The neuroendocrine transition in prostate cancer is dynamic and dependent on ASCL1.

Lineage plasticity is a recognized hallmark of cancer progression that can shape therapy outcomes. The underlying cellular and molecular mechanisms mediating lineage plasticity remain poorly understood. Here, we describe a versatile in vivo platform to identify and interrogate the molecular determinants of neuroendocrine lineage transformation at different stages of prostate cancer progression. Adenocarcinomas reliably develop following orthotopic transplantation of primary mouse prostate organoids acutely engineered with human-relevant driver alterations (e.g., Rb1-/-; Trp53-/-; cMyc+ or Pten-/-; Trp53-/-; cMyc+), but only those with Rb1 deletion progress to ASCL1+ neuroendocrine prostate cancer (NEPC), a highly aggressive, androgen receptor signaling inhibitor (ARSI)-resistant tumor. Importantly, we show this lineage transition requires a native in vivo microenvironment not replicated by conventional organoid culture. By integrating multiplexed immunofluorescence, spatial transcriptomics and PrismSpot to identify cell type-specific spatial gene modules, we reveal that ASCL1+ cells arise from KRT8+ luminal epithelial cells that progressively acquire transcriptional heterogeneity, producing large ASCL1+;KRT8- NEPC clusters. Ascl1 loss in established NEPC results in transient tumor regression followed by recurrence; however, Ascl1 deletion prior to transplantation completely abrogates lineage plasticity, yielding adenocarcinomas with elevated AR expression and marked sensitivity to castration. The dynamic feature of this model reveals the importance of timing of therapies focused on lineage plasticity and offers a platform for identification of additional lineage plasticity drivers.

FASN-mediated fatty acid biosynthesis remodels immune environment in Clonorchis sinensis infection-related intrahepatic cholangiocarcinoma.

BACKGROUND & AIMS: Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver cancer and is highly lethal. Clonorchis sinensis (C. sinensis) infection is an important risk factor for iCCA. Here we investigated the clinical impact and underlying molecular characteristics of C. sinensis infection-related iCCA. METHODS: We performed single-cell RNA sequencing, whole-exome sequencing, RNA sequencing, metabolomics and spatial transcriptomics in 251 patients with iCCA from three medical centers. Alterations in metabolism and the immune microenvironment of C. sinensis-related iCCAs were validated through an in vitro co-culture system and in a mouse model of iCCA. RESULTS: We revealed that C. sinensis infection was significantly associated with iCCA patients' overall survival and response to immunotherapy. Fatty acid biosynthesis and the expression of fatty acid synthase (FASN), a key enzyme catalyzing long-chain fatty acid synthesis, were significantly enriched in C. sinensis-related iCCAs. iCCA cell lines treated with excretory/secretory products of C. sinensis displayed elevated FASN and free fatty acids. The metabolic alteration of tumor cells was closely correlated with the enrichment of tumor-associated macrophage (TAM)-like macrophages and the impaired function of T cells, which led to formation of an immunosuppressive microenvironment and tumor progression. Spatial transcriptomics analysis revealed that malignant cells were in closer juxtaposition with TAM-like macrophages in C. sinensis-related iCCAs than non-C. sinensis-related iCCAs. Importantly, treatment with a FASN inhibitor significantly reversed the immunosuppressive microenvironment and enhanced anti-PD-1 efficacy in iCCA mouse models treated with excretory/secretory products from C. sinensis. CONCLUSIONS: We provide novel insights into metabolic alterations and the immune microenvironment in C. sinensis infection-related iCCAs. We also demonstrate that the combination of a FASN inhibitor with immunotherapy could be a promising strategy for the treatment of C. sinensis-related iCCAs. IMPACT AND IMPLICATIONS: Clonorchis sinensis (C. sinensis)-infected patients with intrahepatic cholangiocarcinoma (iCCA) have a worse prognosis and response to immunotherapy than non-C. sinensis-infected patients with iCCA. The underlying molecular characteristics of C. sinensis infection-related iCCAs remain unclear. Herein, we demonstrate that upregulation of FASN (fatty acid synthase) and free fatty acids in C. sinensis-related iCCAs leads to formation of an immunosuppressive microenvironment and tumor progression. Thus, administration of FASN inhibitors could significantly reverse the immunosuppressive microenvironment and further enhance the efficacy of anti-PD-1 against C. sinensis-related iCCAs.

A subpopulation of luminal progenitors secretes pleiotrophin to promote angiogenesis and metastasis in inflammatory breast cancer.

Inflammatory breast cancer (IBC) is a highly aggressive subtype of breast cancer characterized by rapidly arising diffuse erythema and edema. Genomic studies have not identified consistent alterations and mechanisms that differentiate IBC from non-IBC tumors, suggesting that the microenvironment could be a potential driver of IBC phenotypes. Here, using single-cell RNA sequencing, multiplex staining, and serum analysis in IBC patients, we identified enrichment of a subgroup of luminal progenitor (LP) cells containing high expression of the neurotropic cytokine pleiotrophin (PTN) in IBC tumors. PTN secreted by the LP cells promoted angiogenesis by directly interacting with the NRP1 receptor on endothelial tip cells located in both IBC tumors and the affected skin. NRP1 activation in tip cells led to recruitment of immature perivascular cells in the affected skin of IBC, which are correlated with increased angiogenesis and IBC metastasis. Together, these findings reveal a role for crosstalk between LPs, endothelial tip cells, and immature perivascular cells via PTN-NRP1 axis in the pathogenesis of IBC, which could lead to improved strategies for treating IBC.

PMI-controlled mannose metabolism and glycosylation determines tissue tolerance and virus fitness.

Host survival depends on the elimination of virus and mitigation of tissue damage. Herein, we report the modulation of D-mannose flux rewires the virus-triggered immunometabolic response cascade and reduces tissue damage. Safe and inexpensive D-mannose can compete with glucose for the same transporter and hexokinase. Such competitions suppress glycolysis, reduce mitochondrial reactive-oxygen-species and succinate-mediated hypoxia-inducible factor-1α, and thus reduce virus-induced proinflammatory cytokine production. The combinatorial treatment by D-mannose and antiviral monotherapy exhibits in vivo synergy despite delayed antiviral treatment in mouse model of virus infections. Phosphomannose isomerase (PMI) knockout cells are viable, whereas addition of D-mannose to the PMI knockout cells blocks cell proliferation, indicating that PMI activity determines the beneficial effect of D-mannose. PMI inhibition suppress a panel of virus replication via affecting host and viral surface protein glycosylation. However, D-mannose does not suppress PMI activity or virus fitness. Taken together, PMI-centered therapeutic strategy clears virus infection while D-mannose treatment reprograms glycolysis for control of collateral damage.

Exploring COVID-19 causal genes through disease-specific Cis-eQTLs.

Genome-wide association study (GWAS) analysis has exposed that genetic factors play important roles in COVID-19. Whereas a deeper understanding of the underlying mechanism of COVID-19 was hindered by the lack of expression of quantitative trait loci (eQTL) data specific for disease. To this end, we identified COVID-19-specific cis-eQTLs by integrating nucleotide sequence variations and RNA-Seq data from COVID-19 samples. These identified eQTLs have different regulatory effect on genes between patients and controls, indicating that SARS-CoV-2 infection may cause alterations in the human body's internal environment. Individuals with the TT genotype in the rs1128320 region seemed more susceptible to SARS-CoV-2 infection and developed into severe COVID-19 due to the abnormal expression of IFITM1. We subsequently discovered potential causal genes, of the result, a total of 48 genes from six tissues were identified. siRNA-mediated depletion assays in SARS-CoV-2 infection proved that 14 causal genes were directly associated with SARS-CoV-2 infection. These results enriched existing research on COVID-19 causal genes and provided a new sight in the mechanism exploration for COVID-19.

An enhanced broad-spectrum peptide inhibits Omicron variants in vivo.

The continual emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) poses a major challenge to vaccines and antiviral therapeutics due to their extensive evasion of immunity. Aiming to develop potent and broad-spectrum anticoronavirus inhibitors, we generated A1-(GGGGS)7-HR2m (A1L35HR2m) by introducing an angiotensin-converting enzyme 2 (ACE2)-derived peptide A1 to the N terminus of the viral HR2-derived peptide HR2m through a long flexible linker, which showed significantly improved antiviral activity. Further cholesterol (Chol) modification at the C terminus of A1L35HR2m greatly enhanced the inhibitory activities against SARS-CoV-2, SARS-CoV-2 VOCs, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV) pseudoviruses, with IC50 values ranging from 0.16 to 5.53 nM. A1L35HR2m-Chol also potently inhibits spike-protein-mediated cell-cell fusion and the replication of authentic Omicron BA.2.12.1, BA.5, and EG.5.1. Importantly, A1L35HR2m-Chol distributed widely in respiratory tract tissue and had a long half-life (>10 h) in vivo. Intranasal administration of A1L35HR2m-Chol to K18-hACE2 transgenic mice potently inhibited Omicron BA.5 and EG.5.1 infection both prophylactically and therapeutically.

Hypoxia-driven tumor stromal remodeling and immunosuppressive microenvironment in scirrhous HCC.

BACKGROUND AND AIMS: Scirrhous HCC (SHCC) is one of the unique subtypes of HCC, characterized by abundant fibrous stroma in the tumor microenvironment. However, the molecular traits of SHCC remain unclear, which is essential to develop specialized therapeutic approaches for SHCC. APPROACH AND RESULTS: We presented an integrative analysis containing single-cell RNA-sequencing, whole-exome sequencing, and bulk RNA-sequencing in SHCC and usual HCC samples from 134 patients to delineate genomic features, transcriptomic profiles, and stromal immune microenvironment of SHCC. Multiplexed immunofluorescence staining, flow cytometry, and functional experiments were performed for validation. Here, we identified SHCC presented with less genomic heterogeneity while possessing a unique transcriptomic profile different from usual HCC. Insulin-like growth factor 2 was significantly upregulated in SHCC tumor cells compared to usual HCC, and could serve as a potential diagnostic biomarker for SHCC. Significant tumor stromal remodeling and hypoxia were observed in SHCC with enrichment of matrix cancer-associated fibroblasts and upregulation of hypoxic pathways. Insulin-like growth factor 2 was identified as a key mediator in shaping the hypoxic stromal microenvironment of SHCC. Under this microenvironment, SHCC exhibited an immunosuppressive niche correlated to enhanced VEGFA signaling activity, where CD4 + T cells and CD8 + T cells were dysfunctional. Furthermore, we found that another hypoxic-related molecule SPP1 from SHCC tumor cells suppressed the function of dendritic cells via the SPP1-CD44 axis, which also probably hindered the activation of T cells. CONCLUSION: We uncovered the genomic characteristics of SHCC, and revealed a hypoxia-driven tumor stroma remodeling and immunosuppressive microenvironment in SHCC.

A TGF-ß-dominant chemoresistant phenotype of hepatoblastoma associated with aflatoxin exposure in children.

BACKGROUND AND AIMS: Hepatoblastoma (HB) is the most common liver cancer in children, posing a serious threat to children's health. Chemoresistance is the leading cause of mortality in patients with HB. A more explicit definition of the features of chemotherapy resistance in HB represents a fundamental urgent need. APPROACH AND RESULTS: We performed an integrative analysis including single-cell RNA sequencing, whole-exome sequencing, and bulk RNA sequencing in 180 HB samples, to reveal genomic features, transcriptomic profiles, and the immune microenvironment of HB. Multicolor immunohistochemistry staining and in vitro experiments were performed for validation. Here, we reported four HB transcriptional subtypes primarily defined by differential expression of transcription factors. Among them, the S2A subtype, characterized by strong expression of progenitor ( MYCN , MIXL1 ) and mesenchymal transcription factors ( TWIST1 , TBX5 ), was defined as a new chemoresistant subtype. The S2A subtype showed increased TGF-ß cancer-associated fibroblast and an immunosuppressive microenvironment induced by the upregulated TGF-ß of HB. Interestingly, the S2A subtype enriched SBS24 signature and significantly higher serum aflatoxin B1-albumin (AFB1-ALB) level in comparison with other subtypes. Functional assays indicated that aflatoxin promotes HB to upregulate TGF-ß. Furthermore, clinical prognostic analysis showed that serum AFB1-ALB is a potential indicator of HB chemoresistance and prognosis. CONCLUSIONS: Our studies offer new insights into the relationship between aflatoxin and HB chemoresistance and provide important implications for its diagnosis and treatment.

BioTreasury: a community-based repository enabling indexing and rating of bioinformatics tools.

The exponential growth of bioinformatics tools in recent years has posed challenges for scientists in selecting the most suitable one for their data analysis assignments. Therefore, to aid scientists in making informed choices, a community-based platform that indexes and rates bioinformatics tools is urgently needed. In this study, we introduce BioTreasury ( http://biotreasury.rjmart.cn ), an integrated community-based repository that provides an interactive platform for users and developers to share their experiences in various bioinformatics tools. BioTreasury offers a comprehensive collection of well-indexed bioinformatics software, tools, and databases, totaling over 10,000 entries. In the past two years, we have continuously improved and maintained BioTreasury, adding several exciting features, including creating structured homepages for every tool and user, a hierarchical category of bioinformatics tools and classifying tools using large language model (LLM). BioTreasury streamlines the tool submission process with intelligent auto-completion. Additionally, BioTreasury provides a wide range of social features, for example, enabling users to participate in interactive discussions, rate tools, build and share tool collections for the public. We believe BioTreasury can be a valuable resource and knowledge-sharing platform for the biomedical community. It empowers researchers to effectively discover and evaluate bioinformatics tools, fostering collaboration and advancing bioinformatics research.

Personalized drug screening in patient-derived organoids of biliary tract cancer and its clinical application.

Patients with biliary tract cancer (BTC) show different responses to chemotherapy, and there is no effective way to predict chemotherapeutic response. We have generated 61 BTC patient-derived organoids (PDOs) from 82 tumors (74.4%) that show similar histological and genetic characteristics to the corresponding primary BTC tissues. BTC tumor tissues with enhanced stemness- and proliferation-related gene expression by RNA sequencing can more easily form organoids. As expected, BTC PDOs show different responses to the chemotherapies of gemcitabine, cisplatin, 5-fluoruracil, oxaliplatin, etc. The drug screening results in PDOs are further validated in PDO-based xenografts and confirmed in 92.3% (12/13) of BTC patients with actual clinical response. Moreover, we have identified gene expression signatures of BTC PDOs with different drug responses and established gene expression panels to predict chemotherapy response in BTC patients. In conclusion, BTC PDO is a promising precision medicine tool for anti-cancer therapy in BTC patients.

Alisol B 23-acetate broadly inhibits coronavirus through blocking virus entry and suppresses proinflammatory T cells responses for the treatment of COVID-19.

INTRODUCTION: Emerging severe acute respiratory syndrome (SARS) coronavirus (CoV)-2 causes a global health disaster and pandemic. Seeking effective anti-pan-CoVs drugs benefit critical illness patients of coronavirus disease 2019 (COVID-19) but also may play a role in emerging CoVs of the future. OBJECTIVES: This study tested the hypothesis that alisol B 23-acetate could be a viral entry inhibitor and would have proinflammatory inhibition for COVID-19 treatment. METHODS: SARS-CoV-2 and its variants infected several cell lines were applied to evaluate the anti-CoVs activities of alisol B 23-aceate in vitro. The effects of alisol B 23-acetate on in vivo models were assessed by using SARS-CoV-2 and its variants challenged hamster and human angiotensin-converting enzyme 2 (ACE2) transgenic mice. The target of alisol B 23-acetate to ACE2 was analyzed using hydrogen/deuterium exchange (HDX) mass spectrometry (MS). RESULTS: Alisol B 23-acetate had inhibitory effects on different species of coronavirus. By using HDX-MS, we found that alisol B 23-acetate had inhibition potency toward ACE2. In vivo experiments showed that alisol B 23-acetate treatment remarkably decreased viral copy, reduced CD4+ T lymphocytes and CD11b+ macrophages infiltration and ameliorated lung damages in the hamster model. In Omicron variant infected human ACE2 transgenic mice, alisol B 23-acetate effectively alleviated viral load in nasal turbinate and reduced proinflammatory cytokines interleukin 17 (IL17) and interferon γ (IFNγ) in peripheral blood. The prophylactic treatment of alisol B 23-acetate by intranasal administration significantly attenuated Omicron viral load in the hamster lung tissues. Moreover, alisol B 23-acetate treatment remarkably inhibited proinflammatory responses through mitigating the secretions of IFNγ and IL17 in the cultured human and mice lymphocytes in vitro. CONCLUSION: Alisol B 23-acetate could be a promising therapeutic agent for COVID-19 treatment and its underlying mechanisms might be attributed to viral entry inhibition and anti-inflammatory activities.

Addressing persistent challenges in digital image analysis of cancerous tissues.

The National Cancer Institute (NCI) supports many research programs and consortia, many of which use imaging as a major modality for characterizing cancerous tissue. A trans-consortia Image Analysis Working Group (IAWG) was established in 2019 with a mission to disseminate imaging-related work and foster collaborations. In 2022, the IAWG held a virtual hackathon focused on addressing challenges of analyzing high dimensional datasets from fixed cancerous tissues. Standard image processing techniques have automated feature extraction, but the next generation of imaging data requires more advanced methods to fully utilize the available information. In this perspective, we discuss current limitations of the automated analysis of multiplexed tissue images, the first steps toward deeper understanding of these limitations, what possible solutions have been developed, any new or refined approaches that were developed during the Image Analysis Hackathon 2022, and where further effort is required. The outstanding problems addressed in the hackathon fell into three main themes: 1) challenges to cell type classification and assessment, 2) translation and visual representation of spatial aspects of high dimensional data, and 3) scaling digital image analyses to large (multi-TB) datasets. We describe the rationale for each specific challenge and the progress made toward addressing it during the hackathon. We also suggest areas that would benefit from more focus and offer insight into broader challenges that the community will need to address as new technologies are developed and integrated into the broad range of image-based modalities and analytical resources already in use within the cancer research community.

Identification of the SARS-unique domain of SARS-CoV-2 as an antiviral target.

SARS-CoV-2 nsp3 is essential for viral replication and host responses. The SARS-unique domain (SUD) of nsp3 exerts its function through binding to viral and host proteins and RNAs. Herein, we show that SARS-CoV-2 SUD is highly flexible in solution. The intramolecular disulfide bond of SARS-CoV SUD is absent in SARS-CoV-2 SUD. Incorporating this bond in SARS-CoV-2 SUD allowed crystal structure determination to 1.35 Å resolution. However, introducing this bond in SARS-CoV-2 genome was lethal for the virus. Using biolayer interferometry, we screened compounds directly binding to SARS-CoV-2 SUD and identified theaflavin 3,3'-digallate (TF3) as a potent binder, Kd 2.8 µM. TF3 disrupted the SUD-guanine quadruplex interactions and exhibited anti-SARS-CoV-2 activity in Vero E6-TMPRSS2 cells with an EC50 of 5.9 µM and CC50 of 98.5 µM. In this work, we provide evidence that SARS-CoV-2 SUD harbors druggable sites for antiviral development.

Epigenetic plasticity cooperates with cell-cell interactions to direct pancreatic tumorigenesis.

The response to tumor-initiating inflammatory and genetic insults can vary among morphologically indistinguishable cells, suggesting as yet uncharacterized roles for epigenetic plasticity during early neoplasia. To investigate the origins and impact of such plasticity, we performed single-cell analyses on normal, inflamed, premalignant, and malignant tissues in autochthonous models of pancreatic cancer. We reproducibly identified heterogeneous cell states that are primed for diverse, late-emerging neoplastic fates and linked these to chromatin remodeling at cell-cell communication loci. Using an inference approach, we revealed signaling gene modules and tissue-level cross-talk, including a neoplasia-driving feedback loop between discrete epithelial and immune cell populations that was functionally validated in mice. Our results uncover a neoplasia-specific tissue-remodeling program that may be exploited for pancreatic cancer interception.

Cross-platform dataset of multiplex fluorescent cellular object image annotations.

Defining cellular and subcellular structures in images, referred to as cell segmentation, is an outstanding obstacle to scalable single-cell analysis of multiplex imaging data. While advances in machine learning-based segmentation have led to potentially robust solutions, such algorithms typically rely on large amounts of example annotations, known as training data. Datasets consisting of annotations which are thoroughly assessed for quality are rarely released to the public. As a result, there is a lack of widely available, annotated data suitable for benchmarking and algorithm development. To address this unmet need, we release 105,774 primarily oncological cellular annotations concentrating on tumor and immune cells using over 40 antibody markers spanning three fluorescent imaging platforms, over a dozen tissue types and across various cellular morphologies. We use readily available annotation techniques to provide a modifiable community data set with the goal of advancing cellular segmentation for the greater imaging community.

Single-cell RNA analysis to identify five cytokines signaling in immune-related genes for melanoma survival prognosis.

Melanoma is one of the deadliest skin cancers. Recently, developed single-cell sequencing has revealed fresh insights into melanoma. Cytokine signaling in the immune system is crucial for tumor development in melanoma. To evaluate melanoma patient diagnosis and treatment, the prediction value of cytokine signaling in immune-related genes (CSIRGs) is needed. In this study, the machine learning method of least absolute selection and shrinkage operator (LASSO) regression was used to establish a CSIRG prognostic signature of melanoma at the single-cell level. We discovered a 5-CSIRG signature that was substantially related to the overall survival of melanoma patients. We also constructed a nomogram that combined CSIRGs and clinical features. Overall survival of melanoma patients can be consistently predicted with good performance as well as accuracy by both the 5-CSIRG signature and nomograms. We compared the melanoma patients in the CSIRG high- and low-risk groups in terms of tumor mutation burden, infiltration of the immune system, and gene enrichment. High CSIRG-risk patients had a lower tumor mutational burden than low CSIRG-risk patients. The CSIRG high-risk patients had a higher infiltration of monocytes. Signaling pathways including oxidative phosphorylation, DNA replication, and aminoacyl tRNA biosynthesis were enriched in the high-risk group. For the first time, we constructed and validated a machine-learning model by single-cell RNA-sequencing datasets that have the potential to be a novel treatment target and might serve as a prognostic biomarker panel for melanoma. The 5-CSIRG signature may assist in predicting melanoma patient prognosis, biological characteristics, and appropriate therapy.

Motivation to interaction media: The impact of automation trust and self-determination theory on intention to use the new interaction technology in autonomous vehicles.

Introduction: This research investigated the effects of three psychological needs (competence, autonomy, and relatedness) of self-determination theory (SDT) and automation trust on the intention of users to employ new interaction technology brought by autonomous vehicles (AVs), especially interaction mode and virtual image. Method: This study focuses on the discussion from the perspective of psychological motivation theory applied to AV interaction technology. With the use of a structured questionnaire, participants completed self-report measures related to these two interaction technologies; a total of 155 drivers' responses were analyzed. Result: The results indicated that users' intentions were directly predicted by their perceived competence, autonomy, and relatedness of SDT and automation trust, which jointly explained at least 66% of the variance in behavioral intention. In addition to these results, the contribution of predictive components to behavioral intention is influenced by the type of interaction technology. Relatedness and competence significantly impacted the behavioral intention to use the interaction mode but not the virtual image. Discussion: These findings are essential in that they support the necessity of distinguishing between types of AV interaction technology when predicting users' intentions to use.

An integrated strategy to identify COVID-19 causal genes and characteristics represented by LRRC37A2.

Genome-wide association study (GWAS) could identify host genetic factors associated with coronavirus disease 2019 (COVID-19). The genes or functional DNA elements through which genetic factors affect COVID-19 remain uncharted. The expression quantitative trait locus (eQTL) provides a path to assess the correlation between genetic variations and gene expression. Here, we firstly annotated GWAS data to describe genetic effects, obtaining genome-wide mapped genes. Subsequently, the genetic mechanisms and characteristics of COVID-19 were investigated by an integrated strategy that included three GWAS-eQTL analysis approaches. It was found that 20 genes were significantly associated with immunity and neurological disorders, including prior and novel genes such as OAS3 and LRRC37A2. The findings were then replicated in single-cell datasets to explore the cell-specific expression of causal genes. Furthermore, associations between COVID-19 and neurological disorders were assessed as a causal relationship. Finally, the effects of causal protein-coding genes of COVID-19 were discussed using cell experiments. The results revealed some novel COVID-19-related genes to emphasize disease characteristics, offering a broader insight into the genetic architecture underlying the pathophysiology of COVID-19.

The m6A reader YTHDC1 regulates muscle stem cell proliferation via PI4K-Akt-mTOR signalling.

Muscle stem cells are required for the homeostasis and regeneration of mammalian skeletal muscles. It has been reported that RNA N6-methyladenosine (m6A) modifications play a pivotal role in muscle development and regeneration. Nevertheless, we know little about which m6A reader regulates mammalian muscle stem cells. Here, we discovered that the m6A reader Ythdc1 is indispensable for mouse skeletal muscle regeneration and proliferation of muscle stem cells. In the absence of Ythdc1, Muscle stem cells in adult mice are unable to exit from quiescence. Mechanistically, Ythdc1 binds to m6A-modified Pi4k2a and Pi4kb mRNAs to regulate their alternative splicing and thus PI4K-Akt-mTOR signalling. Ythdc1-null muscle stem cells show a deficiency in phosphatidylinositol (PI) 3,4,5-trisphosphate, phospho-Akt and phospho-S6, which correlates with a failure in exit from quiescence. Our findings connect dynamic RNA methylation to the regulation of PI4K-Akt-mTOR signalling during stem cell proliferation and adult tissue regeneration.

VSOLassoBag: a variable-selection oriented LASSO bagging algorithm for biomarker discovery in omic-based translational research.

Screening biomolecular markers from high-dimensional biological data is one of the long-standing tasks for biomedical translational research. With its advantages in both feature shrinkage and biological interpretability, Least Absolute Shrinkage and Selection Operator (LASSO) algorithm is one of the most popular methods for the scenarios of clinical biomarker development. However, in practice, applying LASSO on omics-based data with high dimensions and low-sample size may usually result in an excess number of predictive variables, leading to the overfitting of the model. Here, we present VSOLassoBag, a wrapped LASSO approach by integrating an ensemble learning strategy to help select efficient and stable variables with high confidence from omics-based data. Using a bagging strategy in combination with a parametric method or inflection point search method, VSOLassoBag can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates. The application of VSOLassoBag on both simulation datasets and real-world datasets shows that the algorithm can effectively identify markers for either case-control binary classification or prognosis prediction. In addition, by comparing with multiple existing algorithms, VSOLassoBag shows a comparable performance under different scenarios while resulting in fewer features than others. In summary, VSOLassoBag, which is available at https://seqworld.com/VSOLassoBag/ under the GPL v3 license, provides an alternative strategy for selecting reliable biomarkers from high-dimensional omics data. For user's convenience, we implement VSOLassoBag as an R package that provides multithreading computing configurations.