Explore the vast range of titles available.

N6-methyladenosine (m6A) is a reversible mRNA modification that has been shown to play important roles in various biological processes. However, the roles of m6A modification in macrophages are still unknown. Here, we discover that ablation of Mettl3 in myeloid cells promotes tumour growth and metastasis in vivo. In contrast to wild-type mice, Mettl3-deficient mice show increased M1/M2-like tumour-associated macrophage and regulatory T cell infiltration into tumours. m6A sequencing reveals that loss of METTL3 impairs the YTHDF1-mediated translation of SPRED2, which enhances the activation of NF-kB and STAT3 through the ERK pathway, leading to increased tumour growth and metastasis. Furthermore, the therapeutic efficacy of PD-1 checkpoint blockade is attenuated in Mettl3-deficient mice, identifying METTL3 as a potential therapeutic target for tumour immunotherapy. N6-methyladenosine (m6A) is a reversible mRNA modification with important roles in cancer biology and immunoregulation. Here, the authors show that myeloid-specific deletion of Mettl3, the catalytic subunit of the methyltransferase complex, promotes tumor growth and metastasis in preclinical tumor models, influencing macrophage reprogramming and attenuating PD-1 blockade.

Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by data-dependent acquisition (DDA) experiments are required prior to DIA analysis, which is time-consuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning-based approach to generate in silico spectral libraries for DIA analysis. We demonstrate that the quality of in silico libraries predicted by instrument-specific models using DeepDIA is comparable to that of experimental libraries, and outperforms libraries generated by global models. With peptide detectability prediction, in silico libraries can be built directly from protein sequence databases. We further illustrate that DeepDIA can break through the limitation of DDA on peptide/protein detection, and enhance DIA analysis on human serum samples compared to the state-of-the-art protocol using a DDA library. We expect this work expanding the toolbox for DIA proteomics. Data-independent acquisition (DIA) is an emerging technology in proteomics but it typically relies on spectral libraries built by data-dependent acquisition (DDA). Here, the authors use deep learning to generate in silico spectral libraries directly from protein sequences that enable more comprehensive DIA experiments than DDA-based libraries.

Double-stranded RNA (dsRNA) is a virus-encoded signature capable of triggering intracellular Rig-like receptors (RLR) to activate antiviral signaling, but whether intercellular dsRNA structural reshaping mediated by the N 6 -methyladenosine (m 6 A) modification modulates this process remains largely unknown. Here, we show that, in response to infection by the RNA virus Vesicular Stomatitis Virus (VSV), the m 6 A methyltransferase METTL3 translocates into the cytoplasm to increase m 6 A modification on virus-derived transcripts and decrease viral dsRNA formation, thereby reducing virus-sensing efficacy by RLRs such as RIG-I and MDA5 and dampening antiviral immune signaling. Meanwhile, the genetic ablation of METTL3 in monocyte or hepatocyte causes enhanced type I IFN expression and accelerates VSV clearance. Our findings thus implicate METTL3-mediated m 6 A RNA modification on viral RNAs as a negative regulator for innate sensing pathways of dsRNA, and also hint METTL3 as a potential therapeutic target for the modulation of anti-viral immunity. N 6 -methyladenosine (m 6 A) RNA modification regulates RNA metabolism, and has been implicated in immune regulation. Here, the authors show that the m 6 A methyltransferase, METTL3, translocates into the cytoplasm to increase viral RNA m 6 A modification, decreases viral ds RNA content, and thereby dampens the RIG/MDA5-induced anti-viral immunity.

Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. Data independent acquisition (DIA) is an emerging technology with deep proteome coverage and accurate quantitative capability in proteomics studies, but is still in the early stage of development in the field of glycoproteomics. We propose GproDIA, a framework for the proteome-wide characterization of intact glycopeptides from DIA data with comprehensive statistical control by a 2-dimentional false discovery rate approach and a glycoform inference algorithm, enabling accurate identification of intact glycopeptides using wide isolation windows. We further utilize a semi-empirical spectrum prediction strategy to expand the coverage of spectral libraries of glycopeptides. We benchmark our method for N-glycopeptide profiling on DIA data of yeast and human serum samples, demonstrating that DIA with GproDIA outperforms the data-dependent acquisition-based methods for glycoproteomics in terms of capacity and data completeness of identification, as well as accuracy and precision of quantification. We expect that this work can provide a powerful tool for glycoproteomic studies. Data independent acquisition (DIA) proteomics provides deep coverage and high quantitative accuracy, but is not yet well established in glycoproteomics. Here, the authors develop a DIA-based glycoproteomics workflow with stringent statistical controls to enable accurate glycopeptide identification.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection often causes severe complications and even death. However, asymptomatic infection has also been reported, highlighting the difference in immune responses among individuals. Here we performed single-cell chromatin accessibility and T cell-receptor analyses of peripheral blood mononuclear cells collected from individuals convalescing from COVID-19 and healthy donors. Chromatin remodelling was observed in both innate and adaptive immune cells in the individuals convalescing from COVID-19. Compared with healthy donors, recovered individuals contained abundant TBET-enriched CD16 + and IRF1-enriched CD14 + monocytes with sequential trained and activated epigenomic states. The B-cell lineage in recovered individuals exhibited an accelerated developmental programme from immature B cells to antibody-producing plasma cells. Finally, an integrated analysis of single-cell T cell-receptor clonality with the chromatin accessibility landscape revealed the expansion of putative SARS-CoV-2-specific CD8 + T cells with epigenomic profiles that promote the differentiation of effector or memory cells. Overall, our data suggest that immune cells of individuals convalescing from COVID-19 exhibit global remodelling of the chromatin accessibility landscape, indicative of the establishment of immunological memory. You et al. report single-cell ATAC-seq profiles of periphery immune cells from patients recovered from COVID-19, which reveals a global remodelling of the chromatin accessibility that may contribute to immune memory formation.

Diabetes is a complex metabolic syndrome that is characterized by prolonged high blood glucose levels and frequently associated with life-threatening complications 1 , 2 . Epidemiological studies have suggested that diabetes is also linked to an increased risk of cancer 3 – 5 . High glucose levels may be a prevailing factor that contributes to the link between diabetes and cancer, but little is known about the molecular basis of this link and how the high glucose state may drive genetic and/or epigenetic alterations that result in a cancer phenotype. Here we show that hyperglycaemic conditions have an adverse effect on the DNA 5-hydroxymethylome. We identify the tumour suppressor TET2 as a substrate of the AMP-activated kinase (AMPK), which phosphorylates TET2 at serine 99, thereby stabilizing the tumour suppressor. Increased glucose levels impede AMPK-mediated phosphorylation at serine 99, which results in the destabilization of TET2 followed by dysregulation of both 5-hydroxymethylcytosine (5hmC) and the tumour suppressive function of TET2 in vitro and in vivo. Treatment with the anti-diabetic drug metformin protects AMPK-mediated phosphorylation of serine 99, thereby increasing TET2 stability and 5hmC levels. These findings define a novel ‘phospho-switch’ that regulates TET2 stability and a regulatory pathway that links glucose and AMPK to TET2 and 5hmC, which connects diabetes to cancer. Our data also unravel an epigenetic pathway by which metformin mediates tumour suppression. Thus, this study presents a new model for how a pernicious environment can directly reprogram the epigenome towards an oncogenic state, offering a potential strategy for cancer prevention and treatment. Modulation of DNA 5-hydroxymethylcytosine by glucose reveals an AMPK–TET2–5hmC axis that links diabetes to cancer.

Over the past decade, the number and diversity of identified protein post-translational modifications (PTMs) have grown significantly. However, most PTMs occur at relatively low abundance, making selective enrichment of modified peptides essential. To address this, we developed a thermodynamic model describing the free beads enrichment in suspension enrichment process and derived a theoretical relationship between material dosage and analyte recovery. The model predicts a non-linear trend, with enrichment efficiency increasing up to an optimal dosage and declining thereafter—a pattern confirmed by experimental data. We validated the model using centrifugation-based enrichment for glycosylated peptides and magnetic-based enrichment for phosphorylated peptides. In both cases, the results aligned with theoretical predictions. Additionally, the optimal dosage varied among peptides with the same modification type, highlighting the importance of tailoring enrichment strategies. This study provides a solid theoretical and experimental basis for optimizing PTMs enrichment and advancing more sensitive, accurate, and efficient mass spectrometry-based proteomic workflows.

Large-scale intact glycopeptide identification has been advanced by software tools. However, tools for quantitative analysis remain lagging behind, which hinders exploring the differential site-specific glycosylation. Here, we report pGlycoQuant, a generic tool for both primary and tandem mass spectrometry-based intact glycopeptide quantitation. pGlycoQuant advances in glycopeptide matching through applying a deep learning model that reduces missing values by 19–89% compared with Byologic, MSFragger-Glyco, Skyline, and Proteome Discoverer, as well as a Match In Run algorithm for more glycopeptide coverage, greatly expanding the quantitative function of several widely used search engines, including pGlyco 2.0, pGlyco3, Byonic and MSFragger-Glyco. Further application of pGlycoQuant to the N-glycoproteomic study in three different metastatic HCC cell lines quantifies 6435 intact N-glycopeptides and, together with in vitro molecular biology experiments, illustrates site 979-core fucosylation of L1CAM as a potential regulator of HCC metastasis. We expected further applications of the freely available pGlycoQuant in glycoproteomic studies. Software tools for larger-scale intact glycopeptide quantification lag far behind, which hinders exploring the differential sitespecific glycosylation. Here, the authors report pGlycoQuant, a generic tool with a deep learning model for quantitative glycoproteomics at intact glycopeptide level.

Non-alcoholic fatty liver disease (NAFLD) is the hepatic manifestation of the metabolic syndrome that elevates the risk of hepatocellular carcinoma (HCC). Although alteration of lipid metabolism has been increasingly recognized as a hallmark of cancer cells, the deregulated metabolic modulation of HCC cells in the NAFLD progression remains obscure. Here, we discovers an endoplasmic reticulum-residential protein, Nogo-B, as a highly expressed metabolic modulator in both murine and human NAFLD-associated HCCs, which accelerates high-fat, high-carbohydrate diet-induced metabolic dysfunction and tumorigenicity. Mechanistically, CD36-mediated oxLDL uptake triggers CEBPβ expression to directly upregulate Nogo-B, which interacts with ATG5 to promote lipophagy leading to lysophosphatidic acid-enhanced YAP oncogenic activity. This CD36-Nogo-B-YAP pathway consequently reprograms oxLDL metabolism and induces carcinogenetic signaling for NAFLD-associated HCCs. Targeting the Nogo-B pathway may represent a therapeutic strategy for HCC arising from the metabolic syndrome. Non alcoholic fatty liver disease (NAFLD) associates with an elevated risk of developing hepatocellular carcinoma (HCC). Here, the authors find that Nogo-B, an endoplasmic reticulum resident protein, is upregulated by lipid uptake and acts as an oncogene in NAFLD-associated HCC by promoting lipid droplet breakdown by lipophagy and triggering Hippo pathway dysregulation

The tumour stroma is an active participant during cancer progression. Stromal cells promote tumour progression and metastasis through multiple mechanisms including enhancing tumour invasiveness and angiogenesis, and suppressing immune surveillance. We report here that miR-126/miR-126 * , a microRNA pair derived from a single precursor, independently suppress the sequential recruitment of mesenchymal stem cells and inflammatory monocytes into the tumour stroma to inhibit lung metastasis by breast tumour cells in a mouse xenograft model. miR-126/miR-126 * directly inhibit stromal cell-derived factor-1 alpha (SDF-1α) expression, and indirectly suppress the expression of chemokine (C–C motif) ligand 2 (Ccl2) by cancer cells in an SDF-1α-dependent manner. miR-126/miR-126 * expression is downregulated in cancer cells by promoter methylation of their host gene Egfl7 . These findings determine how this microRNA pair alters the composition of the primary tumour microenvironment to favour breast cancer metastasis, and demonstrate a correlation between miR-126/126 * downregulation and poor metastasis-free survival of breast cancer patients. Wang and colleagues show that miR-126 and miR-126 * suppress metastasis by inhibiting the production of the Sdf-1α cytokine in mouse mammary tumours, resulting in decreased recruitment of mesenchymal stem cells and inflammatory monocytes to the tumour stroma.