Biology subjects

Yu, W.

Publications and source records attributed to Yu, W..

At least 109 records · Page 6Linked to original sources

bioRxiv2023

Endogenous bystander killing mechanisms enhance the activity of novel FAP-CAR-T cells against glioblastoma

ObjectivesCAR-T cells are being investigated as a novel immunotherapy for glioblastoma, but clinical success has been limited. We recently described fibroblast activation protein (FAP) as an ideal target antigen for glioblastoma immunotherapy, with expression on both tumor cells and tumor blood vessels. However, CAR-T cells targeting FAP have never been investigated as a therapy for glioblastoma. MethodsWe generated a novel FAP targeting CAR with CD3{zeta} and CD28 signaling domains and tested the resulting CAR-T cells for their lytic activity and cytokine secretion function in vitro (using real-time impedance, flow cytometry, imaging, and bead-based cytokine assays), and in vivo (using a xenograft mimicking the natural heterogeneity of human glioblastoma). ResultsFAP-CAR-T cells exhibited target specificity against model cell lines and potent cytotoxicity against patient-derived glioma neural stem cells, even when only a subpopulation expressed FAP, indicating a bystander killing mechanism. Using co-culture assays, we confirmed FAP-CAR-T cells mediate bystander killing of antigen-negative tumor cells, but only after activation by FAP-positive target cells. This bystander killing was at least partially mediated by soluble factors and amplified by IL-2 which activated the non-transduced fraction of the CAR-T product. Finally, a low dose of intravenously administered FAP-CAR-T cells controlled, without overt toxicity, the growth of subcutaneous tumors created using a mixture of antigen-negative and antigen-positive glioblastoma cells. ConclusionsOur findings advance FAP as a leading candidate for clinical CAR-T therapy of glioblastoma and highlight under-recognized antigen non-specific mechanisms that may contribute meaningfully to the antitumor activity of CAR-T cells.

immunology↗

bioRxiv2023

RUNX1 is required in granulocyte-monocyte progenitors to attenuate inflammatory cytokine production by neutrophils

The transcription factor RUNX1 is mutated in familial platelet disorder with associated myeloid malignancies (FPDMM) and in sporadic myelodysplastic syndrome and leukemia. RUNX1 regulates inflammation in multiple cell types. Here we show that RUNX1 is required in granulocyte-monocyte progenitors (GMPs) to restrict the inflammatory response of neutrophils to toll-like receptor 4 (TLR4) signaling. Loss of RUNX1 in GMPs increased the TLR4 coreceptor CD14 on neutrophils, which contributed to neutrophils increased inflammatory cytokine production in response to the TLR4 ligand lipopolysaccharide. RUNX1 loss increased the chromatin accessibility of retrotransposons in GMPs and neutrophils and induced a type I interferon signature characterized by enriched footprints for signal transducer and activator of transcription (STAT1::STAT2) and interferon regulatory factors (IRF) in opened chromatin, and increased expression of interferon-stimulated genes. The overproduction of inflammatory cytokines by neutrophils was reversed by inhibitors of type I IFN signaling. We conclude that RUNX1 restrains the chromatin accessibility of retrotransposons in GMPs and neutrophils, and that loss of RUNX1 increases proinflammatory cytokine production by elevating tonic type I interferon signaling.

developmental biology↗

bioRxiv2023

Artificial intelligence-driven morphology-based enrichment of malignant cells from body fluid

Cell morphology is a fundamental feature used to evaluate patient specimens in pathological analysis. However, traditional cytopathology analysis of patient effusion samples is limited by low tumor cell abundance coupled with high background of non-malignant cells, restricting the ability for downstream molecular and functional analyses to identify actionable therapeutic targets. We applied the Deepcell platform that combines microfluidic sorting, brightfield imaging, and real-time deep learning interpretations based on multi-dimensional morphology to enrich carcinoma cells from malignant effusions without cell staining or labels. Carcinoma cell enrichment was validated with whole genome sequencing and targeted mutation analysis, which showed higher sensitivity for detection of tumor fractions and critical somatic variant mutations that were initially at low-levels or undetectable in pre-sort patient samples. Combined, our study demonstrates the feasibility and added value of supplementing traditional morphology-based cytology with deep learning, multi-dimensional morphology analysis, and microfluidic sorting.

pathology↗

bioRxiv2023

Pan-cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics

The National Cancer Institutes Clinical Proteomic Tumor Analysis Consortium (CPTAC) provides unique opportunities for cancer target discovery using protein expression. Proteomics data from CPTAC tumor types have been primarily generated using a multiplex tandem mass tag (TMT) approach, which is designed to provide protein quantification relative to reference samples. However, relative protein expression data is suboptimal for prioritization of targets within a tissue type, which requires additional reprocessing of the original proteomics data to derive absolute quantitation estimation. We evaluated the feasibility of using differential protein analysis coupled with intensity-based absolute quantification (iBAQ) to identify tumor-enriched and highly expressed cell surface antigens, employing tandem mass tag (TMT) proteomics data from CPTAC. Absolute quantification derived from TMT proteomics data was highly correlated with that of label-free proteomics data from the CPTAC colon adenocarcinoma cohort, which contains proteomics data measured by both approaches. We validated the TMT-iBAQ approach by comparing the iBAQ value to the receptor density value of HER2 and TROP2 measured by flow cytometry in about 30 selected breast and lung cancer cell lines from the Cancer Cell Line Encyclopedia. Collections of these tumor-enriched and highly expressed cell surface antigens could serve as a valuable resource for the development of cancer therapeutics, including antibody-drug conjugates and immunotherapeutic agents.

bioinformatics↗

bioRxiv2023

Acetylated dietary protein contributes to the acetylome in cells via KARS-mediated intra-translational deposition of acetyl-lysine in nascent protein

N6-acetyl-L-lysine residue is abundant in dietary protein but less is known about its potential influences on the diet-consumers. We herein report that KARS mediates intra- translational deposition of diet-derived N6-acetyl-L-lysine in nascent proteins to contribute the acetylome in cells. Acetylated dietary protein is a direct source of N6-acetyl-L-lysine that can widely and substantially contribute the acetylome in multiple organs of mice. By analyzing the co-crystal structure of Lysyl-tRNA synthetase (KARS) in complex with N6- acetyl-L-lysyl-AMP and pyrophosphate, together with in vitro biochemical assays, we learned that KARS can utilize N6-acetyl-L-lysine to produce N6-acetyl-L-lysyl-AMP and transfers the N6-acetyl-L-lysyl-moiety to lysine cognate tRNA to generate N6-acetyl-L- lysyl-tRNA, which introduces N6-acetyl-L-lysine into growing nascent polypeptide and intra-translationally results in protein acetylation. This undocumented protein modification mechanism is inherently different from post-translational modification (PTM) and termed as intra-translational modification (ITM). ITM can functionally mimic PTM mechanisms to deposit acetylation in histones to decondense chromatin. It can also modify PTM- inaccessible regions that are buried inside and functionally important to proteins. ITM is expected to extend the repertoire of acetylome and improve our understandings in protein modification modes in cells.

biochemistry↗

bioRxiv2023

VHL Mutation Drives Human Clear Cell Renal Cell Carcinoma Progression Through PI3K/AKT-Dependent Cholesteryl Ester Accumulation

BackgroundCholesteryl ester (CE) accumulation in intracellular lipid droplets (LDs) is an essential signature of clear cell renal cell carcinoma (ccRCC), but its molecular mechanism and pathological significance remain elusive. MethodsEnabled by the label-free Raman spectromicroscopy, which integrated stimulated Raman scattering microscopy with confocal Raman spectroscopy on the same platform, we quantitatively analyzed LD distribution and composition at the single cell level in intact ccRCC cell and tissue specimens in situ without any processing or exogenous labeling. Since we found that commonly used ccRCC cell lines actually did not show the CE-rich signature, primary cancer cells were isolated from human tissues to retain the lipid signature of ccRCC with CE level as high as the original tissue, which offers a preferable cell model for the study of cholesterol metabolism in ccRCC. Moreover, we established a patient-derived xenograft (PDX) mouse model that retained the CE-rich phenotype of human ccRCC. FindingsSurprisingly, our results revealed that CE accumulation was induced by tumor suppressor VHL mutation, the most common mutation of ccRCC. Moreover, VHL mutation was found to promote CE accumulation by upregulating HIF and subsequent PI3K/AKT/mTOR/SREBPs pathway. Inspiringly, inhibition of cholesterol esterification remarkably suppressed ccRCC aggressiveness in vitro and in vivo with negligible toxicity, through the reduced membrane cholesterol-mediated downregulations of integrin and MAPK signaling pathways. InterpretationCollectively, our study improves current understanding of the role of CE accumulation in ccRCC and opens up new opportunities for treatment. FundingThis work was supported by National Natural Science Foundation of China (No. U23B2046 and No. 62027824), National Key R&D Program of China (No. 2023YFC2415500), Fundamental Research Funds for the Central Universities (No. YWF-22-L-547), PKU-Baidu Fund (No. 2020BD033), Peking University First Hospital Scientific and Technological Achievement Transformation Incubation Guidance Fund (No.2022CX02), and Beijing Municipal Health Commission (No. 2020-2Z-40713).

cancer biology↗

bioRxiv2022

FCGRT, a cancer-derived immunoglobulin G binding protein, mediates the malignant phenotype of glioma

CIgG has received increasing attention, and was first discovered by our group to indicate poor prognosis in glioma. Furthermore, by protein mass spectrometry, we found that FCGRT can combine with CIgG. However, the study of FCGRT in glioma has not been reported. We used the CGGA325, TCGA dataset and immunohistochemistry to verify the importance of FCGRT on the prognosis of glioma patients. Single cell sequencing data analysis evaluated that the role of FCGRT in the microenvironment of glioma. Estimate, ssGSEA, EPIC and xCell were used for immune infiltration analysis. FCGRT was knocked down in U251 cells to detect the effect of FCGRT on the malignant development of glioma. These results showed that patients with higher FCGRT expression had a shorter overall survival. FCGRT was closely related to the tumor microenvironment, especially to macrophages in the tumor microenvironment (r=0.743, p<0.001). Interestingly we also found that FCGRT was positively correlated with IGHG1. Finally, we found that knock-down of FCGRT resulted in a decrease in proliferation, migration and invasion of U251 cells. Taken together, we believe that FCGRT is an independent prognostic factor for glioma patients, and its possible mechanism is to promote proliferation and invasion of tumor cells by interacting with CIgG.

bioinformatics↗

bioRxiv2022

Repetition accelerates neural markers of memory consolidation

No sooner is an experience over than its neural memory representation begins to be strengthened and transformed through the process of memory replay. Using fMRI, we examined how memory strength manipulated through repetition during encoding modulates post-encoding replay in humans. Results revealed that repetition did not increase replay frequency in the hippocampus. However, replay in cortical regions and hippocampal-cortical coordinated replay were significantly enhanced for repeated events, suggesting that repetition accelerates the consolidation process. Interestingly, we found that replay frequency in both hippocampus and cortex modulated behavioral success on an immediate associative recognition test for the weakly encoded information, indicating a significant role for post-encoding replay in rescuing once-presented events. Together, our findings highlight the relationships of replay to stabilizing weak memories and accelerating cortical consolidation for strong memories.

neuroscience↗

bioRxiv2022

Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model

MotivationIdentifying the B-cell epitopes is an essential step for guiding rational vaccine development and immunotherapies. Due to experimental approaches being expensive and time-consuming, many computational methods have been designed to assist B-cell epitope prediction. However, existing sequence-based methods have limited performance since they only use contextual features of the sequential neighbors while neglecting structural information. ResultsBased on the recent breakthrough of AlphaFold2 in protein structure prediction, we propose GraphBepi, a novel graph-based model for accurate B-cell epitope prediction. GraphBepi first generates the effective information sequence representations and protein structures from antigen sequences through the pretrained language model and AlphaFold2, respectively. GraphBepi then applies the edge-enhanced deep graph neural network (EGNN) to capture the spatial information from predicted protein structures and leverages the bidirectional long short-term memory neural networks (BiLSTM) to capture long-range dependencies from sequences. The low-dimensional representation learned by EGNN and BiLSTM is then combined to predict B-cell epitopes through a multilayer perceptron. Through comprehensive tests on the curated epitope dataset, GraphBepi was shown to outperform the state-of-the-art methods by more than 5.5% and 44.0% in terms of AUC and AUPR, respectively. We also provide the GraphBepi web server that is freely available at https://biomed.nscc-gz.cn/apps/GraphBepi. AvailabilityThe datasets, pre-computed features, source codes, and the pretrained model of GraphBepi are available at https://github.com/biomed-AI/GraphBepi. Contactyangyd25@mail.sysu.edu.cn or gaojz@nankai.edu.cn

bioinformatics↗

bioRxiv2022

Atomistic structure of the interlocking claw-like amyloid fibril of full-length glucagon

Glucagon is a peptide hormone which posits a significant potential as a therapeutic molecule for various human diseases. One of the major challenges hampering medicinal application of glucagon, however, is its insoluble and aggregation-prone property. Although glucagon is dissolvable, it aggregates easily and forms amyloid fibrils. To date, despite many studies to understand how glucagon aggregates and fibrillizes, its detailed amyloidogenesis mechanism is still elusive, particularly due to insufficient structural information of glucagon amyloid fibrils. Here we report the novel structure of a glucagon amyloid fibril, which was determined with cryo-electron microscopy (cryo-EM) to a 3.8-[A] resolution. Our model features with tight and extensive inter-monomer interactions, which efficiently stabilize the entire chain of full-length glucagon into the V-shape conformation, clamping the other monomer to construct a dimeric architecture orthogonal to the fibril axis. Notably, the current structure significantly differs from the previous solid-state NMR model, which gives direct evidence for multiple and dynamic amyloidogenesis mechanisms of glucagon. In addition, our results are expected to provide important insights to appreciate the molecular details of glucagon in its initial amyloidogenesis and fibril elongation processes.

molecular biology↗

bioRxiv2022

Spatiotemporal orchestration of multicellular transcriptional programs and communications in spinal cord injury

While spinal cord injury (SCI) involves a complex cascade of cellular and pathological changes that last for months to years, the most dramatic and comprehensive molecular rewiring and multicellular re-organization occur in the first few days, which determine the overall progression and prognosis of SCI, yet remain poorly understood. Here, we resolved the spatiotemporal architecture of multicellular gene expression in a mouse model of acute SCI, and revealed the coordinated gene co-expression networks, the upstream regulatory programs, and in situ cell-cell interactions that underlay the anatomic disorganization as well as the immune and inflammatory responses conferring the secondary injury. The spatial transcriptomic analysis highlights that the genes and cell types in the white matter (WM) play a more active and predominant role in the early stage of SCI. In particular, we identified a distinct population of WM-originated, Igfbp2-expressing reactive astrocytes, which migrated to the grey matter and expressed multiple axon/synapse-supporting molecules that may foster neuron survival and spinal cord recovery in the acute phase. Together, our dataset and analyses not only showcase the spatially-defined molecular features endowing the cell (sub)types with new biological significance but also provide a molecular atlas for disentangling the spatiotemporal organization of the mammalian SCI and advancing the injury management.

neuroscience↗

bioRxiv2022

Reconstruct a eukaryotic chromosome arm by de novo design and synthesis

The genome of an organism is inherited from its ancestor and keeps evolving over time, however, how much the current version could be altered remains unknown. Here, we use the left arm of chromosome XII (chrXIIL) as an example to probe the genome plasticity in Saccharomyces cerevisiae. A neochromosome was designed to harbor originally dispersed genes. The essentiality of sequences in chrXIIL was dissected by targeted DNA removal, chromosome truncation and random deletion. Notably, 12 genes were sufficient for survival, while 25 genes are required to retain robust fitness. Next, we demonstrated these genes could be reconstructed using synthetic regulatory sequences and recoded open-reading frames with "one-amino-acid-one-codon" strategy. Finally, we built a neochromsome, which could substitute for chrXIIL for cell viability, with these reconstructed genes. Our work not only highlights the high plasticity of yeast genome, but also illustrates the possibility of making functional chromosomes with completely artificial sequences. HIGHLIGHTSO_LIA neochromosome was designed to facilitate the assembly of exogenous DNA for stable expression in yeast C_LIO_LIThe left arm of chrXII could be minimized to just 12 genes to maintain viability, but additional genes were required to retain robust fitness C_LIO_LIComprehensive recoding and transcriptional refactoring using artificial regulatory sequences produced a functional chromosome arm C_LIO_LIA completely reconstructed neochromosome could replace the chrXIIL to maintain comparable fitness C_LI

synthetic biology↗

bioRxiv2022

Deciphering Spatial Domains by Integrating Histopathological Image and Transcriptomics via Contrastive Learning

Recent advances in spatial transcriptomics have enabled measurements of gene expression at cell/spot resolution meanwhile retaining both the spatial information and the histopathological images of the tissues. Deciphering the spatial domains of spots in the tissues is a vital step for various downstream tasks in spatial transcriptomics analysis. Existing methods have been developed for this purpose by combining gene expression and histopathological images to conquer noises in gene expression. However, current methods only use the histopathological images to construct spot relations without updating in the training stage, or simply concatenate the information from gene expression and images into one feature vector. Here, we propose a novel method ConGI to accurately decipher spatial domains by integrating gene expression and histopathological images, where the gene expression is adapted to image information through contrastive learning. We introduce three contrastive loss functions within and between modalities to learn the common semantic representations across all modalities while avoiding their meaningless modality-private noise information. The learned representations are then used for deciphering spatial domains through a clustering method. By comprehensive tests on tumor and normal spatial transcriptomics datasets, ConGI was shown to outperform existing methods in terms of spatial domain identification. More importantly, the learned representations from our model have also been used efficiently for various downstream tasks, including trajectory inference, clustering, and visualization.

bioinformatics↗

bioRxiv2022

Subchronic oral toxicity study of Aldicarb sulfoxide in Sprague-Dawley rats

Aldicarb sulfoxide is the metabolite of the exceedingly deadly carbamate insecticide Aldicarb. Aldicarb sulfoxide is easily soluble in water and has been identified in groundwater. Moreover, Aldicarb sulfoxide is highly hazardous to mammals. However, the toxicity data of Aldicarb sulfoxide is scarce. Here, the effects of relatively low dosages of exposure to Aldicarb sulfoxide on the blood biochemical, hematological, and histopathological parameters were examined in SD rats. The rats were treated with Aldicarb sulfoxide orally at dosages of 0, 6.3, 18.9, and 56.7 g per kg b.w. daily for 12 weeks. The results revealed that Aldicarb sulfoxide induced significant alterations in liver-related biochemical parameters, glucose level, cholesterol level, and uric acid level. Histopathological testing demonstrated cellular alterations in the liver of 18.9 and 56.7 g/kg BW. Together, our data demonstrated that Aldicarb sulfoxide exposure causes liver injury. Furthermore, the no observed adverse effect level (NOAEL) was less than 6.3 g/kg BW, and the lowest benchmark dose (BMD) value was 0.03 g/kg BW. Accordingly, Aldicarb sulfoxides derived no-effect level (DNEL) was calculated as 0.00035 g/kg BW. Thus, this work is an important reference for supervising Aldicarb sulfoxide in the environment and determining safe exposure limits for Aldicarb sulfoxide.

pharmacology and toxicology↗

bioRxiv2022

The protein phosphatases MoPtc1 and MoPtc2 are induced during pathogen-host interactions and play synergistic roles in regulating MAPK pathways in Magnaporthe oryzae

Reversible protein phosphorylation is essential in cellular signal transduction. The rice blast fungus Magnaporthe oryzae, contains six putative type 2c protein phosphatases namely; MoPtc1, MoPtc2, MoPtc5, MoPtc6, MoPtc7 and MoPtc8 respectively. In this study, we carried out transcription expression analysis and found that MoPtc1, MoPtc2 and MoPtc7 are significantly induced during pathogen-host interactions. Subsequent deletions of MoPtc1 or MoPtc2 not only resulted in increased sensitivity to cell wall stress mediated by Congo red but also increased phosphorylation of Mps1-MAPK. By immunoblot analysis, we found that deletion of both MoPtc1 and MoPtc2 resulted in overstimulation of both the HOG1 and Pmk1 pathways in M. oryzae. We also demonstrate that MoPtc1 is recruited directly to Osm1 by the adaptor protein MoNbp2 to inactivate the Osm1 during hypoosmotic stress unlike in budding yeast. Finally, we show that type 2c protein phosphatases are localized in different cellular compartments in the life cycle of Magnaporthe oryzae. Taken together type 2C protein phosphatases MoPtc1 and MoPtc2 play synergistic roles in regulating MAPK signaling pathways in M. oryzae. This work expands our understanding of the MAPK signaling regulation circuits in M. oryzae and the essential roles of type 2C phosphatases in fine-tuning phosphorylation levels of MAPK during fungal development.

molecular biology↗

bioRxiv2022

A Meta-learning based Graph-Hierarchical Clustering Method for Single Cell RNA-Seq Data

Single cell sequencing techniques enable researchers view complex bio-tissues from a more precise perspective to identify cell types. However, more and more recent works have been done to find more detailed subtypes within already known cell types. Here, we present MeHi-SCC, a method which utilized meta-learning protocol and brought in multi scRNA-seq datasets information in order to assist graph-based hierarchical sub-clustering process. In result, MeHi-SCC outperformed current-prevailing scRNA clustering methods and successfully identified cell subtypes in two large scale cell atlas. Our codes and datasets are available online at https://github.com/biomed-AI/MeHi-SCC

bioinformatics↗

bioRxiv2022

Chemoenzymatic Measurement of Cell-surface Glycan in Single-cell Multiomics: LacNAc as an Example

Despite the rich information of a cells physiological state encoded in the dynamic changes of cell-surface glycans, methods of capturing glycosylation states at the single-cell level are quite limited. Here we report a chemoenzymatic single-cell N-acetyllactosamine (LacNAc) detection method via tagging the LacNAc with a specific DNA barcode. Compared to the lectin-based glycan detection, the chemoenzymatic labeling does not change the transcriptional status of immune cells and is more compatible with scRNA-seq. Integrated analysis of LacNAc and transcriptome of T cells at a single-cell level reveals that the quantity of cell-surface LacNAc is significantly upregulated in activated CD8+ T cells but maintained at the basal level in quiescent CD8+ T cells (i.e., naive and central memory T cells). Further analysis confirms that the LacNAc level is positively correlated to the glycolytic activity of CD8+ T cells at all statues. Taken together, our study demonstrates the feasibility of chemoenzymatic detection of cell-surface glycan in single-cell RNA sequencing-based multiomics with information of TCR sequence and cell-surface epitopes (i.e., scTCR and CITE-seq) and offers a new way to characterize the biological role of glycan in diversified physiological states.

immunology↗

bioRxiv2022

Capturing the Hierarchically Assorted Modules of Protein Interaction in the Organized Nucleome

Nuclear proteins are major constituents and key regulators of the topological organization of nucleome. To elucidate the global connectivity of nucleomic proteins and to decipher the hierarchically organized modules of protein interaction that are involved in nucleomic organization and nuclear events, both formaldehyde and CBDPS crosslinkers were applied sequentially on the in vivo prefixed nuclei to perform a double chemical crosslinking with mass spectrometry (XL-MS) analysis. The integration of dimethyl-labelling with XL-MS generated a quantitative XL-MS workflow (qXL-MS) that consequently identified 5,340 cross-linked peptides (crosslinks) from nucleome. These crosslinks were construed into 1,297 nuclear protein-protein interactions (PPIs), from which discovered were 250 and 26 novel interactors of histones and nucleolar box C/D snoRNP complex, respectively. MONET-based modulomic analysis of their Arabidopsis orthoglous PPIs constructed 27 and 24 master nuclear protein interaction modules (NPIMs) that contain the condensate-forming protein(s) and the intrinsically disordered region (IDR)-containing proteins, respectively. These NPIMs successfully captured the previously reported nuclear protein complexes and nuclear bodies in nucleome. Surprisingly, modulomic analysis showed that these NPIMs were hierarchically assorted into four communities of NPIMs in nucleome including Genome Community and Nucleolus Community. The qXL-MS-based quantitative interactomics finally revealed 17 Hormone-specific module variants participating in a broad range of nuclear events. Thus, this integrated pipeline of qXL-MS and MONET modulomics, named as CHAMPION, is capable of capturing both nuclear protein complexes and nuclear bodies, constructing the topological architecture of protein interaction modules and module variants in nucleome and probably of mapping the protein compositions of condensates. HighlightsO_LIThe formaldehyde and CBDPS crosslinkers coupled qXL-MS discovered 5,340 crosslinked peptides. These crosslinks were construed into 1,297 nuclear protein-protein interactions (PPIs), protein components of which contained 250 and 26 novel interactors of histone octamer and nucleolar box C/D snoRNP complex, respectively, in the intricately organized nucleome. C_LIO_LIThe MONET-based modulomic analysis of these crosslinks captured 95 nuclear protein interaction modules (NPIMs), a portion of which contain both the condensate-forming and the intrinsically disordered region (IDR)-containing proteins. Especially, some NPIMs captured 6 previously reported nuclear protein complexes. C_LIO_LIA number of Hormone-specific module variants were identified by modulomics upon hormone treatment using the hormone significantly up-regulated crosslinks from qXL-MS. Several PPIs and NPIMs have been substantiated with alternative biological experiments. C_LIO_LIThis CHAMPION pipeline has partitioned these NPIMs into four hierarchically and topologically organized communities in nucleome. The molecular functions of those proteins partitioned into C1 and C2 community are specialized in genome organization and nucleolar functions, respectively. C_LI

bioinformatics↗