Biology subjects

Liu, H.

Publications and source records attributed to Liu, H..

At least 469 records · Page 26Linked to original sources

bioRxiv2023

Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images

Stereo-seq is a cutting-edge technique for spatially resolved transcriptomics that combines subcellular resolution with centimeter-level field-of-view, serving as a technical foundation for analyzing large tissues at the single-cell level. Our previous work presents the first one-stop software that utilizes cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. With recent advancements in Stereo-seq technology, it is possible to acquire cell boundary information, such as cell membrane/wall staining images. To take advantage of this progress, we update our software to a new version, named STCellbin, which utilizes the cell nuclei staining images as a bridge to align cell membrane/wall staining images with spatial gene expression maps. By employing an advanced cell segmentation technique, accurate cell boundaries can be obtained, leading to more reliable single-cell spatial gene expression profiles. Experimental results verify that STCellbin can be applied on the mouse liver (cell membranes) and Arabidopsis seed (cell walls) datasets and outperforms other competitive methods. The improved capability of capturing single cell gene expression profiles by this update results in a deeper understanding of the contribution of single cell phenotypes to tissue biology. Availability & ImplementationThe source code of STCellbin is available at https://github.com/STOmics/STCellbin.

bioinformatics↗

bioRxiv2023

Climate change decreases the likelihood of tropical forest community persistence with a strong mediation of plant-plant network structures

Climate change is known to negatively impact tropical forests; yet how climate change impacts tree community persistence at local scales remains less clear. Using data from a long-term tropical forest census plot over 25 years, we constructed plant- plant interaction networks based on tree growth. We then quantified community persistence as feasibility domain of constituent species using recently developed frameworks of structural stability. We found a decrease in structural stability under warming and precipitation changes over time as evidenced by both direct environmental effects and indirect effects via network structure; and indirect effects were stronger than direct effects. Among these structures, facilitation:competition degree ratio and competitive transitivity were most positively related with structural stability. Our study highlights how the lens of plant-plant interaction networks can identify novel details about risk to tropical forest diversity under climate change at local scales. Insights from this work will be helpful for aligning forest management activities with areas under the greatest risk of species loss.

ecology↗

bioRxiv2023

Cell type specific enrichment of somatic aneuploidy in the mammalian brain

Somatic mutations alter the genomes of a subset of an individuals brain cells1-3, impacting gene regulation and contributing to disease processes4,5. Mosaic single nucleotide variants have been characterized with single-cell resolution in the brain2,3, but we have limited information about large-scale structural variation, including whole-chromosome duplication or loss1,6,7. We used a dataset of over 415,000 single-cell DNA methylation and chromatin conformation profiles across the adult mouse brain to identify aneuploid cells comprehensively. Whole-chromosome loss or duplication occurred in <1% of cells, with rates up to 1.8% in non-neuronal cell types, including oligodendrocyte precursors and pericytes. Among all aneuploidies, we observed a strong enrichment of trisomy on chromosome 16, which is syntenic with human chromosome 21 and constitutively trisomic in Down syndrome. Chromosome 16 trisomy occurred in multiple cell types and across brain regions, suggesting that nondisjunction is a recurrent feature of somatic variation in the brain.

genomics↗

bioRxiv2023

An ancient transcription activator is required for the multiciliogenesis program

Multiciliogenesis is an evolutionarily conserved process required a unique transcriptional program. The mechanisms governing multiciliogenesis are incompletely understood. Here we show that an ancient transcription activator, Edf1, is essential for the multiciliogenesis program. Mice lacking Edf1 exhibit postnatal hydrocephalus and delayed multiciliated cells (MCCs) differentiation. Functional studies reveal that MCCs in Edf1-/- mice have defects in tissue-level polarity and reduced motility. These defects result in abnormal cerebrospinal fluid (CSF) dynamics in vivo, potentially contributing to the development of hydrocephalus. Intriguingly, we found that the expression of pivotal ciliary transcription factors was decreased in Edf1-/- mice. Collectively, our data suggest that Edf1 modulates the multiciliogenesis by regulating the transcription of important ciliary transcription factors required for ciliary gene expression.

cell biology↗

bioRxiv2023

An Integrated Optogenetic and Bioelectronic Platform for Regulating Cardiomyocyte Function

We report an integrated optogenetic and bioelectronic platform for stable and long-term modulation and monitoring of cardiomyocyte function in vitro. Optogenetic inputs were achieved through expression of a photoactivatable adenylyl cyclase (bPAC), that when activated by blue light caused a dose-dependent and time-limited increase in autonomous cardiomyocyte beat rate. Bioelectronic readouts were achieved through an integrated planar multi-electrode array (MEA) that provided real-time readouts of electrophysiological activity from 32 spatially-distinct locations. Irradiation at 27 W/mm2 resulted in a ca. 14% increase in beat rate within 20-25 minutes, which remained stable for at least 2 hours. The beating rate could be cycled through repeated "on" and "off" states, and its magnitude was a monotonic function of irradiation intensity. Our integrated platform opens new avenues in bioelectronic medicine, including closedloop feedback systems, with potential applications for cardiac regulation including arrhythmia diagnosis and intervention. TeaserA system that integrates optogenetic stimulation and bioelectronic recording capabilities allows for on-demand regulation of cardiac cell function.

bioengineering↗

bioRxiv2023

The somato-cognitive action network links diverse neuromodulatory targets for Parkinson's disease

The newly-recognized somato-cognitive action network (SCAN) is posited to be important in movement coordination. Functional disruptions in Parkinsons disease (PD) correspond with complex, non-effector-specific motor symptoms, indicating that SCAN dysfunction may underlie these symptoms. Along the same lines, the SCAN may link multiple neuromodulatory targets used for PD treatment. To investigate the role of the SCAN in PD, we leveraged resting-state precision functional mapping, analyzing data from 673 individuals across 6 independent datasets and 5 types of neuromodulation. Our findings revealed functional abnormalities within the SCAN in PD patients and the selective involvement of the SCAN in diverse neuromodulatory targets. Moreover, our data suggests causal links between SCAN connectivity changes and motor symptom alleviation following both invasive and non-invasive neuromodulation. Collectively, these findings underscore the critical role of the SCAN in the pathophysiology of PD and its brain stimulation treatments, and suggest the SCAN as a promising candidate target for neuromodulation.

neuroscience↗

bioRxiv2023

Utilizing Non-Invasive Prenatal Test Sequencing Data Resource for Human Genetic Investigation

Non-invasive prenatal testing (NIPT) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. With exceptional sensitivity, specificity, and safety, NIPT has gained global adoption, exceeding ten million tests, establishing it as one of the largest human genetic resources. This resource holds immense potential for exploring population genetic variations and their correlations with phenotypes. Here, we present comprehensive methods tailored for analyzing large, low-depth NIPT genetic datasets, involving customized algorithms and software for genetic variation detection, genotype imputation, and genome-wide association analysis. Through evaluations, we demonstrate that, when integrated with appropriate probabilistic models and population-specific haplotype reference panels, accurate allele frequency estimation and high genotype imputation accuracy (0.8 to 0.9) are achievable for genetic variants with alternative allele frequencies between 0.01 and 0.05, at sequencing depths of 0.1x to 0.25x. Additionally, we attained an R-square exceeding 0.9 for estimating genetic effect sizes across various sequencing platforms. These findings establish a robust theoretical and practical foundation for leveraging NIPT data in advancing medical genetic studies, not only in realms of maternal and child health, but also for long-term health outcomes. HighlightsO_LIIntroduction of probabilistic model integration for analyzing large-scale, low-pass non-invasive prenatal test (NIPT) sequencing data C_LIO_LIEvaluation of protocols for variant detection, genotype imputation, and genome-wide association analyses with NIPT data C_LI

bioinformatics↗

bioRxiv2023

Extracellular Matrix Protein 1 Attenuates Hepatic Fibrosis by Inhibiting TSP-, ADAMTS-, and MMP-Mediated Latent TGF-β1 Activation

ObjectiveExtracellular Matrix Protein 1 (ECM1) serves as a gatekeeper of hepatic fibrosis by maintaining transforming growth factor-{beta}1 (TGF-{beta}1) in its latent form. ECM1 knockout (KO) causes latent (L) TGF-{beta}1 activation, resulting in hepatic fibrosis with rapid mortality. In chronic liver disease (CLD), ECM1 decreases with increasing CLD severity. We investigate the regulatory role of ECM1 in TGF-{beta}1 bioavailability and its impact on CLD progression. DesignRNAseq was performed to analyze hepatic gene expression. Functional assays were performed using hepatic stellate cells (HSCs), Ecm1-KO and Fxr-KO mice, patient liver tissue, and computer simulations. ResultsExpression of LTGF-{beta}1 activators, including thrombospondins (TSPs), ADAMTS proteases, and matrix metalloproteinases (MMPs) increased along with pro-fibrotic gene expression in liver tissue of Ecm1-KO mice. In HSCs, overexpression of ECM1 prevented TSP-1-, ADAMTS1-, and MMP-2/9-mediated LTGF-{beta}1 activation. In vitro interaction assays demonstrated that ECM1 inhibited LTGF-{beta}1 activation by interacting with TSP-1 and ADAMTS1 via their respective, intrinsic KRFK or KTFR amino acid sequences, and by suppressing MMP-2/9 proteolytic activity. In mice, ECM1 overexpression attenuated KRFK-induced LTGF-{beta}1 activation, while KTFR treatment reversed Ecm1-KO- and Fxr-KO-mediated liver injury. In patients with CLD, ECM1 expression was inversely correlated with TSP-1, ADAMTS1, MMP-2/9 expression and LTGF-{beta}1 activation. And these results were complemented by a computational compartment model representing the key network of cellular phenotypes and predicted interactions in liver fibrogenesis. ConclusionOur findings underscore the hepatoprotective effect of ECM1, which interferes with mediators of LTGF-{beta}1 activation, suggesting ECM1 or its representative peptide as potential anti-fibrotic therapies in CLD. What is already known on this topic?[tpltrtarr] ECM1 expression is negatively correlated with CLD progression. [tpltrtarr]ECM1 maintains liver homeostasis by keeping TGF-{beta}1 latency. What this study adds?[tpltrtarr] ECM1 inhibits LTGF-{beta}1 activation through interfering with key activators, including TSP-1, ADAMTS1, MMP-2, and MMP-9. [tpltrtarr]ECM1 interacts with TSP-1 and ADAMTS1 via their respective, intrinsic KRFK or KTFR amino acid motifs, and suppresses MMP-2/9 proteolytic activity. [tpltrtarr]In vivo, ECM1 overexpression mitigates KRFK peptide-induced LTGF-{beta}1 activation, while KTFR peptide rescues Ecm1-KO- and Fxr-KO-induced liver injury. [tpltrtarr]ECM1 expression inversely correlates with TSP-1, ADAMTS1, MMP-2/9 expression and LTGF-{beta}1 activation in CLD patients. How might this study affect research, practice or policy?[tpltrtarr] Considering severe adverse effects associated with anti-fibrotic treatments utilizing TGF-{beta}1 receptor inhibitors, our findings indicate that restoration of ECM1 expression or phenocopying peptides might represent a novel and safe route to urgently needed anti-fibrotic therapies in CLD.

cell biology↗

bioRxiv2023

Nuclear cytoophidia assembly represses transcriptional activity to control skeletal development and homeostasis

Compartmentation via filamentation is an evolutionarily conserved subcellular structure that fine-tunes the inherent activity of proteins. Cytoophidia represent a typical class of filamentous structures controlling enzymatic activities. Despite eukaryotic cells containing both cytoplasmic cytoophidia and nuclear cytoophidia, the physiological significance of nuclear cytoophidia is largely unknown. Here we show that nuclear filamentation inhibits the transcriptional activity of Impdh2 required for limb formation and bone resorption. Impdh2 deletion in mouse limb mesenchymal progenitors causes severe skeletal dysplasia by impairing endochondral ossification and chondrocyte differentiation. Additionally, Impdh2 deficiency in myeloid lineages leads to an increased bone mass via impeding osteoclast differentiation. Furthermore, Impdh2 regulates osteoclastic mitochondrial biogenesis and function. We propose that the nuclear compartmentalization of Impdh2 regulates the transcriptional activity during skeletal development and homeostasis.

cell biology↗

bioRxiv2023

FOXA2 is essential for maintaining the urea cycle in acute liver failure

Hepatic encephalopathy is a lethal complication of acute liver failure (ALF), and is caused by hyperammonemia. Ammonia clearance by the liver requires an intact and complete urea cycle comprising six enzymes, including the rate-limiting enzyme carbamoyl phosphate synthetase I (CPS1). To date, the detailed regulation of CPS1 transcription in order to maintain urea cycle in physiological condition and ALF remains largely unknown. This study scrutinizes the role of pioneer factor forkhead box A 2 (FOXA2) in the regulation of CPS1 transcription, urea cycle performance and hyperammonemia. Physiologically, CPS1 transcription requires FOXA2 to maintain chromatin accessibility on its enhancers, which is essential for CCAAT enhancer-binding protein-alpha (C/EBP) binding to activate gene transcription. In ALF, hepatic C/EBP expression is inhibited by inflammatory mediators such as TGF-{beta} and TNF-. In this setting, retinoic acid receptor synergizes with FOXA2 to maintain CPS1 transcriptions. Once ALF patients suffer from massive hepatic necrosis, liver progenitor cells initiate a transcription network comprising FOXA2 and C/EBP to perform the urea cycle and prevent hyperammonemia. In ALF, hepatic encephalopathy occurs in patients lacking hepatic FOXA2 expression. In mice with acetaminophen-induced ALF, injection of Foxa2-AAV8 maintains urea cycle and prevents hyperammonemia. Taken together, FOXA2 is essential for maintaining the urea cycle. Pharmaceutical induction of hepatic FOXA2 expression might represent a novel approach to treat hepatic encephalopathy in ALF. One Sentence SummaryPioneer factor FOXA2 synergizes with C/EBP or RAR to maintain urea cycle in acute liver failure

molecular biology↗

bioRxiv2023

Bi-parental graph strategy to represent and analyze hybrid plant genomes

Hybrid plants are universally existed in wild and often exhibit greater performance of complex traits compared with their parents and other selfing plants. This phenomenon, known as heterosis, has been extensively applied in plant breeding for decades. However, the process of decoding hybrid plant genomes has seriously lagged due to the challenges in their genome assembling and the lack of proper methods to further represent and analyze them. Here we report the assembly and analysis of two hybrids: an intraspecific hybrid between two maize inbred lines and an interspecific hybrid between maize and its wild relative teosinte, based on the combination of PacBio High Fidelity (HiFi) sequencing and chromatin conformation capture sequencing data. The haplotypic assemblies are well-phased at chromosomal scale, successfully resolving the complex loci with extensive parental structural variations (SVs). By integrating into a bi-parental genome graph, the haplotypic assemblies can facilitate downstream short-reads based SV calling and allele-specific gene expression analysis, demonstrating outstanding advantages over one single linear genome. Our work provides an entire workflow which hopefully can promote the deciphering of the large numbers of hybrid plant genomes, especially those whose parents are unknown or unavailable and help to understand genome evolution and heterosis.

bioinformatics↗

bioRxiv2023

Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection

With the widespread use of high-throughput sequencing technologies, understanding biology and cancer heterogeneity has been revolutionized. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patients outcome and clinical response. However, an open-source R package covering state-of-the-art machine learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct machine learning-based integration model with elegant performance (Mime). Mime streamlined the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting outcomes of patients compared with other published models. In addition, PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from PIEZO1-associated signatures presented high-potential role in glioma with targeted prospect. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields.

bioinformatics↗

bioRxiv2023

A genome-wide association study of neonatal metabolites

The hereditary component significantly influences the concentration of metabolites in adults. Nevertheless, the precise influence of genetic factors on neonatal metabolites remains uncertain. To bridge this gap, we employed genotype imputation techniques on large-scale low-pass genome data obtained from non-invasive prenatal testing. Subsequently, we conducted association studies on a total of 75 metabolic components in neonates. The study identified a total 17 previous reported associations and 13 novel discovered associations between single nucleotide polymorphisms and metabolic components. These associations were initially found in the discovery cohort (8,744 participants) and subsequently confirmed in a replication cohort (19,041 participants). The average heritability of metabolic components was calculated to be 76.2%, with a range of 69-78.8%. The aforementioned findings offer valuable insights pertaining to the genetic architecture of neonatal metabolism. In BriefLarge-scale genomes of maternal non-invasive prenatal testing provide insights into the genetic contribution to neonatal metabolism. HighlightsGWAS of 27,785 low-pass genomes revealed 13 novel associations of neonatal metabolic components. Estimated an average of 76.2% heritability of neonatal metabolic components and showed the individual concentration can be accurately predicted from polygenic risk scores. A total of 17 established relationships have been observed, providing evidence that maternal genomes can be utilized in neonatal metabolite GWAS.

genetics↗

bioRxiv2023

SCC3 acts as the cohesin and inhibits inter-sister chromatids repair during rice meiosis

Cohesin is a multi-subunit protein that plays a pivotal role in holding sister chromatids together during cell division. Sister chromatid cohesion 3 (SCC3), constituents of cohesin complex, is highly conserved from yeast to mammals. Since the deletion of individual cohesin subunit always causes lethality, it is difficult to dissect its biological function in both mitosis and meiosis. Here, we obtained scc3 weak mutants using CRISPR-Cas9 system to explore its function during rice mitosis and meiosis. The scc3 weak mutants displayed obvious vegetative defects and complete sterility, underscoring the essential roles of SCC3 in both mitosis and meiosis. SCC3 is localized on chromatin from interphase to prometaphase in mitosis. However, in meiosis, SCC3 acts as an axial element during early prophase I and subsequently situates onto centromeric regions following the disassembly of the synaptonemal complex. The loading of SCC3 onto meiotic chromosomes depends on REC8. scc3 shows severe defects in homologous pairing and synapsis. Consequently, SCC3 functions as an axial element that is essential for maintaining homologous chromosome pairing and synapsis during meiosis.

genetics↗

bioRxiv2023

Accelerating protein engineering with fitness landscape modeling and reinforcement learning

Protein engineering holds significant promise for designing proteins with customized functions, yet the vast landscape of potential mutations versus limited lab capacity constrains the discovery of optimal sequences. To address this, we present the {micro}Protein framework, which accelerates protein engineering by combining {micro}Former, a deep learning model for accurate mutational effect prediction, with {micro}Search, a reinforcement learning algorithm designed to efficiently navigate the protein fitness landscape using {micro}Former as an oracle. {micro}Protein leverages single mutation data to predict optimal sequences with complex, multi-amino acid mutations through its modeling of epistatic interactions and a multi-step search strategy. Except from state-of-the-art performance on benchmark datasets, {micro}Protein identified high-gain-of-function multi-point mutants for the enzyme {beta}-lactamase, surpassing the highest known activity level, in wet-lab, trained solely on single mutation data. These results demonstrate {micro}Proteins capability to discover impactful mutations across vast protein sequence space, offering a robust, efficient approach for protein optimization.

synthetic biology↗

bioRxiv2023

Cell type-specific effects of age and sex on human cortical neurons

Excitatory and inhibitory neurons establish specialized identities early in life through cell type-specific patterns of epigenetic regulation and gene expression. Although cell types are largely stable throughout the lifespan, altered transcriptional and epigenetic regulation may contribute to cognitive changes with advanced age. Using single-nucleus multiomic DNA methylation and transcriptome sequencing (snmCT-seq) in frontal cortex samples from young adult and aged donors, we found widespread age- and sex-related variability in specific neuronal cell types. The proportion of GABAergic inhibitory cells, including SST and VIP expressing cells, was reduced in aged donors. On the other hand, excitatory neurons had more profound age-related changes in their gene expression and DNA methylation compared with inhibitory cells. Hundreds of genes involved in synaptic activity were downregulated, while genes located in subtelomeric regions were upregulated with age and anti-correlated with telomere length. We further mapped sex differences in autosomal gene expression and escape from X-inactivation in specific neuron types. Multiomic single-nucleus epigenomes and transcriptomes provide new insight into the effects of age and sex on human neurons.

neuroscience↗

bioRxiv2023

MYC amplifies mitotic perturbations elicited by LXY18 to enable synthetic lethality

The MYC oncoprotein represents an intriguing target for cancer treatment, but its therapeutic potential has been hindered by the absence of specific pharmacological inhibitors. In this study, we demonstrate that the phenoxy quinoline compound LXY18 selectively targets and eliminates cells overexpressing MYC, leaving non-transformed cells unharmed. This synthetic lethality arises from an acute induction of multipolarity, resulting in a persistent arrest in early mitosis followed by cell death in mitosis or after mitotic slippage. Distinctively, LXY18s action contrasts with other antimitotic compounds, as they either fail to induce mitotic arrest or elicit mitotic arrest irrespective of MYC abundance. Furthermore, the MYC abundance in a panel of 98 tumor cell lines correlates with their sensitivity to LXY18. Collectively, our findings uncover LXY18 as an MYC- enabled mitotic blocker and open a new avenue to selectively target MYC-overexpressing tumor cells without affecting normal cells.

cancer biology↗

bioRxiv2023

An end-to-end approach for protein folding by integrating Cryo-EM maps and sequence evolution

Protein structure modeling is an important but challenging task. Recent breakthroughs in Cryo-EM technology have led to rapid accumulation of Cryo-EM density maps, which facilitate scientists to determine protein structures but it remains time-consuming. Fortunately, artificial intelligence has great potential in automating this process. In this study, we present SMARTFold, a deep learning protein structure prediction model combining sequence alignment features and Cryo-EM density map features. First, using density map, we sample representative points along the predicted high confidence areas of protein backbone. Then we extract geometric features of these points and integrate these features with sequence alignment features in our proposed protein folding model. Extensive experiments confirm that our model performs best on both single-chain and multi-chain benchmark dataset compared with state-of-the-art methods, which makes it a reliable tool for protein atomic structure determination from Cryo-EM maps.

bioinformatics↗