Defining the Design Principles of Skin Epidermis Postnatal Growth.

During embryonic and postnatal development, organs and tissues grow steadily to achieve their final size at the end of puberty. However, little is known about the cellular dynamics that mediate postnatal growth. By combining in vivo clonal lineage tracing, proliferation kinetics, single-cell transcriptomics, and in vitro micro-pattern experiments, we resolved the cellular dynamics taking place during postnatal skin epidermis expansion. Our data revealed that harmonious growth is engineered by a single population of developmental progenitors presenting a fixed fate imbalance of self-renewing divisions with an ever-decreasing proliferation rate. Single-cell RNA sequencing revealed that epidermal developmental progenitors form a more uniform population compared with adult stem and progenitor cells. Finally, we found that the spatial pattern of cell division orientation is dictated locally by the underlying collagen fiber orientation. Our results uncover a simple design principle of organ growth where progenitors and differentiated cells expand in harmony with their surrounding tissues.

KRAS Dimerization Impacts MEK Inhibitor Sensitivity and Oncogenic Activity of Mutant KRAS.

The mechanism by which the wild-type KRAS allele imparts a growth inhibitory effect to oncogenic KRAS in various cancers, including lung adenocarcinoma (LUAD), is poorly understood. Here, using a genetically inducible model of KRAS loss of heterozygosity (LOH), we show that KRAS dimerization mediates wild-type KRAS-dependent fitness of human and murine KRAS mutant LUAD tumor cells and underlies resistance to MEK inhibition. These effects are abrogated when wild-type KRAS is replaced by KRASD154Q, a mutant that disrupts dimerization at the α4-α5 KRAS dimer interface without changing other fundamental biochemical properties of KRAS, both in vitro and in vivo. Moreover, dimerization has a critical role in the oncogenic activity of mutant KRAS. Our studies provide mechanistic and biological insights into the role of KRAS dimerization and highlight a role for disruption of dimerization as a therapeutic strategy for KRAS mutant cancers.

KRAS Allelic Imbalance Enhances Fitness and Modulates MAP Kinase Dependence in Cancer.

Investigating therapeutic "outliers" that show exceptional responses to anti-cancer treatment can uncover biomarkers of drug sensitivity. We performed preclinical trials investigating primary murine acute myeloid leukemias (AMLs) generated by retroviral insertional mutagenesis in KrasG12D "knockin" mice with the MEK inhibitor PD0325901 (PD901). One outlier AML responded and exhibited intrinsic drug resistance at relapse. Loss of wild-type (WT) Kras enhanced the fitness of the dominant clone and rendered it sensitive to MEK inhibition. Similarly, human colorectal cancer cell lines with increased KRAS mutant allele frequency were more sensitive to MAP kinase inhibition, and CRISPR-Cas9-mediated replacement of WT KRAS with a mutant allele sensitized heterozygous mutant HCT116 cells to treatment. In a prospectively characterized cohort of patients with advanced cancer, 642 of 1,168 (55%) with KRAS mutations exhibited allelic imbalance. These studies demonstrate that serial genetic changes at the Kras/KRAS locus are frequent in cancer and modulate competitive fitness and MEK dependency.

Impaired Amino Acid Transport at the Blood Brain Barrier Is a Cause of Autism Spectrum Disorder.

Autism spectrum disorders (ASD) are a group of genetic disorders often overlapping with other neurological conditions. We previously described abnormalities in the branched-chain amino acid (BCAA) catabolic pathway as a cause of ASD. Here, we show that the solute carrier transporter 7a5 (SLC7A5), a large neutral amino acid transporter localized at the blood brain barrier (BBB), has an essential role in maintaining normal levels of brain BCAAs. In mice, deletion of Slc7a5 from the endothelial cells of the BBB leads to atypical brain amino acid profile, abnormal mRNA translation, and severe neurological abnormalities. Furthermore, we identified several patients with autistic traits and motor delay carrying deleterious homozygous mutations in the SLC7A5 gene. Finally, we demonstrate that BCAA intracerebroventricular administration ameliorates abnormal behaviors in adult mutant mice. Our data elucidate a neurological syndrome defined by SLC7A5 mutations and support an essential role for the BCAA in human brain function.

Systematically defining selective autophagy receptor-specific cargo using autophagosome content profiling.

Autophagy deficiency in fed conditions leads to the formation of protein inclusions highlighting the contribution of this lysosomal delivery route to cellular proteostasis. Selective autophagy pathways exist that clear accumulated and aggregated ubiquitinated proteins. Receptors for this type of autophagy (aggrephagy) include p62, NBR1, TOLLIP, and OPTN, which possess LC3-interacting regions and ubiquitin-binding domains (UBDs), thus working as a bridge between LC3/GABARAP proteins and ubiquitinated substrates. However, the identity of aggrephagy substrates and the redundancy of aggrephagy and related UBD-containing receptors remains elusive. Here, we combined proximity labeling and organelle enrichment with quantitative proteomics to systematically map the autophagic degradome targeted by UBD-containing receptors under basal and proteostasis-challenging conditions in human cell lines. We identified various autophagy substrates, some of which were differentially engulfed by autophagosomal and endosomal membranes via p62 and TOLLIP, respectively. Overall, this resource will allow dissection of the proteostasis contribution of autophagy to numerous individual proteins.

Data-balanced transformer for accelerated ionizable lipid nanoparticles screening in mRNA delivery.

Despite the widespread use of ionizable lipid nanoparticles (LNPs) in clinical applications for messenger RNA (mRNA) delivery, the mRNA drug delivery system faces an efficient challenge in the screening of LNPs. Traditional screening methods often require a substantial amount of experimental time and incur high research and development costs. To accelerate the early development stage of LNPs, we propose TransLNP, a transformer-based transfection prediction model designed to aid in the selection of LNPs for mRNA drug delivery systems. TransLNP uses two types of molecular information to perceive the relationship between structure and transfection efficiency: coarse-grained atomic sequence information and fine-grained atomic spatial relationship information. Due to the scarcity of existing LNPs experimental data, we find that pretraining the molecular model is crucial for better understanding the task of predicting LNPs properties, which is achieved through reconstructing atomic 3D coordinates and masking atom predictions. In addition, the issue of data imbalance is particularly prominent in the real-world exploration of LNPs. We introduce the BalMol block to solve this problem by smoothing the distribution of labels and molecular features. Our approach outperforms state-of-the-art works in transfection property prediction under both random and scaffold data splitting. Additionally, we establish a relationship between molecular structural similarity and transfection differences, selecting 4267 pairs of molecular transfection cliffs, which are pairs of molecules that exhibit high structural similarity but significant differences in transfection efficiency, thereby revealing the primary source of prediction errors. The code, model and data are made publicly available at https://github.com/wklix/TransLNP.

A feature extraction free approach for protein interactome inference from co-elution data.

Protein complexes are key functional units in cellular processes. High-throughput techniques, such as co-fractionation coupled with mass spectrometry (CF-MS), have advanced protein complex studies by enabling global interactome inference. However, dealing with complex fractionation characteristics to define true interactions is not a simple task, since CF-MS is prone to false positives due to the co-elution of non-interacting proteins by chance. Several computational methods have been designed to analyze CF-MS data and construct probabilistic protein-protein interaction (PPI) networks. Current methods usually first infer PPIs based on handcrafted CF-MS features, and then use clustering algorithms to form potential protein complexes. While powerful, these methods suffer from the potential bias of handcrafted features and severely imbalanced data distribution. However, the handcrafted features based on domain knowledge might introduce bias, and current methods also tend to overfit due to the severely imbalanced PPI data. To address these issues, we present a balanced end-to-end learning architecture, Software for Prediction of Interactome with Feature-extraction Free Elution Data (SPIFFED), to integrate feature representation from raw CF-MS data and interactome prediction by convolutional neural network. SPIFFED outperforms the state-of-the-art methods in predicting PPIs under the conventional imbalanced training. When trained with balanced data, SPIFFED had greatly improved sensitivity for true PPIs. Moreover, the ensemble SPIFFED model provides different voting schemes to integrate predicted PPIs from multiple CF-MS data. Using the clustering software (i.e. ClusterONE), SPIFFED allows users to infer high-confidence protein complexes depending on the CF-MS experimental designs. The source code of SPIFFED is freely available at: https://github.com/bio-it-station/SPIFFED.

Export of heme by the feline leukemia virus C receptor regulates mitochondrial biogenesis and redox balance in the hematophagous insect Rhodnius prolixus.

Heme is a prosthetic group of proteins involved in vital physiological processes. It participates, for example, in redox reactions crucial for cell metabolism due to the variable oxidation state of its central iron atom. However, excessive heme can be cytotoxic due to its prooxidant properties. Therefore, the control of intracellular heme levels ensures the survival of organisms, especially those that deal with high concentrations of heme during their lives, such as hematophagous insects. The export of heme initially attributed to the feline leukemia virus C receptor (FLVCR) has recently been called into question, following the discovery of choline uptake by the same receptor in mammals. Here, we found that RpFLVCR is a heme exporter in the midgut of the hematophagous insect Rhodnius prolixus, a vector for Chagas disease. Silencing RpFLVCR decreased hemolymphatic heme levels and increased the levels of intracellular dicysteinyl-biliverdin, indicating heme retention inside midgut cells. FLVCR silencing led to increased expression of heme oxygenase (HO), ferritin, and mitoferrin mRNAs while downregulating the iron importers Malvolio 1 and 2. In contrast, HO gene silencing increased FLVCR and Malvolio expression and downregulated ferritin, revealing crosstalk between heme degradation/export and iron transport/storage pathways. Furthermore, RpFLVCR silencing strongly increased oxidant production and lipid peroxidation, reduced cytochrome c oxidase activity, and activated mitochondrial biogenesis, effects not observed in RpHO-silenced insects. These data support FLVCR function as a heme exporter, playing a pivotal role in heme/iron metabolism and maintenance of redox balance, especially in an organism adapted to face extremely high concentrations of heme.

The Limits of the Constant-rate Birth-Death Prior for Phylogenetic Tree Topology Inference.

Birth-death models are stochastic processes describing speciation and extinction through time and across taxa and are widely used in biology for inference of evolutionary timescales. Previous research has highlighted how the expected trees under the constant-rate birth-death (crBD) model tend to differ from empirical trees, for example, with respect to the amount of phylogenetic imbalance. However, our understanding of how trees differ between the crBD model and the signal in empirical data remains incomplete. In this Point of View, we aim to expose the degree to which the crBD model differs from empirically inferred phylogenies and test the limits of the model in practice. Using a wide range of topology indices to compare crBD expectations against a comprehensive dataset of 1189 empirically estimated trees, we confirm that crBD model trees frequently differ topologically compared with empirical trees. To place this in the context of standard practice in the field, we conducted a meta-analysis for a subset of the empirical studies. When comparing studies that used Bayesian methods and crBD priors with those that used other non-crBD priors and non-Bayesian methods (i.e., maximum likelihood methods), we do not find any significant differences in tree topology inferences. To scrutinize this finding for the case of highly imbalanced trees, we selected the 100 trees with the greatest imbalance from our dataset, simulated sequence data for these tree topologies under various evolutionary rates, and re-inferred the trees under maximum likelihood and using the crBD model in a Bayesian setting. We find that when the substitution rate is low, the crBD prior results in overly balanced trees, but the tendency is negligible when substitution rates are sufficiently high. Overall, our findings demonstrate the general robustness of crBD priors across a broad range of phylogenetic inference scenarios but also highlight that empirically observed phylogenetic imbalance is highly improbable under the crBD model, leading to systematic bias in data sets with limited information content.

Increased presynaptic excitability in a migraine with aura mutation.

Migraine is a common and disabling neurological disorder. The headache and sensory amplifications of migraine are attributed to hyperexcitable sensory circuits, but a detailed understanding remains elusive. A mutation in casein kinase 1 delta (CK1δ) was identified in non-hemiplegic familial migraine with aura and advanced sleep phase syndrome. Mice carrying the CK1δT44A mutation were more susceptible to spreading depolarization (the phenomenon that underlies migraine aura), but mechanisms underlying this migraine-relevant phenotype were not known. We used a combination of whole-cell electrophysiology and multiphoton imaging, in vivo and in brain slices, to compare CK1δT44A mice (adult males) to their wild-type littermates. We found that despite comparable synaptic activity at rest, CK1δT44A neurons were more excitable upon repetitive stimulation than wild-type, with a reduction in presynaptic adaptation at excitatory but not inhibitory synapses. The mechanism of this adaptation deficit was a calcium-dependent enhancement of the size of the readily releasable pool of synaptic vesicles, and a resultant increase in glutamate release, in CK1δT44A compared to wild-type synapses. Consistent with this mechanism, CK1δT44A neurons showed an increase in the cumulative amplitude of excitatory post-synaptic currents, and a higher excitation-to-inhibition ratio during sustained activity compared to wild-type. At a local circuit level, action potential bursts elicited in CK1δT44A neurons triggered an increase in recurrent excitation compared to wild-type, and at a network level, CK1δT44A mice showed a longer duration of 'up state' activity, which is dependent on recurrent excitation. Finally, we demonstrated that the spreading depolarization susceptibility of CK1δT44A mice could be returned to wild-type levels with the same intervention (reduced extracellular calcium) that normalized presynaptic adaptation. Taken together, these findings show a stimulus-dependent presynaptic gain of function at glutamatergic synapses in a genetic model of migraine, that accounts for the increased spreading depolarization susceptibility and may also explain the sensory amplifications that are associated with the disease.

Early autism diagnosis based on path signature and Siamese unsupervised feature compressor.

Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in autism spectrum disorder infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.

MicroRNAs play regulatory roles in genomic balance.

Classic genetics studies found that genomic imbalance caused by changing the dosage of part of the genome (aneuploidy) has more detrimental effects than altering the dosage of the whole genome (ploidy). Previous analysis revealed global modulation of gene expression triggered by aneuploidy across various species, including maize (Zea mays), Arabidopsis, yeast, mammals, etc. Plant microRNAs (miRNAs) are a class of 20- to 24-nt endogenous small noncoding RNAs that carry out post-transcriptional gene expression regulation. That miRNAs and their putative targets are preferentially retained as duplicates after whole-genome duplication, as are many transcription factors and signaling components, indicates miRNAs are likely to be dosage-sensitive and potentially involved in genomic balance networks. This review addresses the following questions regarding the role of miRNAs in genomic imbalance. (1) How do aneuploidy and polyploidy impact the expression of miRNAs? (2) Do miRNAs play a regulatory role in modulating the expression of their targets under genomic imbalance?

StackDPP: a stacking ensemble based DNA-binding protein prediction model.

BACKGROUND: DNA-binding proteins (DNA-BPs) are the proteins that bind and interact with DNA. DNA-BPs regulate and affect numerous biological processes, such as, transcription and DNA replication, repair, and organization of the chromosomal DNA. Very few proteins, however, are DNA-binding in nature. Therefore, it is necessary to develop an efficient predictor for identifying DNA-BPs. RESULT: In this work, we have proposed new benchmark datasets for the DNA-binding protein prediction problem. We discovered several quality concerns with the widely used benchmark datasets, PDB1075 (for training) and PDB186 (for independent testing), which necessitated the preparation of new benchmark datasets. Our proposed datasets UNIPROT1424 and UNIPROT356 can be used for model training and independent testing respectively. We have retrained selected state-of-the-art DNA-BP predictors in the new dataset and reported their performance results. We also trained a novel predictor using the new benchmark dataset. We extracted features from various feature categories, then used a Random Forest classifier and Recursive Feature Elimination with Cross-validation (RFECV) to select the optimal set of 452 features. We then proposed a stacking ensemble architecture as our final prediction model. Named Stacking Ensemble Model for DNA-binding Protein Prediction, or StackDPP in short, our model achieved 0.92, 0.92 and 0.93 accuracy in 10-fold cross-validation, jackknife and independent testing respectively. CONCLUSION: StackDPP has performed very well in cross-validation testing and has outperformed all the state-of-the-art prediction models in independent testing. Its performance scores in cross-validation testing generalized very well in the independent test set. The source code of the model is publicly available at https://github.com/HasibAhmed1624/StackDPP . Therefore, we expect this generalized model can be adopted by researchers and practitioners to identify novel DNA-binding proteins.

DeepPhoPred: Accurate Deep Learning Model to Predict Microbial Phosphorylation.

Phosphorylation is a substantial posttranslational modification of proteins that refers to adding a phosphate group to the amino acid side chain after translation process in the ribosome. It is vital to coordinate cellular functions, such as regulating metabolism, proliferation, apoptosis, subcellular trafficking, and other crucial physiological processes. Phosphorylation prediction in a microbial organism can assist in understanding pathogenesis and host-pathogen interaction, drug and antibody design, and antimicrobial agent development. Experimental methods for predicting phosphorylation sites are costly, slow, and tedious. Hence low-cost and high-speed computational approaches are highly desirable. This paper presents a new deep learning tool called DeepPhoPred for predicting microbial phospho-serine (pS), phospho-threonine (pT), and phospho-tyrosine (pY) sites. DeepPhoPred incorporates a two-headed convolutional neural network architecture with the squeeze and excitation blocks followed by fully connected layers that jointly learn significant features from the peptide's structural and evolutionary information to predict phosphorylation sites. Our empirical results demonstrate that DeepPhoPred significantly outperforms the existing microbial phosphorylation site predictors with its highly efficient deep-learning architecture. DeepPhoPred as a standalone predictor, all its source codes, and our employed datasets are publicly available at https://github.com/faisalahm3d/DeepPhoPred.

Aberrant neuronal connectivity and network activity of neurons derived from patients with idiopathic schizophrenia.

Schizophrenia (SCZ) is a psychiatric disorder with a strong genetic determinant. A major hypothesis to explain disease aetiology comprises synaptic dysfunction associated with excitatory-inhibitory imbalance of synaptic transmission, ultimately contributing to impaired network oscillation and cognitive deficits associated with the disease. Here, we studied the morphological and functional properties of a highly defined co-culture of GABAergic and glutamatergic neurons derived from induced pluripotent stem cells (iPSC) from patients with idiopathic SCZ. Our results indicate upregulation of synaptic genes and increased excitatory synapse formation on GABAergic neurons in co-cultures. In parallel, we observed decreased lengths of axon initial segments, concordant with data from postmortem brains from patients with SCZ. In line with increased synapse density, patch-clamp analyses revealed markedly increased spontaneous excitatory postsynaptic currents (EPSC) recorded from GABAergic SCZ neurons. Finally, MEA recordings from neuronal networks indicate increased strength of network activity, potentially in response to altered synaptic transmission and E-I balance in the co-cultures. In conclusion, our results suggest selective deregulation of neuronal activity in SCZ samples, providing evidence for altered synapse formation and synaptic transmission as a potential base for aberrant network synchronization.

Prognostic and therapeutic potential of imbalance between PD-1+CD8 and ICOS+Treg cells in advanced HBV-HCC.

Over 50% of patients with hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC) are diagnosed at an advanced stage, which is characterized by immune imbalance between CD8+ T cells and regulatory T (Treg) cells that accelerates disease progression. However, there is no imbalance indicator to predict clinical outcomes. Here, we show that the proportion of CD8+ T cells decreases and Treg cells increases in advanced HBV-HCC patients. During this stage, CD8+ T cells and Treg cells expressed the coinhibitory molecule PD-1 and the costimulatory molecule ICOS, respectively. Additionally, the ratio between PD-1+CD8 and ICOS+Tregs showed significant changes. Patients were further divided into high- and low-ratio groups: PD-1+CD8 and ICOS+Tregs high- (PD-1/ICOShi) and low-ratio (PD-1/ICOSlo) groups according to ratio median. Compared with PD-1/ICOSlo patients, the PD-1/ICOShi group had better clinical prognosis and weaker CD8+ T cells exhaustion, and the T cell-killing and proliferation functions were more conservative. Surprisingly, the small sample analysis found that PD-1/ICOShi patients exhibited a higher proportion of tissue-resident memory T (TRM) cells and had more stable killing capacity and lower apoptosis capacity than PD-1/ICOSlo advanced HBV-HCC patients treated with immune checkpoint inhibitors (ICIs). In conclusion, the ratio between PD-1+CD8 and ICOS+Tregs was associated with extreme immune imbalance and poor prognosis in advanced HBV-HCC. These findings provide significant clinical implications for the prognosis of advanced HBV-HCC and may serve as a theoretical basis for identifying new targets in immunotherapy.

Novel Therapeutic Approach for Excitatory/Inhibitory Imbalance in Neurodevelopmental and Neurodegenerative Diseases.

Excitatory/inhibitory (E/I) balance, defined as the balance between excitation and inhibition of synaptic activity in a neuronal network, accounts in part for the normal functioning of the brain, controlling, for example, normal spike rate. In many pathological conditions, this fine balance is perturbed, leading to excessive or diminished excitation relative to inhibition, termed E/I imbalance, reflected in network dysfunction. E/I imbalance has emerged as a contributor to neurological disorders that occur particularly at the extremes of life, including autism spectrum disorder and Alzheimer's disease, pointing to the vulnerability of neuronal networks at these critical life stages. Hence, it is important to develop approaches to rebalance neural networks. In this review, we describe emerging therapies that can normalize the E/I ratio or the underlying abnormality that contributes to the imbalance in electrical activity, thus improving neurological function in these maladies.

Immunological factors, important players in the development of asthma.

Asthma is a heterogeneous disease, and its development is the result of a combination of factors, including genetic factors, environmental factors, immune dysfunction and other factors. Its specific mechanism has not yet been fully investigated. With the improvement of disease models, research on the pathogenesis of asthma has made great progress. Immunological disorders play an important role in asthma. Previously, we thought that asthma was mainly caused by an imbalance between Th1 and Th2 immune responses, but this theory cannot fully explain the pathogenesis of asthma. Recent studies have shown that T-cell subsets such as Th1 cells, Th2 cells, Th17 cells, Tregs and their cytokines contribute to asthma through different mechanisms. For the purpose of the present study, asthma was classified into distinct phenotypes based on airway inflammatory cells, such as eosinophilic asthma, characterized by predominant eosinophil aggregates, and neutrophilic asthma, characterized by predominant neutrophil aggregates. This paper will examine the immune mechanisms underlying different types of asthma, and will utilize data from animal models and clinical studies targeting specific immune pathways to inform more precise treatments for this condition.

An alternative miRISC targets a cancer-associated coding sequence mutation in FOXL2.

Recent evidence suggests that animal microRNAs (miRNAs) can target coding sequences (CDSs); however, the pathophysiological importance of such targeting remains unknown. Here, we show that a somatic heterozygous missense mutation (c.402C>G; p.C134W) in FOXL2, a feature shared by virtually all adult-type granulosa cell tumors (AGCTs), introduces a target site for miR-1236, which causes haploinsufficiency of the tumor-suppressor FOXL2. This miR-1236-mediated selective degradation of the variant FOXL2 mRNA is preferentially conducted by a distinct miRNA-loaded RNA-induced silencing complex (miRISC) directed by the Argonaute3 (AGO3) and DHX9 proteins. In both patients and a mouse model of AGCT, abundance of the inversely regulated variant FOXL2 with miR-1236 levels is highly correlated with malignant features of AGCT. Our study provides a molecular basis for understanding the conserved FOXL2 CDS mutation-mediated etiology of AGCT, revealing the existence of a previously unidentified mechanism of miRNA-targeting disease-associated mutations in the CDS by forming a non-canonical miRISC.

The associations of adherence to the Mediterranean diet with chronic dizziness and imbalance in community-dwelling adults: KNHANES 2019-2021.

BACKGROUND: Dizziness and vertigo rank among the top 10 reasons for emergency and clinical referrals to neurologists. Chronic dizziness and imbalance not only reduce quality of life, but also increase mortality. While the Mediterranean diet has long been considered beneficial for human and planetary health, its effects on chronic dizziness or imbalance are understudied. We investigated the associations of adherence to the Mediterranean diet with chronic dizziness and imbalance. METHODS: This study used data from the Korea National Health and Nutrition Examination Survey 2019-2021 and included 4,183 adults aged 40 years and older with complete information from diet, dizziness, and neurotology questionnaires. The alternate Mediterranean diet score (aMed) for nine food groups was calculated from 24-hour dietary recall data. Based on questionnaire responses, chronic dizziness was categorized as either isolated or chronic dizziness with imbalance, characterized by a cluster of difficulties maintaining a standing position, walking, or falling. RESULTS: In a multivariable-adjusted model, the prevalence of chronic imbalance was lower in the top aMed tertile than in the bottom tertile (OR 0.37; 95% CI, 0.18-0.74; p-trend = 0.01). Among the individual aMed components, the intake of whole grains and nuts exhibited an inverse relationship with chronic imbalance (OR 0.50; 95% CI, 0.27-0.93 for whole grains; OR 0.55; 95% CI, 0.31-1.01 for nuts). The aMed score was not associated with isolated chronic dizziness. CONCLUSIONS: Greater adherence to the Mediterranean diet may reduce chronic imbalance, particularly with an adequate intake of whole grains and nuts.