Biology subjects

Huang, Y.

Publications and source records attributed to Huang, Y..

At least 397 records · Page 22Linked to original sources

bioRxiv2024

The aberrant language network dynamics in autism ages 5-40 years

Language impairments across both structural components and pragmatic use are frequently observed in individuals with autism spectrum disorder (ASD). These difficulties are thought to stem from atypical brain development and abnormal network interactions, yet an integrative network-level model accounting for such impairments remains lacking. To bridge this gap, we applied the dynamic meta-networking framework of language, a theoretical model capturing domain-segregation dynamics during rest, to examine age-related changes (5-60 years) in cortical language networks in individuals with ASD. To further probe the biological underpinnings of these dynamics, we quantified spatial correspondences between network state hubs and gene co-expression modules as well as neurotransmitter receptor distributions. We identified distinct language meta-states characterized by domain-segregation connectivity patterns, which exhibited spatial alignment with gene co-expression modules and neurotransmitter systems. Individuals with ASD showed state-dependent developmental trajectories marked by age-related hypo- and hyper-connectivity. Critically, these network alterations strongly predicted verbal IQ and communicative difficulties, but were unrelated to social functioning or stereotyped behaviors. Our findings provide novel evidence that language-related network dynamics in ASD are developmentally altered, biologically grounded, and selectively linked to verbal and communicative impairments. These results advance a network-level model of language dysfunction in ASD and highlight potential mechanistic pathways for targeted interventions. Lay SummaryLanguage challenges are common in autism and can affect both learning language and using it in everyday conversations. In people aged 5-60, we found distinct, age-changing patterns in how the brains language areas connect at rest; these patterns align with basic biology (genes and brain chemicals) and strongly relate to verbal ability and daily communication, but not to social difficulties or repetitive behaviors. These results suggest clear, brain-based targets for earlier and more tailored support for language problems in autism.

neuroscience↗

bioRxiv2024

Principles of nutrition-dependent root-microbiome engineering for regulating crop yield via live microbial inoculants

As the second genome, host-associated microbiota can enhance the functions of host immune systems, improve host nutrient availability and acquisition capacity, and increase host resistance to abiotic stress. Constructing host-associated microbiomes by applying various bioinoculants has become an attractive tool for promoting human health and increasing food production. However, despite their promising properties, the efficacy of bioinoculants vary significantly in actual field and clinical practices. Understanding the global design principles that shape the outcomes of interactions between bioinoculants and target host-microbiome symbionts remains a considerable challenge. In this study, we used a wheat production system as a case study to quantitatively understand how soil nutrient status impacts the establishment of host-microbiome interaction networks and their subsequent interactions with external bioinoculants. We found that soil organic carbon, one of the most general soil properties for global crop productivity and resilience, could affect the outcome of bioinoculant applications in wheat production systems, which led to functional instability in bioinoculant application outcomes. The results of this study significantly improved our understanding of the global design principles of nutrition-dependent root-microbiome engineering for regulating crop yield via the application of live microbial inoculants and provide theoretical guidance for bioinoculant applications in agricultural practice.

microbiology↗

bioRxiv2024

Decipher Fundamental Atomic Interactions to Unify Generative Molecular Docking and Design

Understanding molecular interactions is essential to structural biology and drug discovery. Despite the progress of AI models in revealing and exploiting the interaction mechanisms for various applications, they are predominantly tailored to specific tasks without fully exploiting the underlying transferability across molecular data and tasks. Here, we present PocketXMol, an atom-level generative foundation model to decipher fundamental atomic interactions for general protein-pocket-interacting molecular tasks. It adopts a novel unified generative framework with an innovative task prompt mechanism and an exclusive atom-level representation, making it applicable to diverse tasks covering structure prediction and design of small molecules and peptides, without requiring fine-tuning. PocketXMol was compared to 55 baseline models across 13 typical tasks, achieving state-of-the-art performance in 11 tasks and remaining competitive in the others. We successfully utilized PocketXMol to design novel small molecules that inhibit caspase-9 with efficacy comparable to that of commercial pan-caspase inhibitors. Furthermore, we employed PocketXMol to design PD-L1-binding peptides, demonstrating a success rate substantially higher than random library screening. Three representative peptides underwent further experiments, which validated the cellular specificity and confirmed their potential for molecular probing and therapeutics. PocketXMol presents a powerful and versatile tool with promising prospects for future applications and will have a profound impact on AI-aided drug discovery.

bioinformatics↗

bioRxiv2024

Uncovering Latent Biological Function Associations through Gene Set Embeddings

The complexity of biological systems has increasingly been unraveled through computational methods, with biological network analysis now focusing on the construction and exploration of well-defined interaction networks. Traditional graph-theoretical approaches have been instrumental in mapping key biological processes using high-confidence interaction data. However, these methods often struggle with incomplete or/and heterogeneous datasets. In this study, we extend beyond conventional bipartite models by integrating attribute-driven knowledge from the Molecular Signatures Database (MSigDB) using the node2vec algorithm. Our approach explores unsupervised biological relationships and uncovers potential associations between genes and biological terms through network connectivity analysis. By embedding both human and mouse data into a shared vector space, we validate our findings cross-species, further strengthening the robustness of our method. This integrative framework reveals both expected and novel biological insights, offering a comprehensive perspective that complements traditional biological network analysis and paves the way for deeper understanding of complex biological processes and diseases.

bioinformatics↗

bioRxiv2024

SAMPL-seq reveals micron-scale spatial hubs in the human gut microbiome

The local arrangement of microbes can profoundly impact community assembly, function, and stability. To date, little is known about the spatial organization of the human gut microbiome. Here, we describe a high-throughput and streamlined method, dubbed SAMPL-seq, that samples microbial composition of micron-scale sub-communities with split-and-pool barcoding to capture spatial colocalization in a complex consortium. SAMPL-seq analysis of the gut microbiome of healthy humans identified bacterial taxa pairs that consistently co-occurred both over time and across multiple individuals. These colocalized microbes organize into spatially distinct groups or "spatial hubs" dominated by Bacteroideceae, Ruminococceae, and Lachnospiraceae families. From a dietary perturbation using inulin, we observed reversible spatial rearrangement of the gut microbiome, where specific taxa form new local partnerships. Spatial metagenomics using SAMPL-seq can unlock new insights to improve the study of microbial communities. One Sentence SummaryHigh throughput micron-scale subcommunity sampling and sequencing identifies distinct spatial associations of gut bacteria within and across individuals.

systems biology↗

bioRxiv2024

Identification and predictive machine learning models construction of gut microbiota associated with lymph node metastasis in colorectal cancer

This study focuses on the significant role between gut microbiota and lymph node metastasis (LNM) in colorectal cancer (CRC). By conducting 16S rRNA sequencing on fecal samples from 147 CRC patients and combining it with the linear discriminant analysis effect size (LEfSe) algorithm, we successfully identified significant differences in the gut microbiota between patients with LNM and those with none lymph node metastasis (NLNM). Furthermore, using transcriptome data from 9 CRC patients, we constructed an immune cell infiltration matrix to deeply explore the biological functions associated with LNM. Eventually, using the characteristics of the gut microbiota associated with LNM, we developed random forest (RF) and multilayer perceptron (MLP) machine learning models to predict the lymph node metastasis status of CRC patients. We identified 21 differentially abundant gut microbes between the two groups, among which Bacteroides plebeius, significantly enriched in LNM group, is closely related to the upregulation of resting NK cells and chemokine CXCL8 expression, and this bacterial species is also positively correlated with the enhancement of fructose metabolism. The RF and MLP models constructed based on the LNM-associated gut microbiota showed good predictive efficacy in predicting LNM status in CRC. This study reveals that Bacteroides plebeius may play an important role in the progression of CRC, with its mechanism potentially involving changes in immune modulation and metabolic pathways. The classification model constructed based on gut microbiota characteristics can predict LNM status of CRC, providing a new perspective for personalized and precision treatment of CRC patients. ImportanceThis study highlights the pivotal role of gut microbiota in lymph node metastasis (LNM) of CRC, identifying key microbial differences between LNM and NLNM patients. Our findings implicate Bacteroides plebeius in CRC progression via immune modulation and metabolic alterations. Moreover, machine learning models based on gut microbiota predict LNM status accurately, offering a novel approach for personalized CRC treatment.

microbiology↗

bioRxiv2024

Mapping AAV capsid sequences to functions through function-guided in silico evolution

Artificial intelligence (AI) offers significant potential to accelerate the functional engineering of adeno-associated virus (AAV) capsids in a time- and cost-efficient manner. However, existing approaches lack the ability to systematically map capsid sequences to multifunctional properties. To address this challenge, we developed ALICE, a knowledge-driven platform for in silico AAV capsid engineering that directly maps sequence to multiple functions. Our method incorporating a heuristic algorithm with contrastive learning principles, termed function-guided evolution (FE), to iteratively optimize high-performing capsid sequences generated by a naive language model toward desired functions. We elucidate FEs evolutionary mechanism, demonstrating its ability to navigate a function-guided landscape and generate capsids with tailored properties. After validating ALICEs ability to design murine-specific AAV capsids targeting Ly6a/Ly6c1 with limited training data, we developed ALICE-X by integrating a curriculum learning strategy. This upgrade facilitated exploration of sequence space beyond existing wet-lab datasets, enabling successful targeting of the human transferrin receptor 1 (hTfR1). The resulting engineered AAV variant, AAV.ALICE-H3, demonstrated improved viability, specific receptor targeting, and [~] 251-fold enhanced CNS tropism in human TFRC knock-in (hTFRC KI) mice compared to wildtype controls. This interpretable, knowledge-driven framework advances in silico AAV capsid design, offering broad applicability for translational gene therapy.

bioengineering↗

bioRxiv2024

Metastatic organotropism in small cell lung cancer

Metastasis is the leading cause of cancer-related deaths, yet its regulatory mechanisms are not fully understood. Small-cell lung cancer (SCLC) is the most metastatic form of lung cancer, with most patients presenting with widespread disease, making it an ideal model for studying metastasis. However, the lack of suitable preclinical models has limited such studies. We utilized rapid autopsy-derived tumors to develop xenograft models that mimic key features of SCLC, including histopathology, rapid and widespread development of metastasis to the liver, brain, adrenal, bone marrow, and kidneys within weeks, and response to chemotherapy. By integrating in vivo lineage selection with comprehensive bulk and single cell multiomic profiling of transcriptomes and chromatin accessibility, we identified critical cellular programs driving metastatic organotropism to the liver and brain, the most common sites of SCLC metastasis. Our findings reveal the key role of nuclear-cytoskeletal interactions in SCLC liver metastasis. Specifically, the loss of the nuclear envelope protein lamin A/C, encoded by the LMNA gene, increased nuclear deformability and significantly increased the incidence of liver metastasis. Human liver metastases exhibited reduced LMNA expression compared to other metastatic sites, correlating with poorer patient outcomes and increased mortality. This study introduces novel preclinical models for SCLC metastasis and highlights pathways critical for organ-specific metastasis, offering new avenues for the development of targeted therapies to prevent or treat metastatic disease.

cancer biology↗

bioRxiv2024

After-effects of Parieto-occipital Gamma Transcranial Alternating Current Stimulation on Behavioral Performance and Neural Activity in Visuo-spatial Attention Task

Visuo-spatial attention enables selective focus on spatial locations while ignoring irrelevant stimuli, involving both endogenous and exogenous attention. Recent advancements in transcranial alternating current stimulation (tACS) have shown promise in modulating these attentional processes by targeting electrical oscillations in specific brain areas. Despite evidence of online effects of tACS on visuo-spatial attention performance, whether tACS can produce lasting after-effects on behavioral performance and neural activity remains unknown. This study explored these after-effects using a single-blind, sham-controlled, between-group design. Eighteen young healthy participants were equally divided into two groups receiving either sham or active gamma tACS at 40 Hz targeted at the right parieto-occipital region. Each participant performed a version of the Posner cueing task with EEG recording before and after the tACS intervention. The active tACS group exhibited greater reductions in reaction time compared to the sham group. These changes were not uniform across different attention types, suggesting specific enhancements in cognitive processing. EEG analyses revealed trial-type-specific modulation of event-related potentials, including amplitude and latency of N1 and P3 components, that paralleled the behavioral effects. Additionally, frequency-specific changes in oscillatory power during the cue-target interval--decreased alpha power and increased gamma power--as well as reduced long-range temporal correlations were observed more broadly across conditions. While limited by a small sample size, these preliminary findings provide convergent behavioral and electrophysiological evidence that parieto-occipital gamma tACS can induce lasting, condition-specific after-effects on visuo-spatial attentional networks.

neuroscience↗

bioRxiv2024

IGF2BP3 Promotes Autophagy-Mediated TNBC Metastasis via M6A-Dependent, Cap-Independent c-Met Translation

Tumor metastasis involves a series of complex processes and is a major challenge in clinical treatment. The cooperation of epigenetic modifications and metabolic adaptations endows tumor cells with dynamic adaptations for survival in variable environments, which is crucial for tumor metastasis and worth exploring in depth. In this study, we found IGF2BP3 could link epigenetic modification and metabolic adaptation in promoting autophagy-mediated triple-negative breast cancer (TNBC) metastasis. As a TNBC specifically high expressed N6-methyladenosine (m6A) binding protein, IGF2BP3 could bind to the m6A motif of c-Met mRNA, regulating autophagy-mediated epithelial-to-mesenchymal transition (EMT) progress by c-Met/PI3K/AKT/mTOR pathway. Moreover, through the recruitment of eIF4G2 as a collaborator, IGF2BP3 induced the changes in c-Met protein expression not by affecting mRNA stability, but by facilitating its mRNA translation initiation in an m6A-dependent and cap-independent manner. In conclusion, our study extended the role of IGF2BP3 in autophagy mediated TNBC metastasis, found IGF2BP3 could bind to the m6A motif on the 5', 3'-UTR of c-Met mRNA to facilitate its translation in a cap-independent manner.

cancer biology↗

bioRxiv2024

Assessment of the relationship between synaptic density and metabotropic glutamate receptors in early Alzheimer's disease: a multi-tracer PET study

BackgroundThe pathological effects of amyloid {beta} oligomers (A{beta}o) may be mediated through the metabotropic glutamate receptor subtype 5 (mGluR5), leading to synaptic loss in Alzheimers disease (AD). Positron emission tomography (PET) studies of mGluR5 using [18F]FPEB indicate a reduction of receptor binding that is focused in the medial temporal lobe in AD. Synaptic loss due to AD measured through synaptic vesicle glycoprotein 2A (SV2A) quantification with [11C]UCB-J PET is also focused in the medial temporal lobe, but with clear widespread reductions is commonly AD-affected neocortical regions. In this study, we used [18F]FPEB and [11C]UCB-J PET to investigate the relationship between mGluR5 and synaptic density in early AD. MethodsFifteen amyloid positive participants with early AD and 12 amyloid negative, cognitively normal (CN) participants underwent PET scans with both [18F]FPEB to measure mGluR5 and [11C]UCB-J to measure synaptic density. Parametric DVR images using equilibrium methods were generated from dynamic. For [18F]FPEB PET, DVR was calculated using equilibrium methods and a cerebellum reference region. For [11C]UCB-J PET, DVR was calculated with a simplified reference tissue model - 2 and a whole cerebellum reference region. ResultA strong positive correlation between mGluR5 and synaptic density was present in the hippocampus for participants with AD (r = 0.81, p < 0.001) and in the CN group (r = 0.74, p = 0.005). In the entorhinal cortex, there was a strong positive correlation between mGluR5 and synaptic in the AD group (r = 0.85, p <0.001), but a weaker non-significant correlation in the CN group (r = 0.36, p = 0.245). Exploratory analyses within and between other brain regions suggested significant positive correlations between mGluR5 in the medial temporal lobe and synaptic density in a broader set of commonly AD-affected regions. ConclusionMedial temporal loss of mGluR5 in AD is associated with synaptic loss in both medial temporal regions and more broadly in association cortical regions, indicating that mGluR5 mediated A{beta}o toxicity may lead to early synaptic loss more broadly in AD-affected networks. In CN individuals, an isolated strong association between lower mGluR5 and lower synaptic density may indicate non-AD related synaptic loss.

pathology↗

bioRxiv2024

Building a literature knowledge base towards transparent biomedical AI

As artificial intelligence (AI) continues to advance and scale up in biomedical research, concerns about AIs trustworthiness and transparency have grown. There is a critical need to systematically bring accurate and relevant biomedical knowledge into AI applications for transparency and provenance. Knowledge graphs have emerged as a powerful tool that integrates heterogeneous knowledge by explicitly describing biomedical knowledge as entities and relationships between entities. However, PubMed, the largest and most comprehensive repository of biomedical knowledge, exists primarily as unstructured text and is under utilized for advanced machine learning tasks. To address the challenge, we developed LiteralGraph, a computational framework to extract biomedical terms and relationships from PubMed literature into a unified knowledge graph. Using this framework, we established the Genomic Literature Knowledge Base (GLKB), which consolidates 14,634,427 biomedical relationships between 3,276,336 biomedical terms from over 33 million PubMed abstracts and nine well-established biomedical repositories. The database is coupled with RESTful APIs and a user-friendly web interface that makes it accessible to researchers for various usages. We demonstrated the broad utility of GLKB towards transparent AI in three distinct application scenarios. In the LLM grounding scenario, we developed a Retrieval Augmented Generation (RAG) agent to reduce LLM hallucination in biomedical question answering. In the hypothesis generation scenario, we elucidated the potential functions of RFX6 in type 2 diabetes (T2D) using the vast evidence from PubMed articles. In the machine learning scenario, we utilized GLKB to provide semantic knowledge in predictive tasks and scientific fact-checking.

bioinformatics↗

bioRxiv2024

Traumatic Brain Injury Exacerbates Alcohol Consumption and Neuroinflammation with Decline in Cognition and Cholinergic Activity

Traumatic brain injury (TBI) is a global health challenge, responsible for 30% of injury-related deaths and significantly contributing to disability. Annually, over 50 million TBIs occur worldwide, with most adult patients at emergency departments showing alcohol in their system. TBI is also a known risk factor for alcohol abuse, yet its interaction with alcohol consumption remains poorly understood. In this study, we demonstrate that the fluid percussion injury (FPI) model of TBI in mice significantly increases alcohol consumption and impairs cognitive function. At cellular levels, FPI markedly reduced the number and activity of striatal cholinergic interneurons (CINs) while increasing microglial cells. Notably, depleting microglial cells provided neuroprotection, mitigating cholinergic loss and enhancing cholinergic activity. These findings suggest that TBI may promote alcohol consumption and impair cognitive abilities through microglia activation and consequently reduced cholinergic function. Our research provides critical insights into the mechanisms linking TBI with increased alcohol use and cognitive deficits, potentially guiding future therapeutic strategies.

neuroscience↗

bioRxiv2024

Varying recombination landscapes between individuals are driven by polymorphic transposable elements

AbstractsMeiotic recombination is a prominent force shaping genome evolution, and understanding the causes for varying recombination landscapes within and between species has remained a central, though challenging, question. Recombination rates are widely observed to negatively associate with the abundance of transposable elements (TEs), selfish genetic elements that move between genomic locations. While such associations are usually interpreted as recombination influencing the efficacy of selection at removing TEs, accumulating findings suggest that TEs could instead be the cause rather than the consequence. To test this prediction, we formally investigated the influence of polymorphic, putatively active TEs on recombination rates. We developed and benchmarked a novel approach that uses PacBio long-read sequencing to efficiently, accurately, and cost-effectively identify crossovers (COs), a key recombination product, among large numbers of pooled recombinant individuals. By applying this approach to Drosophila strains with distinct TE insertion profiles, we found that polymorphic TEs, especially RNA-based TEs and TEs with local enrichment of repressive marks, reduce the occurrence of COs. Such an effect leads to different CO frequencies between homologous sequences with and without TEs, contributing to varying CO maps between individuals. The suppressive effect of TEs on CO is further supported by two orthogonal approaches-analyzing the distributions of COs in panels of recombinant inbred lines in relation to TE polymorphism and applying marker-assisted estimations of CO frequencies to isogenic strains with and without transgenically inserted TEs. Our investigations reveal how the constantly changing mobilome can actively modify recombination landscapes, shaping genome evolution within and between species.

genomics↗

bioRxiv2024

Distinct duration and diet dependent lipid profiles and renal complicationsin GCK-MODY

Heterozygous inactivating mutations in the glucokinase (GCK) gene cause one of the most common types of maturity-onset diabetes of the young type 2 (MODY2), also named GCK-MODY. Studies suggest that, unlike other types of diabetes, patients with GCK-MODY do not have increased risk of diabetic complications and therefore do not typically require antihyperglycemic therapy. However, long-term outcomes of GCK-MODY on lipid profiles and chronic complications under different dietary scenarios remain unclear. Herein, using a knock-in mouse model expressing a novel MODY causing mutation GCK-Q26L (GCKMut), we examined age- and diet-related lipid profiles and diabetic complications. We found that, although GCKMut mice exhibited mild elevated blood glucose, the lipid profiles, body fat composition, and diabetic kidney disease (DKD) were initially improved in high-fat-diet (HFD) fed mice at the age of 28 weeks, supporting potential beneficial effects of GCKMut on lipid metabolism and kidney health. Unexpectedly however, those protective effects diminished by 40 weeks, and became more severe dyslipidemia and kidney injury associated with renal fibrosis and inflammation at 60-week-old mice fed with normal diet (ND) or HFD. Those findings revealed distinct duration- and diet-dependent effects of inactivating GCK mutation on lipid profiles and DKD, highlighting previously unrealized long-term chronic complications in GCK-MODY.

physiology↗

bioRxiv2024

Patterned wireless transcranial optogenetics generates artificial perception

Synthesizing perceivable artificial neural inputs independent of typical sensory channels remains a fundamental challenge in the development of next-generation brain-machine interfaces. Establishing a minimally invasive, wirelessly effective, and miniaturized platform with long-term stability is crucial for creating a clinically meaningful interface capable of mediating artificial perceptual feedback. In this study, we demonstrate a miniaturized fully implantable wireless transcranial optogenetic encoder designed to generate artificial perceptions through digitized optogenetic manipulation of large cortical ensembles. This platform enables the spatiotemporal orchestration of large-scale cortical activity for remote perception genesis via real-time wireless communication and control, with optimized device performance achieved by simulation-guided methods addressing light and heat propagation during operation. Cue discrimination during operant learning demonstrates the wireless genesis of artificial percepts sensed by mice, where spatial distance across large cortical networks and sequential order-based analyses of discrimination performance reveal principles that adhere to general perceptual rules. These conceptual and technical advancements expand our understanding of artificial neural syntax and its perception by the brain, guiding the evolution of next-generation brain-machine communication.

neuroscience↗

bioRxiv2024

CRAK-Velo: Chromatin Accessibility Kinetics integration improves RNA Velocity estimation

RNA velocity has recently emerged as a key tool in the analysis of single-cell transcriptomic data, yet connecting RNA velocity analyses to underlying regulatory processes has proved challenging. Here we propose CRAK-Velo, a semi-mechanistic model which integrates chromatin accessibility data in the estimation of RNA velocities. CRAK-Velo provides biologically consistent estimates of developmental flows and enables accurate cell-type deconvolution, while additionally shining light on regulatory processes at the level of interactions between genes and chromatin regions.

bioinformatics↗

bioRxiv2024

The transcription factor ZmbZIP75 promotes both grain filling and kernel dehydration in maize

Selecting both high-yield and low-kernel-moisture varieties is essential for modern maize production, but relevant breeding efforts are hindered by a lack of valuable regulatory genes. Here, we demonstrate that the transcription factor (TF) basic leucine zipper 75 (ZmbZIP75) promotes grain yield and reduces kernel moisture in maize. Knockout of ZmbZIP75 results in defective grain filling and kernel dehydration, whereas ZmbZIP75 overexpression confers increased grain yield per plant and decreased kernel moisture without altering plant architecture. Mechanistically, during the grain filling stage, ZmbZIP75 is transcriptionally induced by maternal-derived basal abscisic acid (ABA) and directly activates multiple core starch synthesis-related genes and key TFs, thereby promoting grain filling and final yield. In the late stage of kernel development, high concentrations of zygotic ABA enhance ZmbZIP75 phosphorylation through SnRK2.10. The phosphorylated ZmbZIP75 subsequently transactivates and interacts with TF VP1 to synergistically promote kernel dehydration. This study thus highlights the potential of ZmbZIP75 for engineering both high-yield and low-kernel-moisture varieties to meet the demands of high-efficient maize production. IN A NUTSHELLO_ST_ABSBackgroundC_ST_ABSHigh grain yield in maize is generally associated with elevated kernel moisture at harvest, which is the main limiting factor for modern maize production. Therefore, it is of importance to select varieties with both high-yield and low-kernel-moisture. These traits are largely controlled by grain filling and kernel dehydration, two tightly connected processes during maize kernel development. Abscisic acid (ABA) is well-documented for its vital role in grain filling and dehydration maturation. However, the molecular mechanisms by which ABA coordinates these two processes remain unclear. QuestionWhat are the regulatory factors involved, and how do they mediate ABA signaling to coordinate grain filling and dehydration maturation in maize? FindingsZmbZIP75 is transcriptionally induced by ABA and directly activates multiple core starch synthesis-related genes and key TFs in developing maize endosperm, thereby promoting grain filling. In developing embryo, ABA enhances ZmbZIP75 phosphorylation via SnRK2.10. The phosphorylated ZmbZIP75 then transactivates and interacts with VP1 to synergistically promote kernel dehydration. Moreover, ZmbZIP75 overexpression confers increased grain yield and reduced kernel moisture in maize. Next stepsWhile ZmbZIP75 is directly phosphorylated to mediate ABA signaling during dehydration maturation, ZmbZIP75 is transcriptionally induced by ABA during grain filling. We plan to identify upstream factors that mediate ABA signaling to regulate ZmbZIP75 expression, thereby enhancing our understanding of ABA-promoted grain filling in maize.

plant biology↗