the underlying computation mechanisms through which language contributes to our word meaning representations are poorly understood. We compared three major types of word computation mechanisms from large language corpus (simple co-occurrence, graph-space relations and neural-network-vector-embedding relations) in terms of the association of words brain activity patterns, measured by two functional magnetic resonance imaging (fMRI) experiments. Word relations derived from a graph-space representation, and not neural-network-vector-embedding, had unique explanatory power for the neural activity patterns in brain regions that have been shown to be particularly sensitive to language processes, including the anterior temporal lobe (capturing graph-common-neighbors), inferior frontal gyrus, and posterior middle/inferior temporal gyrus (capturing graph-shortest-path). These results were robust across different window sizes and graph sizes and were relatively specific to language inputs. These findings highlight the role of cumulative language inputs in organizing word meaning neural representations and provide a mathematical model to explain how different brain regions capture different types of language-derived information.

neuroscience↗

bioRxiv2021

Accelerating RepeatClassifier Based on Spark and Greedy Algorithm with Dynamic Upper Boundary

1Transposable elements (TEs) represent quantitatively important components of genome sequences (e.g. 90% of the wheat genome), and play important roles in genome organization and evolution. The promotion of unsupervised annotation of transposable elements is of great significance. Classification is an important step in TE annotation, which summarize the information about the type or mechanism for the raw repetitive sequences. RepeatClassifier is a basic homology-based classification tool which compares the TE families to both the Repeat Protein Database (DB) and libraries of RepeatMasker. Unfortunately, RepeatClassifier is inefficient and takes a few days to classify the repetitive sequences of large genomes. Hence, we proposed Spark-based RepeatClassifier (SRC) which uses Greedy Algorithm with Dynamic Upper Boundary (GDUB) for data division and load balancing, and Spark to improve the parallelism of RepeatClassifier. Experimental results show that SRC can not only ensure the same level of accuracy as that of RepeatClassifier, but also achieve 42-88 times of acceleration compared to RepeatClassifier. At the same time, SRC shows excellent parallel performance when dealing with input datasets with unbalanced length distribution. SRC is publicly available at https://github.com/BioinformaticsCSU/SRC.

bioinformatics↗

bioRxiv2021

3D-MASNet: 3D Mixed-scale Asymmetric Convolutional Segmentation Network for 6-month-old Infant Brain MR Images

Precise segmentation of infant brain MR images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are essential for studying neuroanatomical hallmarks of early brain development. However, for 6-month-old infants, the extremely low-intensity contrast caused by inherent myelination hinders accurate tissue segmentation. Existing convolutional neural networks (CNNs) based segmentation models for this task generally employ single-scale symmetric convolutions, which are inefficient for encoding the isointense tissue boundaries in baby brain images. Here, we propose a 3D mixed-scale asymmetric convolutional segmentation network (3D-MASNet) framework for brain MR images of 6-month-old infants. We replaced the traditional convolutional layer of an existing to-be-trained network with a 3D mixed-scale convolution block consisting of asymmetric kernels (MixACB) during the training phase and then equivalently converted it into the original network. Five canonical CNN segmentation models were evaluated using both T1- and T2-weighted images of 23 6-month-old infants from iSeg-2019 datasets, which contained manual labels as ground truth. MixACB significantly enhanced the average accuracy of all five models and obtained the most considerable improvement in the fully convolutional network model (CC-3D-FCN) and the highest performance in the Dense U-Net model. This approach further obtained Dice coefficient accuracies of 0.931, 0.912, and 0.961 in GM, WM, and CSF, respectively, ranking first among 30 teams on the validation dataset of the iSeg-2019 Grand Challenge. Thus, the proposed 3D-MASNet can improve the accuracy of existing CNNs-based segmentation models as a plug-and-play solution that offers a promising technique for future infant brain MRI studies.

neuroscience↗

bioRxiv2021

Development of Large-Scale Brain Network Dynamics in Children

Functional brain networks require dynamic reconfiguration to support flexible cognitive function. However, the developmental principles shaping brain network dynamics remain poorly understood. Here, we report the longitudinal development of large-scale brain network dynamics during childhood and adolescence, and its connection with gene expression profiles. Using a multilayer network model, we show the temporally varying modular architecture of child brain networks, with higher network switching primarily in the association cortex and lower switching in the primary regions. This topographical profile exhibits progressive maturation, which manifests as reduced modular dynamics, particularly in the transmodal (e.g., default-mode and frontoparietal) and sensorimotor regions. These developmental refinements mediate age-related enhancements of global network segregation and are linked with the expression profiles of genes associated with the enrichment of ion transport and nucleobase-containing compound transport. These results highlight a progressive stabilization of brain dynamics, which expand our understanding of the neural mechanisms that underlie cognitive development.

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

Kinetics of self-assembly of inclusions due to lipid membrane thickness interactions

Self-assembly of proteins on lipid membranes underlies many important processes in cell biology, such as, exo- and endo-cytosis, assembly of viruses, etc. An attractive force that can cause self-assembly is mediated by membrane thickness interactions between proteins. The free energy profile associated with this attractive force is a result of the overlap of thickness deformation fields around the proteins. The thickness deformation field around proteins of various shapes can be calculated from the solution of a boundary value problem and is relatively well understood. Yet, the time scales over which self-assembly occurs has not been explored. In this paper we compute this time scale as a function of the initial distance between two inclusions by viewing their coalescence as a first passage time problem. The first passage time is computed using both Langevin dynamics and a partial differential equation, and both methods are found to be in excellent agreement. Inclusions of three different shapes are studied and it is found that for two inclusions separated by about hundred nanometers the time to coalescence is hundreds of milliseconds irrespective of shape. Our Langevin dynamics simulation of self-assembly required an efficient computation of the interaction energy of inclusions which was accomplished using a finite difference technique. The interaction energy profiles obtained using this numerical technique were in excellent agreement with those from a previously proposed semi-analytical method based on Fourier-Bessel series. The computational strategies described in this paper could potentially lead to efficient methods to explore the kinetics of self-assembly of proteins on lipid membranes. Author summarySelf-assembly of proteins on lipid membranes occurs during exo- and endo-cytosis and also when viruses exit an infected cell. The forces mediating self-assembly of inclusions on membranes have therefore been of long standing interest. However, the kinetics of self-assembly has received much less attention. As a first step in discerning the kinetics, we examine the time to coalescence of two inclusions on a membrane as a function of the distance separating them. We use both Langevin dynamics simulations and a partial differential equation to compute this time scale. We predict that the time to coalescence is on the scale of hundreds of milliseconds for two inclusions separated by about hundred nanometers. The deformation moduli of the lipid membrane and the membrane tension can affect this time scale.

biophysics↗

bioRxiv2020

Genome-wide association study of TP53 R249S mutation in hepatocellular carcinoma with aflatoxin B1 exposure and hepatitis B virus infection in Guangxi

Background/AimsDietary aflatoxin B1 (AFB1) exposure, which induces DNA damage and codon 249 mutation of the TP53 gene, is one of the major risk factors for hepatocellular carcinoma (HCC). Hepatitis B virus (HBV) infection and AFB1 exert synergistic effects to promote carcinogenesis and TP53 R249S mutation in HCC. MethodsA genome-wide association study (GWAS) was conducted on 485 cases of HCC with chronic HBV infection, followed by a two-stage replication study on 270 cases with chronic HBV infection. Susceptibility variants for the TP53 R249S mutation in HCC were identified based on both GWAS and replication analysis. The associations of identified variants with expression levels of their located genes were validated in 20 paired independent samples. ResultsOur results showed that TP53 R249S was significantly associated with ADAMTS18 rs9930984 (adjusted P = 4.84x10-6), WDR49 rs75218075 (adjusted P = 7.36 x 10-5) and SLC8A3 rs8022091 (adjusted P = 0.042). Additionally, ADAMTS18 mRNA expression was significantly higher in HCC tissue, compared with paired non-tumor tissue (P = 0.041) and patients carrying the TT genotype at rs9930984 showed lower ADAMTS18 expression in non-tumor tissue, compared with those carrying the GT genotype (P = 0.0028). ConclusionsTP53 expression is significantly associated with R249S mutation in HCC. Our collective results suggest that rs9930984, rs75218075 and rs8022091 are associated with susceptibility to the R249S mutation in cases of HCC exposed to AFB1 and HBV infection.

genetics↗

bioRxiv2020

Extensions of the worm-like-chain model to tethered active filaments under tension

Intracellular elastic filaments such as microtubules are subject to thermal Brownian noise and active noise generated by molecular motors that convert chemical energy into mechanical work. Similarly, polymers in living fluids such as bacterial suspensions and swarms suffer bending deformations as they interact with single bacteria or with cell clusters. Often these filaments perform mechanical functions and interact with their networked environment through cross-links, or have other similar constraints placed on them. Here we examine the mechanical properties - under tension - of such constrained active filaments under canonical boundary conditions motivated by experiments. Fluctuations in the filament shape are a consequence of two types of random forces - thermal Brownian forces, and activity derived forces with specified time and space correlation functions. We derive force-extension relationships and expressions for the mean square deflections for tethered filaments under various boundary conditions including hinged and clamped constraints. The expressions for hinged-hinged boundary conditions are reminiscent of the worm-like-chain model and feature effective bending moduli and mode-dependent non-thermodynamic effective temperatures controlled by the imposed force and by the activity. Our results provide methods to estimate the activity by measurements of the force-extension relation of the filaments or their mean-square deflections which can be routinely performed using optical traps, tethered particle experiments, or other single molecule techniques.

biophysics↗

bioRxiv2020

A simple method for isolating high-quality RNA from kenaf containing high polysaccharide and polyphenol contents

The isolation of high-quality RNA from kenaf is crucial for genetic and molecular biology studies. However, high levels of polysaccharide and polyphenol compounds in kenaf tissues could irreversibly bind to and coprecipitate with RNA, which complicates RNA extraction. In the present study, we proposed a simplified, time-saving and low-cost extraction method for isolating high quantities of high-quality RNA from several different kenaf tissues. RNA quality was measured for yield and purity, and the proposed protocol yielded high quantities of RNA (10.1-12.9 μg/g·FW). Spectrophotometric analysis showed that A260/280 ratios of RNA samples were in the range of 2.11 to 2.13, and A260/230 ratios were in the range of 2.04-2.24, indicating that the RNA samples were free of polyphenols, polysaccharides, and protein contaminants after isolation. The method of RNA extraction presented here was superior to the conventional CTAB method in terms of RNA isolation efficiency and was more sample-adaptable and cost-effective than commercial kits. Furthermore, to confirm downstream amenability, the high-quality RNA obtained from this method was successfully used for RT-PCR, real-time RT-PCR and Northern blot analysis. We provide an efficient and low-cost method for extracting high quantities of high-quality RNA from plants that are rich in polyphenols and polysaccharides, and this method was also validated for the isolation of high-quality RNA from other plants.View Full Text

genetics↗

bioRxiv2019

Genetic predisposition to myeloproliferative neoplasms implicates hematopoietic stem cell biology

Myeloproliferative neoplasms (MPNs) are blood cancers characterized by excessive production of mature myeloid cells that result from the acquisition of somatic driver mutations in hematopoietic stem cells (HSCs)1. While substantial progress has been made to define the causal somatic mutation profile for MPNs2, epidemiologic studies indicate a significant heritable component for the disease that is among the highest known for all cancers3. However, only a limited set of genetic risk loci have been identified, and the underlying biological mechanisms leading to MPN acquisition remain unexplained. Here, to define the inherited risk profile, we conducted the largest genome-wide association study of MPNs to date (978,913 individuals with 3,224 cases) and identified 14 genome-wide significant loci, as well as a polygenic signature that increases the odds for disease acquisition by nearly 3-fold between the top and median deciles. Interestingly, we find a shared genetic architecture between MPN risk and several hematopoietic traits spanning distinct lineages, as well as an association between increased MPN risk and longer leukocyte telomere length, collectively implicating HSC function and self-renewal. Strikingly, we find a significant enrichment for risk variants mapping to accessible chromatin in HSCs compared with other hematopoietic populations. Finally, gene mapping identifies modulators of HSC biology and targeted variant-to-function analyses suggest likely roles for CHEK2 and GFI1B in altering HSC function to confer disease risk. Overall, we demonstrate the power of human genetic studies to illuminate a previously unappreciated mechanism for MPN risk through modulation of HSC function.

genetics↗

bioRxiv2019

Comparative analyses of 3654 chloroplast genomes unraveled new insights into the evolutionary mechanism of green plants

BackgroundChloroplast are believed to arise from a cyanobacterium through endosymbiosis and they played vital roles in photosynthesis, oxygen release and metabolites synthesis for the plant. With the advent of next-generation sequencing technologies, until December 2018, about 3,654 complete chloroplast genome sequences have been made available. It is possible to compare the chloroplast genome structure to elucidate the evolutionary history of the green plants.\n\nResultsWe compared the 3654 chloroplast genomes of the green plants and found extreme conservation of gene orders and gene blocks in the green plant such as ATP synthase cluster, Phytosystem, Cytochrome cluster, and Ribosomal cluster. For the chloroplast-based phylogenomics, we used three different data sets to recover the relationships within green plants which accounted for biased GC content and could mitigate the bias in molecular data sets by increasing taxon sampling. The main topology results include: I) Chlorokybales + Mesostigmatales as the earliest-branching lineage and a clade comprising Zygnematales+ Desmidiales formed a grade as the sister group to the land plants, II) Based on matrix AA data, Bryophytes was strongly supported as monophyletic but for matrix nt123 data, hornworts, mosses and liverworts were placed as successive sister lineages of Tracheophytes with strong support, III) Magnoliids were placed in the outside of Monocots using the matrix nt123 data and the matrix AA data, IV) Ceratophyllales + Chloranthales as sister to the Eudicots using matrix nt123 data, but when using matrix nt12 data and AA data, only Ceratophyllales sister to the Eudicots.\n\nConclusionWe present the first of its kind large scale comparative analyses of the chloroplast coding gene constitution for 3654 green plants. Some important genes likely showed co-occurrence and formed gene cluster and gene blocks in Streptophyta. We found a clear expansion of IRs (Inverted Repeats) among seed plants. The comprehensive taxon sampling and different data sets recovered a strong relationship for green plants.

plant biology↗