Biology subjects

Chen, X.

Publications and source records attributed to Chen, X..

At least 505 records · Page 28Linked to original sources

bioRxiv2024

TurboID-based proximity labeling identifies novel germline proteins that maintain E granule integrity and small RNA homeostasis in C. elegans

Germ granules are biomolecular condensates composed of RNA and proteins that play crucial roles in RNA metabolism and posttranscriptional gene regulation. C. elegans germ granules consist of at least seven distinct subcompartments, including P granules, Z granules, Mutator foci, SIMR foci, P-bodies, D granules and E granules. Among these condensates, the E granule, which is nonrandomly positioned within the germ granule, is required for the production of a specialized class of small interfering RNAs (siRNAs). However, the mechanisms underlying E granule formation and its functional significance remain largely unexplored. In this study, via the use of TurboID- based proximity labeling technology combined with an RNAi-based reverse genetic screen, we identified two novel components of the E granule, EGC-2/C27B7.5 and EGC-3/F59G1.8, which initiate E granule assembly. The depletion of EGC-2 or EGC- 3 disrupts the perinuclear localization of the EGO and PICS complexes, both of which are enriched in E granules and are required for E-class siRNA and piRNA biogenesis, respectively. Small RNAomic analyses revealed that both EGC-2 and EGC-3 promote the production of 5 E-class siRNA, whereas prohibiting piRNA maturation. Taken together, our results elucidate the roles of EGC-2 and EGC-3 in maintaining E granule integrity and small RNA homeostasis, and suggest that intracellular condensation may exert distinct regulatory effects on different biological processes. Additionally, the combination of proximity labeling technology and reverse genetic screening provides a robust strategy for studying the assembly of biomolecular condensates.

genetics↗

bioRxiv2024

Drug Response Modeling across Cancers: Proteomics vs. Transcriptomics

Cancer cell lines are the most common in-vitro models for the evaluation of anti-cancer drug sensitivities. Past studies have been conducted to decipher and characterize the pharmacogenomic feature of cell lines based on other omics data, such as genomic mutation data and whole-genome RNA sequencing (RNA-seq) profiles. In particular, proteomic data is also an essential component for the characterization of tumours. However, different from RNA-seq datasets rich in numerous transcriptome profiles of cancer cell lines and cell viability assay of drug responses, the pharmacogenomic protein quantifications are relatively scarce. With the availability of the recently enriched proteomic dataset ProCan-DepMapSanger, we systematically evaluated the interplays among genomic mutations, transcription, and protein expressions across cancer cell lines. In general, blood cancers have higher RNA-protein correlations than those in solid cancers. The differential expression analysis on protein data helped identify more expressional and functional impact of genomic mutations of cancer genes. We also integrated the proteomic map with drug molecular chemical features to construct a bi-modal machine learning model to infer the drug sensitivities of cancer cell lines. Our results demonstrated that protein quantifications can lead to better drug response prediction performance than the model trained on transcriptome profiles. In addition, integrating protein data with drug chemical features, represented as molecular graphs and learned by Graph Neural Network, outperformed the state-of-the-art model DeepOmicNet for drug response prediction in proteomics.

bioinformatics↗

bioRxiv2024

DynaMAP: Dynamic Microbiome Abundance Profiling through high density optical mapping

Microbial abundance profiling is rapidly becoming an essential method in biomedical research, though it is often costly and time intensive. We present DynaMAP, a rapid approach for microbiome profiling that uses single-molecule imaging to develop an optical map of metagenomic DNA. DynaMAP achieves strain-level taxonomic profiling without requiring base-by-base sequencing and with a one-day turnaround time. In doing so, it delivers microbiome profiling that is comparable to shotgun sequencing but operates more efficiently, strongly expanding the possibility for microbiome analysis.

microbiology↗

bioRxiv2024

Degradation of intrinsically disordered proteins using proteolysis targeting nanobody conjugate

Intrinsically disordered proteins (IDPs) play crucial roles in diverse cellular processes and are implicated in numerous diseases. However, due to the inherently unstructured nature, it is challenging to modulate their function using small molecule degraders or inhibitors. To address this, we introduced proteolysis targeting nanobody conjugate (PROTNC) composed of a nanobody that binds to an IDP, an E3 ligase ligand, and a cyclic cell-penetrating peptide for efficient intracellular delivery. We first showed a general-purpose PROTNC system capable to degrade different endogenous IDPs based on a co-condensate formation mechanism. We also introduced a straightforward strategy to degrade individual IDPs using specific PROTNCs equipped with a nanobody recognizing that particular IDP. Notably, we demonstrated the degradation of TPX2 which is an undruggable oncogenic IDP responsible for microtubule nucleation. This targeted degradation effectively inhibited cancer cell proliferation by disrupting bipolar mitotic spindle assembly in metaphase and directed cells to apoptotic pathway.

cell biology↗

bioRxiv2024

De novo non-compartmentalized microorganism for directed evolution of nanobodies

The creation of de novo microorganisms is a fascinating task, and microorganisms offer a wide range of applications for humanity. Herein, we designed a de novo microorganism, termed Syn-phage, as an artificial unencapsulated mimic of naturally occurring phage capable of evolving nanobodies with improved properties. Syn-phage composes unified genomic and phenotypic materials with phenotypic proteins encoded from its genetic information. Since Syn-phage can undergo amplification in E. coli. host, we established a protocol for the evolution of nanobodies with improved avidity or affinity, allowing super-resolution live-cell immunoimaging of endogenous proteins, a challenge posed in immunolabeling with antibodies. Utilizing a Syn-phage evolved nanobody, we visualized the microtubule nucleation factor hTPX2, recorded the dynamics of it during mitotic progression, and imaged it at a resolution beyond the diffraction limit of light via an AI-powered imaging pipeline. Hence, this study demonstrated the possibility to design de novo microorganisms for capitative applications.

biochemistry↗

bioRxiv2024

Association between COVID-19 Disease Severity and T Cell Receptor Repertoire

During the COVID-19 pandemic, while most infected individuals experienced mild to moderate symptoms, a significant subset developed severe illness. A clinical test distinguishing between mild and severe cases could inform effective treatment strategies. Toward the latter stages of the pandemic, it became evident that vaccination or prior infection cannot entirely prevent reinfection. However, they are crucial in reducing the risk of severe disease by inducing T-cell memory. T cell receptors (TCRs), which can be obtained from human blood, serve as valuable biomarkers for monitoring T cell responses to SARS-CoV-2 infection. In this study, we investigated the associations between TCR metrics and COVID-19 severity and found significant associations. Furthermore, such associations could depend on the subset of TCRs used (e.g., TCRs from CD8+ T or CD4+ T cells) and when the TCRs were collected.

bioinformatics↗

bioRxiv2024

Exploring the Neural Basis and Validity of Ordinal Emotion Representation through EEG

Accurately measuring emotion is a major challenge in advancing the understanding of human emotion and developing emotional artificial intelligence. In many existing studies, participants emotional ratings in interval scales are considered the true reflection of their emotional experiences. However, recent research suggests that ordinal annotations of emotions can more accurately capture the emotional expression process, providing a potential method for more precise emotion measurement. However, our understanding of the characteristics and validity of this new form of emotion representation is still relatively lacking. In particular, there is a lack of research using neural signals to explore the validity and neural basis of ordinal emotion representation. In this study, we used a video-elicited electroencephalogram (EEG) dataset (n = 123) to identify the neural basis of ordinal emotion representation and demonstrate its validity from a neural perspective. Furthermore, we explored various characteristics of ordinal emotion representation, showing how it is superior to the interval form. First, we conducted inter-situation representational similarity analysis (RSA) and inter-subject RSA to test the degree to which ordinal representation captures both group commonalities and individual differences of emotion. Next, we investigated the characteristics of ordinal representation under different combinations of emotion items, including uni-variate and multivariate emotions, positive and negative emotions. Our results show that both group commonalities and inter-subject variations in EEG features are better explained by ordinal emotion representations than by interval ones. Multivariate ordinal representations showed better inter-subject reliability and higher representational similarity with EEG features compared to uni-variate counterparts, highlighting the co-occurrence nature of human emotions. Compared to negative emotions, ordinal representation showed greater improvements for positive emotions, suggesting that the complexity of positive emotions is well captured by ordinal representations. Taken together, these findings demonstrate that multivariate ordinal emotion ratings provide a more accurate measure of real emotional experience, which is crucial for enabling machines to precisely understand and express human emotions.

neuroscience↗

bioRxiv2024

Engineered mitochondrial A-to-G editors with enhanced efficiency and targeting scope

Mitochondrial base editing is a powerful technology, but the current A-to-G mitochondrial DNA (mtDNA) base editors are reluctant to achieve high efficiency, which is critical for mtDNA engineering. Through laboratory evolution, TadA-8e variants were discovered with substantially increased activity and expanded targeting compatibility, especially for previously unfavorite sequence contexts, when applied to both nuclear and mitochondrial ABEs. Further engineering of the mtDNA editors (eTd-mtABEs) dramatically reduced both DNA and RNA off-targeting effects and enhanced strand-selective A-to-G editing with substitution of DddA to DNA nickases. Moreover, the eTd-mtABEs induced up to 145-fold editing frequencies compared to previous mtDNA adenine base editors in rat cells, and installed targeted mutations in all injected rat embryos with up to 74% efficiency in founders, resulting generation of SNHL and Leigh syndrome models with severe defects. It suggests that eTd-mtABEs are promising mtDNA engineering technology for basic research and for translational studies. HighlightsO_LIEngineered TadA variants enhance nuclear and mitochondrial ABE activity in all sequence contexts. C_LIO_LIAdditional mutations in eTd-mtABEs further minimize both DNA and RNA off-targeting effects. C_LIO_LIEngineered TadA variants are compatible to DNA nickase assisted strand-selective mtDNA editing. C_LIO_LIeTd-mtABEs enable highly efficient generation of rat mtDNA disease models with severe phenotypes. C_LI

bioengineering↗

bioRxiv2024

GOBoost: Leveraging Long-Tail Gene Ontology Terms for Accurate Protein Function Prediction

MotivationWith the advancement of deep learning, researchers have increasingly proposed computational methods based on deep learning techniques to predict protein function. However, many of these methods treat protein function prediction as a multi-label classification problem, often overlooking the long-tail distribution of functional labels (i.e., Gene Ontology Terms) in datasets. To address this issue, we propose the GOBoost method, which incorporates the proposed long-tail optimization ensemble strategy. Besides, GOBoost introduces the proposed global-local label graph module and multi-granularity focal loss function to enhance long-tail functional information, mitigate the long-tail phenomenon, and improve overall prediction accuracy. ResultsWe evaluate GOBoost and other state-of-the-art (SOTA) protein function prediction methods on the PDB and AF2 datasets. The GOBoost outperformed SOTA methods across all evaluation metrics on both datasets. Notably, in the AUPR evaluation on the PDB test set, GOBoost improved by 10.71%, 35.91%, and 22.71% compared to the SOTA HEAL method in the MF, BP, and CC functions. The experimental results demonstrate the necessity and superiority of designing models from the label long-tail distribution perspective. Availabilityhttps://github.com/Cao-Labs/GOBoost Contactcaora@plu.edu

bioinformatics↗

bioRxiv2024

Time-Dependent Material Properties and Composition of the Nonhuman Primate Uterine Layers Through Gestation

The uterus is central to the establishment, maintenance, and delivery of a healthy pregnancy. Biomechanics is an important contributor to pregnancy success, and alterations to normal uterine biomechanical functions can contribute to an array of obstetric pathologies. Few studies have characterized the passive mechanical properties of the gravid human uterus, and ethical limitations have largely prevented the investigation of mid-gestation periods. To address this key knowledge gap, this study seeks to characterize the structural, compositional, and time-dependent micro-mechanical properties of the nonhuman primate (NHP) uterine layers in nonpregnancy and at three time-points in pregnancy: early 2nd, early 3rd, and late 3rd trimesters. Distinct material and compositional properties were noted across the different tissue layers, with the nonpregnant endometrium and pregnant decidua being the least stiff, most viscous, least diffusible, and most hydrated layers of the NHP uterus. Pregnancy induced notable compositional and structural changes in the decidua and myometrium but had no effect on their micro-mechanical properties. Further comparison to published human data revealed marked similarities across species, with minor differences noted for the perimetrium and nonpregnant endometrium. This work provides insights into the material properties of the NHP uterus and demonstrates the validity of NHPs as a model for studying certain aspects of human uterine biomechanics.

bioengineering↗

bioRxiv2024

Single-cell resolution spatial analysis of antigen-presenting cancer-associated fibroblast niches

Recent studies have identified a unique subtype of cancer-associated fibroblasts (CAFs) termed antigen-presenting CAFs (apCAFs), which remain the least understood CAF subtype. To gain a comprehensive understanding of the origin and function apCAFs, we construct a fibroblast molecular atlas across 14 types of solid tumors. Our integration study unexpectedly reveals two distinct apCAF lineages present in most cancer types: one associated with mesothelial-like cells and the other with fibrocytes. Using a high-resolution single-cell spatial imaging platform, we characterize the spatial niches of these apCAF lineages. We find that mesothelial-like apCAFs are located near cancer cells, while fibrocyte-like apCAFs are associated with tertiary lymphoid structures. Additionally, we discover that both apCAF lineages can up-regulate the secreted protein SPP1, which facilitates primary tumor formation and peritoneal metastasis. Taken together, this study offers an unprecedented resolution in analyzing apCAF lineages and their spatial niches.

cancer biology↗

bioRxiv2024

Systemic genome-epigenome analysis captures the lineage specificity and functional significance for MYB associated super-enhancer in gastrointestinal adenocarcinoma

The gastrointestinal adenocarcinoma is a major cancer type for the digestive system, ranking as the top cause of cancer-related deaths worldwide. In contrast to the large body of studies on the protein-coding regions mutations, the knowledge about the landscape of its non-coding regulatory elements is still insufficient. Combining the analysis of active enhancers profile and genomic structural variation, we discovered and validated a lineage-specific super-enhancer for MYB in gastrointestinal adenocarcinoma. This super-enhancer is constituted by a predominant enhancer e4 and multiple facilitator enhancers, whose transcriptional activity is controlled by the direct binding of HNF4A and MYB itself. Suppression of the super-enhancer downregulated the expression of MYB, inhibited the downstream Notch signaling and prevented the development of gastrointestinal adenocarcinoma in vitro and in vivo. Our study revealed a non-coding variation-based mechanism to affect MYB expression in a lineage-specific manner, which provided an inspiring insight into the carcinogenic mechanism and therapeutic strategies for gastrointestinal adenocarcinoma.

genetics↗

bioRxiv2024

The paternally derived genome opposes seed dormancy induction by the mother plant in Arabidopsis

Seed dormancy in Arabidopsis is known to be mediated by the interaction of maternal and zygotic genomes during seed maturation. While studies have revealed the extensive influence of maternal processes on dormancy and germination, less is known about the influence of the father. Here we exploit differences in ploidy to explore the role of the paternal genome on progeny seed dormancy. We show that paternal genome acts to reduce seed dormancy regardless of maternal genome dose, resulting in lower dormancy in tetraploid Arabidopsis versus genetically identical diploids. We show that this paternal effect requires synthesis of RNA Polymerase IV-dependent RNAs in the male gametophyte which oppose the dormancy-inducing effects of maternal siRNAs on seed coat and endosperm development. We conclude that the paternal genome has evolved to subvert the dormancy-inducing role of the mother plant in progeny seeds.

plant biology↗

bioRxiv2024

DNA StairLoop: Achieving High Error-correcting and Parallel-processing Capabilities in DNA-based Data Storage

DNA-based data storage is a promising solution to the challenges of large-scale data storage. However, the low throughput of the mainstream inkjet-based DNA synthesis method has hindered its widespread adoption. In contrast, high-throughput electrochemical synthesis provides higher throughput but with more nucleotide insertion, deletion, and substitution errors. Here, we propose an innovative coding scheme with high error correction capabilities, called DNA StairLoop. This coding scheme features a staircase interleaver and allows for component codes such as the convolutional code and the ow-density Parity-check code, allowing for flexible adaptation of the coding scheme. Both the row and the column decoders are soft input and soft output, enabling further improvement in data recovery accuracy through iterative decoding. The staircase interleaver facilitates extensive parallel decoding capabilities while effectively preserving parallelism across a multitude of nodes. In the in vitro experiment, DNA StairLoop successfully recovered the raw information in a staircase block with a nucleotide error rate of more than 8%. The simulations revealed that the DNA StairLoop can correct 10% nucleotide errors. Moreover, in parallel computing processing, the decoding time of our code continues to decrease dramatically.

synthetic biology↗

bioRxiv2024

RING1 dictates GSDMD-mediated inflammatory response and host susceptibility to pathogen infection

RING1 is an E3 ligase component of the polycomb repressive complex 1 (PRC1) with known roles in chromatin regulation and cellular processes such as apoptosis and autophagy. However, its involvement in inflammation and pyroptosis needs to be better characterized. Here, we found that human RING1, but not RING2, promoted K48-linked ubiquitination of Gasdermin D (GSDMD) and acted as a negative regulator of pyroptosis and bacterial infection. Ring1 knockout mice increased the bacterial loads and mortality rate by treatment of S. typhimurium. Upon infection by M. tuberculosis (Mtb) H37Rv strain, RING1 deletion initially reduced bacterial loads but later increased lung inflammation and impaired immune defense responses in mice. Also, Ring1 knockout exacerbated acute sepsis induced by lipopolysaccharide (LPS). Mechanistically, RING1 directly interacts with GSDMD and ubiquitinates the K51 and K168 sites of GSDMD for K48-linked proteasomal degradation, thereby inhibiting pyroptosis. Inhibition of RING1 E3 ligase activity by mutation or small molecular compound increased GSDMD level and cell death during pyroptosis. Our findings reveal that RING1 dictates GSDMD-mediated inflammatory response and host susceptibility to pathogen infection, highlighting RING1 as a potential therapeutic target for combating infectious diseases. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/622030v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@e6d4a6org.highwire.dtl.DTLVardef@92f137org.highwire.dtl.DTLVardef@11a69c4org.highwire.dtl.DTLVardef@115dc0b_HPS_FORMAT_FIGEXP M_FIG Graphic Abstract C_FIG HighlightsO_LIE3 ubiquitin ligase RING1 targets GSDMD for proteasomal degradation. C_LIO_LIMice lacking RING1 are more vulnerable to S. typhimurium infection and LPS- induced sepsis. C_LIO_LIIn the early stages of M. tuberculosis (Mtb) infection, Ring1-/- mice exhibit efficient bacterial clearance. C_LIO_LIChronic inflammation in Ring1-/- mice impairs the ability to control Mtb in the late stages of infection. C_LI

cell biology↗

bioRxiv2024

xIV-LDDMM Toolkit: A Suite of Image-Varifold Based Technologies for Representing and Mapping 3D Imaging and Spatial-omics Data Simultaneously Across Scales

Advancements in imaging and molecular techniques enable the collection of subcellular-scale data. Diversity in measured features, resolution, and physical scope of capture across technologies and experimental protocols pose numerous challenges to integrating data with reference coordinate systems and across scales. This paper describes a collection of technologies that we have developed for mapping data across scales and modalities, such as genes to tissues, specifically in a 3D setting. Our collection of technologies include (i) an explicit censored data representation for the partial matching problem mapping whole brains to subsampled subvolumes, (ii) a multi, scale-space optimization technology for generating resampling grids optimized to represent spatial geometry at fixed complexities, and (iii) mutual-information based functional feature selection. We integrate these technologies with our cross-modality mapping algorithm through the use of image-varifold measure norms to represent universally data across scales and imaging modalities. Collectively, these methods afford efficient representations of peta-scale imagery providing the algorithms for mapping from the nano to millimeter scales, which we term cross-modality image-varifold LDDMM (xIV-LDDMM).

bioengineering↗

bioRxiv2024

Structure of Blm10:13S proteasome intermediate reveals parallel assembly pathways for the proteasome core particle

Proteasomes are formed by chaperone-assisted assembly of core particles (CPs) and regulatory particles (RPs). The CP chaperone dimer Pba1/Pba2 binds early to proteasome subunits, and is thought to be replaced by Blm10 to form Blm10:CP, which promotes ATP-independent degradation of disordered proteins. Here, we present evidence of distinct parallel assembly pathways for CP by solving five cryo-EM structures including a Blm10:13S pre-assembly intermediate. Our data conflict with the current model of Blm10 and Pba1/Pba2 sequential activity in a single assembly pathway, as we find their CP binding is mutually exclusive and both are present on early and late assembly intermediates. CP affinity for Pba1/Pba2 is reduced during maturation, promoting Pba1/Pba2 release. We find Blm10 undergoes no such affinity switch, suggesting this pathway predominantly yields mature Blm10-bound CP. Altogether, our findings conflict with the current paradigm of sequential CP binding to instead indicate parallel assembly pathways by Pba1/Pba2 and Blm10.

biochemistry↗

bioRxiv2024

PreDigs: a Database of Context-specific Cell-type Markers and Precise cell subtypes for Digestive Cell Annotation

Research on cell type markers aids investigators in exploring the diverse cellular compositions within gastrointestinal tumors, enhancing our understanding of tumor heterogeneity and its implications for disease progression and treatment response. However, issues such as the integration of large-scale datasets and the lack of standardized cell type identification hinder comprehensive characterization. Here, we developed a user-friendly web interface called PreDigs (Predicted Signatures in Digestive System), which offers 124 tailored scRNA-seq datasets available for download, encompassing over 3.4 million cells. After unsupervised clustering, we unified the identification and naming of subtype labels, ultimately constructing a cell ontology tree that includes 142 cell types, with up to eight hierarchical levels. Meanwhile, we calculated three different context-specific cell-type markers--Cell Markers, Subtype Markers, and TPN Markers--based on various application requirements within or across tissues. Through the integrated analysis of PreDigs gastrointestinal data, we identified distinct cell subpopulations exclusive to tumors, one of which corresponds to tumor-specific endothelial cells (TEC). Furthermore, PreDigs offers online cell annotation tools that empower users to perform single-cell classification with greater flexibility, accessible at https://www.biosino.org/predigs/.

bioinformatics↗