Biology subjects

Shu, M.

Publications and source records attributed to Shu, M..

8 recordsLinked to original sources

bioRxiv2026

Ultra-flexible PINE arrays for month-long, continuous intracellular ion flux monitoring in plants with nanomolar accuracy

High-precision in vivo monitoring of ion fluxes is essential yet challenging studying plant electrophysiology such as growth regulation, signal transduction and stress responses. Existing methods for probing ion dynamics are limited by low sensitivity, high invasiveness that interferes physiological processes, and the inability to accurately resolve ion homeostasis with required spatial and temporal resolution. Here, we introduce ultraflexible, plant implantable nanoelectrode (PINE) arrays manufactured on 1.2-m-thick polymer substrates, which enable ultrasensitive and selective measurement of ionic current for month-long via scalable nanofabrication techniques. The fabricated PINE arrays have a smaller dimension than typical plant cells as well as less stiffness, facilitating minimally invasive integration with living plant cells. This subcellular-scale plant-electronic interface allows for reliable, selective detection of K+ flux with a detection limit of [~]10- M, and thus allows continuous, stable monitoring of tomato stem cells over six weeks, capturing dynamic potassium fluctuations during all key growth stages. More importantly, the method permits long-term, real-time tracking of ion-specific dynamics without disrupting plant cellular structure or altering endogenous ion concentrations. Therefore, PINE provides unprecedented access to ion homeostasis and signaling networks, making it an excellent platform for precision agriculture and a foundational tool for future digital plant engineering.

plant biology↗

bioRxiv2025

Telomere-to-telomere assemblies reveal complex adaptive variation of 3-ketoacyl-CoA-synthases in Populus trichocarpa likely driven by helitrons

BackgroundThe model woody plant Populus trichocarpa displays an atypical alkene-diverse wax cuticle likely driven by copy number variation (CNV) of 3-ketoacyl-CoA synthases (KCS), which has been difficult to confirm based on short-read assemblies. New long-read sequencing provides opportunities to develop telomere-to-telomere resources to detect cryptic variation, including CNVs, which are currently missed in traditional analyses. Integrating this information can improve genomic prediction for breeding and provide insights into the evolutionary basis of important traits. ResultsOur analysis of 78 telomere-to-telomere long-read haplotypes identified more than twice as many KCS genes as previously reported, along with numerous intragenic non-synonymous substitutions. Random forest predictive models highlighted the importance of Potri.010G079500 in producing very long chain alkenes; however, its absence did not predict previously reported alkene-deficient phenotypes. Instead, alkene levels are best predicted by the combinations of KCS copies. Amino acid substitutions clustered around ligand and donor binding pockets, suggesting they contribute to differing wax cuticle composition. Finally, each KCS gene and copy was linked to a helitron transposon. A phylogenetic analysis indicates they are the evolutionary mechanism for generating KCS tandem arrays. ConclusionsLong-read sequencing and telomere-to-telomere assembles revealed large-effect loci critical to genetic studies that are unattainable from short-reads. These approaches also have the potential to reveal novel insights into genome structure and function, such as the helitrons identified here. Our results highlight that, given current challenges in annotation and assembly, detailed and focused long-read sequences are key to interpreting complex genomic regions that contain tandem copy number variants.

genomics↗

bioRxiv2024

Climate adaptation in P. trichocarpa: key adaptive loci identified for stomata and leaf traits

Identifying the genetic basis of traits underlying climate adaptation remains a key goal for predicting species responses to climate change, enabling the elucidation of gene targets for future climate-resilient crops. Here, we measured 14 leaf and stomatal traits under control (well-watered) and drought conditions, subsampling a diversity collection of over 1,300 Populus trichocarpa genotypes, a potential biofuel feedstock crop. Stomatal traits were correlated with the climate of origin for genotypes, such that those originating from environments subject to water deficit tended to have smaller stomata, but with higher density. Stomatal traits were also correlated with leaf morphology, with larger leaves having larger stomata and lower stomatal density mirrored in correlations to climate of origin. The direction of plastic responses - reduced stomatal size under drought - mirrors the correlations seen among genotypes with respect to the aridity of environmental origin. Genome-Wide Association Studies (GWAS) identified loci underlying trait diversity, including candidates contributing to stomatal size. We used climate of origin to predict stomatal size in genotypes with unknown trait values and found that these predicted phenotypes confirmed empirically measured allele effects. Finally, we found evidence that future climates may select for alleles contributing to decreased stomatal size, with the strength of selection depending on the availability of moisture. These findings reveal adaptive variation in stomatal and physiological traits along with underlying genetic loci, with implications for future selection and breeding - providing insights into the responses to future climate change. HighlightResearch on Populus trichocarpa reveals adaptation of physiological and stomatal traits linked to drought tolerance, with genotypes from arid regions exhibiting smaller stomata, offering insights for climate change adaptation and sustainable biofuel production.

plant biology↗

bioRxiv2024

Embryo spatial 3D genomics

The 3D architecture of the genome is crucial for controlling gene expression and organ development. Here, we introduce a spatial 3D genomics approach for assessing chromatin conformation in-situ in tissue sections, by integrating microfluidic deterministic barcoding and SPRITE procedures. This method was applied to mouse embryo sections, revealing a hierarchical model of chromatin interactions within and between compartments in various organs. The intra-compartment interactions vary among organs to orchestrate gene expressions, while the inter-compartment interactions remain identical in the most organs. Beyond this, the liver exhibits overwhelmingly packed chromatin with enhanced adjacent-compartment interactions, possibly related to its physiology. These findings highlight the importance of tissue-spatial information in understanding embryonic chromatin organization. The approach presents a powerful tool for investigating these processes in tissues with high heterogeneity. One Sentence SummaryA spatial 3D genomics approach was developed that accesses hierarchical chromatin conformation in-situ in tissue sections.

genetics↗

bioRxiv2023

The target gene regulatory network of miR-497 in angiosarcoma

Angiosarcoma (AS) is a vascular sarcoma that is highly aggressive and metastatic. Due to its rarity, treatment options for patients are limited, therefore more research is needed to identify possible therapeutic vulnerabilities. We previously found that conditional deletion of Dicer1 drives AS development in mice. Given the role of DICER1 in canonical microRNA (miRNA) biogenesis, this suggests that miRNA loss is important in AS development. After testing miRNAs previously suggested to have a tumor-suppressive role in AS, microRNA-497-5p (miR-497) suppressed cell viability most significantly. We also found that miR-497 overexpression led to significantly reduced cell migration and tumor formation. To understand the mechanism of miR-497 in tumor suppression, we identified clinically relevant target genes using a combination of RNA-sequencing data in an AS cell line, expression data from AS patients, and target prediction algorithms. We validated miR-497 direct regulation of CCND2, CDK6, and VAT1. One of these genes, VAT1, is an understudied protein that has been suggested to promote cell migration and metastasis in other cancers. Indeed, we find that pharmacologic inhibition of VAT1 with the natural product Neocarzilin A reduces AS migration. This work provides insight into the mechanisms of miR-497 and its target genes in AS pathogenesis.

cancer biology↗

bioRxiv2022

Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis

Many enhancers exist as clusters in the genome and control cell identity and disease genes; however, the underlying mechanism remains largely unknown. Here, we introduce an algorithm, eNet, to build enhancer networks by integrating single-cell chromatin accessibility and gene expression profiles. Enhancer network is a gene regulation model we proposed that not only delineates the mapping between enhancers and target genes, but also quantifies the underlying regulatory relationships among enhancers. The complexity of enhancer networks is assessed by two metrics: the number of enhancers and the frequency of predicted enhancer interactions (PEIs) based on chromatin co-accessibility. We apply eNet algorithm to a human blood dataset and find cell identity and disease genes tend to be regulated by complex enhancer networks. The network hub enhancers (enhancers with frequent PEIs) are the most functionally important in enhancer networks. Compared with super-enhancers, enhancer networks show better performance in predicting cell identity and disease genes. The establishment of enhancer networks drives gene expression during lineage commitment. Applying eNet in various datasets in human or mouse tissues across different single-cell platforms, we demonstrate eNet is robust and widely applicable. Thus, we propose a model of enhancer networks containing three modes: Simple, Multiple and Complex, which are distinguished by their complexity in regulating gene expression. Taken together, our work provides an unsupervised approach to simultaneously identify key cell identity and disease genes and explore the underlying regulatory relationships among enhancers in single cells, without requiring the cell type identity in advance. HighlightsO_LIeNet, a computational method to build enhancer network based on scATAC-seq and scRNA-seq data C_LIO_LICell identity and disease genes tend to be regulated by complex enhancer networks, where network hub enhancers are functionally important C_LIO_LIEnhancer network outperforms the existing models in predicting cell identity and disease genes, such as super-enhancer and enhancer cluster C_LIO_LIWe propose a model of enhancer networks in gene regulation containing three modes: Simple, Multiple and Complex C_LI

bioinformatics↗

bioRxiv2021

Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data

Transcriptome profiling and differential gene expression constitute a ubiquitous tool in biomedical research and clinical application. Linear dimensionality reduction methods especially principal component analysis (PCA) are widely used in detecting sample-to-sample heterogeneity in bulk transcriptomic datasets so that appropriate analytic methods can be used to correct batch effects, remove outliers and distinguish subgroups. In response to the challenge in analysing transcriptomic datasets with large sample size such as single-cell RNA-sequencing (scRNA-seq), non-linear dimensionality reduction methods were developed. t-distributed stochastic neighbour embedding (t-SNE) and uniform manifold approximation and projection (UMAP) show the advantage of preserving local information among samples and enable effective identification of heterogeneity and efficient organisation of clusters in scRNA-seq analysis. However, the utility of t-SNE and UMAP in bulk transcriptomic analysis has not been carefully examined. Therefore, we compared major dimensionality reduction methods (linear: PCA; nonlinear: multidimensional scaling (MDS), t-SNE, and UMAP) in analysing 71 bulk transcriptomic datasets with large sample sizes. UMAP was found superior in preserving sample level neighbourhood information and maintaining clustering accuracy, thus conspicuously differentiating batch effects, identifying pre-defined biological groups and revealing in-depth clustering structures. We further verified that new clustering structures visualised by UMAP were associated with biological features and clinical meaning. Therefore, we recommend the adoption of UMAP in visualising and analysing of sizable bulk transcriptomic datasets.

bioinformatics↗

bioRxiv2018

Widespread and polymorphous noncoding amino acid residues in human sperm proteome

Proteins are usually deciphered by translation of the coding genome; however, their amino acid residues are seldom determined directly across the proteome. Herein, we describe a systematic workflow for identifying all possible protein residues that differ from the coding genome, termed noncoded amino acids (ncAAs). By measuring the mass differences between the coding amino acids and the actual protein residues in human spermatozoa, over a million nonzero delta masses were detected, fallen into 424 high-quality Gaussian clusters and 571 high-confidence ncAAs spanning 29,053 protein sites. Most ncAAs are novel with unresolved side-chains and discriminative between healthy individuals and patients with oligoasthenospermia. For validation, 40 out of 98 ncAAs that matched with amino acid substitutions were confirmed by exon sequencing. This workflow revealed the widespread existence of previously unreported ncAAs in the sperm proteome, which represents a new dimension on the understanding of amino acid polymorphisms at the proteomic level.\n\nHighlightsO_LI571 ncAAs spanning 108,000 protein sites were identified in human sperm proteome.\nC_LIO_LIMost ncAAs are novel with unresolved sidechains and found at unreported protein sites.\nC_LIO_LIExon sequencing confirmed 40 of 98 ncAAs that matched with amino acid substitutions.\nC_LIO_LIMany ncAAs are linked with disease and have potential for diagnosis and targeting.\nC_LI\n\neTOC BlurbWe describe a systematic identification of all possible protein residues that were not encoded by their genomic sequences. A total of 571 high-confidence most novel noncoded amino acids were identified in human sperm proteome, corresponding to over 108,000 ncAA-containing protein sites. For validation, 40 out of 98 ncAAs that matched to amino acid substitutions were confirmed by exon sequencing. These ncAAs are discriminative between individuals and expand our understanding of amino acid polymorphisms in human proteomes and diseases.

molecular biology↗