Biology subjects

Deng, M.

Publications and source records attributed to Deng, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Systematic CRISPRi perturbation of 1,408 autism risk genes maps multilevel transcriptional convergence in human cortical neurons

Genetic heterogeneity in autism spectrum disorder (ASD) complicates the identification of shared molecular pathways amenable to therapeutic intervention. Here, we perform a CRISPRi Perturb-seq screen targeting 1,408 ASD risk genes in human embryonic stem cell-derived immature cortical neurons and profile the resulting transcriptomic effects by single-cell RNA sequencing. We identify 215 ASD risk genes whose repression induces significant global transcriptomic dysregulation. Leveraging this functional atlas, we characterize candidate genes based on transcriptional similarity to established ASD hub genes. We identify gene programs recurrently dysregulated across perturbations, anchored by processes governing microtubule dynamics, neuron differentiation, cell migration, and transmembrane transport. We further identify CHAMP1 as a previously unrecognized regulator of Wnt signaling and define specific ASD risk genes that modulate the rate of cortical neuron differentiation. Analysis of differentially expressed genes reveals both convergent and perturbation-specific downstream transcriptional responses. Together, these findings provide a multilevel map of transcriptomic convergence in ASD and establish a framework for identifying both pathway-level and genotype-specific therapeutic strategies.

neuroscience↗

bioRxiv2026

PalmLab: A Comprehensive Computational Platform for Systematic Annotation and Functional Interrogation of Protein Palmitoylation

Protein palmitoylation is a dynamic and reversible post-translational lipid modification with progressively recognized pathophysiological relevance in cancer and a range of human diseases. Current databases, however, predominantly catalog palmitoylation sites derived from isolated literature reports, and suffer from a conspicuous lack of systematic harmonization across publicly available mass spectrometry (MS)-based palmitoylome datasets, which severely impedes standardized annotation and cross-cohort comparative analyses. Here, we introduce PalmLab, an integrative computational platform that synergistically couples MS-based palmitoylome data curation with interactive, multi-dimensional analytical functionalities for both human and mouse proteomes. Through a unified and rigorously standardized bioinformatics pipeline, PalmLab enables systematic reanalysis of all published palmitoylome datasets, yielding a curated compendium of 15,968 human and 9,924 mouse MS-validated palmitoylated proteins, a quantitative increase that more than doubles the total entries available in existing public repositories. The platform further provides user-friendly, code-free analytical modules that support differential palmitoylation profiling, context-specific pattern visualization, protein-protein correlation network inference, mutation-palmitoylation association mapping, and sequence-based motif discovery. Collectively, PalmLab constitutes a comprehensive and openly accessible resource for systematic palmitoylome mining and functional interrogation of palmitoylation events, thereby facilitating the elucidation of palmitoylation-mediated regulatory circuits and aiding the prioritization of novel therapeutic targets in oncology. The platform is freely available at https://palmlab.intelligent-oncology.com.

bioinformatics↗

bioRxiv2026

CellChem: Cellular transcriptional responses reshape molecular representation space for efficient and multi-scale drug discovery

Despite decades of progress in computational drug discovery, deep learning-based molecular representation models remain largely structure-centric, assuming that chemical similarity approximates functional similarity. However, drug effects in cells are shaped not only by chemical similarity but also by molecular interactions in the cellular context. To capture this complexity, we introduce CellChem, a cellular-chemical representation-learning framework that learns cell-guided molecular representations of small-molecule actions within cells. By incorporating large-scale cellular transcriptional profiles during pretraining, CellChem reshapes molecular representation space from structure-centric to a balanced integration of structure and function. The learned CellChem molecular representations exhibit biologically meaningful geometric organization, such that distances between molecules encode not only structural similarity but also similarity in the cellular responses they elicit, independent of downstream tasks. Using downstream derivative models such as cell-guided compound-protein interaction prediction and drug-induced transcriptional response profile generation, CellChem supports highly efficient, multi-scale drug discovery, achieving significantly better performance than traditional models.

molecular biology↗

bioRxiv2026

Deep-Palm：an integrated deep learning framework for structure-aware prediction of protein S-Palmitoylation

Protein S-palmitoylation is a reversible lipid modification that regulates protein localization, trafficking, and signaling. Its dysregulation has been implicated in cancer and therapeutic resistance, making accurate site annotation important for understanding disease-related regulatory mechanisms. However, experimental identification of S-palmitoylation sites remains labor-intensive, highlighting the need for computational tools that can support large-scale candidate-site prioritization. S-palmitoylation-site recognition requires the integration of multiple levels of information surrounding candidate cysteine residues, including sequence context, local protein properties and structural features. Here, we present Deep-Palm, a deep learning framework that integrates four complementary branches: amino acid sequence, physicochemical properties, ESM embedding and spatial structure. On the testing set, Deep-Palm achieved an AUC of 0.950 and outperformed the compared predictors, including pCysMod, MusiteDeep and GPS-Palm. Deep-Palm also showed stable performance across diverse cysteine sequence contexts and Gene Ontology functional groups. Feature-level analyses revealed distinct spatial structural and protein property patterns between palmitoylated and non-palmitoylated peptide windows. Independent mass spectrometry datasets further demonstrated the ability of Deep-Palm to sensitively identify previously unannotated S-palmitoylation sites. Together, Deep-Palm provides an accurate and biologically informative framework for S-palmitoylation-site prediction, facilitating the discovery of novel candidate S-palmitoylation sites.

bioinformatics↗

bioRxiv2026

Interclonal cooperation and suppression shape early Ras-driven tumour growth

Cancer is generally thought to be caused by expansion of a single mutant cell. However, analyses of human early lesions show that tumours can originate from several genetically distinct cell populations1-5. How neighbouring mutant clones interact to shape tumourigenesis, and which driver genes mediate these effects is largely unexplored. Here, we use an in vivo mosaic Drosophila epithelial model to systematically test interclonal interactions during early Ras-driven tumourigenesis. We screened 88 recurrent RAS co-mutated driver genes in human carcinomas for their ability to modify Ras-clone growth when disrupted in neighbouring epithelial clones. This uncovered two opposing classes of interactions: Interclonal cooperativity, where neighbouring mutant clones promote the overgrowth of Ras mutant tumours, and interclonal suppression, in which neighbours restrain Ras tumours, unexpectedly improving host survival. The strongest suppressive modifiers included canonical cell competition regulators, including Myc, archipelago (ago/FBXW7), and taiman (tai/NCOA1-3). In contrast, the strongest cooperative modifiers were disruptions of XNP/ATRX and SWI/SNF chromatin remodelling subunits (including Osa/ARID1A, Bap170/ARID2, Polybromo/PBRM1, among others), which in neighbouring cells induce a wound-like inflammatory program and drive an anabolic, pro-growth state in Ras tumours. Notably, the disruption of SWI/SNF components cell autonomously within Ras tumours confers no cooperativity. We show that interclonal cooperative Ras tumour growth requires reactive oxidative species and prostaglandin synthesis in SWI/SNF-disrupted clones. Together, this study provides a catalogue and emerging principles of cooperative and suppressive interclonal interactions among recurrent RAS co-mutated drivers, extending the rules of oncogenic cooperation beyond cell intrinsic co-mutation.

cancer biology↗

bioRxiv2026

Experimental Time Points Guided Transcriptomic Velocity Inference

Time-series single-cell RNA sequencing enables longitudinal tracking of biological processes, yet cellular trajectory reconstruction informed by experimental time remains challenging. Existing trajectory inference methods either perform de novo reconstruction without leveraging experimental time points, or prioritize transitions between time points while paying less attention to intra-time-point dynamics. To reconcile experimental time points with local precision, we present CellDyc, a semi-supervised learning framework that leverages experimental time-point supervision to reconstruct transcriptomic velocities and recover an intrinsic gene-embedded time. CellDyc consistently outperforms existing approaches in reconstructing cellular trajectories across development, disease, and reprogramming contexts. Biologically, CellDyc provides novel insights, such as resolving temporal heterogeneity in erythroid maturation and quantitatively demonstrating that the immunosuppressive environment delays monocyte differentiation in glioblastoma. CellDyc integrates seamlessly with downstream tools like CellRank and remains robust even when only inferred temporal information is available. Collectively, CellDyc offers a rigorous, data-driven solution for deciphering time-resolved cellular dynamics.

bioinformatics↗

bioRxiv2026

Evolutionary origin of prolonged delayed fertilization in the Fagaceae

The temporal organization of flowering, fertilization, and fruiting is a fundamental axis of life-history evolution in angiosperms. While most species complete fruit development within a single growing season ("1-year fruiting"), many Fagaceae species delay fertilization and fruit maturation until the year following flowering ("2-year fruiting"). Despite its ecological prevalence, the evolutionary origins of this strategy and its coordination with other functional traits remain poorly understood. In this study, we investigated the evolutionary origins of the two-year fruiting strategy by reconstructing ancestral states on a phylogeny comprising 88 species that represent all eight genera of Fagaceae. We further employed phylogenetic comparative analyses to test whether the fruiting trait is evolutionarily constrained by pollination mode (animal versus wind-pollinated) or leaf habit (evergreen versus deciduous). Our results support a single origin of 2-year fruiting in the common ancestor of the major clade excluding Fagus and Trigonobalanus, followed by multiple independent reversions to 1-year fruiting in lineages including Castanea, Quercus, and Castanopsis. Ancestral-state reconstructions of pollination mode and leaf habit strongly support entomophily and an evergreen leaf habit as ancestral traits of Quercoideae, predating the emergence of the 2-year fruiting strategy. Although entomophily and evergreen habit were ancestral in Quercoideae, our correlation analyses indicate that transitions in fruiting strategy did not depend on changes in these traits. These results provide macroevolutionary evidence that extreme extensions of the interval between pollination and fertilization can be evolutionarily stable yet rarely re-evolve once lost over long evolutionary timescales.

ecology↗

bioRxiv2025

Comprehensive perturbation of transcription factors in human cardiomyocytes reveals the regulatory architecture of congenital heart disease

Transcription factors (TFs), including DNA binding proteins and epigenetic co-regulators, control the timing of gene programs during lineage commitment1. However, systematically defining the dynamic activities of the human genomes nearly 2000 TFs during development remains challenging2,3. Here, we use Perturb-Seq to map the transcriptional impact of nearly all human TFs and a subset of enhancers during cardiomyocyte differentiation. Our results suggest that developmental regulatory networks are distributed across highly connected hub TFs4-6 that are dynamic across lineage and time, rather than organizing into top-down hierarchies directed by "master regulators". We also identify distinct TF ensembles that regulate sequential decision points during cardiac cell-fate specification by coordinating lineage-specific activation with alternate lineage repression. Separately, analysis of TF-TF cooperativity uncovers a dynamic interaction between MEF2 family TFs7 and members of the Polycomb Repressive complex 18-10 to execute alternative fate repression. Building on these regulatory interactions, we construct a deep learning transformer model to accurately predict perturbed TFs driving altered regulatory networks in patient-derived transcriptomes. Together, our results define the regulatory network architecture of human lineage specification and provide a platform for predicting TF function and interpreting disease mechanisms.

genomics↗

bioRxiv2025

A Shared Adenocarcinoma Transcriptomic Program Enables Prediction of Therapeutics Applicable Across Tissues

BackgroundAdenocarcinomas are malignancies arising from glandular epithelial cells or secretory tissue, and account for most cancer-related mortalities worldwide, despite advances in treatment. New treatments are often developed in the context of the organ from which the tumor originates. However, there exists shared biology across glandular epithelium at the molecular level regardless of organ system, and there has been a shift towards the molecular classification of tumors. Defining and targeting pan-adenocarcinoma specific molecular features may facilitate the development of treatments applicable across adenocarcinomas, expediting drug discovery efforts and translation to the clinical setting. MethodsWe performed an integrated transcriptomics analysis of adenocarcinomas originating from different organ systems. RNA sequencing expression profiles from lung adenocarcinoma (LUAD), stomach adenocarcinoma (STAD), and colorectal adenocarcinoma (COAD) with matched normal tissue from The Cancer Genome Atlas (TCGA) was used to discover a pan-adenocarcinoma specific transcriptomic module. A standardized DESeq2 based pipeline was used to test for differentially expressed genes (DEGs) between each adenocarcinoma and its matched normal tissues, followed by cross-cancer comparisons to identify a consensus transcriptional module. Enrichment analysis was performed to reveal biological pathways associated with the consensus module, with a particular focus on those involved in oncogenesis. Prognostic significance of the consensus transcriptional module was evaluated using survival modeling, and the module was tested against in vitro transcriptomic drug perturbation signatures using Connectivity Map (cMAP) analysis to identify candidate drugs targeting adenocarcinomas, regardless of organ system of origin. ResultsDespite the diversity of adenocarcinomas tested, there existed a significant and large overlap of the genes dysregulated across tested tumors. A consensus transcriptomic module was defined, and it predicted patient prognosis in each of the three adenocarcinomas. Leveraging the top shared biomarkers through cMAP analysis, we identified 36 FDA-approved drugs that are capable of reversing the shared malignant transcriptional module towards the normal state. Among the FDA-approved drugs were the EGFR and ALK inhibitors gefitinib and crizotinib, both currently used for treating LUAD, providing validation the pipeline could discover efficacious drugs. Taken together, we developed an approach for organ system independent cancer biomarkers and drug discovery, and leveraged it to identify drug candidates for expanded use in adenocarcinomas. The FDA-approved drugs identified through this pipeline serve as candidates to repurpose as pan-adenocarcinoma anti-cancer therapeutics, reducing both time and cost for drug development. ConclusionOur research provided insights into the common molecular mechanisms across multiple adenocarcinomas and unveiled potential drug candidates for future therapeutic testing.

cancer biology↗

bioRxiv2025

Designing smart spatial omics experiments with S2Omics

Spatial omics technologies have transformed biomedical research by enabling high-resolution molecular profiling while preserving the native tissue architecture. These advances provide unprecedented insights into tissue structure and function. However, the high cost and time-intensive nature of spatial omics experiments necessitate careful experimental design, particularly in selecting regions of interest (ROIs) from large tissue sections. Currently, ROI selection is performed manually, which introduces subjectivity, inconsistency, and a lack of reproducibility. Previous studies have shown strong correlations between spatial molecular patterns and histological features, suggesting that readily available and cost-effective histology images can be leveraged to guide spatial omics experiments. Here, we present S2Omics, an end-to-end workflow that automatically selects ROIs from histology images with the goal of maximizing molecular information content in the ROIs. Through comprehensive evaluations across multiple spatial omics platforms and tissue types, we demonstrate that S2Omics enables systematic and reproducible ROI selection and enhances the robustness and impact of downstream biological discovery.

genomics↗

bioRxiv2025

The Impact of Delayed Evacuation on the Quality of Human Fetal Tissue

BackgroundDigoxin and other agents are frequently administered to arrest fetal circulation before the evacuation of human fetal tissue (HFT) in pregnancy terminations to address concerns about fetal viability. However, the impact of delayed evacuation on HFT quality remains unknown. Analyzing HFT is critical for diagnosing pregnancies affected by fetal abnormalities and for driving progress in biomedical research. ObjectiveThis study aims to assess the effects of delayed evacuation following fetal circulation cessation on the quality of HFT for both diagnostic and research purposes. Study DesignHFT samples were collected from second-trimester dilation and evacuation (D&E) procedures, with and without agent injection approximately 24 hours prior, as per standard care protocols. We assessed multiple parameters relevant to diagnostics and research, including: 1) cell morphology, 2) cell proliferation, 3) apoptosis, 4) cell viability in culture, and 5) nucleic acid quality. To simulate in utero conditions and determine the timeline for tissue degradation, we incubated brain tissue obtained from D&E without induced demise at 37{degrees}C for up to 18 hours. ResultsWe analyzed 18 HFT samples from D&E procedures performed 19-25 hours after digoxin or potassium chloride (KCl) injection and compared them to 26 HFT samples from immediate evacuations without prior injection. Induced fetal demise resulted in 1) disrupted cell morphology, 2) decreased cell proliferation, 3) increased apoptosis, 4) reduced cell viability in culture, and 5) lower RNA quality. Despite these findings, all samples yielded DNA of sufficient quality for polymerase chain reaction (PCR). ConclusionD&E procedures performed after fetal demise induced by digoxin or KCl lead to decreased HFT quality, limiting its diagnostic and research potential beyond gross tissue evaluation and DNA extraction. Limiting the time between fetal circulation arrest and evacuation may improve HFT quality for clinical and research applications.

pathology↗

bioRxiv2025

Identification of senescence-related hub genes and potential therapeutic agents for chronic heart failure

BackgroundChronic heart failure (CHF), the terminal phase of cardiovascular disease progression, has emerged as an increasingly severe global public health concern. Despite current therapeutic approaches aimed at symptom relief, their long-term effectiveness remains limited, urgently necessitating the exploration of novel treatment strategies. This study endeavored to explore the role of cellular senescence in CHF, identify the characteristic genes linked to cellular senescence, and predict potential therapeutic agents. MethodsWe acquired CHF-related datasets from the Gene Expression Omnibus database and cell senescence-related genes from the CellAge database to identify differentially expressed cell senescence-related genes. We then conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to elucidate the functions of these differentially expressed genes, constructed a protein-protein interaction network, and screened hub genes. Using receiver operating curve (ROC) analysis, we developed a diagnostic model based on these hub genes. Furthermore, we constructed networks for the hub genes involving miRNAs, lncRNAs, transcription factors, and drugs. Subsequently, we explored the potential mechanism of action of metformin in the treatment of CHF through molecular docking studies. Lastly, we verified the expression of the hub genes in a doxorubicin-induced CHF model in rats. Results We ultimately identified nine hub genes associated with cellular senescence: STAT1, MMP9, MAP2K1, SOCS1, SDC1, MET, EIF4EBP1, ATF3, and NAMPT. These genes exhibited significant differential expression between CHF and normal tissues. The constructed diagnostic model demonstrated robust diagnostic performance in ROC curve analysis, with an area under the curve exceeding 0.7, thereby providing biomarker support for early CHF diagnosis. Furthermore, we identified a regulatory network comprising 94 lncRNAs, 63 miRNAs, and 11 transcription factors and screened 42 potential therapeutic drugs. Subsequent molecular docking simulations revealed that metformin could effectively bind to the hub genes. Finally, we validated the expression of some hub genes in a rodent CHF model, in which gene expression regulation varied across diverse experimental settings. ConclusionsOur research identified nine genes cellular senescence-related genes with potential roles in CHF pathogenesis, offering fresh perspectives concerning the diagnosis and treatment of CHF.

bioinformatics↗

bioRxiv2025

Compositional data modeling of high-dimensional single cell RNA-seq (CoDA-hd): its advantages over commonly used normalization approaches

Compositional data analysis (CoDA) is an emerging statistical framework and has been extended to microbiome, bulk RNA-seq, and cell type proportions in single-cell RNA-seq (scRNA-seq) (with 50-200 components). Here, we explore the high-dimensional application of CoDA (CoDA-hd) and its various log-ratio (LR) transformations to raw count matrices of scRNA-seq which has over 20,000 components (e.g., protein coding genes). scRNA-seq matrices are typically sparse and high-dimensional. Common approaches of normalization such as log-normalization may lead to suspicious findings as previously shown for trajectory inference. Although RNA-seq is compositional data by nature, the geometry of CoDA in high-dimensional simplex is not compatible with most downstream analyses of scRNA-seq which are based on Euclidean space. In this study, we explored if CoDA is adaptable with scRNA-seq in various downstream applications. Specifically, we attempted to study: (1) CoDA adaptability to scRNA-seq; (2) handling of zero data: prior-log-normalized, imputation or with specific count addition; (3) transformation to Euclidean space and compatibility with downstream analyses. Our results suggested that (1) the innovative count addition schemes (e.g., SGM) enable the application of CoDA to high dimensional sparse data (i.e., scRNA-seq); (2) log-normalized data could be transformed to CoDA LR transformation as an approximation; (3) CoDA LR transformations such as count-added centered-log-ratio (CLR) had some advantages in dimension reduction visualization, clustering, and trajectory inference in the tested real & simulated datasets. CLR provided more decent and separated clusters in dimension reductions, improved the Slingshot trajectory inference, and eliminated the suspicious trajectory that is probably caused by the dropouts. We therefore concluded that CoDA may be a preferred scale-free model to handle scRNA-seq data for these downstream applications. Additionally, an R package CoDAhd was developed for conducting CoDA LR transformations for high dimensional scRNA-seq data. The code for implementing CoDA-hd and some example datasets were placed at https://github.com/GO3295/CoDAhd.

bioinformatics↗

bioRxiv2025

Genome modeling and design across all domains of life with Evo 2

All of life encodes information with DNA. While tools for sequencing, synthesis, and editing of genomic code have transformed biological research, intelligently composing new biological systems would also require a deep understanding of the immense complexity encoded by genomes. We introduce Evo 2, a biological foundation model trained on 9.3 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life. We train Evo 2 with 7B and 40B parameters to have an unprecedented 1 million token context window with single-nucleotide resolution. Evo 2 learns from DNA sequence alone to accurately predict the functional impacts of genetic variation--from noncoding pathogenic mutations to clinically significant BRCA1 variants--without task-specific finetuning. Applying mechanistic interpretability analyses, we reveal that Evo 2 autonomously learns a breadth of biological features, including exon-intron boundaries, transcription factor binding sites, protein structural elements, and prophage genomic regions. Beyond its predictive capabilities, Evo 2 generates mitochondrial, prokaryotic, and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Guiding Evo 2 via inference-time search enables controllable generation of epigenomic structure, for which we demonstrate the first inference-time scaling results in biology. We make Evo 2 fully open, including model parameters, training code, inference code, and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.

genomics↗

bioRxiv2024

Epigenetic reprogramming induced by Acetyl-CoA and SAM depletion is an evolutionarily-ancient path to malignant growth

Despite the vast diversity of life forms and living histories, it appears that all branches of Metazoa face the challenge of tumor growth. Contrary to human tumors, which take years to form, tumors in short-living species can arise within days without accumulating multiple mutations, raising the question whether the paths to tumorigenesis in diverse species have any commonalities. In a fly tumor model caused by loss of cell polarity genes, we first identified the rise of a glycolytic cell population over time, resembling the Wartburg effect observed in human tumors. We further identified two key metabolic changes in these fly tumors. First, a systemic depletion of acetyl-CoA leads to a reduction in histone acetylation levels and stochastic silencing of actively-transcribed genes. Second, defects in the methionine cycle cause a systemic depletion of S-Adenosyl methionine, which further reduces histone methylation levels and causes stochastic activation of transposons. Perturbation of the methionine metabolic process strongly inhibits tumor growth. Finally, to understand the evolutionary origin of tumorigenesis, we performed comparative studies of fly and human tumors, and identified human tumors that exhibit metabolic signatures similar to those of fly tumors. We found that human tumors with high metabolic similarity to fly tumors have a lower mutational load, younger patient age, and lower DNA methylation levels. This study suggests that tumorigenesis processes have a deep evolutionary origin and highlights that depletion of key metabolites is an evolutionarily-ancient driving force for tumorigenesis.

developmental biology↗

bioRxiv2024

Cardiolipin-mimic lipid nanoparticles without antibody modification delivered senolytic in-vivo CAR-T therapy for inflamm-aging

mRNA-based in vivo CAR T cell engineering offers advantages over ex vivo therapies, including streamlined manufacturing and transient expression. However, current delivery requires antibody-modified vehicles with manufacturing challenges. In this study, inspired by cardiolipin, we identified a cardiolipin-like di-phosphoramide lipid that improved T cell transfection without targeting ligands, both in vivo and in vitro. The T cell-favored tropism is likely due to the lipids packing, shape, and rigidity. Encapsulating circular RNA further prolonged mRNA expression in the spleen and T cells. Using PL40 lipid nanoparticles, we delivered mRNA encoding a CAR targeting the senolytic and inflammatory antigen urokinase-type plasminogen activator receptor (uPAR), alleviating uPAR-related liver fibrosis and rheumatoid arthritis (RA). Single cell sequencing in humans confirmed uPARs relevance to senescence and inflammation in RA. To further enhance clinical translation, we screened and humanized scFvs against uPAR, establishing PL40 mRNA encoding a circular human uPAR CAR, with potential for treating aging-inflamed disorders. One Sentence SummaryWeve developed a unique class of Cardiolipin-mimic lipids that facilitate mRNA delivery to T cells in vivo without the need for antibody modification, enhancing the treatment of liver fibrosis and rheumatoid arthritis through circular CAR uPAR RNA and propelling the clinical application of humanized CAR against human uPAR.

bioengineering↗

bioRxiv2023

Distinct roles of H3K27me3 and H3K36me3 in vernalization response, maintenance and resetting in winter wheat

Winter plants rely on vernalization, a vital process for adapting to cold and ensuring successful reproduction. However, understanding the role of histone modifications in guiding the vernalization process in winter wheat is limited. In this study, we investigate the transcriptome and chromatin dynamics in the shoot apex throughout the life cycle of winter wheat in the field. Two core histone modifications, H3K27me3 and H3K36me3, exhibit opposite pattern on the key vernalization gene VERNALIZATION1 (VRN1), correlated with its induction during cold exposure. Additionally, H3K36me3 remains high at VRN1 after cold exposure, maintaining its active state. Mutations in FERTILIZATION-INDEPENDENT ENDOSPERM (TaFIE) and SET DOMAIN GROUP 8 (TaSDG8), writer complex components of H3K27me3 and H3K36me3, respectively, affect flowering time. Interestingly, VRN1 loses its high expression after cold exposure memory in the absence of H3K36me3. During embryo development, VRN1 is silenced with the removal of H3K36me3 in both winter and spring alleles. H3K27me3 is selectively added to the winter allele, influencing the cold exposure requirement for the next generation. Integrating gene expression with H3K27me3 and H3K36me3 patterns identified potential regulators of flowering. This study reveals distinct roles of H3K27me3 and H3K36me3 in controlling vernalization response, maintenance, and resetting in winter wheat. Significance StatementVernalization, initially observed in cereals, lacks a comprehensive understanding of its underlying mechanism, particularly regarding chromatin-mediated transcriptional regulation in winter wheat. By delving into the transcriptome and chromatin dynamics in the shoot apex throughout winter wheats life cycle, we pinpointed two crucial histone modifications, H3K27me3 and H3K36me3, each playing distinct roles at different vernalization stages. H3K27me3 is implicated in establishing and resetting the extended cold exposure requirement for winter wheat, gradually diminishing during vernalization. On the other hand, H3K36me3 is crucial for maintaining VRN1s active state post-cold exposure, contributing to the memory of the vernalization treatment. Additionally, the integration of transcriptome and histone modification profiles unveiled potential novel regulators of flowering in winter wheat.

plant biology↗

bioRxiv2023

Cultivation of host-adapted Cryptosporidium parvum and Cryptosporidium hominis using enteroids for cryopreservation of isolates and transcriptomic studies of infection

Cryptosporidium hominis and Cryptosporidium parvum are major causes of severe diarrhea in humans. Comparative studies of them are hampered by the lack of effective cultivation and cryopreservation methods, especially for C. hominis. Here, we described adapted murine enteroids for the cultivation of one C. parvum IId subtype and nonhuman primate-adapted C. hominis Ib, Im, and In subtypes, which allowed the complete development of the pathogens, producing oocysts infectious to mice. Using the system, we developed a novel cryopreservation method for Cryptosporidium isolates. In comparative RNA-seq analyses of C. hominis cultures, the enteroid system generated significantly more transcriptomic responses of both pathogen and host genes than the conventional HCT-8 cell system. In particular, the infection was shown to upregulate PI3K-Akt, Wnt, Ras,TNF, NF-{kappa}B, IL-17, MAPK, and innate immunity signaling pathways and downregulate Wnt and Hippo signaling pathways, host cell metabolism, and parasites in enteroid cultures had significantly higher expression of genes involved in oocyst formation. Therefore, the new culture model provides a valuable tool for comparative studies of the biology of divergent Cryptosporidium species. IMPORTANCE The two dominant species for human cryptosporidiosis, Cryptosporidium hominis and Cryptosporidium parvum, differ significantly in host range and virulence. Up to date, biological studies of Cryptosporidium spp. are almost exclusively done with bovine-adapted IIa subtypes of C. parvum, which is the species with effective laboratory animal models and in vitro cultivation methods. Here, we describe modified procedures for the generation of murine enteroids for successful cultivation of both nonhuman primate-adapted C. hominis subtypes and a C. parvum IId subtype, producing oocysts infective to mice. In addition, we have developed a novel cryopreservation method using the system for long-term storage of Cryptosporidium isolates. RNA-seq analyses of C. hominis cultures indicate that the enteroid culture system generates host and pathogen transcriptomic responses similar to those in natural infection. This new development alleviates a technical bottleneck in cryptosporidiosis research, and provides an example for other difficult-to-culture pathogens of major public health importance.

microbiology↗