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Wei, N.

Publications and source records attributed to Wei, N..

At least 19 recordsLinked to original sources

Genome evolution at the extreme of angiosperm miniaturization

Eukaryotic genomes vary by several orders of magnitude, yet this vast variation bears little relation to the complexity of the organisms they encode. Thus, how genome evolution accompanies changes in organismal complexity remains unresolved. A central obstacle is that major differences in body plan usually occur among deeply divergent lineages, entangling body plan evolution with the genomic divergence accumulated over long independent histories. Duckweeds offer a rare system in which successive body plan reductions can be traced within a single plant family. Across this trajectory, body size declined by nearly an order of magnitude and roots were progressively lost, culminating in the extreme of angiosperm miniaturization. Yet genome size increased nearly sixfold. Here, using a new chromosome-scale genome of Wolffia globosa and comparative genomics across nested evolutionary scales, we show that genome size, gene number, and functional repertoire followed distinct trajectories during miniaturization. Genome expansion was driven largely by transposable element accumulation, whereas the number of protein-coding genes remained stable. Aquatic adaptation itself promotes functional simplification, but establishes only a baseline. Duckweeds pushed this streamlining much further through additional contraction of developmental, structural, and biotic defense functions, alongside selective expansion of functions associated with growth and abiotic adaptation. This remodeling accumulated across successive evolutionary transitions through continued contraction of the same gene families and, more commonly, contraction of different families affecting the same biological processes. Organismal complexity may therefore reflect not simply the size of a genome or its functional repertoire, but how that repertoire is selectively reconfigured through evolution.

evolutionary biology↗

Phyllosphere microbes decouple nutrient enrichment from plant productivity

Resource limitation theory predicts that nutrient enrichment enhances plant productivity. Yet plant-associated microbes can modify this relationship by facilitating nutrient acquisition, competing for resources, or restructuring the plant-environment interface. These processes generate contrasting predictions for whether added nutrients are converted into plant population growth. Whether phyllosphere microbiomes mediate this resource-productivity relationship remains unclear. Here we show that phyllosphere microbiomes decoupled nutrient enrichment from productivity in a duckweed polyploid complex. In microbe-free microcosm ecosystems, nutrient enrichment increased productivity, whereas with microbes, enrichment failed to increase productivity despite abundant residual nutrients. This decoupling was not explained by direct microbial competition for nutrients or predicted microbial functions associated with pathogenicity, oxygen depletion, or acidification. Instead, nutrient enrichment stimulated biofilm formation, potentially restricting plant access to nutrients. Predicted microbial phosphorus immobilization and transformation also increased, but neither explained the decline in ecosystem phosphorus removal. This decline instead reflected lower plant productivity associated with biofilm formation, leaving much of the phosphorus unused. These patterns were consistent across ploidy levels, with the productivity advantage of polyploids associated with greater nutrient-use efficiency rather than greater tolerance of microbial effects. Our results reveal that phyllosphere microbiomes mediate how nutrient enrichment translates into ecosystem functioning.

ecology↗

Ancient transposable elements sustain global ecological adaptation despite chronically low nucleotide diversity

Transposable elements (TEs) are dynamic components of eukaryotic genomes and a major source of structural and regulatory variation, yet their contribution to ecological adaptation over evolutionary time remains unresolved. This uncertainty is particularly acute in species that occupy broad environmental niches despite chronically low nucleotide diversity, where conventional models predict limited adaptive potential. Here we show that ancient TE polymorphisms underpin global ecological adaptation in Spirodela polyrhiza, one of the smallest flowering plants with low genome-wide nucleotide diversity. Most TE polymorphisms predate continental population divergence. Cold-season temperature emerges as the dominant selective axis, with adaptive signals overwhelmingly associated with TE polymorphisms rather than SNPs. These adaptive TEs bear signatures of selection on standing variation and are embedded in genomic regions shaped by relaxed purifying selection rather than recent hard sweeps. Our results reveal how ancient TE variation sustains ecological adaptation despite chronically depleted nucleotide diversity, resolving a longstanding evolutionary paradox. One sentence summaryHow species adapt with little genetic diversity is a longstanding evolutionary puzzle solved by ancient transposon insertions

evolutionary biology↗

ALKBH5 Promotes Hepatic Gluconeogenesis via m6A-mediated Stabilization of Ogt mRNA

Excessive hepatic glucose production is a hallmark of fasting hyperglycemia in type 2 diabetes mellitus, yet the epitranscriptomic mechanisms that sustain this dysregulation remain incompletely understood. Here, we show that dexamethasone/forskolin-induced gluconeogenic activation triggers widespread remodeling of RNA m6A methylation in mouse primary hepatocytes. Among the m6A regulators examined, the demethylase ALKBH5 is robustly induced both in vitro and in fasted livers. This induction is mediated by the glucocorticoid receptor, which binds to the Alkbh5 promoter and activates its transcription. Functionally, ALKBH5 overexpression enhances gluconeogenic gene expression and glucose production, whereas both global and hepatocyte-specific Alkbh5 knockout mice exhibit suppressed hepatic gluconeogenesis. Mechanistically, we identify Ogt as a primary downstream target, defining an ALKBH5-OGT epitranscriptomic axis in hepatocytes. ALKBH5 demethylates and stabilizes Ogt mRNA, thereby elevating OGT expression and promoting gluconeogenic gene expression. Consistently, the pro-gluconeogenic effects of ALKBH5 are largely abrogated by OGT knockdown. Finally, pharmacological inhibition of ALKBH5 with 18l effectively improves glucose tolerance and suppresses hepatic gluconeogenesis in both wild-type and db/db mice. Together, these findings reveal a glucocorticoid-responsive epitranscriptomic mechanism whereby ALKBH5 stabilizes Ogt mRNA to drive gluconeogenesis and highlight ALKBH5 as a promising therapeutic target for type 2 diabetes mellitus.

molecular biology↗

Exploring multi-level microbial interactions from individual 3D genomes to community networks

Microbial communities interact with their hosts through complex genomic networks that influence ecosystem stability and disease progression. Here, we present FindMeta3D, a computational framework that simultaneously identifies microbial three-dimensional (3D) genome structures and cross-domain interaction networks. Applying this approach to 528 Hi-C samples, we resolved 3D genome structures of 344 microbial species, revealing five evolutionarily conserved chromatin folding patterns linked to intrinsic sequence features. Further analyses demonstrated distinct microbial-host interaction preferences and identified functional interaction hotspots that are critical for infection. Experimental deletion of such hotspots in the EBV genome resulted in significant infection defects, demonstrating their essential role in viral infectivity. Additionally, we constructed the first Cross-domain Microbial Interaction Network (CMIN), which uncovered pathogen-specific subnetworks and demonstrate dramatic restructuring of gut microbial communities in neutropenic patients, including enhanced Klebsiella-phage interactions. Subnetwork analysis identified potential phage therapy targets, such as Klebsiella phage ST16-OXA48phi5.4. These findings provide fundamental insights into microbial 3D genomics and establish FindMeta3D as a powerful platform for studying microbial genome structure and communities and developing antimicrobial strategies.

microbiology↗

Cross-task, explainable, and real-time decoding of human emotion states by integrating grey and white matter intracranial neural activity

Decoding human emotion states from intracranial neural activity is key in developing brain-computer interfaces and new therapies for affective disorders. However, real-world application of decoding requires high performance that integrates neural activity from both grey and white matter, stable generalization across different contexts, sufficient neural encoding explainability, and robust real-time implementation, all of which remain elusive. Here, we simultaneously recorded intracranial electroencephalogram (iEEG) and abundant self-rated valence and arousal scores across two emotion-eliciting tasks in eighteen subjects, forming the largest intracranial neural dataset for emotion decoding. We then developed personalized decoding models within a hybrid deep learning framework involving both self-supervised and supervised components. The models achieved high-performance decoding of continuous valence/arousal states, doubling the R-squared performance of prior EEG/iEEG decoding. Critically, the models significantly improved performance by integrating grey and white matter signals and demonstrated cross-task generalization. The models further revealed shared and preferred mesolimbic-thalamo-cortical subnetworks encoding valence and arousal, showing neurophysiological explainability. Finally, the models realized robust real-time decoding in four new subjects. Our results have implications for advancing emotion decoding neurotechnology toward deployable affective brain-computer interfaces and closed-loop therapeutic systems for affective disorders.

neuroscience↗

Cyclin-dependent kinase 9 inhibitors as oncogene signaling modulators in combination with targeted therapy for the treatment of colorectal cancer

BackgroundColorectal cancer (CRC) is the second deadliest cancer worldwide and new treatment options are urgently needed. Cyclin dependent kinase 9 (CDK9) promotes aberrant RNA transcription in cancer and is a promising target for cancer therapies. MethodsUsing CRC cell lines as well as newly established patient-derived organoid models of CRC, we studied the clinically promising CDK9 inhibitors (AZD4573, BAY1125152/VIP152/enitociclib, and NVP2) to determine their therapeutic potential. We investigated the efficacy and mechanisms of action through cell growth and cytotoxicity assays, RNAseq, immunoblotting, and IHC. ResultsWe found CDK9 inhibitors to be highly potent against CRC, through suppression of proliferation and induction of apoptosis. Our results demonstrated CDK9 inhibitors to be broadly active against a set of CRC models derived from a diverse patient population. Our mechanistic studies showed significant suppression of the Mitogen-active protein kinase (MAPK) signaling pathway due to CDK9 inhibitor treatment, suggesting that CDK9 inhibitor efficacy could be enhanced when combined with MAPK pathway inhibitors. As proof-of-concept, we found that CDK9 inhibitors and MEK inhibitors could be combined to synergistically suppress CRC growth and survival. ConclusionsCDK9 inhibitors show promising activity against patient-derived models of CRC. MAPK signaling is particularly suppressed by CDK9 inhibitors. Combining CDK9 inhibitors and targeted therapy against MAPK signaling pathway may be a viable strategy worthy of further investigation preclinically and clinically. State of translational relevanceNovel therapies for chemotherapy-refractory CRC remain urgently needed. Rapid validation of efficacy in appropriate translational models can provide the necessary rationale to test new therapies in clinical trials. Using newly established patient-derived organoids, we validate the preclinical efficacy of transcriptional CDK9 inhibitors against CRC. This is a therapeutic class that has been scarcely evaluated for efficacy against CRC. We found that CDK9 inhibitors potently inhibit patient-derived organoid models of CRC. Our mechanistic work also reveals that clinically achievable concentrations of CDK9 inhibitor treatment results in suppression of MAPK signaling. As proof-of-concept of the potential actionability of our findings, we demonstrate that CDK9 inhibitors can be combined with MEK inhibitors to synergistically suppress CRC growth. Our study provides new patient-derived CRC models as a resource available to others for therapeutic testing, as well as a roadmap for novel hypotheses about synergistic combination treatments using CDK9 inhibitors.

cancer biology↗

High Throughput Screening Identifies Small Molecules that Synergize with MRTX1133 Against Acquired Resistant KRAS G12D Mutated CRC

Colorectal cancer (CRC) remains a significant clinical challenge, with a 5-year survival rate of 10%. Over half of all CRCs harbor mutations in the KRAS gene, leading to poor response to standard therapy. This underscores the crucial need for novel therapeutics targeting KRAS and overcoming the growing barrier of resistance. To address these critical challenges, we conducted a high-throughput screen to identify small molecules that synergize with KRAS G12D inhibitor MRTX1133 against CRC. Through screening a 2,652 kinase inhibitor library, we discovered that Osimertinib and its analogs strongly synergize with MRTX1133 against both parental and MRTX1133-resistant cells. The top compound from the screen, NT-1, is a chemical analog of Osimertinib. NT-1 strongly synergized with MRTX1133 to suppress EGFR/MAPK signaling and induce apoptosis in an MRTX1133-resistant patient-derived organoid model of CRC. We present novel small molecule combinations with the potential to overcome the limitations of MRTX1133 with direct clinical translational applications. One Sentence SummaryHigh throughput screening and validation in colon cancer PDOs identifies novel KRAS inhibitor combinations with potential for clinical translation.

cancer biology↗

CellPatch: a Highly Efficient Foundation Model for Single-Cell Transcriptomics with Heuristic Patching

The rapid advancement of foundation models has significantly enhanced the analysis of single-cell omics data, enabling researchers to gain deeper insights into the complex interactions between cells and genes across diverse tissues. However, existing foundation models often exhibit excessive complexity, hindering their practical utility for downstream tasks. Here, we present CellPatch, a lightweight foundation model that leverages the strengths of the cross-attention mechanism and patch tokenization to reduce model complexity while extracting efficient biological representations. Comprehensive evaluations conducted on single-cell RNA-sequencing datasets across multiple organs and tissue states demonstrate that CellPatch achieves state-of-the-art performance in downstream analytical tasks while maintaining ultra-low computational costs during both pretraining and finetuning phases. Moreover, the flexibility and scalability of CellPatch allow it to serve as a general framework that can be incorporated with other well established single-cell analysis software, thereby enhancing their performance through transfer learning on diverse downstream tasks.

bioinformatics↗

Deep Learning with Evolutionary Scale Modeling to discover Antibiotic-Resistance Drug against Klebsiella Pneumoniae

The development of effective antibiotics is crucial in combating antibiotic-resistant pathogens such as Klebsiella pneumoniae (KP). This study utilizes deep learning, specifically Evolutionary Scale Modeling (ESM), to identify potential drug candidates. By analyzing molecular similarities between known drugs and KP strains, we aim to accelerate the discovery of new treatments and reduce the risks associated with clinical trials. This approach may shed new lights in the field of drug repositioning.

bioinformatics↗

Contrasting dynamics of soil fungal functional groups in the plant rhizosphere

Background and aimsSoil microbiomes, critical for plant productivity and ecosystem functioning, mediate essential functions such as pathogenesis, mutualism, and decomposition through different fungal functional groups. Yet, our understanding of the dynamics of co-existing soil fungal functional groups in the plant rhizosphere remains limited. MethodsBy leveraging a natural experiment in urban farming with fields of different ages and multiple plant genotypes, we tracked the relative abundance, richness, and microbial networks of plant pathogens, mycorrhizal fungi, and saprotrophic fungi across fields over two years. ResultsWe observed an increase in the relative abundance of plant pathogens in older fields relative to younger fields, supporting the prediction of pathogen accumulation over time. In contrast, there was a decrease in the relative abundance of mycorrhizal fungi in older fields. Unlike plant pathogens and mycorrhizal fungi, the relative abundance of saprotrophic fungi remained similar among fields. While the richness of plant pathogens and saprotrophic fungi were similar across fields, the community structure of both groups differed between younger and older fields. For mycorrhizal fungi, the richness declined in older fields and over the two years. These dynamics led to distinct microbial networks, with decreased network links for mycorrhizal fungi and increased links for saprotrophic fungi in older fields, whereas the links for plant pathogens remained similar across fields. ConclusionOur study reveals contrasting dynamics of essential soil fungal functional groups in the plant rhizosphere, and provides a predictive insight into the potential shifts in soil function and their impact on plant productivity.

ecology↗

Nanoparticles alter the nature and strength of intraploidy and interploidy interactions in plants

Engineered nanoparticles have profound impacts on organisms, yet there is limited understanding of how nanoparticle exposure shapes species interactions that are key for natural community dynamics. By growing plants of the same (intraploidy) and different ploidy levels (interploidy) of Fragaria in axenic microcosms, we examined the influence of nanoparticles on species interactions in polyploid and diploid plants. We found that, under copper oxide (CuO) nanoparticle exposure, polyploids experienced reduced competition when growing with polyploids (the effect of polyploids on polyploids, RII8x,8x), and a shift towards facilitation when growing with diploids (the effect of diploids on polyploids, RII8x,2x). This reduction in competitive interactions in polyploids, in line with the stress gradient hypothesis, was primarily caused by nanoscale effects, because the strength of competitive interactions (RII8x,8x and RII8x,2x) remained relatively unchanged under CuO bulk particles compared to control conditions. In contrast, diploids experienced a shift from facilitation (RII2x,2x and RII2x,8x) under control conditions to neutrality under CuO nanoparticles, with a similar reduction in facilitation observed with both nanoparticles and bulk particles. These findings underscore ploidy specific interaction dynamics and the need of considering species interactions when predicting organismal responses to nanoparticle pollution in ecological communities.

ecology↗

STCC: consensus clustering enhances spatial domain detection for spatial transcriptomics data

The rapid advance of spatially resolved transcriptomics technologies has yielded substantial spatial transcriptomics data. Deriving biological insights from these data poses non-trivial computational and analysis challenges, of which the most fundamental step is spatial domain detection (or spatial clustering). Although a number of tools for spatial domain detection have been proposed in recent years, their performance varies across datasets and experimental platforms. It is thus an important task to take full advantage of different tools to get a more accurate and stable result through consensus strategy. In this work, we developed STCC, a novel consensus clustering framework for spatial transcriptomics data that aggregates outcomes from state-of-the-art tools using a variety of consensus strategies, including Onehot-based, Average-based, Hypergraph-based and wNMF-based methods. Comprehensive assessments on simulated and real data from distinct experimental platforms show that consensus clustering significantly improves clustering accuracy over individual methods under varied input parameters. For normal tissue samples exhibiting clear layered structure, consensus clustering by integrating multiple baseline methods leads to improved results. Conversely, when analyzing tumor samples that display scattered cell type distribution patterns, integration of a single baseline method yields satisfactory performance. For consensus strategies, Average-based and Hypergraph-based approaches demonstrated optimal precision and stability. Overall, STCC provides a scalable and practical solution for spatial domain detection in spatial transcriptomic data, laying a solid foundation for future research and applications in spatial transcriptomics.

bioinformatics↗

Spider: a flexible and unified framework for simulating spatial transcriptomics data

Spatial transcriptomics technology provides a valuable view for studying cellular heterogeneity due to its ability to simultaneously acquire gene expression profile and cell location information. However, benchmarking these rapidly accumulating spatial transcriptomics analysis tools is challenging owing to the limited diversity and accuracy of "gold standard" data sets annotated by pathologists. To address this issue, we proposed Spider, a flexible and unified simulator for spatial transcriptomics data guided by cell type proportion and transition matrix of adjacent cell types. Taking advantage of a heuristic batched simulated annealing algorithm (BSA) in assigning simulated cell type labels, Spider can generate spatial transcriptomics data for one million cells in just five minutes. Furthermore, Spider can generate various types of spatial transcriptomics data, including immune hot/cold tumor samples by specifying different immune cell proportions and transition matrices and layered tissue samples via an interactive interface. In addition, Spider is also a unified framework for ST data simulation in which we have implemented diverse simulators proposed by other researchers as special cases. We have systematically evaluated the performance of Spider and competing tools, and demonstrated Spiders remarkable power to capture the spatial pattern of the reference dataset. Spider is available at https://github.com/YANG-ERA/Artist.

bioinformatics↗

Environments and host genetics influence the geographic distribution of plant microbiome structure

O_LITo understand how microbiota influence plant populations in nature, it is important to examine the geographic distribution of plant-associated microbiomes and the underlying mechanisms. However, we currently lack a fundamental understanding of the biogeography of plant microbiomes and the environmental and host genetic factors that shape their distribution. C_LIO_LILeveraging the broad distribution and extensive genetic variation in duckweeds (the Lemna species complex), we identified the key factors that influenced the geographic distribution of plant microbiome diversity and compositional variation. C_LIO_LIIn line with the pattern observed in microbial biogeography based on free-living environmental microbiomes, we observed higher bacterial richness in temperate regions relative to lower latitudes in duckweed microbiomes (with 10% higher in temperate populations). Our analyses revealed that temperature and sodium concentration in aquatic environments had a negative impact on duckweed bacterial richness, whereas temperature, precipitation, pH, and concentrations of phosphorus and calcium, along with duckweed genetic variation, influenced the geographic variation of duckweed bacterial community composition. C_LIO_LIThe findings add significantly to our understanding of host-associated microbial biogeography and provide insights into the relative impact of different ecological processes, such as selection by environments and host genetics, dispersal, and chance, on plant microbiome assembly. These insights have important implications for predicting plant microbiome vulnerability and resilience under changing climates and intensifying anthropogenic activities. C_LI

microbiology↗

Trophic interactions in microbiomes influence plant host population size and ecosystem function

Plant microbiomes that comprise diverse microorganisms, including prokaryotes, eukaryotes, and viruses are the key determinant of plant population dynamics and ecosystem function. Despite their importance, little is known about how interactions, especially trophic interactions, between microbes from different domains modify the importance of microbiomes for plant hosts and ecosystems. Using the common duckweed Lemna minor, we experimentally examined the effects of predation (by bacterivorous protozoa) and parasitism (by bacteriophage) within microbiomes on plant population size and ecosystem phosphorus removal. Our results revealed that predation increased plant population size and phosphorus removal whereas parasitism showed the opposite pattern. The structural equation modeling further pointed out that predation and parasitism affected plant population size and ecosystem function via distinct mechanisms that were both mediated by microbiomes. Our results highlight the importance of understanding microbial trophic interactions for predicting the outcomes and ecosystem impacts of plant-microbiome symbiosis.

microbiology↗

Competitive asymmetry confers polyploid advantage under environmental stress

O_LICompetitive asymmetry across heterogeneous environments is crucial for the success of polyploid plants, however, little is known about it. As the major force in plant evolution, polyploids are predicted to maintain the competitive dominance relative to diploids under increased stress conditions. C_LIO_LITo evaluate the hypothesis of competitive asymmetry, we competed tetraploid and diploid plants of perennial herbaceous Chrysanthemum indicum L. (Asteraceae) at different relative frequencies under low and high water stress. We quantified the interaction intensity between competing plants of the same (intraploidy) and different ploidy levels (interploidy), and measured functional traits related to gas exchange and plant water use to understand the underlying mechanisms. C_LIO_LIStronger competitive effects of the tetraploid on diploid provided evidence for the competitive asymmetry between polyploid and diploid plants in C. indicum. Such competitive asymmetry was not only maintained under drought (increased water stress), but also translated into higher fitness of the tetraploid consistently across water stress conditions. Functional traits associated with fast growth and efficient water use likely explained the competitive dominance of the tetraploid. C_LIO_LIThese results will advance our understanding of species interactions between polyploid and diploid plants, and provide insights into population dynamics and species distribution under environmental change. C_LI

ecology↗

Pollinators mediate floral microbial diversity and network under agrochemical disturbance

How pollinators mediate microbiome assembly in the anthosphere is a major unresolved question of theoretical and applied importance in the face of anthropogenic disturbance. We addressed this question by linking visitation of diverse pollinator functional groups (bees, wasps, flies, butterflies, beetles, true bugs and other taxa) to the key properties of floral microbiome (microbial - and {beta}-diversity and microbial network) under agrochemical disturbance, using a field experiment of bactericide and fungicide treatments on cultivated strawberries that differ in flower abundance. Structural equation modeling was used to link agrochemical disturbance and flower abundance to pollinator visitation to floral microbiome properties. Our results revealed that (1) pollinator visitation influenced the - and {beta}-diversity and network centrality of floral microbiome, with different pollinator functional groups affecting different microbiome properties; (2) flower abundance influenced floral microbiome both directly by governing the source pool of microbes and indirectly by enhancing pollinator visitation; and (3) agrochemical disturbance affected floral microbiome primarily directly by fungicide, and less so indirectly via pollinator visitation. These findings improve the mechanistic understanding of floral microbiome assembly, and may be generalizable to many other plants that are visited by diverse insect pollinators in natural and managed ecosystems.

ecology↗