Biology subjects

Yao, W.

Publications and source records attributed to Yao, W..

At least 19 recordsLinked to original sources

bioRxiv2026

Incorporation of single-neuron projectome-based connectivity motifs enhances the cortex-specific performance of artificial neural networks

The organizational principles of natural neural networks could inspire the new architecture design of artificial neural networks (ANNs). Analysis of single-neuron connectomes of mouse brains revealed distinct profiles of three-node connectivity motifs in various cortical areas and hippocampal formation. A connectome-informed neural network algorithm ("CINA") was developed to incorporate natural connectivity motifs into ANN algorithms represented by recurrent neural network (RNN) and transformer-based large language model (LLM). We found that incorporation of the average profile of cortical motifs improved the RNNs performance in noise-resistant categorization and motor learning benchmark tasks, as compared with RNNs with random connectivity. Notably, incorporating cortex-specific motifs further elevated the RNNs performance in tasks related to the cortical function, and this effect was enhanced by artificially increasing the bias in the motif profile. Similar experimental results were verified on an LLM using Motif-Transformer for natural language question answering and brain-signal decoding tasks. Graph-theoretic analyses showed that incorporating natural motifs drove the emergence of modular and small-world properties in ANNs. Together, we demonstrated not only connectome-inspired optimization of ANN architecture but also functional significance of specific motif profiles in various cortices.

neuroscience↗

bioRxiv2026

DigiMus: a connectome-informed spiking framework for multi-region mouse neural-behavior modeling

Computational models are increasingly used to relate mouse brain structure, neural activity and behavior, but most models still learn from task data with limited constraints from biological circuit organization. Here we present DigiMus, a connectome-informed spiking framework for multi-region-capable mouse neural-behavior modeling. DigiMus combines leaky integrate-and-fire spiking dynamics with brain-region-specific motif regularization in a trainable sequence-modeling architecture, allowing directed three-node circuit motifs derived from 38,481 reconstructed neuronal morphologies across approximately 50 brain regions to guide recurrent coupling during learning. We evaluate DigiMus on 18 rule-based cognitive tasks spanning sensorimotor mapping and perceptual decision-making, and on three mouse neural decoding datasets involving auditory discrimination, fixed-interval licking and visual decoding. Across synthetic tasks, DigiMus showed stable performance relative to TCN, LSTM and Transformer baselines, with stronger advantages in more complex decision-making settings. In real neural datasets, single-region instantiations of DigiMus produced small, consistent and dataset-dependent improvements over a structure-free sequence baseline, while retaining motif-prior signatures in trained connectivity. Internal state analyses further linked task-dependent state dynamics to behavioral error patterns. These results suggest that connectome-derived structural priors can shape neural sequence models, and establish DigiMus as a modular, connectome-informed workflow for mouse neural-behavior modeling and hypothesis generation, rather than a complete digital reconstruction.

neuroscience↗

bioRxiv2026

Particle Biology: A Perspective on a First-Principles Theory of Life

This Perspective formally proposes Particle Biology as a unifying theoretical framework to address the critical bottleneck in current life science research. Current life science research has reached a critical bottleneck. While the field has advanced to the study of 3D genomic spatial configurations and chromosomal organization, it remains largely descriptive and confined to the macromolecular level. This approach lacks a first-principles understanding of the underlying physical forces that drive biological processes. This Perspective formally proposes Particle Biology as a unifying theoretical framework. We establish an axiomatic system positing that life phenomena are fundamentally emergent spatiotemporal patterns of electromagnetic forces among atoms, electrons, and nuclei operating far from thermodynamic equilibrium. By defining biological states through the Biological Hamiltonian and mapping biochemical pathways to multidimensional Potential Energy Surfaces (PES), we bridge the gap between descriptive biology and predictive physics. We categorize core research technologies into three modalities--seeing, computing, and controlling particles--facilitated by advancements in Cryo-EM, AlphaFold 3, and Boron Neutron Capture Therapy (BNCT). Ultimately, the trajectory of molecular biology has evolved from cells to DNA and onto the 3D spatial genome, yet it cannot go deeper within current paradigms. The next logical evolution is to move beyond the macromolecular bottleneck to focus on the electromagnetic interactions between atoms and ions--the true Particle Biology level--to redefine disease and intervention.

biophysics↗

bioRxiv2026

The Magnaporthe oryzae Effector AvrPii Attenuates Rice Immunity by Targeting the Calcium-Associated ANK Protein AVIN8

Rice blast, caused by the fungus Magnaporthe oryzae, poses a severe threat to global rice production. Although numerous M. oryzae effectors have been identified, the molecular mechanisms remain poorly understood. The effector AvrPii triggers ETI upon recognition by NLR receptor Pii and has been shown to target host exocytosis and oxidative metabolic pathways to suppress immunity. Here, we identify AVIN8 (AvrPii-interacting protein 8) as a new target of AvrPii. AVIN8 is an ankyrin-repeat (ANK) protein localized to the plasma membrane and nuclear envelope, with sequence similarity to known ANK calcium channels. AVIN8 expression is induced during early M. oryzae infection. Overexpression of AVIN8 enhances blast resistance, whereas its silencing increases susceptibility. AVIN8 promotes chitin-triggered ROS production in a Ca2+-dependent manner. In contrast, AvrPii impairs Ca2+ influx and chitin-triggered ROS burst. Our findings reveal a virulence strategy in which M. oryzae effector AvrPii hijacks the Ca2+ influx-associated ANK protein AVIN8 to suppress early calcium and PTI signaling in rice.

plant biology↗

bioRxiv2025

Bispecific T cell engagers control solid tumors through clonal replacement and IL2-driven effector differentiation of CD8 T-cells

Bispecific T cell engagers (TCEs) often exhibit limited efficacy in solid tumors, in part due to immunosuppressive cues in the tumor microenvironment and low expression of targetable tumor antigens. Therapeutic strategies to improve TCE target sensitivity and enhance T cell effector functions therefore have significant translational potential. Here, we engineered TCEs that induce T cell activation in vitro against the low-abundance target antigens, TRP2/Kb and DLL3. Despite in vitro activity in these models, TCE monotherapy showed limited control of tumor growth in immunocompetent mice. Leveraging this in vivo model of TCE treatment failure, we discovered that co-treatment with TCE and a CD25-biased Interleukin-2 (IL2) rescues anti-tumor activity. Further, multimodal single-cell transcriptomic and immune repertoire analyses revealed that TCE-IL2 combination therapy controlled tumors by recruiting and activating new CD8+ T cells into the tumor microenvironment. These findings demonstrate that TCE-mediated anti-tumor responses function through a CD8+ T cell clonal replacement mechanism that can be augmented by cytokine therapy. One Sentence SummaryCombining TCE therapy with IL-2 enhances TCE efficacy in aggressive small-cell lung cancer and melanoma models with low target antigen density through T cell clonal replacement and CD8+ effector T cell differentiation.

cancer biology↗

bioRxiv2025

QKI ensures splicing fidelity during cardiogenesis by engaging the U6 tri-snRNP to activate splicing at weak 5' splice sites

During organogenesis, precise pre-mRNA splicing is essential to assemble tissue architecture. Many developmentally essential exons bear weak 5' splice sites (5'SS) yet are spliced with high precision, implying unknown yet active splicing fidelity mechanisms. By combining transcriptome and alternative splicing profiling with temporal eCLIP mapping of RNA interactions across development, we identify the RNA-binding protein QKI as an essential direct regulator of splicing fidelity in key cardiac transcripts. Although QKI is dispensable for cardiac specification, its loss disrupts sarcomere assembly despite intact expression of sarcomere mRNAs through exon skipping and nuclear retention of mis-spliced RNAs. QKI-dependent exons in essential cardiac genes have weak 5'SS and frequently show poor complementarity with U6 snRNA. We show that QKI directly interacts with U6 snRNA using an overlapping interface to its traditional intronic binding activity, securing U4/U6{middle dot}U5 tri-snRNP to ensure splicing fidelity. Thus, QKI exemplifies how context-aware RBPs enforce splicing fidelity at structurally vulnerable splice sites during organogenesis.

cell biology↗

bioRxiv2025

ArchLips: A comprehensive in silico database for high-throughput identification of archaeal lipids

Archaeal membrane lipids are markedly distinct from those in bacteria and eukaryotes, serving as unique biomarkers for unraveling their ecological and biogeochemical roles. Recent advancements in lipidomics have facilitated detailed cellular-level characterizations of lipid compounds. However, the lack of a comprehensive and dedicated database has severely limited large-scale, high-throughput investigations of archaeal lipids. We present ArchLips, a comprehensive database containing 218,868 in silico molecular structures and tandem mass spectra of 199,248 corresponding archaeal lipids. ArchLips enables the rapid and accurate identification of characterized archaeal lipid compounds from both pure cultures and environmental samples, serving as a transformative tool for enhancing our understanding of archaeal diversity and their ecological and evolutionary significance within global ecosystems.

biochemistry↗

bioRxiv2024

WRAD core perturbation impairs DNA replication fidelity promoting immunoediting in pancreatic cancer

It is unclear how cells counteract the potentially harmful effects of uncoordinated DNA replication in the context of oncogenic stress. Here, we identify the WRAD (WDR5/RBBP5/ASH2L/DPY30) core as a modulator of DNA replication in pancreatic ductal adenocarcinoma (PDAC) models. Molecular analyses demonstrated that the WRAD core interacts with the replisome complex, with disruption of DPY30 resulting in DNA re-replication, DNA damage, and chromosomal instability (CIN) without affecting cancer cell proliferation. Consequently, in immunocompetent models, DPY30 loss induced T cell infiltration and immune-mediated clearance of highly proliferating cancer cells with complex karyotypes, thus improving anti-tumor efficacy upon anti-PD-1 treatment. In PDAC patients, DPY30 expression was associated with high tumor grade, worse prognosis, and limited response to immune checkpoint blockade. Together, our findings indicate that the WRAD core sustains genome stability and suggest that low intratumor DPY30 levels may identify PDAC patients who will benefit from immune checkpoint inhibitors.

cancer biology↗

bioRxiv2024

Therapeutic targeting of Syndecan-1 axis overcomes acquired resistance to KRAS-targeted therapy in gastrointestinal cancers

The therapeutic benefit of recently developed mutant KRAS (mKRAS) inhibitors has been limited by the rapid onset of resistance. Here, we aimed to delineate the mechanisms underlying acquired resistance to mKRAS inhibition and identify actionable targets for overcoming this clinical challenge. Previously, we identified Syndecan-1 (SDC1) as a key effector for pancreatic cancer progression whose surface expression is driven by mKRAS. By leveraging both pancreatic and colorectal cancer models, we found that surface SDC1 expression was initially diminished upon mKRAS inhibition, but recovered in tumor cells that bypass mKRAS dependency. Functional studies showed that these tumors depended on SDC1 for survival, further establishing SDC1 as a driver for the acquired resistance to mKRAS inhibition. Mechanistically, we revealed that the YAP1-SDC1 axis was the major driving force for bypassing mKRAS dependency to sustain nutrient salvage machinery and tumor maintenance. Specifically, YAP1 activation mediated the recovery of SDC1 localization on cell surface that sustained macropinocytosis and enhanced the activation of multiple RTKs, promoting resistance to KRAS-targeted therapy. Overall, our study has provided the rationale for targeting the YAP-SDC1 axis to overcome resistance to mKRAS inhibition, thereby revealing new therapeutic opportunities for improving the clinical outcome of patients with KRAS-mutated cancers.

cancer biology↗

bioRxiv2024

Lysophosphatidylcholine acyltransferase 1 suppresses nanoclustering and function of KRAS

Small GTPase KRAS mutated at hotspots, such as G12, G13 and Q61, are major drivers of cancer and display allele-specific oncogenic properties, which are not well understood. KRAS mutants require precise spatiotemporal distribution to the proteolipid nanoclusters on the plasma membrane (PM) for efficient signaling. We recently reported allele-specific lipid sensing of KRAS mutants. KRASG12D, KRASG12V and KRASQ61H favor the unsaturated phosphatidylserine (PS), while KRASG12C and KRASG13D gain enrichment of the saturated PS, cholesterol and/or phosphoinositol 4,5-bisphosphate (PIP2). We, here, examined how the allele-specific lipid sensing of KRAS mutants contributes to their allele-specific signaling and activities. We now show that the stable expression of lysophosphatidylcholine acyltransferase 1 (LPCAT1) elevates the saturated phospholipids and reduces the mixed-chain lipids, especially PS species. Our super-resolution electron microscopy (EM)-spatial analysis revealed that the LPCAT1 expression perturbs the PM nanoclustering of KRASG12D, without affecting that of KRASG12C. LPCAT1 suppresses the KRAS-dependent mitogen-activated protein kinases (MAPKs) signaling and the MAPK-regulated proliferation and colony formation of the KRASG12D-expressing human pancreatic PANC1 cells, while promoting those of the KRASG12C-expressing MiaPaCa-2 cells. Mouse embryonic fibroblasts (MEF) transformed with KRASG12C contain more saturated lipids than those expressing KRASG12D. Concordantly, patient tumor genomics analysis illustrated that expression of LPCAT1 and KRAS mutants negatively correlate in pancreatic adenocarcinoma with KRASG12D as a dominant driver, but loses correlation in lung adenocarcinoma with KRASG12C as a major driver. Thus, the allele-specific lipid sensing of KRAS mutants contributes to their pathological activities.

cancer biology↗

bioRxiv2023

Temporal genomic analysis of melanoma rejection identifies regulators of tumor immune evasion

Decreased intra-tumor heterogeneity (ITH) correlates with increased patient survival and immunotherapy response. However, even highly homogenous tumors may display variability in their aggressiveness, and how immunologic-factors impinge on their aggressiveness remains understudied. Here we studied the mechanisms responsible for the immune-escape of murine tumors with low ITH. We compared the temporal growth of homogeneous, genetically-similar single-cell clones that are rejected vs. those that are not- rejected after transplantation in-vivo using single-cell RNA sequencing and immunophenotyping. Non- rejected clones showed high infiltration of tumor-associated-macrophages (TAMs), lower T-cell infiltration, and increased T-cell exhaustion compared to rejected clones. Comparative analysis of rejection- associated gene expression programs, combined with in-vivo CRISPR knockout screens of candidate mediators, identified Mif (macrophage migration inhibitory factor) as a regulator of immune rejection. Mif knockout led to smaller tumors and reversed non-rejection-associated immune composition, particularly, leading to the reduction of immunosuppressive macrophage infiltration. Finally, we validated these results in melanoma patient data. Statement of significanceHere, we uncover the association of Mif expression with tumor growth and aggressiveness, specifically in low ITH tumors. These findings could facilitate the development of new strategies to treat patients with homogeneous, high-MIF expressing tumors that are unresponsive to immune checkpoint therapy.

cancer biology↗

bioRxiv2023

Digital profiling of cancer transcriptomes from histology images with grouped vision attention

Cancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. Recently, deep learning has demonstrated potentials for cost-efficient prediction of molecular alterations from histology images. While transformer-based deep learning architectures have enabled significant progress in non-medical domains, their application to histology images remains limited due to small dataset sizes coupled with the explosion of trainable parameters. Here, we develop SEQUOIA, a transformer model to predict cancer transcriptomes from whole-slide histology images. To enable the full potential of transformers, we first pre-train the model using data from 1,802 normal tissues. Then, we fine-tune and evaluate the model in 4,331 tumor samples across nine cancer types. The prediction performance is assessed at individual gene levels and pathway levels through Pearson correlation analysis and root mean square error. The generalization capacity is validated across two independent cohorts comprising 1,305 tumors. In predicting the expression levels of 25,749 genes, the highest performance is observed in cancers from breast, kidney and lung, where SEQUOIA accurately predicts the expression of 11,069, 10,086 and 8,759 genes, respectively. The accurately predicted genes are associated with the regulation of inflammatory response, cell cycles and metabolisms. While the model is trained at the tissue level, we showcase its potential in predicting spatial gene expression patterns using spatial transcriptomics datasets. Leveraging the prediction performance, we develop a digital gene expression signature that predicts the risk of recurrence in breast cancer. SEQUOIA deciphers clinically relevant gene expression patterns from histology images, opening avenues for improved cancer management and personalized therapies.

bioinformatics↗

bioRxiv2023

KRAS inhibition activates an actionable CD24 do-not-eat-me signal in pancreas cancer

KRASG12C inhibitors (G12Ci) have produced encouraging, albeit modest and transient, clinical benefit in pancreatic ductal adenocarcinoma (PDAC). Identifying and targeting resistance mechanisms to G12Ci treatment is therefore crucial. To better understand the function of KRASG12C and possible G12Ci bypass mechanisms, we developed an autochthonous KRASG12C- driven PDAC model. Compared to the classical KRASG12D PDAC model, the G12C model exhibits slower tumor growth, yet similar histopathological and molecular features. Aligned with clinical experience, G12Ci treatment of KRASG12C tumors produced modest impact despite stimulating a hot tumor immune microenvironment. Immunoprofiling revealed that CD24, a dont eat me signal, is significantly upregulated on cancer cells upon G12Ci treatment. Blocking CD24 enhanced macrophage phagocytosis of cancer cells and significantly sensitized tumors to G12Ci treatment. Similar findings were observed in KRASG12D-driven PDAC. Together, this study reveals common and distinct oncogenic KRAS allele-specific biology and identifies a clinically actionable adaptive mechanism that may improve the efficacy of oncogenic KRAS inhibitor therapy in PDAC. SignificanceGeneration of an autochthonous KRASG12C-driven pancreatic cancer model enabled elucidation of specific effects of KRASG12C during tumor development, revealing CD24 as an actionable adaptive mechanism in cancer cells induced upon KRASG12C inhibition.

cancer biology↗

bioRxiv2023

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↗

bioRxiv2023

Isolation and nitrogen removal characteristics of a novel aerobic denitrifying strain Achromobacter xylosoxidans GR7397

Aerobic denitrifying bacteria have the potential for engineering applications due to the efficient nitrate removal capacity from wastewater. In this study, a novel aerobic denitrifying strain was isolated and identified as Achromobacter xylosoxidans GR7397 from the activated sludge of a wastewater treatment plant, which possessed efficient nitrate removal capacity. Moreover, the denitrification capacity and properties of the strain were investigated in the presence of nitrate as the only nitrogen source. Five denitrification reductases encoding genes were harbored by strain GR7397 determined by electrophoretic analysis of PCR amplification products, consisting of periplasmic nitrate reductase (NAP), nitrate reductase (NAR), nitrite reductase (NIR), nitrous oxide reductase (NOS), and nitric oxide reductase (NOR), demonstrating that the strain has a complete denitrification metabolic pathway. The optimum denitrifying condition of strain GR7397 included sodium acetate adopted as the electron donor, COD/TN ratio at 4, pH at 8, temperature at 30{degrees}C, under which condition, the nitrate removal rate reached 14.86 mg {middle dot} L-1 {middle dot} h-1 that the [Formula] concentration decreased from 93.90 mg/L to 4.73 mg/L within 6 h with no accumulation of nitrite. In addition, the bioaugmentation performance of strain GR7397 to enhance nitrate removal was evaluated to be effective and stabilized in a sequential batch reactor (SBR). The removal rate of [Formula] was the highest during each cycle with a range of 15.48-28.56 mg{middle dot}L-1{middle dot}h-1 in the SBR with inoculating 30% of the strain concentrate. The current research demonstrated that strain GR7397 has significant potential for application in enhancing nitrogen removal in wastewater treatment.

bioengineering↗

bioRxiv2023

Broadly effective ACE2 decoy proteins protect mice from lethal SARS-CoV-2 infection

As SARS-CoV-2 variants have been causing increasingly serious drug resistance problem, development of broadly effective and hard-to-escape anti-SARS-CoV-2 agents is in urgent need. Here we describe further development and characterization of two SARS-CoV-2 receptor decoy proteins, ACE2-Ig-95 and ACE2-Ig-105/106. We found that both proteins had potent and robust in vitro neutralization activities against diverse SARS-CoV-2 variants including Omicron, with an average IC50 of up to 37 pM. In a stringent lethal SARS-CoV-2 infection mouse model, both proteins lowered lung viral load by up to [~]1000 fold, prevented the emergence of clinical signs in >75% animals, and increased animal survival rate from 0% (untreated) to >87.5% (treated). These results demonstrate that both proteins are good drug candidates for protecting animals from severe COVID-19. In a head-to-head comparison of these two proteins with five previously-described ACE2-Ig constructs, we found that two of these constructs, each carrying five surface mutations in the ACE2 region, had partial loss of neutralization potency against three SARS-CoV-2 variants. These data suggest that extensively mutating ACE2 residues near the RBD-binding interface should be avoided or performed with extra caution. Further, we found that both ACE2-Ig-95 and ACE2-Ig-105/106 could be produced to gram/liter level, demonstrating the developability of them as biologic drug candidates. Stress-condition stability test of them further suggests that more studies are required in the future to improve the stability of these proteins. These studies provide useful insight into critical factors for engineering and preclinical development of ACE2 decoys as broadly effective therapeutics against diverse ACE2-utilizing coronaviruses. Abstract ImportanceEngineering soluble ACE2 proteins that function as a receptor decoy to block SARS-CoV-2 infection is a very attractive approach to broadly effective and hard-to-escape anti-SARS-CoV-2 agents. This study here describes development of two antibody-like soluble ACE2 proteins that broadly block diverse SARS-CoV-2 variants including Omicron. In a stringent COVID-19 mouse model, both proteins successfully protected >87.5% animals from lethal SARS-CoV-2 infection. In addition, a head-to-head comparison of the two constructs developed in this study with five previously-described ACE2 decoy constructs were performed here. Two previously-described constructs with relatively more ACE2-surface mutations were found with less robust neutralization activities against diverse SARS-CoV-2 variants. Further, the developability of the two proteins as biologic drug candidates was also assessed here. This study provides two broadly anti-SARS-CoV-2 drug candidates and useful insight into critical factors for engineering and preclinical development of ACE2 decoy as broadly effective therapeutics against diverse ACE2-utilizing coronaviruses. TweetTwo antibody-like ACE2 decoy proteins could block diverse SARS-CoV-2 variants and prevent animals from severe COVID-19.

microbiology↗

bioRxiv2022

A compendium of genetic regulatory effects across pig tissues

The Farm animal Genotype-Tissue Expression (FarmGTEx, https://www.farmgtex.org/) project has been established to develop a comprehensive public resource of genetic regulatory variants in domestic animal species, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biology discovery and exploitation in animal breeding and human biomedicine. Here we present results from the pilot phase of PigGTEx (http://piggtex.farmgtex.org/), where we processed 9,530 RNA-sequencing and 1,602 whole-genome sequencing samples from pigs. We build a pig genotype imputation panel, characterize the transcriptional landscape across over 100 tissues, and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We study interactions between genotype and breed/cell type, evaluate tissue specificity of regulatory effects, and elucidate the molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying about 80% of the genetic associations for 207 pig complex phenotypes, and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, corroborating the importance of pigs as a human biomedical model.

genomics↗

bioRxiv2022

The investigation on metabolites, genes and open chromatins involved in colored leaves of Eucommia ulmoides 'Ziye'

Eucommia ulmoides Oliver Ziye has unique purple-red leaves, which contain a variety of flavonoids, so it has high ornamental and medicinal value. However, the categories of flavonoids and molecular mechanism of specific accumulation of flavonoids in Ziye leaves is still unclear. Here, differences in metabolic level, gene expression level, chromatin accessibility and cis-regulatory elements were compared between Ziye and Huazhong 1 with green leaf color by metabolome profiling, RNA-seq, and ATAC-seq. A total of 205 flavonoids were identified from these two varieties using ultraperformance liquid chromatography-mass spectrometry (UPLC-MS). The accumulation of most delphinidin, cyaniding, quercetin, myricetin, and isorhamnetin derivatives peaked in old leaves of Ziye. Single-molecule long-read sequencing indicated that genes in the phenylpropanoid biosynthesis and flavonoid biosynthetic pathway, as well as many transcription factors including MYB, ERF, and WRKY were highly expressed in Ziye leaves. ATAC-seq revealed the presence of cell preferentially enriched peaks, which annotated to 6114 genes. Analysis of the genomic regions preferentially accessible in each cell type identified hundreds of overrepresented TF-binding motifs, highlighting sets of TFs such as MYB, ERF, and WRKY that are probably important for color formation of Ziye cell. Interestingly, the TFs within each of these cell type-enriched sets also showed evidence of extensively co-regulating each other. Our work demonstrated how chromatin accessibility and TF expression level influenced the expression of flavonoid biosynthesis associated genes, resulted in flavonoids accumulation in Ziye leaf. Our results could lay a foundation for further studies of gene expression and functional genomics in E. ulmoides.

genetics↗