Biology subjects

Chen, Q.

Publications and source records attributed to Chen, Q..

At least 163 records · Page 9Linked to original sources

bioRxiv2025

Adaptive selection of p53 mutation metaplastic phenotypes in estrogen-independent progression of ER+ tumors: A mechanism for acquired resistance to hormonal therapy.

Estrogen receptor positive (ER+) subtypes of mammary adenocarcinoma comprise 79% of all breast cancer diagnosis and 67% of all breast cancer mortality. The paucity of models of ER+ mammary cancer that mimic human disease and response to treatment has limited critical preclinical study of mechanisms and new therapies for ER+ breast cancer. The Stat1 knockout, 129S6/SvEvTac-Stat1tm1Rds (Stat1-/-), females develop luminal type FoxA1+, ER+, and PR+ mammary carcinomas after prolonged latencies. Initial studies showed that a cell line derived from a Stat1-/- mammary carcinoma was tumorigenic in syngeneic mice, but non-tumorigenic in ovariectomized (Ovx) mice. Here, data shows that Ovx performed after SSM2 tumors establish growth results in ovarian hormone independent growth. The viable post-Ovx tumors were primarily composed of metaplastic CK14+ basal type cells with a high percentage p53 immunohistochemistry (IHC) positive "mutation pattern", rather than the original luminal type tumors with low percent "wild type" pattern p53. Comparing whole exome sequences of ER+ Stat1-/- mammary tumors before and after Ovx, revealed basal keratins, mesenchymal (EMT) phenotypes, and unique mutation profiles in genes, including Trp53 and Prlr, in the estrogen-independent tumors. Our experimental findings are consistent with the clinical evidence of tumor heterogeneity of ER+ breast cancers in patients in recent whole genome sequencing studies. Similarly, spontaneous Stat1-/- tumors with high percentage p53 "mutation pattern" were more basaloid and grew rapidly after Ovx, while retaining high expression of ER and FoxA1. This study demonstrates that the STAT1-/-, ER+ estrogen dependent breast cancers can become resistant to through clonal selection of mammary cells comprised of metaplastic p53+/CK14+ basaloid cells.

cancer biology↗

bioRxiv2025

RegFormer: A Single-Cell Foundation Model Powered by Gene Regulatory Hierarchies

Single-cell RNA sequencing (scRNA-seq) enables high-resolution profiling of cellular diversity, but current computational models often fail to incorporate regulatory priors, handle data sparsity, or efficiently process long gene sequences. Here, we present RegFormer, a foundation model that integrates gene regulatory networks (GRNs) with Mamba-based state-space modeling, overcoming the scalability and context-length limitations of Transformer architectures. RegFormer encodes each gene through dual embeddings, a value embedding for quantitative expression and a token embedding for regulatory identity, organized within a GRN-guided gene order to capture both expression dynamics and hierarchical regulation. Pretrained on 26 million human single cells spanning 45 tissues and diverse biological contexts, RegFormer achieves superior scalability and biological fidelity. Across comprehensive benchmarks, it consistently outperforms state-of-the-art single-cell foundation models (scGPT, Geneformer, scFoundation, and scBERT), delivering higher clustering accuracy, improved batch integration, and more precise cell type annotation. RegFormer also reconstructs biologically coherent GRNs, accurately models transcriptional responses to genetic perturbations, and enhances drug response prediction across cancer cell lines. By combining regulatory priors with efficient long-sequence Mamba modeling, RegFormer establishes a biologically grounded and scalable framework for single-cell representation learning, enabling deeper mechanistic insight into gene regulation and cellular state transitions.

bioinformatics↗

bioRxiv2025

Sequencing-based Spatial Transcriptomics with scRNA-seq Sensitivity

The advent of spatial transcriptomics has dramatically expanded our ability to study the vast network of cell-cell interactions at the molecular level in tissue. Among current methods, sequencing-based approaches have great potential in discovering because of its unbiased capture. In the last couple of years, the spatial resolution for the capture addresses has been significantly improved from 100 m to <1 m, well below the size of a mammalian cell. However, the capture efficiency has always been a pain point, [~]one order of magnitude lower than that of single cell RNA sequencing (scRNA-seq). The low capture efficiency limits the depth and breadth of its applications in the study of complex biological systems and diseases. Here, we introduce Salus Spatial transcriptomic system (Salus-STS), which provides [~]1 m capture resolution and a capture efficiency [~]1 order of magnitude higher than other current methods. Analysis with sub-cellular resolution becomes practical for sequencing-based spatial transcriptomics.

molecular biology↗

bioRxiv2025

Targeted stimulation of motor cortex neural ensembles drives learned movements

During the execution of learned motor skills, the neural population in the layer 2/3 (L2/3) of the primary motor cortex (M1) expresses a reproducible spatiotemporal activity pattern. It is debated whether M1 actively participates in generating this activity pattern and whether this learned pattern causally drives the learned movement. To address these questions, we utilized in vivo two-photon calcium imaging combined with holographic optogenetic stimulation of functionally defined M1 L2/3 neuronal ensembles in mice performing a skilled lever-pressing task. A brief and synchronous stimulation of [~]20 neurons whose activity onset in voluntary trials precedes movement onsets induced movements that resembled the learned movement, while producing spatiotemporal activity patterns in other M1 neurons that resembled those during the voluntary learned movement. Moreover, trial-by-trial variability of optogenetically triggered population activity correlated with the variability in the induced movements. These trial-by-trial variabilities were predicted by the initial state of M1 population activity immediately preceding the stimulation. In some trials, the stimulation induced the learned activity without inducing overt movements, indicating that the learned activity is not simply a reflection of movements and instead can be induced internally. The stimulation failed to generate movements or learned activity when mice were disengaged from the task. Stimulation of the neurons whose activity followed voluntary movement onsets failed to induce the learned movement in task-engaged mice. Taken together, the learned activity pattern in M1 L2/3 can be generated when the M1 network is prepared at the optimal initial state and receives precise triggering inputs, supporting the active role of M1 in the generation of learned activity and learned movements.

neuroscience↗

bioRxiv2025

Characterization of RNA interference in the model cnidarian Nematostella vectensis reveals partial target silencing but lack of small RNA amplification

RNA interference (RNAi) is a sequence-specific mRNA degradation mechanism, which serves as an antiviral pathway by processing viral double-stranded RNA (dsRNA) into short interfering RNAs (siRNAs), leading to virus destruction. It is considered as an ancestral mechanism which invertebrates rely on for defense against viruses, whereas vertebrates have evolved instead the interferon pathway. Recent studies suggest that sea anemones, members of the basally-branching phylum Cnidaria, might possess an immune response with more vertebrate characteristics than previously thought; however, it is unknown whether cnidarians also employ RNAi as an antiviral response similarly to nematodes and insects. Here, we characterize the response of the model cnidarian Nematostella vectensis to simulated viral infection. We injected dsRNA with eGFP sequence into eGFP-expressing transgenic zygotes and show that siRNAs mapping to the eGFP sequence are generated and induce a moderate but significant knockdown of eGFP expression. Interestingly, we detected no evidence for secondary siRNA production, despite their crucial role in the amplification of antiviral response in other organisms. Notably, siRNA pathway components are specifically upregulated upon dsRNA injection, while microRNA pathway components are downregulated. Furthermore, injection of mRNA coding for self-replicating viral gene fused to eGFP, also induced upregulation of siRNA-related genes and a mild decrease in transgene expression. Overall, we propose that N. vectensis possesses an siRNA-mediated antiviral response that lacks secondary amplification and functions as a short-term antiviral mechanism.

evolutionary biology↗

bioRxiv2025

ORP2 regulates free cholesterol accumulation in hepatocytes during MASH

BackgroundCholesterol crystals in hepatocytes are known to strongly associate with human Metabolic Dysfunction-Associated Steatohepatitis (MASH). However, it remains unclear which molecular pathway(s) regulates free cholesterol accumulation, and therefore the formation of cholesterol crystals in hepatocytes. In cultured cell lines, oxysterol-binding protein-related protein 2 (ORP2) functions to deliver cholesterol to the plasma membrane from internal organelles. MethodsHere, we generated liver-specific ORP2 knockout (ORP2-LKO) mice and characterized their metabolic phenotypes on chow and high fat diet. ResultsThe ORP2-LKO mice developed much more severe hepatic steatosis than wild type mice after high-fat diet feeding. They also demonstrated more severe liver inflammation and damage. Notably, free but not esterified cholesterol, as well as cholesterol crystals accumulated in ORP2-LKO liver. The expression of Cyp7a1 was significantly upregulated in ORP2-LKO liver, accompanied with accumulation of taurocholic acid. Our results thus unveil an important in vivo function of ORP2 in preventing free cholesterol from accumulating in mouse liver. Conclusionsour results suggest that impaired cholesterol trafficking may enhance the deposition of cholesterol crystals in hepatocytes, promoting the development of MASH.

cell biology↗

bioRxiv2024

Kun-peng: an ultra-memory-efficient, fast, and accurate pan-domain taxonomic classifier for all

Comprehensive metagenomic sequence classification of diverse environmental samples faces significant computing memory challenges due to exponentially expanding genome databases. Here, we present Kun-peng, featuring a unique ordered 4GB block database design for ultra-efficient resource management, faster processing, and higher accuracy. When benchmarked on mock communities (Amos HiLo, Mixed, and NIST) against Kraken2, Centrifuge, and Sylph. Kun-peng matched Sylph, achieving the highest precision and lowest false-positive rates while demonstrating superior time and memory efficiency among all tested tools. Furthermore, Kun-pengs efficient database architecture enables the practical utilization of large-scale reference databases that were previously computationally prohibitive. In comprehensive testing across 586 air, water, soil, and human metagenomic samples using an expansive pan-domain database (204,477 genomes, 4.3TB), Kun-peng classified 69.78-94.29% of reads, achieving 38-43% higher classification rates than Kraken2 with the standard database. Unexpectedly, Sylph failed to classify any reads in air samples and left > 99.85% of reads unclassified in water and soil samples. In terms of computational efficiency, Kun-peng processed each sample in 0.2[~]11.2 minutes using only 4.0[~]35.4GB peak memory. Remarkably, these processing times were comparable to Kraken2 using the standard database (81GB, 5% of the pan-domain database). Memory-wise, Kun-peng required only 35.4GB peak memory with the pan-domain database, representing a 473-fold reduction compared to Kraken2. When compared to Sylph, Kun-peng processes samples up to 46.3 times faster while using up to 20.6 times less memory. Overall, Kun-peng offers an ultra-memory-efficient, fast, and accurate solution for pan-domain metagenomic classifications.

bioinformatics↗

bioRxiv2024

Benchmarking AI Models for In Silico Gene Perturbation of Cells

Understanding perturbations at the single-cell level is essential for unraveling cellular mechanisms and their implications in health and disease. The growing availability of biological data has driven the development of a variety of in silico perturbation methods designed for single-cell analysis, which offer a means to address many inherent limitations of experimental approaches. However, these computational methods are often tailored to specific scenarios and validated on limited datasets and metrics, making their evaluation and comparison challenging. In this work, we introduce a comprehensive benchmarking framework to systematically evaluate in silico perturbation methods across four key scenarios: predicting effects of unseen perturbations in known cell types, predicting effects of observed perturbations in unseen cell types, zero-shot transfer to bulk RNA-seq of cell lines, and application to real-world biological cases. For each scenario, we curated diverse and abundant datasets, standardizing them into flexible formats to enable efficient analysis. Additionally, we developed multiple metrics tailored to each scenario, facilitating a thorough and comparative evaluation of these methods. Our benchmarking study assessed 10 methods, ranging from linear baselines to advanced machine learning approaches, across these scenarios. While some methods demonstrated surprising efficacy in specific contexts, significant challenges remain, particularly in zero-shot predictions and the modeling of complex biological processes. This work provides a valuable resource for evaluating and improving in silico perturbation methods, serving as a foundation for bridging computational predictions with experimental validation and real-world biological applications.

bioinformatics↗

bioRxiv2024

GlioME: A Novel Organoid Model Retaining the Glioma Mi-croenvironment for Personalized Drug Screening and Thera-peutic Evaluation

Glioma is an aggressive brain tumor with a poor prognosis. Establishing an in vitro culture model that closely replicates the cellular composition and microenvironment of the original tumor has been challenging, limiting its clinical applications. Here, we present a novel approach to generate glioma organoids with a microenvironment (GlioME) from patient-derived glioma tissue. These organoids maintain the genetic and epigenetic characteristics of the primary tumor and preserve cell-to-cell interactions within the tumor microenvironment, including resident immune cells. Bulk RNA sequencing, whole exome sequencing, and DNA methylation analysis were used to confirm the molecular similarities between the organoids and primary glioma tissues. Immunofluorescence and flow cytometry were used to assess immune cell viability, comparing GlioME with floating glioma organoids. GlioME exhibited high responsiveness to chemotherapy and targeted therapy, demonstrating its potential for therapeutic screening applications. Notably, GlioME accurately predicted patient response to the recently approved MET inhibitor, vebreltinib. Thus, this organoid model provides a reliable in vitro platform for glioma microenvironment-related research and clinical drug screening.

cancer biology↗

bioRxiv2024

PRMT1-Mediated Metabolic Reprogramming Promotes Leukemogenesis

Copious expression of protein arginine methyltransferase 1 (PRMT1) is associated with poor survival in many types of cancers, including acute myeloid leukemia. We observed that a specific acute megakaryocytic leukemia (AMKL) cell line (6133) derived from RBM15-MKL1 knock-in mice exhibited heterogeneity in Prmt1 expression levels. Interestingly, only a subpopulation of 6133 cells expressing high levels of Prmt1 caused leukemia when transplanted into congenic mice. The PRMT1 inhibitor, MS023, effectively cured this PRMT1-driven leukemia. Seahorse analysis revealed that PRMT1 increased the extracellular acidification rate (ECAR) and decreased the oxygen consumption rate (OCR). Consistently, PRMT1 accelerated glucose consumption and led to the accumulation of lactic acid in the leukemia cells. The metabolomic analysis supported that PRMT1 stimulated the intracellular accumulation of lipids, which was further validated by FACS analysis with BODIPY 493/503. In line with fatty acid accumulation, PRMT1 downregulated the protein level of CPT1A, which is involved in the rate-limiting step of fatty acid oxidation. Furthermore, administering the glucose analogue 2-deoxy-glucose (2-DG) delayed AMKL progression and promoted cell differentiation. Ectopic expression of Cpt1a rescued the proliferation of 6133 cells ectopically expressing PRMT1 in the glucose-minus medium. In conclusion, PRMT1 upregulates glycolysis and downregulates fatty acid oxidation to enhance the proliferation capability of AMKL cells. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/628174v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1ae96borg.highwire.dtl.DTLVardef@d27916org.highwire.dtl.DTLVardef@ba63corg.highwire.dtl.DTLVardef@cee9af_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

The Impact of Memory and Stress on Choice Consistency

Choice consistency is a fundamental aspect of rational decision-making, reflecting the stability and reliability of an individuals preferences. However, real-world decision-making often deviates from this ideal, as individuals frequently make irrational or inconsistent choices in value-based decision-making. This study combined computational modeling, neuroimaging, and behavioral assessments to elucidate the mechanisms by which stress and memory affect choice consistency. Remembered items exhibited higher choice consistency compared to forgotten items. Computational modeling further indicated that the drift rate was higher, and the decision threshold lower, for remembered food items compared to forgotten ones. Stress was found to impair both choice consistency and memory retrieval, with stress-induced declines in memory accuracy positively correlating with reductions in choice reaction times. Activation of the dorsolateral prefrontal cortex (DLPFC) during the pre-choice anticipation period was positively associated with choice consistency. Similarly, activation of the orbitofrontal cortex (OFC) during the memory retrieval of food stimuli correlated with improved memory accuracy. These findings suggest that stress may impair choice consistency by disrupting memory retrieval processes. Overall, our study provides novel insights into the role of stress and memory in decision-making, offering a more nuanced understanding of the neural and cognitive processes that govern choice behavior.

neuroscience↗

bioRxiv2024

Employing Microsoft Sway as a Repository for Data Generated in Pharmaceutics Experimental Courses: A Case study on Topical Formulation Characterization

Microsoft Sway is an easy-to-use online creative software developed by Microsoft. It has many advantages as follows: easy to operate and does not require long-term practice and learning; network links can be created and accessed by devices such as Personal Computer (PC) and mobile terminals; Educators and Learners are allowed to browse this repository at anytime to facilitate the application prospects of mining relevant data. Therefore, the author proposes that Microsoft Sway may be a very promising data repository for pharmaceutical experimental teaching courses, and is expected to be extended to undergraduate experimental teaching in other natural science fields.

scientific communication and education↗

bioRxiv2024

A highly conserved sRNA downregulates multiple genes, including a sigma54 transcriptional activator, in the virulence mode of Bordetella pertussis

Bacterial sRNAs together with the RNA chaperone Hfq post-transcriptionally regulate gene expression by affecting ribosome binding or mRNA stability. In the human pathogen Bordetella pertussis, the causative agent of whooping cough, hundreds of sRNAs have been identified, but their roles in B. pertussis biology are mostly unknown. Here we characterize a Hfq-dependent sRNA (S17), whose level is dramatically higher in the virulence (Bvg+) mode. We show that transcription from a {sigma}A-dependent promoter yields a long form of 190 nucleotides (nts) that is processed by RNase E to generate a shorter, more stable form (S17S) of 67 nts. Using RNA-seq and RT-qPCR, we identify 92 genes whose expression significantly increases in the absence of S17. Of these genes, 70 contain sequences at/near their ribosome binding sites (RBSs) that are complementary to single-stranded (ss) regions (Sites 1 or 2) of S17S. The identified genes include those encoding multiple transporters and 3 transcriptional regulators. Using a lacZ translational reporter system, we demonstrate that S17S directly represses one of these genes, BP2158, a {sigma}54- dependent transcriptional regulator, suggesting the repression of a {sigma}54 regulon in the Bvg+ mode. We find that the S17S region containing Sites 1 and 2 is 100% conserved throughout various Betaproteobacteria species, and the S17S target sites are often conserved in the homologs of the B. pertussis target genes. We speculate that S17S regulation represents a highly conserved process that fine-tunes gene expression in the Bvg+ mode of B. pertussis and perhaps under other conditions in related bacteria. IMPORTANCERegulation of gene expression involves controlling transcription, translation, and transcript degradation. sRNAs with short sequences complementary to an mRNA sequence are involved in post-transcriptional regulation by aiding or interfering with either ribosome binding or nuclease attack. In the human pathogen Bordetella pertussis, the causative agent of whooping cough, hundreds of sRNA have been identified, but their functions remain largely unknown. We have characterized a sRNA that is abundant in the virulence mode of B. pertussis and serves to down-regulate multiple genes including transcriptional regulators and various transporters. We demonstrate that this sRNA directly represses a transcriptional factor, suggesting that it influences the regulation of specific B. pertussis regulons. The high conservation of this sRNA and its targets within Betaproteobacteria suggests a conserved pathway for gene regulation.

microbiology↗

bioRxiv2024

Aberrant expression of collagen type X in solid tumor stroma is associated with EMT, immunosuppressive and pro-metastatic pathways, bone marrow stromal cell signatures, and poor survival prognosis

BackgroundCollagen type X (ColX1, encoded by COL10A1) is expressed specifically in the cartilage-to-bone transition, in bone marrow cells, and in osteoarthritic (OA) cartilage. We have previously shown that ColX1 is expressed in breast tumor stroma, correlates with tumor-infiltrating lymphocytes, and predicts poor adjuvant therapy outcomes in ER+/HER2+ breast cancer. However, the underlying molecular mechanisms for these effects are unknown. In this study, we performed bioinformatic analysis of COL10A1-associated gene modules in breast and pancreatic cancer as well as in cells from bone marrow and OA cartilage. These findings provide important insights into the mechanisms of transcriptional and extracellular matrix changes which impact the local stromal microenvironment and tumor progression. MethodsImmunohistochemistry was performed to examine collagen type X expression in solid tumors. WGCNA was used to generate COL10A1-associated gene networks in breast and pancreatic tumor cohorts using RNA-Seq data from The Cancer Genome Atlas. Computational analysis was employed to assess the impact of these gene networks on development and progression of cancer and OA. Data processing and statistical analysis was performed using R and various publicly-available computational tools. ResultsExpression of COL10A1 and its associated gene networks highlights inflammatory and immunosuppressive microenvironments, which identify aggressive breast and pancreatic tumors and contribute to metastatic potential in a sex-dependent manner. Both cancer types are enriched in stroma, and COL10A1 implicates bone marrow-derived fibroblasts as drivers of the epithelial-to-mesenchymal transition (EMT) in these tumors. Heightened expression of COL10A1 and its associated gene networks is correlated with poorer patient outcomes in both breast and pancreatic cancer. Common transcriptional changes and chondrogenic activity are shared between cancer and OA cartilage, suggesting that similar microenvironmental alterations may underlie both diseases. ConclusionsCOL10A1-associated gene networks may hold substantial value as regulators and biomarkers of aggressive tumor phenotypes with implications for therapy development and clinical outcomes. Identification of tumors which exhibit high expression of COL10A1 and its associated genes may reveal the presence of bone marrow-derived stromal microenvironments with heightened EMT capacity and metastatic potential. Our analysis may enable more effective risk assessment and more precise treatment of patients with breast and pancreatic cancer. Research HighlightsO_LIColX highlights features of EMT in breast and pancreatic cancer C_LIO_LIColX gene modules are immunosuppressive and pro-metastatic C_LIO_LIColX-associated gene networks contribute to sex differences in pancreatic cancer C_LIO_LIColX-positive fibroblasts define more aggressive tumors with poorer survival C_LIO_LIColX is emerging as a biomarker for bone marrow-derived cells in cancer C_LI

cancer biology↗

bioRxiv2024

DGRNA: a long-context RNA foundation model with bidirectional attention Mamba2

Ribonucleic acid (RNA) is an important biomolecule with diverse functions i.e. genetic information transfer, regulation of gene expression and cellular functions. In recent years, the rapid development of sequencing technology has significantly enhanced our understanding of RNA biology and advanced RNA-based therapies, resulting in a huge volume of RNA data. Data-driven methods, particularly unsupervised large language models, have been used to automatically hidden semantic information from these RNA data. Current RNA large language models are primarily based on Transformer architecture, which cannot efficiently process long RNA sequences, while the Mamba architecture can effectively alleviate the quadratic complexity associated with Transformers. In this study, we propose a large foundational model DGRNA based on the bidirectional Mamba trained on 100 million RNA sequences, which has demonstrated exceptional performance across six RNA downstream tasks compared to existing RNA language models.

bioinformatics↗

bioRxiv2024

Revolutionizing large-scale DNA synthesis with microchip-based massive in parallel synthesis system

DNA synthesis serves as the fundamental enabling technology of engineering biology, aiming to provide DNA molecules of designed composition, length, and complexity at scale and low cost. Current high-throughput DNA synthesis technologies rely on intricate chip manufacturing and microfluidic systems to provide large-scale synthetic oligonucleotides, at the expense of low concentration and limited compatibility in the processing of longer DNA constructs assembly. Here, we report a microchip-based massive in parallel synthesis (mMPS), pioneering an "identification-sorting-synthesis-recycling" iteration mechanism to microchips for high throughput DNA synthesis. In comparison to microarray-based methods, we demonstrate that our method can increase the DNA product concentration by 4 magnitudes (to picomole-scale per sequence) and greatly simplifies the downstream processes for large-scale gene synthesis construction. By the construction of 1.97 million-diversity variant libraries that cover 1,254 human protein domains, we demonstrated the uniformity of the constructed variant libraries using mMPS-derived oligos is greatly improved, with amino acid distribution highly consistent as designed. In addition, by synthesizing 285 1kb-to-3kb genes with varying degrees of sequence complexity from previously reported strains A501 and 3DAC, potential ancestor of early archaea and bacteria, our result shows that the overall gene assembly success rate using mMPS-derived oligos is increased by 10-fold in comparison to other methods. Our mMPS technology holds the potential to close the gap between the quality and cost of writing DNA in increasing demand across many sectors of research and industrial activities.

synthetic biology↗

bioRxiv2024

The E3 ligase NEDD4L prevents colorectal cancer liver metastasis via degradation of PRMT5 to inhibit the AKT/mTOR signaling pathway

Colorectal cancer is the second most common cause of cancer mortality worldwide, and liver metastasis is the major cause of death in patients with colorectal cancer. Dysfunctional E3 ligase activity has recently been shown to be associated with colorectal cancer. However, the key E3 ligase affecting colorectal cancer liver metastasis remains unknown. Therefore, we used an shRNA library targeting 156 E3 ubiquitin ligases to perform an in vivo loss-of-function screen of a human colorectal cancer cell line in a mouse model of colorectal cancer liver metastasis. The screen revealed that NEDD4L knockdown promoted colorectal cancer liver metastasis. Moreover, overexpression of NEDD4L prevented colorectal cancer liver metastasis. Mechanistic studies revealed that NEDD4L bound to the PPNAY motif in PRMT5 and ubiquitinated PRMT5 to promote its degradation. PRMT5 degradation attenuated the methylation of an arginine residue in AKT1 to inhibit the AKT/mTOR signaling pathway, consequently decreasing colorectal cancer cell proliferation. We are the first to show that PRMT5 is a substrate protein of the E3 ligase NEDD4L and reveals not only the metastasis-inhibiting function of NEDD4L but also a novel mechanism by which NEDD4L suppresses colorectal cancer liver metastasis. These findings may provide a new preventive strategy for liver metastasis.

cancer biology↗

bioRxiv2024

Mitochondrial small RNA alterations associated with increased lysosome activity in an Alzheimer's Disease Mouse Model uncovered by PANDORA-seq

Emerging small noncoding RNAs (sncRNAs), including tRNA-derived small RNAs (tsRNAs) and rRNA-derived small RNAs (rsRNAs), are critical in various biological processes, such as neurological diseases. Traditional sncRNA-sequencing (seq) protocols often miss these sncRNAs due to their modifications, such as internal and terminal modifications, that can interfere with sequencing. We recently developed panoramic RNA display by overcoming RNA modification aborted sequencing (PANDORA-seq), a method enabling comprehensive detection of modified sncRNAs by overcoming the RNA modifications. Using PANDORA-seq, we revealed a novel sncRNA profile enriched by tsRNAs/rsRNAs in the mouse prefrontal cortex and found a significant downregulation of mitochondrial tsRNAs and rsRNAs in an Alzheimers disease (AD) mouse model compared to wild-type controls, while this pattern is not present in the genomic tsRNAs and rsRNAs. Moreover, our integrated analysis of gene expression and sncRNA profiles reveals that those downregulated mitochondrial sncRNAs negatively correlate with enhanced lysosomal activity, suggesting a crucial interplay between mitochondrial RNA dynamics and lysosomal function in AD. Given the versatile tsRNA/tsRNA molecular actions in cellular regulation, our data provide insights for future mechanistic study of AD with potential therapeutic strategies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/619155v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1b6308borg.highwire.dtl.DTLVardef@2d02ddorg.highwire.dtl.DTLVardef@116ff5corg.highwire.dtl.DTLVardef@d7136d_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗