Biology subjects

Xie, Z.

Publications and source records attributed to Xie, Z..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

The Data Distillery: A Graph Framework for Semantic Integration and Querying of Biomedical Data

The Data Distillery Knowledge Graph (DDKG) is a framework for semantic integration and querying of biomedical data across domains. Built for the NIH Common Fund Data Ecosystem, it supports translational research by linking clinical and experimental datasets in a unified graph model. Clinical standards such as ICD-10, SNOMED, and DrugBank are integrated through UMLS, while genomics and basic science data are structured using ontologies and standards such as HPO, GENCODE, Ensembl, STRING, and ClinVar. The DDKG uses a property graph architecture based on the UBKG infrastructure and supports ontology-based ingestion, identifier normalization, and graph-native querying. The system is modular and can be extended with new datasets or schema modules. We demonstrate its utility for informatics queries across eight use cases, including regulatory variant analysis, tissue-specific expression, biomarker discovery, and cross-species variant prioritization. The DDKG is accessible via a public interface, a programmatic API, and downloadable builds for local use.

bioinformatics↗

bioRxiv2025

Efficient spatial gene expression profiling using split-probe ligation and rolling circle amplification

Spatial transcriptomics has transformed our understanding of gene regulation by enabling high-resolution mapping of RNA molecules within their native cellular and tissue environments. This is typically accomplished by capturing or imaging RNA in situ, thereby preserving spatial context. Here, we introduce an in situ RNA imaging method based on split-probe ligation and rolling circle amplification (RCA) for profiling spatial gene expression. In this approach, split-probes hybridize to adjacent regions of a target RNA fragment and are then enzymatically ligated to form circular DNA templates, which are subsequently amplified via RCA to boost signal. We demonstrate that this method enables robust in situ RNA detection and genotyping in both tissue sections and whole-mount tissue samples. By coupling this technique with in situ sequencing, we mapped the spatial expression patterns of 82 genes in the kidneys of healthy and diabetic male and female mice. This analysis revealed distinct localization of Aqp4 in proximal tubules and principal cells of the collecting ducts, and uncovered sex-specific transcriptomic alterations in diabetic kidneys with spatial resolution. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/668044v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@c8efaborg.highwire.dtl.DTLVardef@1eef010org.highwire.dtl.DTLVardef@1d2bbd7org.highwire.dtl.DTLVardef@2a482b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

An expanded reference catalog of translated open reading frames for biomedical research

Non-canonical (i.e., unannotated) open reading frames (ncORFs) have until recently been omitted from reference genome annotations, despite evidence of their translation, limiting their incorporation into biomedical research. To address this, in 2022, we initiated the TransCODE consortium and built the first community-driven consensus catalog of human ncORFs, which was openly distributed to the research community via Ensembl-GENCODE. While this catalog represented a starting point for reference ncORF annotation, major technical and scientific issues remained. In particular, this initial catalogue had no standardized framework to judge the evidence of translation for individual ncORFs. Here, we present an expanded and refined catalog of the human reference annotation of ncORFs. By incorporating more datasets and by lifting constraints on ORF length and start-codon, we define a comprehensive set of 28,359 ncORFs that is nearly four times the size of the previous catalog. Furthermore, to aid users who wish to work with ncORFs with the strongest and most reproducible signals of translation, we utilized a data-driven framework (i.e. translation signature scores) to assess the accumulated evidence for any individual ncORF. Using this approach, we derive a subset of 7,888 ncORFs with translation evidence on par with canonical protein-coding genes, which we refer to as the Primary set. This set can serve as a reliable reference for downstream analyses and validation, with a particular emphasis on high quality. Overall, this update reflects continual community-driven efforts to make ncORFs accessible and actionable to the broader research public and further iterations of the catalog will continue to expand and refine this resource.

genomics↗

bioRxiv2025

Genomic Touchstone: Benchmarking Genomic Language Models in the Context of the Central Dogma

The emergence of genomic language models (gLMs) has revolutionized the analysis of genomic sequences, enabling robust capture of biologically meaningful patterns from DNA sequences for an improved understanding of human genome-wide regulatory programs, variant pathogenicity and therapeutic discovery. Given that DNA serves as the foundational blueprint within the central dogma, the ultimate evaluation of a gLM is its ability to generalize across this entire biological cascade. However, existing evaluations lack this holistic and consistent framework, leaving researchers uncertain about which model best translates sequence understanding into downstream biological prediction. Here we present Genomic Touchstone, a comprehensive benchmark designed to evaluate gLMs across 36 diverse tasks and 88 datasets structured along the central dogmas modalities of DNA, RNA, and protein, encompassing 5.34 billion base pairs of genomic sequences. We evaluate 34 representative models encompassing transformers and convolutional neural networks, as well as emerging efficient architectures such as Hyena and Mamba. Our analysis yield four key insights. First, gLMs achieve comparable or superior performance on RNA and protein tasks compared to models pretrained on these molecules. Second, transformer-based models continue to lead in overall performance, yet efficient sequence models show promising task-specific capabilities and deserve further exploration. Third, the scaling behavior of gLMs remains incompletely understood. While longer input sequences and more diverse pretraining data consistently improve performance, increases in model size do not always translate into better results. Fourth, pretraining strategies, including the choice of training objectives and the composition of pretraining corpora, exert substantial influence on downstream generalization across different genomic contexts. Genomic Touchstone establishes a unified evaluation framework tailored to human genomics. By spanning multiple molecular modalities and biological tasks, it offers a valuable foundation for guiding future gLM design and understanding model generalizability in complex biological contexts.

bioinformatics↗

bioRxiv2025

Evolution of hunt, kill, devour in a Vibrio ecospecies

Understanding the molecular and evolutionary basis of adaptation is a central challenge in biology. We have recently identified bacterial "ecospecies": strains that share a well-mixed gene pool with the rest of the species in most of the genome, but carry nearly fixed differences in [~]100 coadapted genes. We hypothesized this structure reflects ecological selection, but ecospecies have so far been characterized only genomically. Here, using "Molassodon" of the marine bacterium Vibrio parahaemolyticus, we provide first experimental evidence linking a multilocus ecospecies genotype to a laboratory phenotype. We find that Molassodon swimming in viscous environments is enhanced by differentiated lateral flagella that no longer drive surface swarming. Using Tn-seq, RNA-seq, and molecular genetics, we link differentiated Type VI secretion and nutrient uptake genes to the swimming phenotype, potentially invoking a natural adaptation related to "hunting". We also identify a convergent genotype also linking lateral flagella and nutrient uptake, suggesting ecological differentiation in V. parahaemolyticus occurs along recurring phenotypic axes and involves borrowing traits from a broader Vibrio gene pool. Together, our results provide first experimental evidence that ecospecies are functionally coherent units and illustrate how they can provide a window into the complex adaptations that shape bacterial ecology.

microbiology↗

bioRxiv2025

Ecological Toxicity Effects of Marine Pollutants Based on Microalgal Locomotory Behavior

Understanding the ecological toxicity of marine pollutants is essential for assessing marine environmental quality and developing effective protection strategies. This study presents a novel approach using the motility of the marine microalgae as a sensitive physiological indicator for rapid toxicity assessment. Results demonstrated that both copper (0{micro}mol l-1-4.4{micro}mol l-1) and phenol (0mmol l-1-9.1mmol l-1) significantly inhibited algal locomotory behavior parameters (movement patterns, mobility, and swimming speed) within 2 hours, exhibiting clear dose-response relationships. The 2 h-EC50 values determined by logistic regression were 2.2{micro}mol l-1-2.7{micro}mol l-1 for copper and 4.5{micro}mol l-1-5.7 mmol l-1 for phenol. Notably, combined exposure showed antagonistic effects (2 h-EC50 >1 TU). The motility-based assay showed strong correlation with traditional toxicity tests (72h growth inhibition, 24-48 h Daphnia immobilization, and 96h fish mortality), while providing significant advantages in speed and sensitivity. These findings establish microalgal motility as an efficient, ecologically relevant biomarker for comprehensive marine pollutant assessment, offering great potential for environmental monitoring and risk evaluation.

ecology↗

bioRxiv2025

EasyMultiProfiler: An Efficient Multi-Omics Data Integration and Analysis Workflow for Microbiome Research

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/649266v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@14dd0b1org.highwire.dtl.DTLVardef@1c04a79org.highwire.dtl.DTLVardef@159894dorg.highwire.dtl.DTLVardef@1454a01_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG HighlightsO_LIEasyMultiProfiler provides unified integration of microbial multi-omics data C_LIO_LIEasyMultiProfiler enables natural language-style analytical workflows C_LIO_LIEasyMultiProfiler solves key challenges in standardization and reproducibility C_LIO_LIEasyMultiProfiler extracts meaningful biological insights efficiently C_LI Host-microbiome interactions are crucial in maintaining physiological homeostasis and influencing disease progression. While traditional microbiome research, focused on microbial diversity and abundance, provided essential frameworks, the integration of multi-omics approaches established a far more comprehensive, systems-level understanding of microbiome functionality. However, significant methodological challenges in multi-omics data integration remain, including inconsistent sample coverage, heterogeneous data formats, and complex downstream analytical workflows. These challenges impact the reproducibility and reliability of results and underscore the critical requirement for developing standardized, systematic workflows. Thus, we developed the EasyMultiProfiler (EMP) workflow, a streamlined and efficient analytical framework for multi-omics data analysis. EMP provides a comprehensive infrastructure based on the SummarizedExperiment and MultiAssayExperiment classes, creating a unified framework for storing and analyzing omics data. The frameworks architecture comprises five interconnected functional modules: data extraction, preparation, support, analysis, and visualization. These modules are smoothly integrated into natural language-style analytical workflows, offering users an efficient and standardized solution for multi-omics analysis. EMP addresses critical challenges in multi-omics data analysis, including data integration, workflow standardization, and result reproducibility. The platforms modular architecture and intuitive interface provide researchers and clinicians with a robust, flexible workflow to systematically extract biologically relevant insights from complex multi-omics datasets.

bioinformatics↗

bioRxiv2025

Cerebellin-2 positive (Cbln2+) Neurons in the Parafascicular Thalamic Nucleus Regulate Self-Grooming Behavior

Self-grooming represents a stereotyped repetitive behavior which is commonly observed in various neuropsychiatric disorders. Deep brain stimulation (DBS) targeting the centromedian / parafascicular complex (CM/PF) has been shown to alleviate obsessive-compulsive disorder/behavior (OCD/OCB) symptoms in human. However, little is known about the neural circuits of the PF that are involved in the regulation of self-grooming behavior in rodents. Here, we report that cerebellin-2 positive (Cbln2+) neurons in PF bidirectionally encode self-grooming. Chemogenetic activation of Cbln2+ PF neurons significantly reduces both physiological and pathological self-grooming, and chemogenetic inhibition of Cbln2+ PF neurons increases physiological self-grooming. Moreover, activating synaptic excitatory inputs to Cbln2+ PF neurons inhibits self-grooming, while synaptic inhibitory inputs enhance it. Cbln2+ PF neurons independently transmit neural signals to the dorsal and ventral striatum. Activation of PF-dorsal striatum and PF-ventral striatum pathways both inhibit self-grooming behavior. Most excitingly, we alleviate excessive grooming in OCD mice by the activation of PF neurons using non-invasive ultrasound stimulation. Together, these data reveal that Cbln2+ PF neurons are integral components of a brain network involved in self-grooming behavior and highly promising targets in the non-invasive therapy of OCD.

neuroscience↗

bioRxiv2025

National Scale Genomic Engine for Precision Medicine: Singapore PRECISE-SG100K Experience

The whole-of-nation National Precision Medicine (NPM) programme in Singapore aims to integrate a genomics backbone into standard healthcare. Phase II recently completed the sequencing of 102,202 genomes from the diverse Asian population of Singapore, generating the PRECISE-SG100K dataset. The scaling from the pilot SG10K_Health phase to national production scale demanded a central shift: from earlier generation workflows to an integrated, industrial grade "Genomic Engine". The initiative was co-developed under a public-private partnership model. Our Genomic Engine was designed with an automation-first approach, as a modular platform linking a purpose-built, centralized biospecimen processing and DNA extraction facility (EXTRACT Lab), a high-throughput sequencing operation (Illumina Data Generation Infrastructure, IDGI), with the cloud-native analytics (GRIDS) running on a fully integrated PRECISE Sample Tracking System (STS). Here we describe the key design features of our Genomic Engine. Highlighting the Stress Test phase where the automated DNA extraction yields failed to meet expectations. We share the operational adjustments which were implemented to stabilize production and resulted in a three-fold reduction in sample triage due to improved DNA recovery. Without establishing the operational guardrails during the Stress Test, the Engine would not have reached a steady-state production evidenced by >3,000 genomes per month, median turnaround time 11 days, 99.6% operational success, within in six months. This framework offers a practical blueprint for other population-scale initiatives seeking to bridge the gap between scientific piloting and industrial delivery.

genomics↗

bioRxiv2025

The CFDE Workbench: Integrating Metadata and Processed Data from Common Fund Programs

The NIH Common Fund Data Ecosystem (CFDE) program was established to facilitate data accessibility and interoperability across multiple Common Fund (CF) programs and promote collaborations and new discoveries by combining data from different CF programs. The CFDE Data Resource Center (DRC) was tasked with developing two web-based portals: an Information Portal to serve information about the CFDE, and a Data Portal to host harmonized metadata and processed data contributed by participating CF Data Coordination Centers (DCCs) and other sources. To achieve these goals, the CFDE DRC developed the CFDE Workbench, a web-based platform that hosts highly processed data, metadata, tools, use cases, and analyses developed by the CFDE. The Crosscut Metadata Model (C2M2) and several other processed data formats are hosted by the CFDE Workbench, including set libraries (XMTs), Knowledge Graph (KG) assertions, and attribute tables. These processed data formats make information derived from CF programs more findable, accessible, interoperable, and reusable (FAIR) and artificial intelligence (AI)-ready for cross-DCC knowledge discovery. In addition to these processed data formats, several other assets are served by the Workbench, including Extract Transform Load (ETL) pipelines, OpenAPI-documented and SmartAPI-registered Representational State Transfer (REST) Application Programming Interfaces (APIs), entity pages such as gene, drug, disease, or tissue landing pages, Playbook Workflow Builder (PWB) metanodes which are codified workflow steps involving DCC APIs, and chatbot specifications which are prompts helping chatbots to use DCC APIs to answer a wide range of user queries. Besides serving data, metadata, and code assets, the CFDE Workbench also has several tools that use these data, metadata, and code assets to enable cross-CF-program knowledge discovery use cases. Overall, the CFDE Workbench is a platform that consolidates efforts toward making CF programs funded resources harmonized, FAIR, and AI-ready. The CFDE Workbench website is available from: https://cfde.info/.

bioinformatics↗

bioRxiv2025

Exploiting Endogenous Cas9-Based Genome Editing of Lacticaseibacillus rhamnosus GG (LGG) for Precision Food Fermentation and Live Biotherapeutics

Lacticaseibacillus rhamnosus GG (LGG) is one of the most extensively studied probiotic strains, widely used in food and health applications. However, the absence of efficient, precise genome editing methods has limited its broader potential and functional versatility. Here, we present an endogenous type II-A CRISPR-Cas9 genome editing workflow for LGG designed for functional strain construction. Using a plasmid interference assay together with single-nucleotide substitutions, we confirm the precise PAM requirement as 5'-NGAAA-3'. We pair a synthetic sgRNA cassette with homology-directed repair donors to enable precise deletions and insertions across multiple loci. Using this optimized and precise genome engineering method, we generated a {beta}-glucuronidase (GUS)-expressing LGG strain for robust strain tracking within complex gut microbial communities. This work removes barriers to LGG engineering, expands the probiotic CRISPR toolkit, and provides broadly applicable strategies for designing next-generation probiotics with applications in food biotechnology and microbial therapeutics. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/634116v2_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@4c471eorg.highwire.dtl.DTLVardef@1d133f6org.highwire.dtl.DTLVardef@1aa7400org.highwire.dtl.DTLVardef@1a5d4dc_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

A molecularly defined brain circuit module for regulating the panic-like defensive state

Panic is an episode of strong defensive state, characterized by intense fear and severe physical symptoms such as elevated cardiorespiratory activities. How the brain generates panic state remains poorly understood. Here, we developed a robot-based experimental paradigm to evoke panic-like defensive state in mice. When stimulated by the robot, the mice exhibited jumping escapes and elevated cardiorespiratory activities. With this paradigm, we identified Cbln2-expressing (Cbln2+) neurons in the posterior hypothalamic nucleus (PHN) as a key neuronal population essential for the induction of panic-like defensive state. Activation of Cbln2+ PHN neurons induced behavioral and physical symptoms of panic-like defensive state. These neurons were strongly activated by noxious mechanical stimuli and encode jumping escape vigor. They were synaptically innervated by anxiety-associated brain areas and provoked panic-like defensive state via their projection to the periaqueductal gray. Together, our results reveal a molecularly defined circuit module that regulates the panic-like defensive state in mice.

neuroscience↗

bioRxiv2024

Structural Characterization of mRNA Lipid Nanoparticles in the Presence of Intrinsic Drug-free Lipid Nanoparticles

Lipid nanoparticles (LNPs) have emerged as a versatile platform for mRNA delivery across a range of applications, including disease prevention, cancer immunotherapy, and gene editing. Structural models of mRNA-containing lipid nanoparticles (mRNA-LNPs) have also been proposed based on characterization of samples by using various advanced techniques. Among these, small angle neutron scattering (SANS) has proven essential for elucidating the lipid distribution within mRNA-LNPs, a factor crucial to both their preparation and efficacy. However, recent findings suggest that the mRNA-LNP samples prepared via commercial microfluidic techniques may contain a substantial fraction of drug-free LNPs, casting doubt on the validity of earlier structural models. In this study, we employed contrast variation SANS to characterize both drug-free LNPs and our mRNA-LNP sample, and quantified the proportion of drug-free LNPs present to be [~]30% in our mRNA-LNP sample using nano flow cytometry. By removing the contributions of drug-free LNPs from the SANS data of our mRNA-LNP sample, we were able to precisely characterize the structure of mRNA-LNPs. Consequently, we proposed structural models for both drug-free LNPs and mRNA-LNPs. Notably, our analysis revealed similar lipid distributions and shell thicknesses between the two particle types, while the solvent content in mRNA-LNPs was significantly higher, leading to a larger core size. This work not only offers a method for accurately characterizing the structure of mRNA-LNPs, but also establishes criteria for selecting appropriate analytical techniques based on the structural parameters of interest. Therefore, our findings hold significant implications for the mechanistic understanding and quality control of mRNA-based vaccines. SignificancePrecise structural determination of mRNA-containing lipid nanoparticles (mRNA-LNPs) is vital for mechanistic insights into their preparation, delivery, immunogenicity, and storage, which are critical to the development of mRNA-based vaccines. However, most previous studies overlooked the substantial presence of drug-free LNPs within these samples. Here, we identified that approximately 30% of the nanoparticles in our mRNA-LNP sample were drug-free. By integrating contrast variation small angle neutron scattering (SANS) data from both drug-free LNPs and mRNA-LNPs, we developed structural models for both particle types, and provided a guidance for characterization technique selection based on concerned structural features. Beyond mechanistic insight on structure, our approach offers a robust method for quality assessment and process monitoring in mRNA-based vaccine production.

biophysics↗

bioRxiv2024

High-quality peptide evidence for annotating non-canonical open reading frames as human proteins

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.

molecular biology↗

bioRxiv2024

Deep Generative Optimization of mRNA Codon Sequences for Enhanced Protein Production and Therapeutic Efficacy

Messenger RNA (mRNA) therapeutics show immense promise, but their efficacy is limited by suboptimal protein expression. Here, we present RiboDecode, a deep learning framework that generates mRNA codon sequences for enhanced mRNA translation. RiboDecode introduces several advances, including direct learning from large-scale ribosome profiling data and generative exploration of a large sequence space. In silico analysis demonstrates RiboDecode s robust predictive accuracy for unseen genes and cellular environments. In vitro experiments showed substantial improvements in protein expression, significantly outperforming past methods. In addition, RiboDecode enables mRNA design with consideration of cellular context and demonstrates robust performance across different mRNA formats, including m1{Psi}-modified and circular mRNAs, an important feature for mRNA therapeutics. In vivo mouse studies showed that optimized influenza hemagglutinin mRNAs induce ten times stronger neutralizing antibody responses against influenza virus compared to the unoptimized sequence. In an optic nerve crush model, optimized nerve growth factor mRNAs achieve equivalent neuroprotection of retinal ganglion cells at one-fifth the dose of the unoptimized sequence. Collectively, RiboDecode represents a paradigm shift from rule-based to a data-driven, context-aware approach for mRNA therapeutic applications, enabling the development of more potent and dose-efficient treatments.

bioinformatics↗

bioRxiv2024

A pontine-specific axonal niche supports de novo gliomagenesis

Diffuse intrinsic pontine gliomas (DIPGs), a major type of pediatric high-grade gliomas located in the pons, are the leading cause of death in children with brain cancer. A subset (20-25%) of DIPGs harbor a lysine 27-to-methionine (K27M) mutation in HIST1H3B, which encodes histone H3.1, and an activating ACVR1 mutation. The occurrence of this pair of mutations in DIPGs, but not in pediatric gliomas in other anatomical locations, suggests the existence of a pontine-specific niche that favors DIPG gliomagenesis. Unfortunately, the identity of the underlying pontine niche remains elusive as available mouse models fail to recapitulate the anatomic specificity that characterizes DIPGs. Herein we show that the trigeminal root entry zone (TREZ), a pontine structure where several major axon tracts intersect, is enriched with proliferating oligodendrocyte-lineage cells during brainstem development. Introducing both H3.1K27M and activating Acvr1 and Pik3ca mutations (which co-occur frequently with H3.1K27M in human DIPGs) into the mouse brain leads to rapid gliomagenesis. This pathology recapitulates the pons specificity of DIPGs as glioma cells proliferate on axon tracts at the TREZ. We further show that a hyaluronan receptor important for cell stemness (HMMR) plays a key role in glioma cell proliferation at the TREZ. We propose that H3.1K27M and its co-occurring mutations drive pontine specific gliomagenesis by inducing a proliferative response of oligodendrocyte-lineage cells with enhanced stemness on large TREZ axon tracts. One-Sentence SummaryThe trigeminal root entry zone underlies pontine-specific gliomagenesis driven by H3.1K27M and its co-occurring mutations.

cancer biology↗

bioRxiv2024

Haplotype-resolved genome assembly of upland switchgrass provides insights into cold tolerance

Switchgrass (Panicum virgatum L.) is a bioenergy and forage crop. Upland switchgrass exhibits superior cold tolerance than lowland ecotype, but the underlying molecular mechanisms remain unclear. Here, we presented a high-quality haplotype-resolved genome of the upland ecotype Jingji31 and conducted multi-omics analysis to understand its cold tolerance. The divergence between upland and lowland ecotypes of switchgrass occurred after the differentiation of the two subgenomes (K and N). Under cold stress, the K subgenome has more differentially expressed genes (DEGs). Transcriptome analysis revealed ecotype-specific differential expressions among members of the cold-responsive (COR) gene families. Specifically, certain members of the AFB1, ATL80, HOS10, and STRS2 gene families exhibited opposite expression changes between the two ecotypes, potentially contributing to their differential cold tolerance. By using haplotype-resolved genome, we identified more cold-induced allele-specific expressions (ASEs) in the upland ecotype, and these ASEs were significantly enriched in the COR gene families. Genome-wide association study detected an association signal on Chr3K related to overwintering rate, which overlapped with a selective sweep region and contained a cytochrome P450 (CYP450) gene highly expressed under cold stress. Heterologous overexpression of CYP450 in rice alleviated leaf wilting and improved cold tolerance. Our study provides a high-quality haplotype-resolved genome of upland switchgrass, to advance conceptual understanding of plant cold tolerance for breeding crops with enhanced cold adaptation.

genomics↗

bioRxiv2024

Distinct pathogenic influence of anti-HMGCR+ and anti-SRP+ immune-mediated necrotizing myopathy autoantibodies on engineered muscle function

Immune-mediated necrotizing myopathy (IMNM) is a subgroup of idiopathic inflammatory myopathies associated with anti-signal recognition particle (SRP) or anti-3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) autoantibodies. However, the demonstration of a direct pathogenic effect of IMNM patient autoantibodies on skeletal muscle contractile force, independent of the downstream activation of the complement pathway, has yet to be reported. Thus, the goal of this study was to leverage a custom 3D-human skeletal muscle microtissue (hMMT) culture platform, that enables muscle cell contractile apparatus maturation and the analysis of contractile function, to evaluate the direct effect of total immunoglobulins (IgGs) isolated from IMNM patients with amplification of anti-SRP+ or anti-HMGCR+ autoantibodies. hMMTs capable of force generation were treated with total IgGs, isolated from 3 SRP+ and 3 HMGCR+ patients plasma, and delivered in complement inactivated media for 4 days. hMMT health was then evaluated by quantifying the peak force and contraction kinetics in response to electrical field stimulation and by performing histological analysis of sarcomere and myotube structures. Treating hMMTs with total IgGs from anti-HMGCR+ patients resulted in a decline in tetanus contractile force, though sarcomere Z-line architecture analysis revealed no significant influences on sarcomere organization. hMMT treatment with total IgGs from anti-SRP+ patients induced muscle atrophy, observed via significantly smaller myotube diameter, but this did not translate to a decline in contractile function. This study demonstrates that anti-SRP and anti-HMGCR autoantibodies exert direct, but distinct influences on IMNM-associated skeletal muscle pathogenesis, which may inform IMNM therapy development.

bioengineering↗