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Ma, C.

Publications and source records attributed to Ma, C..

At least 91 records · Page 5Linked to original sources

Sarbecovirus RBD indels and specific residues dictating ACE2 multi-species adaptiveness

Sarbecoviruses exhibit varying abilities in using angiotensin-converting enzyme 2 (ACE2) receptor1-3. However, a comprehensive understanding of their multi-species ACE2 adaptiveness and the underlying mechanism remains elusive, particularly for many sarbecoviruses with various receptor binding motif (RBM) insertions/deletions (indels)4-11. Here, we analyzed RBM sequences from 268 sarbecoviruses categorized into four RBM indel types. We extensively examined the capability of 14 representative sarbecoviruses and their derivatives in using ACE2 orthologues from 51 bats and five non-bat mammals. We revealed that most sarbecoviruses with longer RBMs (type-I), present broad ACE2 tropism, whereas viruses with single deletions in Region 1 (type-II) or Region 2 (type-III) generally exhibit narrow ACE2 tropism, typically favoring their hosts ACE2. Sarbecoviruses with double region deletions (type-IV) exhibit a complete loss of ACE2 usage. Subsequent investigations unveiled that both loop deletions and critical RBM residues significantly impact multi-species ACE2 tropism in different ways. Additionally, fine mapping based on type-IV sarbecoviruses elucidated the role of several clade-specific residues, both within and outside the RBM, in restricting ACE2 usage. Lastly, we hypothesized the evolution of sarbecovirus RBM indels and illustrated how loop length, disulfide, and adaptive mutations shape their multi-species ACE2 adaptiveness. This study provides profound insights into the mechanisms governing ACE2 usage and spillover risks of sarbecoviruses.

microbiology↗

Architecture of intact mammalian pyruvate dehydrogenase complex revealed by in-situ structural analysis

The multi-enzyme pyruvate dehydrogenase complex (PDC) links glycolysis to the citric acid cycle by converting pyruvate into acetyl-coenzyme A and is essential for cellular energy metabolism. Although individual components have been characterized, the structure of intact PDC remains unclear, hampering our understanding of its composition and multi-step catalytic reactions. Here, we report the in-situ architecture of intact PDC within mammalian mitochondria by cryo-electron tomography. The organization of peripheral E1 and E3 multimers varies substantially among PDCs. We discovered 1-46 E1 heterotetramers surrounding each PDC core, and up to 12 E3 dimers locating primarily along the pentagonal openings. Furthermore, we observed dynamic interactions of the substrate translocating lipoyl domains (LDs) with both E1 and E2, implicating the mechanism of substrate channeling. Our findings reveal the intrinsic dynamics of PDC peripheral compositions, suggesting an additional model in which the number of assembled E1 heterotetramer, E3 dimer, and functional LDs of PDC may be regulated to further control its catalytic activity.

cell biology↗

The Gut Microbiome Controls Liver Tumors via the Vagus Nerve

Liver cancer ranks amongst the deadliest cancers. Nerves have emerged as an understudied regulator of tumor progression. The parasympathetic vagus nerve influences systemic immunity via acetylcholine (ACh). Whether cholinergic neuroimmune interactions influence hepatocellular carcinoma (HCC) remains uncertain. Liver denervation via hepatic vagotomy (HV) significantly reduced liver tumor burden, while pharmacological enhancement of parasympathetic tone promoted tumor growth. Cholinergic disruption in Rag1KO mice revealed that cholinergic regulation requires adaptive immunity. Further scRNA-seq and in vitro studies indicated that vagal ACh dampens CD8+ T cell activity via muscarinic ACh receptor (AChR) CHRM3. Depletion of CD8+ T cells abrogated HV outcomes and selective deletion of Chrm3 on CD8+ T cells inhibited liver tumor growth. Beyond tumor-specific outcomes, vagotomy improved cancer-associated fatigue and anxiety-like behavior. As microbiota transplantation from HCC donors was sufficient to impair behavior, we investigated putative microbiota-neuroimmune crosstalk. Tumor, rather than vagotomy, robustly altered fecal bacterial composition, increasing Desulfovibrionales and Clostridial taxa. Strikingly, in tumor-free mice, vagotomy permitted HCC-associated microbiota to activate hepatic CD8+ T cells. These findings reveal that gut bacteria influence behavior and liver anti-tumor immunity via a dynamic and pharmaceutically targetable, vagus-liver axis.

cancer biology↗

DNA sensor ABCF1 phase separates with cccDNA to inhibit HBV replication

Objectives: Hepatitis B virus (HBV) covalently closed circular DNA (cccDNA) contributes to viral persistence and recurrence. However, it is still less known how the host immune system responds to cccDNA and suppresses HBV replication. Therefore, it is essential to explore host proteins that interact with cccDNA and efficiently suppress HBV replication. Design: The interaction of cccDNA and ABCF1 was assessed by ChIP and pull-down assay. HBV replication was assessed in different cell models, including cccDNA surrogate-transfected and HBV-infected hepatocytes models. Phase separation of recombinant ABCF1 fusion proteins with cccDNA was observed by fluorescence microscopy. Results: Our data found that ABCF1 interacts with cccDNA to form phase-separated condensates via the poly-glutamine (PolyQ) of N-terminal intrinsically disordered low-complexity domain (LCD). Subsequently, ABCF1-cccDNA phase separation not only activates the type I/III interferon (IFN-I/III) pathway but also prevents Pol II accumulation on cccDNA to inhibit HBV transcription. Conclusions: Taken together, our study showed that ABCF1 acts as an antiviral restriction factor of HBV cccDNA by phase-separation-driven innate immune signaling and transcription inhibition. These findings shed new light on the understanding of host defense against cccDNA and provide a novel promising therapeutic strategy for HBV infection.

microbiology↗

Epithelial organoid supports resident memory CD8 T cell differentiation

Resident Memory T cells (TRM) play a vital role in regional immune defense in barrier organs. Although laboratory rodents have been extensively used to study fundamental TRM biology, poor isolation efficiency, sampling bias and low cell survival rates have limited our ability to conduct TRM-focused high-throughput assays. Here, we engineered a murine vaginal epithelial organoid (VEO)-CD8 T cell co-culture system that supports CD8 TRM differentiation in vitro. The three-dimensional VEOs established from murine adult stem cells resembled stratified squamous vaginal epithelium and induced gradual differentiation of activated CD8 T cells into epithelial TRM. These in vitro generated TRM were phenotypically and transcriptionally similar to in vivo TRM, and key tissue residency features were reinforced with a second cognate-antigen exposure during co-culture. TRM differentiation was not affected even when VEOs and CD8 T cells were separated by a semipermeable barrier, indicating soluble factors involvement. Pharmacological and genetic approaches showed that TGF-{beta} signaling played a crucial role in their differentiation. We found that the VEOs in our model remained susceptible to viral infections and the CD8 T cells were amenable to genetic manipulation; both of which will allow detailed interrogation of antiviral CD8 T cell biology in a reductionist setting. In summary, we established a robust model which captures bonafide TRM differentiation that is scalable, open to iterative sampling, and can be subjected to high throughput assays that will rapidly add to our understanding of TRM.

immunology↗

Human brain glycoform co-regulation network and glycan modification alterations in Alzheimer's disease

Despite the importance of protein glycosylation to brain health, current knowledge of glycosylated proteoforms or glycoforms in human brain and their alterations in Alzheimers disease (AD) is limited. Here, we present a new paradigm of proteome-wide glycoform profiling study of human AD and control brains using intact glycopeptide-based quantitative glycoproteomics coupled with systems biology. Our study identified over 10,000 human brain N-glycoforms from nearly 1200 glycoproteins and uncovered disease signatures of altered glycoforms and glycan modifications, including reduced sialylation and N-glycan branching as well as elevated mannosylation and N-glycan truncation in AD. Network analyses revealed a higher-order organization of brain glycoproteome into networks of co-regulated glycoforms and glycans and discovered glycoform and glycan modules associated with AD clinical phenotype, amyloid-{beta} accumulation, and tau pathology. Our findings provide novel insights and a rich resource of glycoform and glycan changes in AD and pave the way forward for developing glycosylation-based therapies and biomarkers for AD.

neuroscience↗

Fast, lightweight, and accurate metagenomic functional profiling using FracMinHash sketches

MotivationFunctional profiling of metagenomic samples is essential to decipher the functional capabilities of microbial communities. Traditional and more widely used functional profilers in the context of metagenomics rely on aligning reads against a known reference database. However, aligning sequencing reads against a large and fast-growing database is computationally expensive. In general, k-mer-based sketching techniques have been successfully used in metagenomics to address this bottleneck, notably in taxonomic profiling. In this work, we describe leveraging FracMinHash (implemented in sourmash, a publicly available software), a k-mer-sketching algorithm, to obtain functional profiles of metagenome samples. ResultsWe show how pieces of the sourmash software (and the resulting FracMinHash sketches) can be put together in a pipeline to functionally profile a metagenomic sample. We named our pipeline fmh-funprofiler. We report that the functional profiles obtained using this pipeline demonstrate comparable completeness and better purity compared to the profiles obtained using other alignment-based methods when applied to simulated metagenomic data. We also report that fmh-funprofiler is 39-99x faster in wall-clock time, and consumes up to 40-55x less memory. Coupled with the KEGG database, this method not only replicates fundamental biological insights but also highlights novel signals from the Human Microbiome Project datasets. ReproducibilityThis fast and lightweight metagenomic functional profiler is freely available and can be accessed here: https://github.com/KoslickiLab/fmh-funprofiler. All scripts of the analyses we present in this manuscript can be found on GitHub.

bioinformatics↗

Platelet-specific TGFβ1 deficiency aggravates atherosclerosis, vascular inflammation, and hypercholesterolemia in mice

Atherosclerosis involves inflammatory and thrombotic mechanisms, to which both platelets and transforming growth factor {beta} (TGF{beta}) contribute. The effect of platelet-derived TGF{beta} on atherosclerosis is, however, unknown and therefore investigated. Murine platelet-selective TGF{beta}-deficiency (plt-TGF{beta}-/-) was created by a Pf4-Cre approach, and an atherosclerotic mouse model was established by functional abrogation of Ldlr and 10-15 weeks of a high-fat diet in plt-TGF{beta}-/- mice and their non-plt-TGF{beta}-/- littermates. En face Oil Red O staining of the aorta showed more atherosclerotic lesion formation in plt-TGF{beta}-/- mice, with significant increases in both lesion size and lesion coverage of the total aortic area. Cryosections of the aortic root confirmed the aggravation of atherogenesis. Platelet-derived TGF{beta} deficiency increased circulating platelets and plasma levels of total cholesterol, LDL-cholesterol, and triglycerides after a 10 or 15 week high-fat diet period. RNA sequencing and proteomic analyses of the aorta showed signs of CD4+ T effector cell and macrophage activation in plt-TGF{beta}-/- mice. In conclusion, platelet-specific TGF{beta} deficiency aggravates atherosclerosis, via increasing arterial inflammation and plasma levels of cholesterol. Our findings demonstrate that platelet-derived TGF{beta} is prominently athero-protective. Key pointsO_LIPlatelet-specific transforming growth factor {beta} (TGF{beta}) deficiency markedly enhances atherosclerosis in a high-fat diet-fed murine model. C_LIO_LIPlatelet TGF{beta} deficiency aggravates hyperlipidemia, with further elevations of total cholesterol, LDL-cholesterol, and triglycerides. C_LI

cell biology↗

Mapping the topography of spatial gene expression with interpretable deep learning

Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of this data complicates the analysis of spatial gene expression patterns such as gene expression gradients. We address these issues by deriving a topographic map of a tissue slice--analogous to a map of elevation in a landscape--using a novel quantity called the isodepth. Contours of constant isodepth enclose spatial domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in gene expression. We develop GASTON, an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gene expression gradients, and piecewise linear functions of the isodepth that model both continuous gradients and discontinuous spatial variation in the expression of individual genes. We validate GASTON by showing that it accurately identifies spatial domains and marker genes across several biological systems. In SRT data from the brain, GASTON reveals gradients of neuronal differentiation and firing, and in SRT data from a tumor sample, GASTON infers gradients of metabolic activity and epithelial-mesenchymal transition (EMT)-related gene expression in the tumor microenvironment.

bioinformatics↗

Identification of ACE2 as the Entry Receptor for Two Novel European Bat Merbecoviruses

The angiotensin-converting enzyme 2 (ACE2) receptor is shared by various coronaviruses with distinct receptor-binding domain (RBD) architectures, yet our understanding of these convergent acquisition events remains elusive. Here, we report that two European bat MERS-related coronaviruses (MERSr-CoVs) infecting Pipistrellus nathusii (P.nat), MOW15-22 and PnNL2018B, use ACE2 as their receptor, with narrow ortholog specificity. Cryo-electron microscopy structures of the MOW15-22 RBD-ACE2 complex unveil an unexpected and entirely distinct binding mode, mapping 50[A] away from that of any other known ACE2-using coronaviruses. Functional profiling of ACE2 orthologs from 105 mammalian species led to the identification of host tropism determinants, including an ACE2 N432-glycosylation restricting viral recognition, and the design of a soluble P.nat ACE2 mutant with potent viral neutralizing activity. Our findings reveal convergent acquisition of ACE2 usage for merbecoviruses found in European bats, underscoring the extraordinary diversity of ACE2 recognition modes among coronaviruses and the promiscuity of this receptor. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/560486v2_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1967686org.highwire.dtl.DTLVardef@13f8c8borg.highwire.dtl.DTLVardef@1241e38org.highwire.dtl.DTLVardef@3bfd7a_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

Multilevel regulation of NF-kappa B signaling by NSD2 suppresses Kras-driven pancreatic tumorigenesis

Pancreatic ductal adenocarcinoma (PDAC) is a clinically challenging cancer with a dismal overall prognosis. NSD2 is an H3K36-specific di-methyltransferase which has been reported to play a crucial role in promoting tumorigenesis. Here, we demonstrate that NSD2 acts as a putative tumor suppressor in Kras-driven pancreatic tumorigenesis. Low level of NSD2 indicates aggressive feature of PDAC. NSD2 restrains the mice from inflammation and Kras-induced ductal metaplasia, while NSD2 loss facilitates pancreatic tumorigenesis. Mechanistically, NSD2-mediated H3K36me2 promotes the expression of I{kappa}B, which inhibits the phosphorylation of p65 and NF-{kappa}B nuclear translocation. More importantly, NSD2 interacts with the DNA binding domain of p65, attenuating NF-{kappa}B transcriptional activity. Furthermore, inhibition of NF-{kappa}B signaling relieves the symptoms of Nsd2-deficient mice. Together, our study reveals the important tumor suppressor role of NSD2 and multiple mechanisms by which NSD2 suppresses both p65 phosphorylation and downstream transcriptional activity during pancreatic tumorigenesis. This study contributes to understanding the pathogenesis of pancreatic tumorigenesis and identifies a novel negative regulator of NF-{kappa}B signaling.

cancer biology↗

Comparing methylation levels assayed in GC-rich regions with current and emerging methods

DNA methylation is an epigenetic mechanism that regulates gene expression, and for mammals typically occurs on cytosines within CpG dinucleotides. A significant challenge for methylation detection methods is accurately measuring methylation levels within GC-rich regions such as gene promoters, as inaccuracies compromise downstream biological interpretation of the data. To address this challenge, we compared methylation levels assayed using four different methods: Enzymatic Methyl-seq (EM-seq), whole genome bisulphite sequencing (WGBS), Infinium arrays (Illumina MethylationEPIC, "EPIC"), and Oxford Nanopore Technologies nanopore sequencing (ONT) applied to human DNA. Overall, all methods produced comparable and consistent methylation readouts across the human genome. The flexibility offered by current gold standard WGBS in interrogating genome-wide cytosines is surpassed technically by both EM-seq and ONT, as their coverages and methylation readouts are less prone to GC bias. These advantages are tempered by increased laboratory time (EM-seq) and higher complexity (ONT). We further assess the strengths and weaknesses of each method, and provide recommendations in choosing the most appropriate methylation method for specific scientific questions or translational needs.

genomics↗

AI in Paleontology

Accumulating data have led to the emergence of data-driven paleontological studies, which reveal an unprecedented picture of evolutionary history. However, the fast-growing quantity and complication of data modalities make data processing laborious and inconsistent, while also lacking clear benchmarks to evaluate data collection and generation, and the performances of different methods on similar tasks. Recently, Artificial Intelligence (AI) is widely practiced across scientific disciplines, but has not become mainstream in paleontology where manual workflows are still typical. In this study, we review more than 70 paleontological AI studies since the 1980s, covering major tasks including micro-and macrofossil classification, image segmentation, and prediction. These studies feature a wide range of techniques such as Knowledge Based Systems (KBS), neural networks, transfer learning, and many other machine learning methods to automate a variety of paleontological research workflows. Here, we discuss their methods, datasets, and performance and compare them with more conventional AI studies. We attribute the recent increase in paleontological AI studies to the lowering bar in training and deployment of AI models rather than real progress. We also present recently developed AI implementations such as diffusion model content generation and Large Language Models (LLMs) to speculate how these approaches may interface with paleontological research. Even though AI has not yet flourished in paleontological research, successful implementation of AI is growing and show promise for transformative effect on the workflow in paleontological research in the years to come. HighlightsO_LIFirst systematic review of AI applications in paleontology. C_LIO_LIThere is a 10 to 20-year gap between AI in paleontology and mainstream studies. C_LIO_LIRecent progress in paleontological AI studies is likely a result of lowering bar in training and deployment. C_LIO_LIFuture direction discussed for interactions between paleontology and AI. C_LI

paleontology↗

Microglia Regulate Sleep via Calcium-Dependent Modulation of Norepinephrine Transmission

Sleep interacts reciprocally with immune system activity, but its specific relationship with microglia - the resident immune cells in the brain - remains poorly understood. Here we show that microglia can regulate sleep through a mechanism involving Gi-coupled GPCRs, intracellular Ca2+ signaling, and suppression of norepinephrine transmission. Chemogenetic activation of microglia Gi signaling strongly promoted sleep, whereas pharmacological blockade of Gi-coupled P2Y12 receptors decreased sleep. Two-photon imaging showed that P2Y12/Gi activation elevated microglia intracellular Ca2+, and blockade of this Ca2+ elevation largely abolished the Gi-induced sleep increase. Microglia Ca2+ level also increased at natural wake-to-sleep transitions, caused partly by reduced norepinephrine. Furthermore, imaging of norepinephrine activity with its biosensor showed that microglia P2Y12/Gi activation significantly reduced norepinephrine, partly by increasing the adenosine concentration. Thus, microglia can regulate sleep through reciprocal interactions with norepinephrine transmission.

neuroscience↗

Source-Free Random Forest Model Calibration for Myoelectric Control

ObjectiveMost existing machine learning models for myoelectric control require a large amount of data to learn user-specific characteristics of the electromyographic (EMG) signals, which is burdensome. Our objective is to develop an approach to enable the calibration of a pre-trained model with minimal data from a new myoelectric user. ApproachWe trained a random forest model with EMG data from 20 people collected during the performance of multiple hand grips. To adapt the decision rules for a new user, first, the branches of the pre-trained decision trees were pruned using the validation data from the new user. Then new decision trees trained merely with data from the new user were appended to the pruned pre-trained model. ResultsReal-time myoelectric experiments with 18 participants over two days demonstrated the improved accuracy of the proposed approach when compared to benchmark user-specific random forest and the linear discriminant analysis models. Furthermore, the random forest model that was calibrated on day one for a new participant yielded significantly higher accuracy on day two, when compared to the benchmark approaches, which reflects the robustness of the proposed approach. SignificanceThe proposed model calibration procedure is completely source-free, that is, once the base model is pre-trained, no access to the source data from the original 20 people is required. Our work promotes the use of efficient, explainable, and simple models for myoelectric control.

neuroscience↗

On-Demand Expansion Fluorescence and Photoacoustic Microscopy (ExFLPAM)

Expansion microscopy (ExM) is a promising technology that enables nanoscale imaging on conventional optical microscopes by physically magnifying the specimens. Here, we report the development of a strategy that enables i) on-demand labeling of subcellular organelles in live cells for ExM through transfection of fluorescent proteins that are well-retained during the expansion procedure; and ii) non-fluorescent chromogenic color-development towards efficient bright-field and photoacoustic imaging in both planar and volumetric formats, which is applicable to both cultured cells and biological tissues. Compared to the conventional ExM methods, our strategy provides an expanded toolkit, which we term as expansion fluorescence and photoacoustic microscopy (ExFLPAM), by allowing on-demand fluorescent protein labeling of cultured cells, as well as non-fluorescent absorption contrast-imaging of biological samples.

bioengineering↗

GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning

Molecule property prediction has gained significant attention in recent years. The main bottleneck is the label insufficiency caused by expensive lab experiments. In order to alleviate this issue and to better leverage textual knowledge for tasks, this study investigates the feasibility of employing natural language instructions to accomplish molecule-related tasks in a zero-shot setting. We discover that existing molecule-text models perform poorly in this setting due to inadequate treatment of instructions and limited capacity for graphs. To overcome these issues, we propose GIMLET, which unifies language models for both graph and text data. By adopting generalized position embedding, our model is extended to encode both graph structures and instruction text without additional graph encoding modules. GIMLET also decouples encoding of the graph from tasks instructions in the attention mechanism, enhancing the generalization of graph features across novel tasks. We construct a dataset consisting of more than two thousand molecule tasks with corresponding instructions derived from task descriptions. We pretrain GIMLET on the molecule tasks along with instructions, enabling the model to transfer effectively to a broad range of tasks. Experimental results demonstrate that GIMLET significantly outperforms molecule-text baselines in instruction-based zero-shot learning, even achieving closed results to supervised GNN models on tasks such as toxcast and muv.1

bioinformatics↗

Synergistic effects of multiple pesticides on the flight ability of honey bees (Apis cerana F.)

Pesticides cause risks in the environment for Asian honey bees (Apis cerana). Imidacloprid (I), chlorpyrifos (C) and glyphosate (G) are among the most-widely used pesticides in the world. It is not clear on how these pesticides and combination of them affect the flight ability of A. cerana. Here we utilized flight mills to show new results that acute treatment of individual pesticides, such as 0.2 ng/bees imidacloprid (20 L, 10 ng/g), 0.6 ng/bees chlorpyrifos (20 L, 30 ng/g) and 1.2 ng/bees glyphosate (20 L, 60 ng/g), had no effect on the flight ability of bees. However, forager bees showed a significantly decrease in the flying duration and flying distance when oral exposed to two or three these pesticides. This evidence indicated that two or three pesticides can produce synergistic changes in the flight ability and behavior of honeybees. Results showed a light on new understandings of complex effects and potential risks of these three pesticide on bee behavior including homing ability and food-collecting ability. Our results are key information to understand new synergistic potential among pesticide formulations and how they impair bee behavior. Table of Contents Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/541595v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@70e24dorg.highwire.dtl.DTLVardef@1195fb8org.highwire.dtl.DTLVardef@db5ad5org.highwire.dtl.DTLVardef@1287b51_HPS_FORMAT_FIGEXP M_FIG C_FIG

animal behavior and cognition↗