Biology subjects

Song, B.

Publications and source records attributed to Song, B..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Identification of biomarkers for ulcerative morphology in carotid atherosclerosis plaques through single-cell RNA sequencing data

BackgroundCarotid atherosclerotic stenosis (CAS) is a major risk factor for stroke, particularly when associated with ulcerative plaque morphology. However, the molecular mechanisms underlying ulceration remain inadequately defined. In this study, we aimed to construct single-cell atlases of ulcerative and non-ulcerative CAS plaques to identify biomarkers associated with ulcerative lesions. MethodsPlaque tissues from four ulcerative and three non-ulcerative CAS samples were collected and subjected to single-cell RNA sequencing (scRNA-seq). Cell types were identified through scRNA-seq analysis, and differential cell types were determined by comparing their proportions between the 2 groups. Candidate genes were identified by intersecting differentially expressed genes (DEGs) within these differential cell populations. Subsequently, the Molecular Complex Detection (MCODE) plugin in Cytoscape was employed to confirm the biomarkers. Functional annotation and regulatory network analysis were then performed to investigate underlying biological mechanisms. ResultsscRNA-seq analysis identified 7 cell types, of which 6 showed differential proportions, leading to the identification of 61 candidate genes and 7 key biomarkers (KLF2, JUNB, FOS, HSPA1A, DUSP1, JUND, and ZFP36). Enrichment analysis indicated that these biomarkers may influence ulcerative CAS plaques via RNA polymerase II-mediated transcription and stress-response pathways. Regulatory network analysis revealed complex interactions, with RBMX, STAT3, YTHDF3, DDX3X, and ELAVL1 identified as potential upstream regulators. ConclusionsKLF2, JUNB, FOS, HSPA1A, DUSP1, JUND, and ZFP36 were identified as biomarkers, offering novel insights into the molecular mechanisms underlying ulcerative morphology in CAS.

pathology↗

bioRxiv2025

New alleles of Arabidopsis BIK1 reinforce its predominant role in pattern-triggered immunity and caution interpretations of other reported functions

The receptor-like cytoplasmic kinase BIK1 and its close homolog PBL1 have been widely recognized as central components of plant immunity. However, most genetic studies of BIK1 and PBL1 functions were carried out with single T-DNA insertional mutant alleles. Some phenotypes observed in these mutants, e.g. autoimmunity, have been difficult to reconcile with the proposed role of BIK1 and PBL1 in pattern-triggered immunity. In this study, we generated multiple new alleles of bik1 and pbl1 by CRISPR-Cas9-based gene editing and systematically analyzed these mutants alongside existing T-DNA insertional lines. These analyses reinforced the central role of BIK1 and PBL1 in pattern-triggered immunity mediated by both receptor kinases and receptor-like proteins. At the same time, however, we revealed several pleiotropic phenotypes associated with T-DNA insertions that are not necessarily linked to loss of BIK1 or PBL1 function. Further analyses of newly generated bik1 pbl1 double mutants uncovered an even greater contribution of these kinases to immune signaling and disease resistance than previously appreciated. These findings clarify longstanding ambiguities surrounding BIK1 and PBL1 functions.

plant biology↗

bioRxiv2025

Machine Learning Matches Human Performance at Segmenting the Human Visual Cortex

A major problem in human visual neuroscience research is the localization of visual areas on the cortical surface. Currently available methods are capable of making detailed predictions about many areas, but human raters do not agree as well with these methods as they do with each other. Although highly accurate, human raters require substantial time and expertise that many researchers do not have. Additionally, human raters require functional data for drawing visual area boundaries that requires additional scan time, budget, and expertise to collect. Here, we train convolutional neural network (CNN) models to predict the boundaries and iso-eccentric regions of V1, V2, and V3 in both the Human Connectome Project dataset and the NYU Retinotopy dataset. CNNs trained to use the same functional data available to human raters predicted these boundaries with an accuracy similar to humans, while CNNs trained to use only anatomical data had a lower accuracy that was nonetheless higher than that of any currently available method. A comparison of the model accuracies when predicting eccentricity-based boundaries and polar angle-based boundaries suggests that eccentricity is substantially less closely tied to anatomical structure than polar angle and that the cortical magnification function, at least in terms of eccentricity, varies substantially between subjects. We further find that the fraction of V1, V2, and V3 that can be accurately parcellated into function regions using gray matter structural data alone is [~]75% ([~]80% of the inter-rater reliability of human experts), implying a much tighter coupling between structure and function in these areas than previously estimated. We conclude that machine learning techniques such as CNNs provide a powerful tool for mapping the brain with human accuracy and predict that such tools will become integral to neuroscience research going forward.

neuroscience↗

bioRxiv2025

Unraveling Tyrosine-Kinase Inhibitor Resistance in NSCLC Cells via Same-cell Measurement of RNA, Protein, and Morphological Responses

Non-small cell lung cancer (NSCLC) frequently develops resistance to tyrosine kinase inhibitors (TKIs), limiting the long-term success of targeted therapies. A deeper understanding of resistance mechanisms at the molecular and cellular levels may enable the development of more effective treatment strategies. Here, we applied the Teton detection assay on the AVITI24 platform to measure concurrently RNA, protein, and cellular morphology in NSCLC cell lines treated with the TKIs gefitinib and osimertinib. This single-cell, multiomic analysis revealed distinct expression and morphological profiles between drug-sensitive and resistant cells, including differences in MAPK-related pathway activity. Stratifying responses at the single-cell level uncovered subtle responses not detectable in bulk measurements. We identified CDK4/6 activity as a route of cell survival under TKI treatment and demonstrated that co-treatment with the CDK4/6 inhibitor palbociclib enhanced TKI efficacy. The ability to measure multiomics and cellular morphology in the same cells opens new avenues for future studies aimed at improving personalized treatment strategies in NSCLC and overcoming the obstacles posed by drug resistance.

genomics↗

bioRxiv2025

Embryonic Durotaxis: A Mechanical Framework for Understanding Cesarean Scar Pregnancy

Cesarean scar pregnancy (CSP) is an increasingly common complication associated with the rise in cesarean section rates, yet its underlying mechanisms remain poorly understood. In this study, we identified a stiffness gradient between healthy and scarred uterine tissues in both human cesarean samples and mouse uterine scar models. By employing gradient stiffness hydrogels, we demonstrated that mouse embryos and human trophoblast spheroids exhibit durotaxis, migrating preferentially toward stiffer areas. This migration occurs through three-dimensional complex behaviors--translation, swinging, and rolling--characterized by periodic patterns that align with cavity oscillations. Embryonic durotaxis is initiated from asymmetric adhesion forces and is driven by embryonic protrusive forces, generated from Marangoni-like tissue flows. The intrinsic cavity oscillations further amplify and sustain embryonic durotaxis. Finally, pharmacological inhibition of embryonic durotaxis significantly reduced CSP incidence in the mouse model. Our findings establish the concept of embryonic durotaxis, which may provide novel therapeutic avenues for CSP prevention.

developmental biology↗

bioRxiv2025

Meta-analysis of the brain transcriptomes of multiple genetic mouse models of schizophrenia highlights dysregulation in striatum and thalamus

Schizophrenia is a severe mental illness with high heritability, but its underlying mechanisms are poorly understood. We meta-analyzed large-scale brain transcriptomic data from mice harboring individual loss-of-function mutations in seven schizophrenia risk genes (Akap11, Dagla, Gria3, Grin2a, Sp4, Srrm2, Zmym2). While all studied brain regions were affected, the striatum and the thalamus emerged as key brain regions of convergence. Striatum showed downregulation of synapse-and oxidative phosphorylation-related gene sets in all models. In the thalamus, mutants separated into two groups based on transcriptomic phenotype: synapse-related gene sets were upregulated in mutants with only schizophrenia and bipolar association, and were downregulated in mutants that are associated with developmental delay/intellectual disability in addition to schizophrenia. Overall, our meta-analysis reveals convergence and divergence in brain transcriptomic phenotype in these schizophrenia genetic models, supports the involvement of striatal disturbance and synapse dysfunction in schizophrenia, and points to a key role of the thalamus.

neuroscience↗

bioRxiv2025

Patchy Striatonigral Neurons Modulate Locomotor Vigor in Response to Environmental Valence

Spiny projection neurons (SPNs) in the dorsal striatum play crucial roles in locomotion control and value-based decision-making. SPNs, which include both direct-pathway striatonigral and indirect-pathway striatopallidal neurons, can be further classified into subtypes based on distinct transcriptomic profiles and cell body distribution patterns. However, how these SPN subtypes regulate spontaneous locomotion in the context of environmental valence remains unclear. Using Sepw1-Cre transgenic mice, which label a specific SPN subtype characterized by a patchy distribution of cell bodies in the dorsal striatum, we found that these patchy striatonigral neurons constrain motor vigor in response to valence differentials. In a modified light/dark box test, mice exhibited differential walking speeds between the light and dark zones. Genetic ablation of these patchy SPNs disrupted restful slowing in the dark zone and increased zone discrimination by speed. In vivo recordings linked the activity of these neurons to zone occupancy, speed, and deceleration, with a specific role in mediating deceleration. Furthermore, chemogenetic activation of patchy SPNs--and optical activation of striatonigral neurons in particular--reduced locomotion and attenuated speed-based zone discrimination. These findings reveal that a subtype of patchy striatonigral neurons regulates implicit walking speed selection based on innate valence differentials.

neuroscience↗

bioRxiv2024

Deep Ensemble Model for Quantitative Optical Property and Chromophore Concentration Images of Biological Tissues

The ability to quantify widefield tissue optical properties (OPs, i.e., absorption and scattering) has major implications on the characterization of various physiological and disease processes. However, conventional image processing methods for tissue optical properties are either limited to qualitative analysis, or have tradeoffs in speed and accuracy. The key to quantification of optical properties is the extraction of amplitude maps from reflectance images under sinusoidal illumination of different spatial frequencies. Conventional three-phase demodulation (TPD) method has been demonstrated for the mapping of OPs, but it requires as many as 14 measurement images for accurate OP extraction, which leads to limited throughput and hinders practical translation. Although single-phase demodulation (SPD) method has been proposed to map OPs with a single measurement image, it is typically subject to image artifacts and decreased measurement accuracy. To tackle those challenges, here we develop a deep ensemble model (DEM) that can map tissue optical properties with high accuracy in a single snapshot, increasing the measurement speed by 14x compared to conventional TPD method. The proposed method was validated with measurements on an array of optical phantoms, ex vivo tissues, and in vivo tissues. The errors for OP extraction were 0.83 {+/-} 5.0% for absorption and 0.40 {+/-} 1.9% for reduced scattering, dramatically lower than that of the state-of-the-art SPD method (2.5 {+/-} 15% for absorption and -1.2 {+/-} 11% for reduced scattering). It was further demonstrated that while trained with data from a single wavelength, the DEM can be directly applied to other wavelengths and effectively obtain optical property and chromophore concentration images of biological tissues. Together, these results highlight the potential of DEM to enable new capabilities for quantitative monitoring of tissue physiological and disease processes.

biophysics↗

bioRxiv2024

Identification of Intrinsic Features for Cortical Separability of Human and Mouse Neurons

This work introduces a novel framework for holistic comparative analysis of cortical regions in mouse and human brains at single-neuron resolution, with a primary focus on the morphological and molecular characteristics of neurons. To do so, we generated one of the largest dendritic reconstruction datasets of cortical neurons to date, comprising 2,363 human neurons and 16,011 mouse neurons from the frontal, parietal, and temporal lobes, followed by establishing a rigorous procedure to identify anatomically and functionally corresponding brain regions with minimal variability in brain mapping. Additionally, we leveraged single nucleus/cell transcriptomic data from independent groups to validate the molecular correspondence of the brain regions identified in this study. The significance of these anatomically, functionally, and molecularly corresponding mouse-human region pairs is highlighted by examining the intrinsic features of their respective cortical regions. Our findings reveal that human neuron branching patterns differ dramatically from those in mouse brains, particularly in terms of dendritic branching frequency and normalized dendritic branching intervals. This difference is pronounced in the frontal and temporal lobes, underscoring the distinct neuronal architectures between the two species. At the single-neuron level, we found that neurons from the human frontal and parietal lobes are six times more separable than those from the same regions in mouse brains. This heightened separability is also observed between the frontal and temporal lobes, as well as between the parietal and temporal lobes in humans. We thoroughly explored the entire morphological feature space, along with its characteristic subspaces, and consistently found this distinct separability. Remarkably, this neuronal separability can be partially recapitulated when examining the global functional states of these brain lobes--using newly acquired Electroencephalography (EEG) and Magnetoencephalography (MEG) signals as physiological measures--as well as their global metabolic states, molecular profiles, and cortical geometry. These findings suggest that our comparative analysis of single-neuron intrinsic features could serve as a valuable foundation for future comprehensive studies of cross-species brain structures and functions.

neuroscience↗

bioRxiv2024

MaskMol: Knowledge-guided Molecular Image Pre-Training Framework for Activity Cliffs with Pixel Masking

Activity cliffs, which refer to pairs of molecules that are structurally similar but show significant differences in their potency, can lead to model representation collapse and make the model challenging to distinguish them. Our research indicates that as molecular similarity increases, graph-based methods struggle to capture these nuances, whereas imagebased approaches effectively retain the distinctions. Thus, we developed MaskMol, a knowledge-guided molecular image selfsupervised learning framework. MaskMol accurately learns the representation of molecular images by considering multiple levels of molecular knowledge, such as atoms, bonds, and substructures. By utilizing pixel masking tasks, MaskMol extracts fine-grained information from molecular images, overcoming the limitations of existing deep learning models in identifying subtle structural changes. Experimental results demonstrate MaskMols high accuracy and transferability in activity cliff estimation and compound potency prediction across 20 different macromolecular targets, outperforming 25 state-of-the-art deep learning and machine learning approaches. Visualization analyses reveal MaskMols high biological interpretability in identifying activity cliff-relevant molecular substructures. Notably, through MaskMol, we identified candidate EP4 inhibitors that could be used to treat tumors. This study not only raises awareness about activity cliffs but also introduces a novel method for molecular image representation learning and virtual screening, advancing drug discovery and providing new insights into structure-activity relationships (SAR). Code is available at https://github.com/ZhixiangCheng/MaskMol.

bioinformatics↗

bioRxiv2024

Extensively acquired antimicrobial resistant bacteria restructure the individual microbial community in post-antibiotic conditions

In recent years, the overuse of antibiotics has led to the emergence of antimicrobial resistant (AMR) bacteria. To evaluate the spread of AMR bacteria, the reservoir of AMR genes (resistome) has traditionally been identified from environmental samples, hospital environments, and human populations; however, the functional role of AMR bacteria in the human gut microbiome and their persistency within individuals has not been fully investigated. Here, we performed a strain-resolved in-depth analysis of the resistome changes by reconstructing a large number of metagenome-assembled genomes (MAGs) of antibiotics- treated individuals gut microbiome. Interestingly, we identified two bacterial populations with different resistome profiles, extensively acquired antimicrobial resistant bacteria (EARB) and sporadically acquired antimicrobial resistant bacteria (SARB), and found that EARB showed broader drug resistance and a significant functional role in shaping individual microbiome composition after antibiotic treatment. Furthermore, longitudinal strain analysis revealed that EARB bacteria were inherently carried by individuals and can reemerge through strain switching in the human gut microbiome. Our data on the presence of AMR bacteria in the human gut microbiome provides a new avenue for controlling the spread of AMR bacteria in the human community.

genomics↗

bioRxiv2024

Overcrowding Drives Tumor Invasion by Inducing Nanotopographical Transition of Plasma Membrane

During the progression from epithelial neoplasms to invasive carcinoma, cells are subjected to prolonged confinement. However, the response of cancer cells to such mild yet sustained compressive pressure during the initial stages of tumor invasion remain poorly understood. Here, using a spontaneous crowding model to recapitulate the progressive compressive stress caused by cell proliferation, we demonstrated that prolonged crowding alone is sufficient to induce the acquisition of an invasive phenotype and associated gene expression patterns in cancer cells. This invasiveness persisted even after cells were removed from the crowded environment, a phenomenon mediated by mechanomemory. By combining genetic manipulations, mechanical modeling, and biophysical measurements, we revealed that the disaggregation of membrane domains--driven by a nanoscale smooth-corrugated topography transition of plasma membranes induced by Laplace pressure under crowded conditions--is essential for initiating cancer cell invasion. Inhibiting membrane domains disaggregation through membrane-to-cortex-attachment effectively suppresses cancer cell invasion in both cellular crowding models and mouse xenograft models. This study underscores the critical role of tissue-scale mechanics in regulating the biophysics of mechanosensitive membrane domains during the early stages of tumor invasion.

cancer biology↗

bioRxiv2024

An Artificial Intelligence Model for Profiling the Landscape of Antigen-binding Affinities of Massive BCR Sequencing Data

The interaction between antigens and antibodies (B cell receptors, BCRs) is the key step underlying the function of the humoral immune system in cancers and other biological contexts. The capability to profile the landscape of antigen-binding affinities of a vast number of BCRs will provide a powerful tool to reveal novel insights at unprecedented levels and will yield powerful tools for translational development. However, current experimental approaches for detecting antibody-antigen interactions are costly and time-consuming, and can only achieve low-to-mid throughput. On the other hand, bioinformatics tools in the field of antibody informatics mostly focus on optimization of antibodies given known binding antigens, which is a very different research question and of limited scope. In this work, we developed an innovative Artificial Intelligence tool, Cmai, to address the prediction of binding between antibodies and antigens that can be scaled to high-throughput sequencing data. We devised a biomarker metric based on the output from Cmai applied to high-throughput BCR sequencing data to predict the landscape of the antigen-binding affinities of the BCRs. We found that extracellular antigens on malignant tumor cells are inducing B cell infiltrations, and the infiltrating B cells have a greater tendency to co-localize with tumor cells expressing these antigens. We further found that the abundance of tumor antigen-targeting antibodies is predictive of immune-checkpoint inhibitor (ICI) treatment response. On the other hand, we found that, during immune-related adverse events (irAEs) caused by ICI treatment, humoral immunity is preferentially responsive to intracellular antigens from the organs affected by the irAEs. Overall, we developed a powerful tool for elucidating the antigen binding landscape of BCR repertoires, and it shall also propell antibody-centric translational applications for cancers and other diseases.

immunology↗

bioRxiv2024

Poxvirus A52 is a host range factor for modified vaccinia virus Ankara (MVA) and promotes viral replication by disturbing the formation of autolysosomes

Many members of the poxvirus family are important zoonotic pathogens that pose a significant threat to human and animal health worldwide. Autophagy is a multi-step degradation pathway within cells, and one of its primary biological functions includes the clearance of invading viruses. Nevertheless, the interplay between poxviruses and host cell autophagy has not been fully elucidated. Here, we demonstrate that vaccinia virus (VACV) and lumpy skin disease virus (LSDV) induce incomplete autophagy and inhibit the fusion of autophagosomes and lysosomes, while modified vaccinia virus Ankara (MVA), an attenuated strain of VACV unable to replicate in almost all human cells, does not. Additionally, we screened and identified the VACV protein A52 as a key factor that obstruct the formation of autolysosomes. Mechanistically, A52 interacts with SNAP29 and inhibits its interaction with STX17 and VAMP8, both of which are binding partners of SNAP29 and are essential for complete autophagy. Moreover, A52 promotes the proteasomal degradation of SNAP29, which facilitates viral replication. We further revealed that SNAP29 functions as a restriction factor for MVA, as the suppression of SNAP29 allowed the replication of MVA in human cells. In summary, our data present a molecular mechanism by which poxviruses manipulate the cellular autophagic machinery and provide additional explanation for the restriction of MVA in human cells.

microbiology↗

bioRxiv2024

The Role of ADGRA3 in Adipose Thermogenesis: A Potential Therapeutic Target for Obesity

The induction of adipose thermogenesis plays a critical role in maintaining body temperature and improving metabolic homeostasis to combat obesity. {beta}3-adrenoceptor ({beta}3-AR) is widely recognized as a canonical {beta}-adrenergic G protein-coupled receptor (GPCR) that plays a crucial role in mediating adipose thermogenesis in mice. Nonetheless, the limited expression of {beta}3-AR in human adipocytes restricts its clinical application. The objective of this study was to identify a GPCR that is highly expressed in human adipocytes and to explore its potential involvement in adipose thermogenesis. Our research findings have demonstrated that the adhesion G protein-coupled receptor A3 (ADGRA3), an orphan GPCR, plays a significant role in adipose thermogenesis through its constitutively active effects. ADGRA3 exhibited high expression levels in human adipocytes and mouse brown fat. Furthermore, the knockdown of Adgra3 resulted in an exacerbated obese phenotype and a reduction in the expression of thermogenic markers. Conversely, Adgra3 overexpression activated the adipose thermogenic program and improved metabolic homeostasis without an exogenous ligand supplementation. We found that ADGRA3 facilitates the biogenesis of beige adipocytes through the Gs-PKA-CREB axis. Moreover, hesperetin was identified as a potential agonist of ADGRA3, capable of inducing adipocyte browning and ameliorating insulin resistance. In conclusion, our study demonstrated that the overexpression of constitutively active ADGRA3 or the activation of ADGRA3 by hesperetin can induce adipocyte browning by Gs-PKA-CREB axis. These findings indicate that the utilization of hesperetin and the selectively overexpression of ADGRA3 in adipose tissue could serve as promising therapeutic strategies in the fight against obesity.

cell biology↗

bioRxiv2024

Genomic and Genetic Insights into Mendel's Pea Genes

Pea, Pisum sativum, is an excellent model system through which Gregor Mendel established the foundational principles of inheritance. Surprisingly, till today, the molecular nature of the genetic differences underlying the seven pairs of contrasting traits that Mendel studied in detail remains partially understood. Here, we present a genomic and phenotypic variation map, coupled with haplotype-phenotype association analyses across a wide range of traits in a global Pisum diversity panel. We focus on a genomics-enabled genetic dissection of each of the seven traits Mendel studied, revealing many previously undescribed alleles for the four characterized genes, R, Le, I and A, and elucidating the gene identities and mutations for the remaining three uncharacterized traits. Notably, we identify: (1) a ca. 100kb deletion upstream of the Chlorophyll synthase (ChlG) gene, which generates aberrant transcripts and confers the yellow pod phenotype of gp mutants; (2) an in-frame premature stop codon mutation in a Dodeca-CLE41/44 signalling peptide which explains the parchmentless mutant phenotype corresponding to p; and (3) a 5bp in-frame deletion in a CIK-like receptor kinase gene corresponding to the fasciated stem phenotype fa, which Mendel described in terms of flower position, and we postulate the existence of a Modifier of fa (Mfa) locus that masks this meristem defect. Mendel noted the pleiotropy of the a mutation, including inhibition of axil ring anthocyanin pigmentation, a trait we found to be controlled by allelic variants of the gene D within an R2R3-MYB gene cluster. Furthermore, we characterize and validate natural variation of a quantitative genetic locus governing both pod width and seed weight, characters that Mendel deemed were not sufficiently demarcated for his analyses. This study establishes a cornerstone for fundamental research, education in biology and genetics, and pea breeding practices.

genetics↗

bioRxiv2024

Biochemical and structural insights into a 5' to 3' RNA ligase reveal a potential role in tRNA ligation

ATP-grasp superfamily enzymes contain a hand-like ATP-binding fold and catalyze a variety of reactions using a similar catalytic mechanism. More than 30 protein families are categorized in this superfamily, and they are involved in a plethora of cellular processes and human diseases. Here we identify C12orf29 as an atypical ATP-grasp enzyme that ligates RNA. Human C12orf29 and its homologs auto-adenylate on an active site Lys residue as part of a reaction intermediate that specifically ligates RNA halves containing a 5-phosphate and a 3-hydroxyl. C12orf29 binds tRNA in cells and can ligate tRNA within the anticodon loop in vitro. Genetic depletion of c12orf29 in female mice alters global tRNA levels in brain. Furthermore, crystal structures of a C12orf29 homolog from Yasminevirus bound to nucleotides reveal a minimal and atypical RNA ligase fold with a unique active site architecture that participates in catalysis. Collectively, our results identify C12orf29 as an RNA ligase and suggest its involvement in tRNA biology. Significance StatementATP-grasp enzymes share an atypical ATP-binding fold and catalyze a diverse set of reactions involved in many essential cellular processes. We identified C12orf29 as an atypical ATP-grasp enzyme. Our biochemical and structural characterizations reveal this enzyme to be a 5 to 3 RNA ligase, structurally and functionally similar to the phage T4 RNA ligase. C12orf29 can ligate tRNAs in vitro and C12orf29 knockout female mice have altered tRNA levels in brain. We also report structures of C12orf29, which have revealed critical insights into the mode of ATP binding and catalysis. Our work suggests that C12orf29 may be a new player in the regulation of tRNAs.

biochemistry↗

bioRxiv2024

Differential seasonal effects of ephemeral legumes in response to moisture-nitrogen coupling in the deserts of northwestern China

Desert ecosystems are ecosystems limited by both nitrogen and moisture, while legumes, as an important source of nitrogen in desert ecosystems, are extremely sensitive to respond to moisture and nitrogen changes. In order to reveal the growth and physiological metabolic processes of desert legumes in response to moisture and nitrogen changes. In this study, two dominant ephemeral legumes, Trigonella arcuata C. A. Mey. and Astragalus arpilobus Kar. & Kir. were selected from the Gurbantunggut Desert through a controlled experiment in greenhouse pots: four moisture treatment levels were established: W1(soil moisture content of 5 %), W2(soil moisture content of 7 %), W3( 9 % soil moisture content), W4(11 % soil moisture content) and three N treatment levels: N1(0 mmol/L 15NH4 15NO3), N2(18 mmol/L 15NH4 15NO3), N3(72 mmol/L 15NH4 15NO3), and we conducted a 15N tracking experiment for the pot treatments throughout the growing season. The results showed that: (1) moisture and nitrogen treatments had similar patterns on above-ground and below-ground as well as total fresh weight, plant height and basal diameter, Chla, Chlb and Chla+b of the two legumes, all of which were greatest in the W3N3 treatment and smallest in the W4N1 treatment, but the growth characteristics and chlorophyll parameters had cumulative differences over the growing season. (2) When subjected to severe moisture and nitrogen stresses, the 2 plants would increase the physiological levels of stress tolerance in vivo to enhance cellular resistance, with the osmoregulatory substance content being the most sensitive in response to moisture, and the cell membrane and antioxidant enzyme systems being the most sensitive in response to nitrogen stress. (3) The adaptive characteristics of physiological metabolic processes in plants to seasonal changes, in which growth characteristics, cell membrane and antioxidant enzyme content accumulation showed: rapid growth period < maximum biomass period, while chlorophyll content and osmoregulatory substance content accumulation content accumulation showed: rapid growth period > maximum biomass period. In general, drought can hinder nitrogen uptake and transport, while sufficient moisture can accelerate the nutrient metabolism process of plants, and there is a nutrient dilution effect once the moisture is in excess, but in the nitrogen starvation state, the moisture factor has almost no effect on plants, therefore, the moisture-nitrogen coupling effect is more reflective of the physiological response characteristics of the plants when they encountered the adversity stress, and the degree of accumulation is different in different growing seasons, and the plants in the The differences in different growing seasons reflect the changes in photosynthetic intensity and energy demand.

ecology↗