Biology subjects

Yu, Z.

Publications and source records attributed to Yu, Z..

At least 199 records · Page 11Linked to original sources

bioRxiv2023

HEARTSVG: a fast and accurate method for spatially variable gene identification in large-scale spatial transcriptomic data

Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higher F1 scores (average F1 score=0.903), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average recall=0.985, average AUC=0.788) than other comparative methods without prespecifing spatial patterns. Furthermore, by clustering SVGs, we uncover two distinct tumor spatial domains characterized by unique spatial expression patterns, spatial-temporal locations, and biological functions in human colorectal cancer data, unraveling the complexity of tumors.

bioinformatics↗

bioRxiv2023

Comprehensive Assessment of Isoform Detection Methods for Third-Generation Sequencing Data

The advancement of Third-Generation Sequencing (TGS) techniques has significantly increased the length of sequencing to several kilobases, thereby facilitating the identification of alternative splicing (AS) events and isoform expressions. Recently, numerous computational methods for isoform detection using long-read sequencing data have been developed. However, there is lack of prior comparative studies that systemically evaluates the performance of these software tools, implemented with different algorithms, under various simulations that encompass potential influencing factors. In this study, we conducted a benchmarking analysis of eleven methods implemented in eight computational tools capable of identifying isoform structures from TGS RNA sequencing data. We evaluated their performances using simulated data, which represented diverse sequencing platforms generated by an in-house simulator, as well as experimental data. Our comprehensive results demonstrate the guided mode of StringTie2 and Bambu achieved the best performance in sensitivity and precision, respectively. This study provides valuable guidance for future research on AS analysis and the ongoing improvement of tools for isoform detection using TGS data.

bioinformatics↗

bioRxiv2023

DOCK2-deficiency causes defects in anti-viral T cell responses and poor control of herpes simplex virus infection

The expanding number of rare immunodeficiency syndromes offers an opportunity to understand key genes that support immune defence against infectious diseases. However, patients with these diseases are by definition rare. In addition, any analysis is complicated by treatments and co-morbid infections requiring the use of mouse models for detailed investigations. Here we develop a mouse model of DOCK2 immunodeficiency and demonstrate that these mice have delayed clearance of herpes simplex virus type 1 (HSV-1) infections. Further, we found that they have a critical, cell intrinsic role of DOCK2 in the clonal expansion of anti-viral CD8+ T cells despite normal early activation of these cells. Finally, while the major deficiency is in clonal expansion, the ability of primed and expanded DOCK2-deficient CD8+ T cells to protect against HSV-1-infection is also compromised. These results provide a contributing cause for the frequent and devastating viral infections seen in DOCK2-deficient patients and improve our understanding of anti-viral CD8+ T cell immunity.

immunology↗

bioRxiv2023

Melatonin alleviates valproic acid-induced neural tube defects by modulating Src/PI3K/ERK signaling and oxidative stress

Neural tube defects (NTDs) represent a developmental disorder of the nervous system that can lead to significant disability in children and impose substantial social burdens. Valproic acid (VPA), a widely prescribed first-line antiepileptic drug for epilepsy and various neurological conditions, has been associated with a fourfold increase in the risk of NTDs when used during pregnancy. Consequently, urgent efforts are required to identify innovative prevention and treatment approaches for VPA-induced NTDs. Studies have demonstrated that the disruption in the delicate balance between cell proliferation and apoptosis is a crucial factor contributing to NTDs induced by VPA. Encouragingly, our current data reveal that melatonin (MT) exerts significant inhibition on apoptosis while promoting the restoration of neuroepithelial cells proliferation impaired by VPA. Moreover, further investigations demonstrate that MT substantially reduces the incidence of neural tube malformations resulting from VPA exposure, primarily achieved by suppressing apoptosis through the modulation of intracellular reactive oxygen species levels. In addition, the Src/PI3K/ERK signaling pathway appears to play a pivotal role in VPA-induced NTDs, with a significant inhibition observed in the affected samples. Notably, MT treatment successfully reinstates the Src/PI3K/ERK signals, thereby offering a potential underlying mechanism for MTs protective effects against VPA-induced NTDs. In summary, our current study substantiates the considerable protective potential of MT in mitigating VPA-triggered NTDs, thereby offering valuable strategies for the clinical management of VPA-related birth defects.

developmental biology↗

bioRxiv2023

Expansion of profibrotic monocyte-derived alveolar macrophages in patients with persistent respiratory symptoms and radiographic abnormalities after COVID-19

As many as 10-30% of the over 760 million survivors of COVID-19 develop persistent symptoms, of which respiratory symptoms are among the most common. To understand the cellular and molecular basis for respiratory PASC, we combined a machine learning based analysis of lung computed tomography (CT) with flow cytometry, single-cell RNA-sequencing analysis of bronchoalveolar lavage fluid and nasal curettage samples, and alveolar cytokine profiling in a cohort of thirty-five patients with respiratory symptoms and radiographic abnormalities more than 90 days after infection with COVID-19. CT images from patients with PASC revealed abnormalities involving 73% of the lung, which improved on subsequent imaging. Interstitial abnormalities suggestive of fibrosis on CT were associated with the increased numbers of neutrophils and presence of profibrotic monocyte-derived alveolar macrophages in BAL fluid, reflecting unresolved epithelial injury. Persistent infection with SARS-CoV-2 was identified in six patients and secondary bacterial or viral infections in two others. These findings suggest that despite its heterogenous clinical presentations, respiratory PASC with radiographic abnormalities results from a common pathobiology characterized by the ongoing recruitment of neutrophils and profibrotic monocyte-derived alveolar macrophages driving lung fibrosis with implications for diagnosis and therapy.

immunology↗

bioRxiv2023

Human Plasma Proteomic Profile of Clonal Hematopoiesis

Plasma proteomic profiles associated with subclinical somatic mutations in blood cells may offer novel insights into downstream clinical consequences. Here, we explore such patterns in clonal hematopoiesis of indeterminate potential (CHIP), which is linked to several cancer and non-cancer outcomes, including coronary artery disease (CAD). Among 61,833 ancestrally diverse participants (3,881 with CHIP) from NHLBI TOPMed and UK Biobank with blood-based DNA sequencing and proteomic measurements (1,148 proteins by SomaScan in TOPMed and 2,917 proteins by Olink in UK Biobank), we identified 32 and 345 unique proteins from TOPMed and UK Biobank, respectively, associated with the most prevalent driver genes (DNMT3A, TET2, and ASXL1). These associations showed substantial heterogeneity by driver genes, sex, and race, and were enriched for immune response and inflammation pathways. Mendelian randomization in humans, coupled with ELISA in hematopoietic Tet2-/- vs wild-type mice validation, disentangled causal proteomic perturbations from TET2 CHIP. Lastly, we identified plasma proteins shared between CHIP and CAD.

genetics↗

bioRxiv2023

Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE Slides

Interpreting the tumor microenvironment (TME) heterogeneity within solid tumors presents a cornerstone for precise disease diagnosis and prognosis. However, while spatial transcriptomics offers a wealth of data, ranging from gene expression and spatial location to corresponding Hematoxylin and Eosin (HE) images, to explore the TME of various cancers, its high cost and demanding infrastructural needs significantly limit its clinical application, highlighting the need for more accessible alternatives. To bridge this gap, we introduce the Integrated Graph and Image Deep Learning (IGI-DL) model. This innovation, a fusion of Convolutional Neural Networks and Graph Neural Networks, is designed to predict gene spatial expression using HE images. The IGI-DL model outperforms its predecessors in analyzing colorectal cancer (CRC), breast cancer, and cutaneous squamous cell carcinoma (cSCC) by leveraging both pixel intensity and structural features in images. Significantly, across all cancer types, the IGI-DL model enhances the mean correlation of the top five genes by an average of 0.125 in internal and external test sets, rising from 0.306 to 0.431, surpassing existing state-of-the-art (SOTA) models. We further present a novel risk score derived from a super-patch graph, where gene expression predicted by IGI-DL serves as node features. Demonstrating superior prognostic accuracy, this risk score, with a C-index of 0.713 and 0.741 for CRC and breast cancer, supersedes traditional HE-based risk scores. In summary, the approach augments our understanding of the TME from the aspect of histological images, portending a transformation in cancer prognostics and treatment planning and ushering in a new era of personalized and precision oncology.

bioinformatics↗

bioRxiv2023

RNA structure and multiple weak interactions balance the interplay between RNA binding and phase separation of SARS-CoV-2 nucleocapsid

The nucleocapsid (N) protein of SARS-CoV-2 binds viral RNA, condensing it inside the virion, and phase separating with RNA to form liquid-liquid condensates. There is little consensus on what differentiates sequence-independent N-RNA interactions in the virion or in liquid droplets from those with specific genomic RNA motifs necessary for viral function inside infected cells. To identify the RNA structures and the N domains responsible for specific interactions and phase separation, we use the first 1000nt of viral RNA and short RNA segments designed as models for single-stranded and paired RNA. Binding affinities estimated from fluorescence anisotropy of these RNAs to the two folded domains of N (the NTD and CTD) and comparison to full-length N demonstrate that the NTD binds preferentially to single-stranded RNA, and while it is the primary RNA binding site, it is not essential to phase separation. Nuclear magnetic resonance spectroscopy identifies two RNA binding sites on the NTD: a previously characterized site and an additional although weaker RNA-binding face that becomes prominent when binding to the primary site is weak, such as with dsRNA or a binding-impaired mutant. Phase separation assays of nucleocapsid domains with different RNA structures support a model where multiple weak interactions, such as with the CTD or the NTDs secondary face promote phase separation, while strong, specific interactions do not. These studies indicate that both strong and multivalent weak N-RNA interactions underlie the multifunctional abilities of N. SignificanceThe nucleocapsid protein of the SARS-CoV-2 coronavirus binds to viral RNA, both to protect and condense it inside the viral particle and to facilitate viral transcription inside infected host cells. Evidence suggests that variations in RNA structure impact how and where it binds to the nucleocapsid, but these differences are not well understood at a structural level. Using nuclear magnetic resonance spectroscopy, we examine the interactions between each folded domain of the nucleocapsid and different RNA structures. Binding affinities and NMR chemical shift profiles demonstrate that binding between the N-terminal domain and single stranded RNA is driven by strong interactions at a specific site, while multiple weak nonspecific interactions at newly discovered sites lead to phase separation and RNA condensation.

biophysics↗

bioRxiv2023

Assessing RNAi feasibility and susceptibility to environmental RNAi in Trichogramma dendrolimi (Hymenoptera: Trichogrammatidae)

Trichogramma, a genus of egg parasitoid wasps, are widely used as biological control agents and serve as model organisms in parasitoid research. Despite their significance, the understanding of RNA interference (RNAi) in Trichogramma remains very limited. In this study, we investigated RNAi-associated genes by bioinformatic approaches and experimentally assessed the feasibility of RNAi and the susceptibility of environmental RNAi in Trichogramma. We found that Trichogramma genomes contain a complete set of genes in the RNAi pathway and exhibit extensive gene expansion of dsRNase, which may influence RNAi efficiency by degrading dsRNA. We demonstrated successful RNAi through pupal microinjection in T. dendrolimi Matsumura, providing a technical approach for future gene functional studies. In addition, we observed no evidence of susceptibility to environmental RNAi in either T. dendrolimi adults or larvae, which might be attributed to the extensive expansion of dsRNase. This low environmental RNAi sensitivity in Trichogramma could suggest a reduced risk of RNAi-based pest management strategies affecting nontarget Trichogramma populations. Overall, this study presents a technical approach for conducting gene functional studies in Trichogramma and provides a foundation for evaluating the nontarget effects of RNAi-based pest control strategies on Trichogramma.

molecular biology↗

bioRxiv2023

HPC-based genome variant calling workflow (HPC-GVCW)

A high-performance computing genome variant calling workflow was designed to run GATK on HPC platforms. This workflow efficiently called an average of 27.3 M, 32.6 M, 168.9 M, and 16.2 M SNPs for rice, sorghum, maize, and soybean, respectively, on the most recently released high-quality reference sequences. Analysis of a rice pan-genome reference panel revealed 2.1 M novel SNPs that have yet to be publicly released.

genomics↗

bioRxiv2023

Functionalized graphene-oxide grids enable high-resolution cryo-EM structures of the SNF2h-nucleosome complex without crosslinking

Single-particle cryo-EM is widely used to determine enzyme-nucleosome complex structures. However, cryo-EM sample preparation remains challenging and inconsistent due to complex denaturation at the air-water interface (AWI). To address this issue, we developed graphene-oxide-coated EM grids functionalized with either single-stranded DNA (ssDNA) or thiol-poly(acrylic acid-co-styrene) (TAASTY) co-polymer. These grids protect complexes between the chromatin remodeler SNF2h and nucleosomes from the AWI and facilitated collection of high-quality micrographs of intact SNF2h-nucleosome complexes in the absence of crosslinking. The data yields maps ranging from 2.3 to 3 [A] in resolution. 3D variability analysis reveals nucleotide-state linked conformational changes in SNF2h bound to a nucleosome. In addition, the analysis provides structural evidence for asymmetric coordination between two SNF2h protomers acting on the same nucleosome. We envision these grids will enable similar detailed structural analyses for other enzyme-nucleosome complexes and possibly other protein-nucleic acid complexes in general.

biochemistry↗

bioRxiv2023

Automated cell annotation in multi-cell images using an improved CRF_ID algorithm

Cell identification is an important yet difficult process in data analysis of biological images. Previously, we developed an automated cell identification method called CRF_ID and demonstrated its high performance in C. elegans whole-brain images (Chaudhary et al, 2021). However, because the method was optimized for whole-brain imaging, comparable performance could not be guaranteed for application in commonly used C. elegans multi-cell images that display a subpopulation of cells. Here, we present an advance CRF_ID 2.0 that expands the generalizability of the method to multi-cell imaging beyond whole-brain imaging. To illustrate the application of the advance, we show the characterization of CRF_ID 2.0 in multi-cell imaging and cell-specific gene expression analysis in C. elegans. This work demonstrates that high accuracy automated cell annotation in multi-cell imaging can expedite cell identification and reduce its subjectivity in C. elegans and potentially other biological images of various origins.

bioengineering↗

bioRxiv2023

VitTCR: A deep learning method for peptide recognition prediction

The identification of the interaction between T-cell receptors (TCRs) and immunogenic peptides is important for the development of novel cancer immunotherapies and vaccines. However, experimentally determining whether a TCR recognizes a peptide is still time- and labour-consuming. In this study, we introduced VitTCR, a predictive model based on the architecture of the vision transformer (ViT), designed to forecast TCR-peptide interactions. Prior to prediction, VitTCR converts the TCR-peptide interactions into a numerical tensor named AtchleyMaps using Atchley factors. Subsequently, VitTCR takes AtchleyMaps as inputs and predicts whether an interaction between a TCR and a peptide exists. Through comprehensive evaluations, we demonstrate that VitTCR surpasses other published methods in classifying TCR-peptide pairs, exhibiting superior performance in terms of the area under the receiver operating characteristic (AUROC) and the area under the precision-recall curve (AUPR). To determine the focal contact point between TCRs and peptides, we obtained a positional bias weight matrix (PBWM) from the empirical amino acid (AA) contact probabilities derived from 83 structurally resolved pMHC-TCR complexes. The comparison of VitTCR with and without the integration of the PBWM revealed significant enhancements in the performance of the model. Moreover, the predicted probabilities generated by VitTCR exhibit significant correlations with immunological factors such as the clonal expansion and activation percentages of T cells. This further supports the efficacy of VitTCR in capturing biologically meaningful TCR-peptide interactions. In conclusion, VitTCR provides a useful computational tool for the prediction of TCR-peptide interactions, thereby contributing to our understanding in this field.

bioinformatics↗

bioRxiv2023

Modifications to the SR-Rich Region of the SARS-CoV-2 Nucleocapsid Regulate Self-Association and Attenuate RNA Interactions

The nucleocapsid protein (N) of SARS-CoV-2 is essential for virus replication, genome packaging, and maturation. N is comprised of two folded domains that are separated by a highly conserved, disordered, Ser/Arg-rich linker, and flanked by disordered tails. Using NMR spectroscopy and analytical ultracentrifugation we identify an alpha-helical region in the linker that undergoes concentration dependent self-association. NMR and gel shift assays show that the linker binds viral RNA but this binding is dampened by both phosphorylation and a naturally occurring mutation, whereas in contrast, RNA binding to the full-length protein is not affected. Interestingly, phase separation with RNA is significantly reduced upon phosphorylation but enhanced with the mutation. We attribute these differences to changes in the linker helix self-association which dissociates upon phosphorylation but forms more stable higher order oligomers in the variant. These data provide a structural mechanism for how the linker region contributes to protein-protein interactions, RNA-protein interactions, liquid-liquid phase separation and N protein regulation.

biophysics↗

bioRxiv2023

Projectome-defined subtypes and modular intra-hypothalamic subnetworks of peptidergic neurons

The hypothalamus plays a vital role in coordinating essential neuroendocrine, autonomic, and somatomotor responses for survival and reproduction. While previous studies have explored population-level projections of hypothalamic neurons, the specific innervation patterns of individual hypothalamic axons remain unclear. To understand the organization of hypothalamic axon projections, we conducted a comprehensive reconstruction of single-cell projectomes from 7,180 mouse hypothalamic neurons expressing specific neuropeptides. Our analysis identified 31 distinct subtypes based on projectome-defined characteristics, with many exhibiting long-range axon collateral projections to multiple brain regions. Notably, these subtypes selectively targeted specific subdomains within downstream areas, either unilaterally or bilaterally. Furthermore, we observed that individual peptidergic neuronal types encompassed multiple projectome-defined subtypes, explaining their diverse functional roles. Additionally, by examining intra-hypothalamic axon projections, we uncovered six modular subnetworks characterized by enriched intramodular connections and distinct preferences for downstream targets. This modular organization of the intra-hypothalamic network likely contributes to the coordinated organization of hypothalamic outputs. In summary, our comprehensive projectome analysis reveals the organizational principles governing hypothalamic axon projections, providing a framework for understanding the neural circuit mechanisms underlying the diverse and coordinated functions of the hypothalamus.

neuroscience↗

bioRxiv2023

Defining a core configuration for human centromeres during mitosis

The biorientation of sister chromatids on the mitotic spindle, essential for accurate sister chromatid segregation, relies on critical centromere components including cohesin, the centromere-specific H3 variant CENP-A, and centromeric DNA. Centromeric DNA is highly variable between chromosomes yet must accomplish a similar function. Moreover, how the 50 nm cohesin ring, proposed to encircle sister chromatids, accommodates inter-sister centromeric distances of hundreds of nanometers on the metaphase spindle is a conundrum. Insight into the 3D organization of centromere components would help resolve how centromeres function on the mitotic spindle. We used ChIP-seq and super-resolution microscopy to examine the geometry of essential centromeric components on human chromosomes. ChIP-seq demonstrates that cohesin subunits are depleted in -satellite arrays where CENP-A nucleosomes and kinetochores assemble. Cohesin is instead enriched at pericentromeric DNA. Structured illumination microscopy of sister centromeres is consistent, revealing a non-overlapping pattern of CENP-A and cohesin. We used single particle averaging of hundreds of mitotic sister chromatids to develop an average centromere model. CENP-A clusters on sister chromatids, connected by -satellite, are separated by [~]562 nm with a perpendicular intervening [~]190 nM wide axis of cohesin. Two differently sized -satellite arrays on chromosome 7 display similar inter-sister CENP-A cluster distance, demonstrating different sized arrays can achieve a common spacing. Our data suggest a working model for a common core configuration of essential centromeric components that includes CENP-A nucleosomes at the outer edge of extensible -satellite DNA and pericentromeric cohesion. This configuration helps reconcile how centromeres function and serves as a foundation for future studies of additional components required for centromere function.

cell biology↗

bioRxiv2023

A novel bacterial signal transduction system specifically senses and responds to peptidoglycan damage

The peptidoglycan (PG) layer is a mesh-like structure within the cell envelope essential for maintenance of cell shape and resistance to osmotic stress, and therefore is a primary target of many important and widely used antibiotics, such as {beta}-lactams. In Gram-negative bacteria, while signal transduction systems that monitor the state of the inner- and outer-membranes have been extensively studied and well understood, much less is known about how cells sense and respond to PG-targeting stresses. Here we show that a novel bacterial two-component system (PghKR) from Shewanella oneidensis is capable of sensing and responding to PG damage. This system is specifically activated in cells exposed to various PG-targeting antibiotics or carrying a defect in PG synthesis, resulting in induced expression of blaA and relV, which encode a {beta}-lactamase conferring resistance to {beta}-lactams and a small ppGpp synthetase responsible for antibiotic tolerance, respectively. Intriguingly, the PghKR homologs are widespread among several classes of Proteobacteria and the periplasmic domain of sensor kinase PghK contains a family 9 carbohydrate-binding module that is required for signal perception, implying that the signals could be the glycan fragments of PG. Overall, our results provide critical insights into the regulation of PG homeostasis in Gram-negative bacteria.

microbiology↗

bioRxiv2023

Photosynthetic living fibers fabrication from algal-bacterial consortia with controlled spatial distribution

Living materials that combine active cells and synthetic matrix materials have become a promising research field in recent years. While multicellular systems present exclusive benefits in developing living materials over single-cell systems, creating artificial multicellular systems can be challenging due to the difficulty in controlling the multicellular assemblies and the complexity of cell-to-cell interactions. Here, we propose a co-culture platform capable of isolating and controlling the spatial distribution of algal-bacterial consortia, which can be used to construct photosynthetic living fibers. Through coaxial extrusion-based 3D printing, hydrogel fibers containing bacteria or algae can be deposited into designated structures and further processed into materials with precise geometries. In addition, the photosynthetic living fibers demonstrate a significant synergistic catalytic effect resulting from the immobilization of both bacteria and algae, which effectively optimize sewage treatment for bioremediation purposes. The integration of microbial consortia and 3D printing gives functional living materials that have promising applications in biocatalysis, biosensing, and biomedicine. Our approach provides an optimized solution for constructing efficient multicellular systems and opens a new avenue for the development of advanced materials.

bioengineering↗