Biology subjects

Wang, Y.-P.

Publications and source records attributed to Wang, Y.-P..

17 recordsLinked to original sources

bioRxiv2026

CpxR-CpxS two-component system exacerbates development of antibiotic resistance in Pseudomonas aeruginosa

Pseudomonas aeruginosa, as a member of the ESKAPE pathogens, is well known for its ability to develop multidrug resistance following antibiotic therapy. Overexpression of the efflux pumps, often caused by dysfunction of related transcriptional factors, is well known as a common mechanism underpins multidrug resistance. Previous work showed direct regulatory link of CpxR-CpxS, an OmpR-EnvZ family two-component system, with the expression of the predominant MexAB-OprM efflux pump in P. aeruginosa. However, the contribution of this regulatory link to the development of antibiotic resistance has not been fully elucidated. In this work, it has been shown that deletion of cpxR, but not cpxS, reduces the emergence of multidrug resistant colonies almost 1000 times lower compared to the parental wild type P. aeruginosa PA14 following sub-lethal antibiotic exposure, suggesting that apart from CpxS, other sensory component(s) or pathway(s) can also mediate CpxR activity in the development of antibiotic resistance in P. aeruginosa. We further demonstrate that when glucose is used as the sole carbon source, it leads to increased expression levels of mexA and reduced antibiotics susceptibilities in the PA14{Delta}cpxS strain in a cpxR dependent manner. GtrS, another EnvZ family sensor kinase, but not the well-known AckA-Pta pathway, mediates this glucose effects. Both in vivo and in vitro evidences support that GtrS can activate CpxR in a cross-talk manner in the absence of CpxS. Besides gtrS, at least four other sensor kinase genes (envZ, tctE, phoR and colS) can exert activation effect upon CpxR in the PA14{Delta}cpxS strain. Having the risk of multiple way to antibiotic resistance development, cpxS null mutations can be emerged in P. aeruginosa clinical isolates. Since many OmpR-EnvZ family two-component system have been shown to be engaged in antibiotic resistance, its potential risk of exacerbating antibiotic resistance in ESKAPE pathogens has been discussed.

microbiology↗

bioRxiv2026

NeuroMark dFNC: Transferable Dynamic-State Templates for Subject-Specific and Frequency-Resolved Chronnectomics

Dynamic functional network connectivity (dFNC) commonly identifies recurring connectivity states from resting-state fMRI, but states derived independently in each dataset lack guaranteed correspondence across studies and cannot be directly estimated in new participants. We introduce NeuroMark dFNC, which extends the NeuroMark principle of prior-guided network estimation to dynamic connectivity by using reproducible reference states as priors for estimating corresponding subject-specific states. Reference-state templates were derived from UK Biobank using windowed functional network connectivity and time-frequency moments (TFMs), with split-sample reproducibility evaluated for k-means, independent component analysis (ICA), and principal component analysis (PCA) template-generation approaches. Constrained ICA then used the templates to estimate subject-specific state patterns and continuous loadings in independent FBIRN schizophrenia and ADNI Alzheimer's disease cohorts. ICA loadings were calibrated by their contribution to reconstruction of the original feature matrix. Fractional occupancy and conditional mean dwell time were calculated from the state with the largest absolute calibrated loading at each observation. The predefined templates avoided the need for dataset-specific state discovery and post-hoc state matching. In both clinical cohorts, the analysis identified state-dependent, distributed edgewise differences and differences in selected temporal summaries. Nominal frequency-resolved findings varied across states and moment-frequency bins and were treated as exploratory. NeuroMark dFNC provides a common state space for cross-cohort comparison while retaining subject-specific connectivity patterns and continuous state expression.

neuroscience↗

bioRxiv2026

Modeling Complex Effects and Individual Variability in Multi-Paradigm fMRI with Nonlinear Mixed Models

Functional magnetic resonance imaging (fMRI) data are inherently complex, characterized by high dimensionality, intricate inter-regional dependencies, and substantial individual variability across experimental paradigms. Traditional linear mixed models (LMMs) provide a principled framework that models population-level fixed effects while estimating variance components arising from subject-level random effects; however, they often fail to adequately capture nonlinear relationships inherent in neuroimaging data. To address these limitations, we introduce the nonlinear mixed model (NMM) approach, an innovative extension of the LMM framework that integrates neural networks to flexibly model complex fixed-effect relationships while preserving the random-effects structure to account for individual differences. NMM advances fMRI analysis by: (1) identifying robust functional connectivity (FC) patterns consistently observed across multiple paradigms; (2) leveraging SHapley Additive exPlanations (SHAP) analysis to provide post-hoc interpretability of the nonlinear fixed effects, quantifying how age, sex, and paradigm contribute to predicted FC and how these effects are distributed across large-scale brain networks; and (3) using subject-specific random effects as neural fingerprints that not only show systematic variability across attention and default mode systems but also predict standardized cognitive scores, demonstrating biological relevance. Applied to the Philadelphia Neurodevelopmental Cohort (PNC) across emotion, n-back, and resting-state paradigms, NMM achieved superior model fit relative to classical LMMs, as evidenced by lower mean squared error (MSE) in predicting FC. This framework offers a statistically rigorous and practically explainable approach for modeling large-scale FC from modest covariates while explicitly separating population-level effects from stable individual variability in functional brain organization.

bioengineering↗

bioRxiv2026

Integrated microbiome and metabolomic profiling reveals alterations across the adenoma-colorectal cancer sequence

The incidence of colorectal cancer (CRC) has been increasing in Taiwan and is associated with multiple risk factors, including aging, obesity, and dietary habits. Increasing evidence suggests that gut microbiota dysbiosis contributes to CRC development. This study aimed to characterize microbial and metabolic alterations across premalignant and malignant colorectal lesions and to identify potential microbiome-associated biomarkers. Individuals undergoing colonoscopy for screening or surveillance at Taipei Veterans General Hospital were enrolled. Gut microbial composition was analyzed using full-length 16S rRNA gene sequencing to achieve high-resolution taxonomic profiling. Predicted functional pathways were inferred from microbial communities, and targeted metabolomic profiling was performed to evaluate microbial metabolic outputs. A total of 122 individuals were included, comprising 62 healthy controls, 15 adenoma cases, and 45 CRC cases. Progressive shifts in microbial composition and predicted functional pathways were observed along the adenoma-carcinoma sequence. Several bacterial taxa, including Phocaeicola dorei, Anaerotignum faecicola, Negativibacillus massiliensis, and Dysosmobacter segnis, were enriched in CRC. At the functional level, CRC samples showed enrichment of pathways associated with energy metabolism and bacterial stress responses. Metabolomic analysis further revealed increased levels of tauro-ursocholanic acid in CRC samples, whereas short-chain fatty acids (SCFAs) were reduced compared with controls. Integrative analysis combining full-length 16S sequencing, functional pathway prediction, and metabolomic profiling revealed coordinated microbial and metabolic alterations across the adenoma-carcinoma sequence. These findings provide insight into microbiome-associated processes in colorectal tumorigenesis and suggest potential microbial and metabolic biomarkers for CRC. ImportanceColorectal cancer (CRC) develops through a adenoma-carcinoma sequence, yet the microbial and metabolic alterations accompanying this progression remain incompletely understood. In this study, we integrated full-length 16S rRNA gene sequencing with metabolomic profiling to characterize taxonomic, functional, and metabolic changes across healthy controls, adenoma, and CRC. Our results reveal synchronized shifts in specific microbial taxa, predicted metabolic pathways, and fecal metabolites along the adenoma-carcinoma sequence. Several bacterial species, including Phocaeicola dorei, Anaerotignum faecicola, and Dysosmobacter segnis, increased in CRC, whereas short-chain fatty acids decreased progressively from controls to adenoma and CRC. Functional pathway analysis further indicated alterations in microbial fermentation, amino acid metabolism, and energy-related pathways. Together, these findings highlight the potential role of microbiome-associated metabolic changes in colorectal tumorigenesis and suggest candidate microbial and metabolic markers that may aid in understanding disease development and improving risk stratification.

microbiology↗

bioRxiv2025

Dynamic Inter-Modality Source Coupling Reveals Sex Differences in Children based on Brain Structural-Functional Network Connectivity: A Multimodal MRI Study of the ABCD Dataset

BackgroundSex differences in brain development are well-documented, yet the dynamic coupling between structure and function remains underexplored. We introduce dynamic inter-modality source coupling (dIMSC), extending our previous work to link structural MRI source-based morphometry (SBM) with dynamic functional network connectivity (dFNC). MethodsWe used data from the Adolescent Brain Cognitive Development (ABCD) study (ages 9-11) and combined SBM-derived gray matter sources with sliding-window dFNC. dIMSC was computed as the time-resolved cross-correlation between these modalities to quantify structure-function coupling strength. We evaluated sex differences in these profiles and their interaction with cognitive performance. ResultsSignificant sex-specific patterns emerged: males exhibited stronger positive coupling in sensorimotor regions (postcentral gyrus), while females showed stronger coupling in higher-order associative regions (inferior parietal lobule). These configurations were functionally distinct: higher positive coupling occupancy predicted better crystallized cognition (vocabulary) in females, whereas it predicted better fluid cognition (working memory) in males. ConclusionTogether, these findings suggest that males and females utilize distinct structural-functional configurations to support cognitive processing, males relying on a sensorimotor-anchored organization and females on an associative-anchored one. The dIMSC method advances our earlier work by enabling time-resolved analysis of brain coupling, providing a powerful framework for investigating sex-specific neurodevelopmental mechanisms.

neuroscience↗

bioRxiv2025

Beyond Pairwise Connections in Complex Systems: Insights into the Human Multiscale Psychotic Brain

Complex biological systems, like the brain, exhibit intricate multiway and multiscale interactions that drive emergent behaviors. In psychiatry, neural processes extend beyond pairwise connectivity, involving higher-order interactions critical for understanding mental disorders. Conventional brain network studies focus on pairwise links, offering insights into basic connectivity but failing to capture the complexity of neural dysfunction in psychiatric conditions. This study aims to bridge this gap by applying a matrix-based entropy functional to estimate total correlation, a mathematical framework that incorporates multivariate information measures extending beyond pairwise interactions. We apply this framework to fMRI-ICA-derived multiscale brain networks, enabling the investigation of interactions beyond pairwise relationships in the human multiscale brain. Additionally, this approach holds promise for psychiatric studies, providing a new lens through which to explore beyond pairwise brain network interactions. By examining both triple interactions and the latent factors underlying the triadic relationships among intrinsic brain connectivity networks through tensor decomposition, our study presents a novel approach to understanding higher-order brain dynamics. This framework not only enhances our understanding of complex brain functions but also offers new opportunities for investigating pathophysiology, potentially informing more targeted diagnostic and therapeutic strategies. Moreover, the methodology of analyzing multiway interactions beyond pairwise connections can be applied to any signal analysis. In this study, we specifically explore its application to neural signals, demonstrating its power in uncovering complex multiway interaction patterns of brain activity, which provide a window to explore connectivity beyond pairwise interactions in the multiscale functionality of the brain.

neuroscience↗

bioRxiv2025

Spatiotemporal Complexity in the Psychotic Brain

Psychotic disorders, such as schizophrenia and bipolar disorder, pose significant diagnostic challenges with major implications on mental health. The measures of resting-state fMRI spatiotemporal complexity offer a powerful tool for identifying irregularities in brain activity. To capture global brain connectivity, we employed information-theoretic metrics, overcoming the limitations of pairwise correlation analysis approaches. This enables a more comprehensive exploration of higher-order interactions and multiscale intrinsic connectivity networks (ICNs) in the psychotic brain. In this study, we provide converging evidence suggesting that the psychotic brain exhibits states of randomness across both spatial and temporal dimensions. To further investigate these disruptions, we estimated brain network connectivity using redundancy and synergy measures, aiming to assess the integration and segregation of topological information in the psychotic brain. Our findings reveal a disruption in the balance between redundant and synergistic information, a phenomenon we term brainquake in this study, which highlights the instability and disorganization of brain networks in psychosis. Moreover, our exploration of higher-order topological functional connectivity reveals profound disruptions in brain information integration. Aberrant information interactions were observed across both cortical and subcortical ICNs. We specifically identified the most easily affected irregularities in the sensorimotor, visual, temporal, default mode, and fronto-parietal networks, as well as in the hippocampal and amygdalar regions, all of which showed disruptions. These findings underscore the severe impact of psychotic states on multiscale critical brain networks, suggesting a profound alteration in the brains complexity and organizational states.

neuroscience↗

bioRxiv2024

Anterior pituitary gland volume mediates associations between pubertal hormones and changes in transdiagnostic symptoms in youth

The pituitary gland (PG) plays a central role in the production and secretion of pubertal hormones, with documented links to the emergence and increase in mental health symptoms known to occur during adolescence. Although much of the literature has focused on examining whole PG volume, recent findings suggest that there are associations among pubertal hormone levels, including dehydroepiandrosterone (DHEA), subregions of the PG, and elevated mental health symptoms (e.g., internalizing symptoms) during adolescence. Surprisingly, studies have not yet examined associations among these factors and increasing transdiagnostic symptomology, despite DHEA being a primary output of the anterior PG. Therefore, the current study sought to fill this gap by examining whether anterior PG volume specifically mediates associations between DHEA levels and changes in dysregulation symptoms in an adolescent sample (N = 114, 9 - 17 years, Mage = 12.87, SD = 1.88). Following manual tracing of the anterior and posterior PG, structural equation modeling revealed that greater anterior, not posterior, PG volume mediated the association between greater DHEA levels and increasing dysregulation symptoms across time, controlling for baseline dysregulation symptom levels. These results suggest specificity in the role of the anterior PG in adrenarcheal processes that may confer risk for psychopathology during adolescence. This work not only highlights the importance of separately tracing the anterior and posterior PG, but also suggests that transdiagnostic factors like dysregulation are useful in parsing hormone-related increases in mental health symptoms in youth.

neuroscience↗

bioRxiv2024

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds

ObjectivefMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. MethodsWe create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevel-opmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). ResultsWe find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. ConclusionOur model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. SignificanceOur DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

neuroscience↗

bioRxiv2023

Reply to: Contribution of carbon inputs to soil carbon accumulation cannot be neglected

In the accompanying Comment1, He et al. argue that the determinant role of microbial carbon use efficiency in global soil organic carbon (SOC) storage shown in Tao et al. (2023)2 was overestimated because carbon inputs were neglected in our data analysis while they suggest that our model-based analysis could be biased and model-dependent. Their argument is based on a different choice of independent variables in the data analysis and a sensitivity analysis of two process-based models other than that used in our study. We agree that both carbon inputs and outputs (as mediated by microbial processes) matter when predicting SOC storage - the question is their relative contributions. While we encourage further studies to examine how the evaluation of the relative importance of CUE to global SOC storage may vary with different model structures, He et al.s claims about Tao et al. (2023) need to be taken as an alternative, unproven hypothesis until empirical data support their specific parameterization. Here we show that an additional literature assessment of global data does not support He et al.s argument, in contrast to our study, and that further study on this topic is essential.

ecology↗

bioRxiv2021

Disrupting hierarchical control of nitrogen fixation enables carbon-dependent regulation of ammonia excretion in soil diazotrophs

The energetic requirements for biological nitrogen fixation necessitate stringent regulation of this process in response to diverse environmental constraints. To ensure that the nitrogen fixation machinery is expressed only under appropriate physiological conditions, the dedicated NifL-NifA regulatory system, prevalent in Proteobacteria, plays a crucial role in integrating signals of the oxygen, carbon and nitrogen status to control transcription of nitrogen fixation (nif) genes. Greater understanding of the intricate molecular mechanisms driving transcriptional control of nif genes may provide a blueprint for engineering diazotrophs that associate with cereals. In this study, we investigated the properties of a single amino acid substitution in NifA, (NifA-E356K) which disrupts the hierarchy of nif regulation in response to carbon and nitrogen status in Azotobacter vinelandii. The NifA-E356K substitution enabled overexpression of nitrogenase in the presence of excess fixed nitrogen and release of ammonia outside the cell. However, both of these properties were conditional upon the nature of the carbon source. Our studies reveal that the uncoupling of nitrogen fixation from its assimilation is likely to result from feedback regulation of glutamine synthetase, allowing surplus fixed nitrogen to be excreted. Reciprocal substitutions in NifA from other Proteobacteria yielded similar properties to the A. vinelandii counterpart, suggesting that this variant protein may facilitate engineering of carbon source-dependent ammonia excretion amongst diverse members of this family. SignificanceThe NifL-NifA regulatory system provides dedicated signal transduction machinery to regulate nitrogen fixation in diverse Proteobacteria. Understanding how the balance of nitrogen and carbon resources is signalled via NifL-NifA for precise control of nitrogen fixation may lead to broadly applicable translational outputs. Here, we characterize a NifA variant that bypasses nitrogen regulation but is still dependent on the carbon status to enable ammonia excretion in soil diazotrophs. Disruption of the regulatory hierarchy in response to nitrogen and carbon suggests how the integration of environmental stimuli could be harnessed to engineer conditional release of fixed nitrogen for the benefit of cereal crops.

microbiology↗

bioRxiv2020

Modular and state-relevant connectivity in high-frequency resting-state BOLD fMRI data: An independent component analysis

Resting-state fMRI (rs-fMRI) data are typically filtered at different frequency bins between 0.008[~]0.2 Hz (varies across the literature) prior to analysis to mitigate nuisance variables (e.g., drift, motion, cardiac, and respiratory) and maximize the sensitivity to neuronal-mediated BOLD signal. However, multiple lines of evidence suggest meaningful BOLD signal may also be parsed at higher frequencies. To test this notion, a functional network connectivity (FNC) analysis based on a spatially informed independent component analysis (ICA) was performed at seven different bandpass frequency bins to examine FNC matrices across spectra. Further, eyes open (EO) vs. eyes closed (EC) resting-state acquisitions from the same participants were compared across frequency bins to examine if EO vs. EC FNC matrices and randomness estimations of FNC matrices are distinguishable at different frequencies. Results show that FNCs in higher-frequency bins display modular FNC similar to the lowest frequency bin, while r-to-z FNC and FNC-based measures indicating matrix non-randomness were highest in the 0.31-0.46 Hz range relative to all frequency bins above and below this range. As such, the FNC within this range appears to be the most temporally correlated, but the mechanisms facilitating this coherence require further analyses. Compared to EO, EC displayed greater FNC (involved in visual, cognitive control, somatomotor, and auditory domains) and randomness values at lower frequency bins, but this phenomenon flipped (EO > EC) at frequency bins greater than 0.46 Hz, particularly within visual regions. While the effect sizes range from small to large specific to frequency range and resting state (EO vs. EC), with little influence from common artifacts. These differences indicate that unique information can be derived from FNC between BOLD signals at different frequencies relative to a given restingstate acquisition and support the hypothesis meaningful BOLD signal is present at higher frequency ranges.

neuroscience↗

bioRxiv2020

A systematic dissection of human primary osteoblasts in vivo at single-cell resolution

Osteoblasts are multifunctional bone cells, which play essential roles in bone formation, angiogenesis regulation, as well as maintenance of hematopoiesis. Although both in vivo and in vitro studies on mice have identified several potential osteoblast subtypes based on their different transition stages or biological responses to external stimuli, the categorization of primary osteoblast subtypes in vivo in humans has not yet been achieved. Here, we used single-cell RNA sequencing (scRNA-seq) to perform a systematic cellular taxonomy dissection of freshly isolated human osteoblasts. Based on the gene expression patterns and cell lineage reconstruction, we identified three distinct cell clusters including preosteoblasts, mature osteoblasts, and an undetermined rare osteoblast subpopulation. This novel subtype was mainly characterized by the nuclear receptor subfamily 4 group A member 1 and 2 (NR4A1 and NR4A2), and its existence was confirmed by immunofluorescence staining. Trajectory inference analysis suggested that the undetermined cluster, together with the preosteoblasts, are involved in the regulation of osteoblastogenesis and also give rise to mature osteoblasts. Investigation of the biological processes and signaling pathways enriched in each subpopulation revealed that in addition to bone formation, preosteoblasts and undetermined osteoblasts may also regulate both angiogenesis and hemopoiesis. Finally, we demonstrated that there are systematic differences between the transcriptional profiles of human osteoblasts in vivo and mouse osteoblasts both in vivo and in vitro, highlighting the necessity for studying bone physiological processes in humans rather than solely relying on mouse models. Our findings provide novel insights into the cellular heterogeneity and potential biological functions of human primary osteoblasts at the single-cell level, which is an important and necessary step to further dissect the biological roles of osteoblasts in bone metabolism under various (patho-) physiological conditions.

bioinformatics↗

bioRxiv2020

Accurate Diagnosis of Colorectal Cancer Based on Histopathology Images Using Artificial Intelligence

BackgroundAccurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on their daily image analyses. MethodsBased on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC prediction/diagnosis using weakly labeled pathological whole slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, >14,680 WSIs, from >9,631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, U.S., and Germany. ResultsOur innovative AI tool was consistently nearly perfectly agreed with (average Kappa-statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multi-centers. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.981 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. ConclusionsThis first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. Hence, it will drastically alleviate the heavy clinical burden of daily pathology diagnosis, and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

pathology↗

bioRxiv2020

Plagues of Desert Locust: No invasion risk to China

Recently, the most serious upsurge of desert locust (Schistocerca gregaria) in the last 25 years is spreading across eastern Africa and southwestern Asia. Parts of the desert locust invasion area, namely the northern border areas of Pakistan and India are very close to China, and whether locust swarms will invade China is of wide concern. To answer this question, we identified areas of potentially suitable habitat for the desert locust within China based on historical precipitation and temperature data, and found that parts of Xinjiang and Inner Mongolia provinces could provide ephemeral habitat in summer, but these places are remote from any other desert locust breeding area. Presently, the desert locust populations in Pakistan and India are mature and have laid eggs, and are less likely to spread long distances. The next generation of adults will appear in April and May, and so we examined twenty years historical wind data (2000-2019) for this period. Our results showed that winds at the height of locust swarm flight blew eastward during April and May, but the wind speeds were quite slow and would not facilitate desert locust eastward migration over large distances. Furthermore, simulated trajectories of desert locust swarms with 10 days migration mostly ended within India. The most easterly point of these trajectories just reached eastern India, close to the border between India and Myanmar, and this is very close to the eastern border of the invasion area of desert locust described in previous studies. In conclusion, the risk that the desert locust will invade China is very low.

ecology↗

bioRxiv2020

Rice NIN-LIKE PROTEIN 4 is a master regulator of nitrogen use efficiency

Nitrogen (N) is one of the key essential macronutrients that affects rice growth and yield. Inorganic N fertilizers are excessively used to boost yield and generate serious collateral environmental pollution. Therefore, improving crop N use efficiency (NUE) is highly desirable and has been a major endeavor in crop improvement. However, only a few regulators have been identified that can be used to improve NUE in rice to date. Here we show that the NIN-like protein OsNLP4 significantly improves the rice NUE and yield. Field trials consistently showed that loss-of-OsNLP4 dramatically reduced yield and NUE compared with wild type under different N regimes. In contrast, the OsNLP4 overexpression lines remarkably increased yield by 30% and NUE by 47% under moderate N level compared with wild type. Transcriptomic analyses revealed that OsNLP4 orchestrates the expression of a majority of known N uptake, assimilation and signaling genes by directly binding to the nitrate-responsive cis-element in their promoters to regulate their expression. Moreover, overexpression of OsNLP4 can recover the phenotype of Arabidopsis nlp7 mutant and enhance its biomass. Our results demonstrate that OsNLP4 is a master regulator of NUE in rice and sheds light on crop NUE improvement.

plant biology↗

bioRxiv2019

High risk of the Fall Armyworm invading into Japan and the Korean Peninsula via overseas migration

The fall armyworm, Spodoptera frugiperda (J.E. Smith) is an emerging and most severe pest species in the Old World. It is originally native in the Americas. Since 2016 it has spread widely and rapidly to throughout Africa, the Middle East, India, Southeast Asia and most recently southern China. By May 2019 it has appeared in 13 provinces in most southern China and would spread further to northern China. It is highly likely that S. frugiperda would enter into Japan and Korea via overseas migrations as many other migratory pests did before. To evaluate the invasion risk of S. frugiperda into Japan and Korean Peninsula, we modelled the rate of expansion and future potential migratory range of the insect by a trajectory analytical approach with flight behaviour of S. frugiperda implemented, and meteorological data of past five years (2014-2018) used. Our results predicted that S. frugiperda would migrate from southern and eastern China into Japan and Korea soon. Most likely, Japan would be invaded from Fujian and Zhejiang on 1 June - 15 July, and Kyushu, Shikoku and south-western Honshu could face the highest risk of S. frugiperdas invasion. Korea would be most possibly reached by S. frugiperda from northern Zhejiang, Jiangsu, Anhui, and Shandong on 1 June - 15 July and later. Our results indicated a very high risk that S. frugiperda would annually invade Japan and the Korean Peninsula and cause a possible significant decrease in agricultural productivity.

animal behavior and cognition↗