Biology subjects

Okada, M.

Publications and source records attributed to Okada, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Trans-Allosteric Activation Releases Distinct Conformational Traps in Kinase Heterodimers

Protein kinases function as dynamic, mechanically coupled nodes, yet the conformational drivers of multimeric activation remain unclear. Here, we present AlloQuant, a computational suite that translates AlphaFold3 structural ensembles into quantitative metrics of kinase regulation, including internal network rigidity, metastable-state populations, and sub-angstrom conformational drivers. Applying AlloQuant to CDK1, we demonstrate that binding of the Cyclin B1 (CCNB1) cofactor mechanically decouples a hyper-rigid inactive kinase core, allowing activating phosphorylation (pT161) to subsequently re-impose localized tension on the catalytic machinery. Conversely, the C-terminal Src kinase (CSK) faces a distinct conformational trap. While nucleotide-free monomeric CSK spontaneously samples a pre-active geometry, ATP binding excludes the active C-In conformation in all but 1 of 225 models. We show that docking partner engagement overcomes this blockade. Autophosphorylation of SRC at the activation loop (Y419) redistributes SRC conformational states without altering bulk rigidity. This redistribution is structurally coupled to the conformational state of CSK via the regulatory spine, not the catalytic machinery. Rather than mechanically deforming CSK, SRC engagement acts by conformational selection, committing roughly a quarter of CSK molecules to a fully active state. Thus, trans-allosteric kinase activation operates by defining the accessible conformational landscape of the receiver kinase. That control is exerted through mechanical remodeling in cofactor-dependent complexes and through conformational selection in transient kinase-kinase heterodimers. These findings establish AlloQuant as a general framework for quantifying how a binding partner reshapes a kinase's conformational landscape, applicable across the kinome because it assigns landmarks by profile-HMM alignment.

biophysics↗

bioRxiv2026

Short-term dietary change rapidly remodels microbial community assemblages and reprogrammed systemic immune phenotypes

Diet is a major determinant of the gut microbiome and immune homeostasis, yet the extent to which short-term dietary interventions can remodel established microbial communities and reprogramme immune phenotypes following long-term western diet consumption remains poorly understood. Here, we investigated temporal dynamics of the gut microbiome, microbial metabolites, intestinal barrier function and local and systemic immune responses following diet switching. Mice were fed either a standard chow or a western diet for 8 weeks before remaining on these diets or switching to the alternate diet for 2 or 4 weeks. Long term consumption of chow and western diets resulted in distinct gut microbial communities and differences in intestinal permeability. Diet switching rapidly remodelled microbial community structure within two weeks, with substantial bidirectional changes in community composition. Despite these changes, the relative abundance of several taxa remained influenced by prior dietary exposure. In contrast, faecal SCFA profiles remained largely associated with long-term diet, indicating that microbial metabolic outputs were altered more slowly than microbial community composition. Mass cytometry revealed progressive remodelling of local (MLN) and systemic (PBMC and spleen) immune responses following dietary switching. Activation-associated immune phenotypes, including Ki67+ and PD-1+ B and T cells, inflammatory monocytes and ROR{gamma}t+ regulatory T cells, rapidly responded to diet switching, whereas overall B cells, regulatory T cells and effector memory T cells retained signatures of long-term dietary exposure. Together, these findings demonstrate distinct temporal dynamics across the diet-microbiome-immune axis, whereby gut microbial composition and immune activation states remain highly plastic, while microbial metabolic outputs and several memory and regulatory immune phenotypes exhibit persistent dietary imprinting. These findings highlight the potential utility of short-term dietary interventions to modulate host-microbiome interactions and immune homeostasis.

immunology↗

bioRxiv2026

Reduction of Pollen Number and Anther Length in Bread Wheat Studied by a Nested Association Mapping Population

The number of pollen grains, which carry male gametes in seed plants, has attracted interest in genetics, evolution, and breeding. Rapid evolutionary reductions in pollen number and anther length were reported in selfing species as well as domesticated species, although this poses a challenge for hybrid breeding. Here, we studied the variation of pollen number and anther length of the hexaploid bread wheat (Triticum aestivum) by employing a quick pollen counting method. Pollen numbers in cultivars were lower than those in landraces among 54 lines of diverse geographic origins. Using the year of registration of traditional and modern cultivars, we found a reduction in pollen number over the past 150 years. We detected high heritability and variation among Asian landraces and cultivars. Thus, we conducted QTL mapping of pollen number as well as of anther length using nested association mapping lines in which Norin 61 was the common parent. Genomic loci encompassing Green Revolution genes (Rht-B1, Rht-D1, and Ppd-D1) showed significant effects on pollen number and anther length, but their contributions were relatively minor. Although anther length has often been used as a proxy for pollen number in bread wheat, our data showed that their correlations are not necessarily high. Interestingly, we identified a new QTL of pollen number that was not detected by measuring anther length, and, vice versa, a new QTL specific to anther length. These data suggest that pollen number has reduced rapidly in bread wheat but can be modified using the genetic diversity of landraces. Significance statementWe found that modern cultivars of bread wheat have reduced pollen number and shorter anther length, which are common in domesticated species but can be a challenge for hybrid breeding. Using underutilized Asian landraces and cultivars, we reported that new quantitative trait loci as well as loci used in the Green Revolution, are responsible for the traits, which can be employed to increase pollen numbers.

plant biology↗

bioRxiv2026

Acyl Carrier Protein is Essential for Apicoplast Biogenesis in Malaria Parasites Independent of Fatty Acid Synthesis

Acyl carrier protein (ACP) and its 4-phosphopantetheine prosthetic group canonically function as the soluble scaffold for acyl chain assembly and elongation during type-II fatty acid synthesis (FASII). Plasmodium malaria parasites retain a FASII pathway in the apicoplast organelle that has been the subject of considerable scrutiny and confusion. Although apicoplast FASII is essential for P. falciparum growth within mosquitoes and the human liver, this pathway is dispensable and largely inactive in blood-stage parasites that can scavenge host fatty acids. In contrast to FASII enzymes that can be disrupted without fitness defect, we report that knockout or ligand-dependent knockdown of apicoplast ACP is lethal to blood-stage P. falciparum, indicating an essential FASII-independent function. Loss of ACP impairs the biosynthesis of essential isoprenoid precursors and blocks apicoplast biogenesis. Using proximity biotinylation and biochemical interaction studies, we identified a key role for ACP in binding and stabilizing apicoplast pyruvate kinase II (PKII). This critical enzyme is the only known source of nucleoside triphosphates (NTPs) in this organelle and is required for isoprenoid synthesis and apicoplast biogenesis. Our work reveals that ACP knockdown results in destabilization and loss of PKII, which is sufficient to explain ACP essentiality in this stage. This work unveils essential ACP function at a key biochemical hub controlling broad apicoplast metabolism in malaria parasites that is independent of the canonical ACP role in FASII.

biochemistry↗

bioRxiv2025

Minimal Perturbation of Activation Loop Dynamics Rewires KinaseSignaling

Enzymes are central to life, with their catalytic activity often shaped by the dynamic conformations of regulatory loops. In hub enzymes such as tyrosine kinases, the activation loop critically controls substrate specificity, catalytic efficiency, and downstream signaling, shaping cellular fate. Yet, the molecular mechanisms by which loop dynamics encode these functions remain incompletely understood. Here, we used SRC kinase as a model to dissect how minimal perturbations of the activation loop reprogram kinase behavior. By generating and characterizing multiple variants, we identified a triple-deletion mutant with altered loop dynamics. Structural and biochemical analyses revealed that this variant explores distinct loop conformations and exhibits a subtle shift in substrate preference toward more acidic motifs. These fine-tuned conformational changes translated into specific cellular signaling outcomes, as demonstrated by phosphoproteomic profiling. Comparative analysis across species further showed that nature exploits similar loop remodeling strategies to modulate kinase function. Together, our findings provide a blueprint for rationally tuning kinase activity and offer a generalizable framework for rewiring signaling pathways in diverse cellular contexts.

biochemistry↗

bioRxiv2025

Literature-derived, context-aware gene regulatory networks improve biological predictions and mathematical modeling

MotivationComplex gene regulatory networks (GRNs) underlie most disease processes, and understanding disease-specific network structures and dynamics is crucial for developing effective treatments. Yet, literature-based analyses of GRNs often treat gene regulations as context-independent interactions, overlooking how their biological relevance can differ depending on the disease type, cell lineage, or experimental condition. ResultsIn an attempt to improve on existing methods for leveraging knowledge present in the scientific literature, we developed a framework to assign quantitative, context-dependent weights to gene regulations extracted from literature. We demonstrate that the context-specific GRNs reconstructed with our method can effectively capture disease biology, showing strong correlation with transcriptomics across a wide range of diseases. Furthermore, we show that utilizing contextual information improves accuracy in drug-target prediction tasks. Finally, we showcase the utility of the contextualized GRNs through the automated construction of an ordinary differential equation model of a breast cancer-specific signaling network. The large language model-based framework allows the integration of literature- and experimentally derived information and streamlines the process of assembling a biologically relevant and functional mathematical model. Our findings indicate the importance of considering the context when making biological predictions, and we demonstrate the use of natural language processing tools to effectively mine associations between gene regulations and biological contexts. Availability and implementationAll reproducibility code is available at https://github.com/okadalabipr/context-dependent-GRNs, along with the automated mathematical model construction package at https://github.com/okadalabipr/BioMathForge. The dataset used in this study is available at Zenodo, DOI: 10.5281/zenodo.16416117.

bioinformatics↗

bioRxiv2025

Comparative Extraction of Cellular Features from High-Resolution Volume Imaging

High-resolution volume imaging techniques, such as lattice light-sheet microscopy (LLSM), generate vast and complex datasets that demand advanced analytical approaches to uncover biologically meaningful insights. While LLSMs high spatial and temporal resolution provides critical data for understanding cellular processes, distinguishing subtle differences between cells in distinct states remains challenging. Here, using an adaptive, human-interpretable kernel-based calculation strategy, we developed Machine-Learning-Based Visual Extraction of Structural Features (M-VEST), a method designed to identify and interpret structural differences with high precision. By applying M-VEST to mitotic cells, we uncovered novel functions of the oncogene Aurora kinase A, demonstrating its utility in revealing previously undetected features. Validated using LLSM datasets, M-VEST offers a scalable framework for analyzing large and complex imaging data, advancing insights into cellular dynamics and beyond.

bioinformatics↗

bioRxiv2024

An attention-based deep neural network model to detect cis-regulatory elements at the single-cell level from multi-omics data

Cis-regulatory elements (cREs) play a crucial role in regulating gene expression and determining cell differentiation and state transitions. To capture the heterogeneous transitions of cell states associated with these processes, detecting cRE activity at the single-cell level is essential. However, current analytical methods can only capture the average behavior of cREs in cell populations, thereby obscuring cell-specific variations. To address this limitation, we proposed an attention-based deep neural network framework that integrates DNA sequences, genomic distances, and single-cell multi-omics data to detect cREs and their activities in individual cells. Our model shows higher accuracy in identifying cREs within single-cell multi- omics data from healthy human peripheral blood mononuclear cells than other existing methods. Furthermore, it clusters cells more precisely based on predicted cRE activities, enabling a finer differentiation of cell states. When applied to publicly available single-cell data from patients with glioma, the model successfully identified tumor-specific SOX2 activity. Additionally, it revealed the heterogeneous activation of the ZEB1 transcription factor, a regulator of epithelial-to-mesenchymal transition-related genes, which conventional methods struggle to detect. Overall, our model is a powerful tool for detecting cRE regulation at the single-cell level, which may contribute to revealing drug resistance mechanims in cell sub- populations.

bioinformatics↗

bioRxiv2024

Modeling of transcriptomic variation among subgenomes in 25 accessions of common wheat reveals cis- and trans-regulation architectures

Common wheat is an allohexaploid plant, thus making it difficult to obtain homoeolog-distinguished transcriptome data. Lasy-Seq, a type of 3 RNA-seq, is efficient for obtaining homoeolog-distinguished transcriptomes and can thus overcome this measurement difficulty. This study obtained transcriptome data from the seedlings, second leaves, and root tips of 25 lines from mainly eastern transmitted area using Lasy-Seq. Roots and seedlings exhibited similar transcriptome profiles; however, they were different from those of the leaves. We determined the effects of subgenomes, lines and their interactions with leaves, roots, and seedlings on the expression levels of each homoeolog triad. Of the 19,805 homoeolog triads, 50.9-55.4%, 24.2-29.5%, and 7.7-9.0% showed significant effects on their expression levels from subgenome, line, and interaction, respectively. 51-55% and 24-30% have genetic variation in the cis- and trans-regulation. Hierarchical clustering and co-trans regulation network analysis of homoeolog triads revealed that the patterns of expression polymorphisms among the lines were shared in different genes. The triads in which the statistical model detected as line effects imply that expression variation between lines is caused by changes in a smaller number of common trans-factors. We assigned gene ontology (GO) terms of the Arabidopsis orthologs to wheat homoeolog triads via reciprocal BLAST between common wheat and Arabidopsis, thus improving the percentage of gene-assigned GO terms to all analyzed GO terms from 19.1% to 90.6%. GO term enrichment analysis revealed that GO terms related to each tissue type function were enriched in genes expressed in the leaves and roots. Our information provides fundamental knowledge for the future breeding of plants possessing complex gene regulatory networks such as common wheat.

plant biology↗

bioRxiv2024

Fc-independent SARS-CoV-2 infection-enhancing antibodies decouple N-terminal and receptor-binding domains by cross-linking neighboring spikes

Antibody dependent enhancement (ADE) is a serious concern in vaccine development. The canonical ADE pathways are dependent on the fragment crystallizable (Fc) region of the antibody. In SARS-CoV-2 several antibodies have been discovered that inflict ADE in vitro. These antibodies target the N-terminal domain (NTD) of the SARS-CoV-2 spike protein. We previously proposed that these NTD-targeting infection-enhancing antibodies (NIEAs) cross-link neighboring spike proteins via their NTDs, and that this results in a decoupling between the NTD and receptor binding domain (RBD), facilitating the "RBD down" to "up" transition. In this study we present a combination of molecular dynamics simulations and cryogenic electron microscopy data that, together, demonstrate that NIEAs are indeed able to cross-link neighboring SARS-CoV-2 spike proteins, and that this cross-linking results in a decoupling of the NTD and RBD domains. These findings provide support for an Fc region independent ADE pathway that is not only relevant for SARS-CoV-2 but also for other viruses of which the spike proteins undergo a conformational change upon host cell entry.

molecular biology↗

bioRxiv2024

Patho-transcriptomic analysis of invasive mucinous adenocarcinoma of the lung (IMA): comparison with lung adenocarcinoma with signet ring cell features (SRCC)

BackgroundInvasive mucinous adenocarcinoma (IMA) comprises [~]5% of lung adenocarcinoma. There is no effective therapy for IMA when surgical resection is not possible. IMA is sometimes confused with adenocarcinoma with signet ring cell features (SRCC) pathologically since both adenocarcinomas feature tumor cells with abundant intracellular mucin. The molecular mechanisms by which such mucin-producing lung adenocarcinomas develop remain unknown. MethodsUsing a Visium spatial transcriptomics approach, we analyzed IMA and compared it with SRCC patho-transcriptomically. Combining spatial transcriptomics data with in vitro studies using RNA-seq and ChIP-seq, we assessed downstream targets of transcription factors HNF4A and SPDEF that are highly expressed in IMA and/or SRCC. ResultsSpatial transcriptomics analysis indicated that there are 6 distinct cell clusters in IMA and SRCC. Notably, two clusters (C1 and C3) of mucinous tumor cells exist in both adenocarcinomas albeit at a different ratio. Importantly, a portion of genes (e.g., NKX2-1, GKN1, HNF4A and FOXA3) are distinctly expressed while some mucous-related genes (e.g., SPDEF and FOXA2) are expressed in both adenocarcinomas. We determined that HNF4A induces MUC3A/B and TM4SF4 and that BI 6015, an HNF4A antagonist, suppressed the growth of IMA cells. Using mutant SPDEF that is associated with COVID-19, we also determined that an intact DNA-binding domain of SPDEF is required for SPDEF-mediated induction of mucin genes (MUC5AC, MUC5B and AGR2). Additionally, we found that XMU-MP-1, a SPDEF inhibitor, suppressed the growth of IMA cells. ConclusionThese results revealed that IMA and SRCC contain heterogenous tumor cell types, some of which are targetable.

cancer biology↗

bioRxiv2024

High-throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models

High throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles. In this study, we developed models to estimate the phenotypes of biomass-related traits in soybean (Glycine max) using unmanned aerial vehicle (UAV) remote sensing and deep learning models. In 2018, a field experiment was conducted using 198 soybean germplasm accessions with known whole genome sequences under two irrigation conditions: drought and control. We used a convolutional neural network (CNN) as a model to estimate the phenotypic values of five conventional biomass-related traits: dry weight, main stem length, numbers of nodes and branches, and plant height. We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models. The accuracy of the developed models was assessed through 10-fold cross-validation, which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously. Deep learning enabled us to extract features that exhibited strong correlations with the output (i.e., phenotypes of the target traits) and accurately estimate the values of the features from the input data. We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits. Furthermore, we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions. The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.

plant biology↗

bioRxiv2024

ErbB2/HER2 expression level determines CDK4-inhibitor sensitivity and cyclin D1 and c-Myc dependency at the G1/S transition

Cell cycle entry and the irreversible transition from the G1 to S phase are crucial for mammalian cell proliferation. Among the ErbB family, the ErbB2/HER2 receptor is a key driver of cancer growth. However, the quantitative mechanisms underlying the ErbB2-mediated G1/S transition remain unclarified. Here, we performed an extensive time-course analysis of high and low ErbB2-expressing breast cancer cells to describe the regulatory mechanisms of the ErbB2-mediated G1/S transition. Live-cell imaging using cell cycle reporters revealed that the G1/S transition occurs 20 h after ErbB2 activation, driven primarily by the cyclin D1/CDK4-RB axis. Hsp90 is regulated by CDK4 activity and controls the stability of ErbB2 protein in a time-dependent manner. CDK4 inhibitor treatment arrested the cell cycle in most cells; a subpopulation showed a 25-h delay in G1/S entry associated with enhanced c-Myc activation. In high ErbB2-expressing cells, CDK4 inhibition led to c-Myc overactivation, a rapid decrease in cyclin D1 expression, and cell cycle arrest. Overall, we demonstrate how ErbB2 receptor levels modulate the roles of cyclin D1 and c-Myc in the G1/S transition and suggest that variations in ErbB2 levels within breast cancer tissues confer heterogeneous sensitivity to CDK4 inhibitors, potentially complicating treatment.

cancer biology↗

bioRxiv2024

Identifying key regulatory genes in drug resistance acquisition: Modeling pseudotime trajectories of single-cell transcriptome

Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560-680 genes out of 6,000 exhibiting multistable expression states in each subpopulation, including key estrogen receptor-positive breast cancer cell survival genes, such as RPS6KB1. Bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer. SummaryDespite recent advancements in experimental technology for genome-wide molecular profiling, our understanding of the dynamic mechanism underlying cancer drug resistance remains limited. In this study, we present an approach that combines mathematical modeling with the pseudotime analysis of single-cell time-series transcriptome data of drug-treated breast cancer cells. Our method identifies approximately 600 genes out of 6,000 exhibiting multistable expression states, including RPS6KB1, a predictor of poor prognosis, cell survival, and growth in estrogen receptor-positive breast cancers. The bifurcation analysis elucidates the regulatory mechanisms of the key regulatory genes, which can also be mapped into a molecular network based on cell survival and metastasis-related pathways, providing a comprehensive understanding of the interplay between signaling pathways and regulatory genes. Our method serves as a powerful tool for deciphering the complexities of drug resistance mechanisms in human diseases.

bioinformatics↗

bioRxiv2024

De novo annotation of the wheat pangenome reveals complexity and diversity of the hexaploid wheat pan-transcriptome

Wheat is the most widely cultivated crop in the world with over 215 million hectares grown annually. However, to meet the demands of a growing global population, breeders face the challenge of increasing wheat production by approximately 60% within the next 40 years. The 10+ Wheat Genomes Project recently sequenced and assembled to chromosome level the genomes of nine wheat cultivars to develop our understanding of genetic diversity and selection within the pan-genome of wheat. Here, we provide a wheat pan-transcriptome with de novo annotation and differential expression analysis for these wheat cultivars, across multiple different tissues and whole seedlings sampled at dusk/dawn. Analysis of these de novo annotations facilitated the discovery of genes absent from the Chinese Spring reference, identified genes specific to particular cultivars and defined the core and dispensable genomes. Expression analysis across cultivars and tissues revealed conservation in expression between a large core set of homeologous genes, but also widespread changes in subgenome homeolog expression bias between cultivars. Co-expression network analysis revealed the impact of divergence of sub-genome homeolog expression and identified cultivar-specific expression profiles. In a case study utilising both the newly constructed wheat pan-genome and pan-transcriptome we demonstrate prevalent variation in the prolamin superfamily and immune-reactive proteins across the pan-cultivars.In summary, this work provides both a valuable resource for the wider wheat community and reveals diversity in gene content and expression patterns between global wheat cultivars.

genomics↗

bioRxiv2023

Positive and negative feedback regulation of the TGF-β1-SMAD4 axis explains two equilibrium states in human skin aging

Skin homeostasis during aging is critical not only for the appearance but also biological defense of the human body. In this study, we identified thrombospondin-1 (THBS1) and fibromodulin (FMOD) as positive and negative regulators, respectively, of the TGF-{beta}1-SMAD4 axis in human skin aging based on in vitro and in vivo omics analyses and mathematical modeling. Transcriptomic and epigenetic analyses of senescent dermal fibroblasts identified TGF-{beta}1 as the key upstream regulator. Bifurcation analysis identified a binary senescent/non-senescent switch, with THBS1 as the main controller. Sensitivity analysis of the TGF-{beta}1 signaling pathway indicated that THBS1 expression was sensitively regulated while FMOD was robustly regulated, suggesting that THBS1 is a controllable factor. Inhibition of SMAD4 complex formation was experimentally validated as a promising manner to control THBS1 production and senescence. This study demonstrates the potential of a data-driven mathematical approach in determining the mechanisms of skin aging. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/546970v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@12b4f29org.highwire.dtl.DTLVardef@5a3e95org.highwire.dtl.DTLVardef@c2daa5org.highwire.dtl.DTLVardef@a09042_HPS_FORMAT_FIGEXP M_FIG C_FIG eTOC BlurbHaga, Iida and Okada revealed that the balance between FMOD and THBS1 determines the two equilibrium states of skin homeostasis and THBS1 is a controllable factor in skin aging HighlightsO_LIMulti-omics analysis identified TGF-{beta}1-SMAD4 as key regulators in human skin aging C_LIO_LITHBS1 and FMOD promoted and suppressed factors in skin aging, respectively C_LIO_LITHBS1 controlled the senescent and non-senescent states of the aging switch C_LIO_LISMAD4 is a potential target for inhibiting THBS1 expression and cell senescence C_LI

systems biology↗

bioRxiv2023

Slow gamma oscillations in the mouse olfactory bulb are correlated with sniffing in the dark period

Neural activity in the olfactory bulb is reflected in local field potentials (LFPs). Functionally, LFPs in the olfactory bulb are categorized into different frequency bands: 1-4 Hz, 6-12 Hz, 25-50 Hz, and 65-130 Hz, which respectively correspond to respiration, sniffing, slow gamma, and fast gamma oscillations. While gamma oscillations in the olfactory bulb are modulated by respiration and sniffing, it remains unknown how and whether the modulation of LFP oscillations is affected by the time of day. To address this question, we recorded LFPs in the olfactory bulb, hippocampus, and neocortex of unrestrained mice for up to 3 d. For each recording site, we calculated the correlation coefficients of normalized LFP powers between pairs of frequency bands in the three regions during the dark and light periods. We then compared these correlations with those generated by surrogate data to investigate whether the correlation was statistically significant. We found that the correlation between sniffing and slow gamma oscillations was higher in the dark period than in the light period. Our finding has the potential to shed light on the coding scheme of olfactory information that is dependent on the light/dark cycle.

neuroscience↗

bioRxiv2023

NFκB nuclear dynamics orchestrate inflammatory aging

Upregulation of nuclear factor {kappa}B (NF{kappa}B) signaling is a hallmark of aging and major cause of age-related chronic inflammation; however, its physiological functions and mechanisms remain unclear. By combining mathematical modeling and experiments, we show that dysfunction of negative feedback regulators of NF{kappa}B, I{kappa}B and A20, alters the NF{kappa}B nuclear dynamics from oscillatory to sustained, promoting cellular senescence by remodeling epigenetic regulation and metabolic landscape. Sustained NF{kappa}B activity by I{kappa}B downregulation enhanced inflammation- and senescence-associated gene expression through increased NF{kappa}B-DNA binding and slowed the cell cycle by upregulating purine catabolism via mTORC2/AKT pathways. Notably, I{kappa}B knockdown combined with A20 overexpression resulted in lower NF{kappa}B amplitude, cytokine expression, and SA-{beta}-gal activity than I{kappa}B knockdown alone. I{kappa}B downregulation is correlated with hypoxanthine phosphoribosyltransferase 1 (HPRT1) expression in the purine salvage pathway in aged mouse hearts. Our study suggests that nuclear NF{kappa}B homeostasis is critical for balancing purine metabolism associated with chronic inflammation and tissue aging.

systems biology↗