Biology subjects

Yamada, T.

Publications and source records attributed to Yamada, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Reconstructing human gene regulatory evolution using deep learning

Understanding the genetic changes that drove the evolution of human-specific traits is a central goal in biology. Changes in gene expression are thought to be a major source of phenotypic innovations, but identifying the underlying changes in cis-regulatory elements (CREs) along the human lineage has been difficult: functional data from great apes are scarce and unobtainable for extinct hominins, and pinpointing causal substitutions requires knowing which sequences determine CRE activity. Here, we used AlphaGenome, a sequence-to-function deep-learning model, to predict the cis-regulatory effects of ~17 million single-nucleotide substitutions specific to the human lineage and traced their consequences for gene expression evolution across 305 tissues and cell types. The 71,345 predicted cis-regulatory gains lie mostly outside human accelerated regions and arose either through creation of new CREs or, more often, through co-option of CREs ancestrally accessible in other tissues. They are also enriched for distal enhancers and for target gene functions characteristic of the corresponding tissues, suggesting that they contributed to the evolution of tissue-specific biology in humans. Genes linked to more gains were more likely to be upregulated in humans, and in silico reversion to ancestral alleles traced such upregulations to individual substitutions creating transcription factor binding motifs. Comparison with Neandertal and Denisovan genomes revealed that nearly all gains now fixed in humans predate the split from archaic hominins, yet a few modern-human-specific gains contribute substantially to gene upregulation. Our study provides a genome-wide, tissue-resolved map of cis-regulatory changes and their contribution to gene expression evolution along the human lineage.

evolutionary biology↗

bioRxiv2026

Androgen receptor determines skeletal muscle sexual dimorphism

The molecular and functional bases of sexual dimorphism in skeletal muscle remain poorly understood. The androgen receptor (AR) is a major regulator of sex-biased gene expression in muscle, but its genomic targets and associated coregulators in vivo are incompletely defined. Using ChIL-seq and an AirID-AR knock-in mouse, we mapped AR-bound genes and AR-associated proteins in skeletal muscle and identified histone deacetylase-linked corepressors. We further identified myosin binding protein H (Mybph) as a female-biased AR-repressed gene conserved in mouse and human muscle. Mybph loss disrupted sarcomeric organization and selectively delayed postinjury force recovery in female mice. These findings define an in vivo AR regulatory network and identify AR-dependent Mybph repression as a potential mechanism contributing to skeletal muscle sexual dimorphism.

molecular biology↗

bioRxiv2026

Postmitotic Regulation of Neuronal Subpopulations in the Cerebellum

Hundreds of neuronal cell types and subtypes have been identified through transcriptome profiling of the mammalian brain and are thought to arise from lineage-restricted neural progenitors during early development. However, how neurons might further diversify their transcriptional identities during postmitotic development and through adulthood remains poorly understood. Here, we combine genetic and biochemical approaches to uncover granule neuron subpopulations in the anterior cerebellum, which we term C1 and C2. We find that a large proportion of granule neurons predominantly express the C2 gene program during early postmitotic differentiation, but that C2 genes are downregulated in a subset of neurons during late postnatal development, leading to comparable proportions of C1 and C2 neurons in adulthood. Using an in vivo genetic mini-screen, we identify calcium signaling pathways, together with the transcription factor ETV1, that establish the transcriptional program of C2 neurons. Finally, C1 and C2 granule neurons are differentially engaged during behavior and this reflects their roles in cerebellar-dependent associative learning. Together, these findings reveal postmitotic mechanisms that continue to shape neuronal identity in the brain.

neuroscience↗

bioRxiv2026

Nonlinear influence of reward volatility on arbitration between multiple learning strategies reflects cost-benefit optimization

Action selection involves two systems: a model-free reinforcement learning strategy, which relies on experience with action-outcome pairs, and a model-based reinforcement learning strategy, which enables more flexible behavior via inference using a model of the invariant environmental structure. Although environmental change requires more flexible behavior, the ability of volatility, a higher-order statistic that captures how rapidly or frequently the environment changes, to systematically modulate these strategies remains unclear. We examined the effects of reward volatility on arbitration between model-free and model-based reinforcement learning strategies using two modified two-step decision tasks. In Experiment 1, participants performed tasks with different levels of reward volatility and time pressure. In Experiment 2, we systematically manipulated reward volatility across a broader range to assess the relationship between volatility and learning strategy. Behavioral data were analyzed using model-agnostic one-trial and multitrial back analyses, reinforcement learning simulations, and hierarchical Bayesian model fitting. Across experiments, reward volatility exerted an inverse U-shaped nonlinear effect on the arbitration between model-free and model-based reinforcement learning strategies, as the model-based learning strategy was strongly driven at intermediate levels of reward volatility. These modulation effects were observed only in individuals who had learned the transition structure in the task, whereas those who had not learned the transition structure relied on the model-free learning strategy regardless of reward volatility. Reinforcement learning simulations revealed that the relative advantage of the model-based learning strategy over the model-free learning strategy peaked at intermediate levels of reward volatility. Additionally, increased time pressure shifted behavior toward the model-free learning strategy. These results demonstrated that, humans do not always use the model-based reinforcement learning strategy in uncertain and dynamic environments, even when they are aware of the task structure, supporting cost-benefit optimization. Author SummaryThe ability to flexibly guide behavior by carefully considering future consequences is fundamental to a prominent property of human intelligence and rationality. However, what drives this deliberative system? In this study, we investigated the factors that promote deliberative versus habitual behavior using decision-making tasks with uncertain structures and changing rewards. We found that participants who spontaneously learned the hidden transition structure in the task used this knowledge to guide deliberative behavior. Conversely, participants who did not learn the structure relied primarily on habitual strategies, repeating actions that had previously been rewarded. Among participants who learned the structure, the degree of deliberative behavior changed nonlinearly with reward volatility, in which the speed at which rewards changed over time. We also observed that limiting the decision time reduced deliberative behavior and promoted habitual responding. These findings suggest that under uncertain and dynamic environments, deliberative control is adaptively regulated according to cost-benefit optimization. Our results contribute to understanding how humans flexibly adjust their behavioral control systems in response to environmental conditions.

animal behavior and cognition↗

bioRxiv2026

Benchmarking long-context genome language models on biosynthetic gene clusters

Recent advances in language models for natural language processing have spread to the field of genomics, driving the development of genome language models (gLMs) to decipher genomic information. Cutting-edge long-context gLMs are promising approaches for understanding and designing biological complexity, but their evaluation remains underdeveloped. In this study, we introduce BGCs-Bench, a unified benchmark focused on biosynthetic gene clusters for assessing long-range genomic modeling on three downstream tasks: biosynthetic class prediction, taxonomic classification and coding sequence annotation. Using BGCs-Bench, we perform systematic and layer-wise evaluations of the embedding representations of long-context gLMs, demonstrating that layer selection is crucial for downstream task performance. In addition to the evaluation results, the logit lens analysis of autoregressive gLMs suggests that StripedHyena-based models consist of earlier layers to encode biologically meaningful information from input DNA sequences and deeper layers to optimize embeddings for sequence generation. These findings provide insights for more effective development and application of long-context gLMs.

bioinformatics↗

bioRxiv2026

Optimizing resource allocation in Miscanthus breeding with sparse testing designs for genomic prediction

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse biobased products. Increasing biomass yield will increase profitability and environmental benefits, so is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; GxE interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for GxE interaction presented the highest PA and the lowest MSE for CNN (PA: [~]0.77, MSE: [~]0.5) and YDY (PA: [~]0.70, MSE: [~]1.3) while for TCM and AIL these ranged from [~]0.28 to 0.41 and [~]1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

genomics↗

bioRxiv2026

Multi-trait Multi-environment Genomic Prediction Strategies for Miscanthus sacchariflorus Populations

Genomic selection holds the potential to serve as a strategic tool to enhance the genetic gain of complex traits in Miscanthus breeding programs. The development of improved cultivars requires their assessment for various traits across diverse environments to ensure suitable overall performance. Hence, the multi-trait multi-environment (MTME) genomic prediction (GP) models offer an opportunity to improve selection accuracy. This study aims to evaluate the potential of five GP models: (1) three MTME models including genotype-by-trait-by-environment interaction (GxExT) and (2) two single-trait multi-environment (STME) models (with and without GxE interaction). A Miscanthus sacchariflorus population comprising 336 genotypes evaluated in three environments and scored for four traits (biomass yield YDY, total culm number TCM, average internode length AIL, and culm node number CNN) was analyzed. The predictive ability of the models was evaluated considering three cross-validation schemes resembling realistic scenarios (CV1: predicting new genotypes, CVP: predicting missing traits in a given environment, and CV2: predicting partially observed genotypes). On average, in all cross-validation schemes compared to the STME the predictive ability of the MTME models was 10% to 70% higher for TCM and AIL. On the other hand, for YDY and CNN, both STME models performed similarly or slightly better (between 5 to 64%) than the MTME models in most environments. While the MTME models were not successful for all traits when compared to their STME counterparts, MTME models improved the prediction of the performance of genotypes that were untested across environments or lacked trait information in a specific environment. Overall, our study suggests that MTME GP models can be implemented in Miscanthus breeding programs to improve the predictive ability of the complex traits, shorten breeding cycles, and accelerate selection decisions.

genomics↗

bioRxiv2026

Weather Characterization for Optimizing Genomic Prediction in Miscanthus sacchariflorus

Environmental factors affect crop growth and development thus their consideration across sites and years become essential for genotypic evaluation. Genomic selection (GS) has been broadly implemented to accelerate breeding cycles by skipping field evaluations thus allowing early identification of outperforming genotypes. In this study, 7,740 phenotypic records corresponding to 516 Miscanthus sacchariflorus genotypes evaluated in five locations across three years were considered for analysis. Additionally, environmental data on six weather covariates was implemented to characterize similarities between locations. Different sets of locations of variable sizes were used for model calibration based on two cross-validations (CV00 and CV0) schemes leaving out one location at a time. Predictive ability across locations of the best model varied between 0.45 and 0.90 for both schemes. These results were compared to associate predictive ability in function of weather patterns between training and testing sets to allow models calibration optimization. We found it is feasible to optimize resource allocation by considering environmentally correlated sets. In most cases, the information from only one and, at most, two locations were enough to deliver better results than using all four locations, reducing training sets by up to 75%. The results obtained shed light on helping breeders make informed decisions considering weather data when designing evaluations.

genomics↗

bioRxiv2026

ENHANCING GENOMIC PREDICTION MODELS IN MISCANTHUS POPULATIONS BY INCORPORATING THE GENOTYPE-BY-ENVIRONMENT INTERACTION

Giant Miscanthus giganteus (Mxg) is one of the most promising perennial crops to generate biomass feedstock for bioenergy and biobased products. It is derived from the natural inter-species hybridization of Miscanthus sacchariflorus (Msa) and Miscanthus sinensis (Msi) species, thus population improvement within these species is crucial. Genomic selection (GS) is an attractive option to accelerate breeding of perennial grasses, such as Miscanthus, which requires up to three years of evaluation to produce reliable phenotypic data. Hence, genotypes are observed in multiple years and locations causing inconsistent response patterns from one year to the next, between location, and/or location-by-year combinations. These inconsistencies are known as the genotype-by-environment interaction effect (GxE). Although GS has been successfully implemented in multiple annual crops where straightforward cross-validation schemes exist to assess the levels of predictive ability that can be reached, for perennial crops new cross-validation schemes will help avoid data contamination. Here, we propose a series of cross-validation schemes to evaluate model performance for perennial crops. We perform a case study by analyzing one panel of each species (516 genotypes of Msa, 280 genotypes of Msi) scored for biomass yield at different locations around the world over several years. The results of the different cross-validation schemes provide insights about the usefulness of GS to accelerate the breeding process of Miscanthus species. In addition, leveraging the GxE effects of different types significantly increases predictive ability (up to 10% in Msa and 30% for Msi) compared to the conventional approaches based on main effects only.

genomics↗

bioRxiv2026

A multi-omics atlas of human hindbrain development

The human hindbrain controls essential motor and autonomic functions and is the site of neurodevelopmental diseases. Yet, its cellular diversity, developmental trajectories and underlying regulatory logic remain poorly understood. We present a comprehensive multi-omics atlas of human hindbrain development spanning embryonic to adult stages, encompassing 594,817 transcriptomic and 422,568 chromatin-accessibility single-nucleus profiles. This dataset resolved the cellular architecture of hindbrain cellular lineages, and delineated coordinated gene expression, cis-regulatory programs and regulatory grammar guiding their developmental trajectories. By integrating multi-omics data, we discovered context-specific roles of transcription factors across cell types and deciphered the role of HOX genes in driving divergent cellular identity in related lineages. We further leveraged the atlas to contextualize pediatric gliomas to decode how subtle yet coordinated shifts in gene expression context can define oncogenic transformation. Together, this atlas provides a foundational resource for hindbrain biology and establishes a gene-regulatory framework linking development and disease.

neuroscience↗

bioRxiv2026

Hepatic Ketogenesis Attenuates Cardiac Hypertrophy via Metabolic Reprogramming

BackgroundHeart failure with preserved ejection fraction (HfpEF) is increasingly recognized as a multisystem disorder linked to the cardiovascular-kidney-metabolic (CKM) syndrome. While the falling heart undergoes metabolic reprogramming, the interorgan crosstalk regulating myocardial substrate preference in HFpEF remains elusive. We aimed to clarify the role of systemic and local ketogenesis in the pathogenesis of cardiac hypertrophy and HFpEF. MethodsA mouse model of HFpEF was employed using a high-fat diet combined with NG-Nitro-L-arginine methyl ester hydrochloride (L-NAME). Cardiac hypertrophy and systemic metabolic profiling including ketogenesis were evaluated. To dissect the role of site-specific ketogenesis, we generated inducible cardiomyocyte-specific (Hmgcs2{Delta}iCM) and hepatocyte-specific (Hmgcs2{Delta}Hep) knockout mice of HMG-CoA synthase 2 (Hmgcs2), deficient in the rate-limiting enzyme for ketogenesis. Cardiomyocyte -specific nuclei were isolated for transcriptomic (RNA-seq) and in vitro assays in H9C2 cells were used to elucidate molecular mechanisms. ResultsThe HFpEF model successfully exhibited diastolic dysfunction, impaired exercise capacity and cardiac hypertrophy with elevated circulating ketone body concentration. Myocardial metabolomics and snRNA-seq identified a profound metabolic shift characterized by the accumulation of long-chain fatty acids and Krebs cycle intermediates, coupled with the transcriptional downregulation of insulin signaling and fatty acid degradation pathways. Although circulating ketone body level was upregulated, Hmgcs2{Delta}iCM mice showed no exacerbation of the HFpEF phenotype. In contrast, Hmgcs2{Delta}Hep mice exhibited significantly aggravated cardiac hypertrophy (HW/TL; Hmgcs2flox: 7.41 {+/-} 0.87: Hmgcs2{Delta}Hep: 8.29 {+/-} 0.73; p = 0.0154). Mechanistically, hepatic ketogenesis was required to maintain circulating beta-hydroxybutyrate (BHB) levels, which directly modulated cardiomyocyte metabolism. BHB acted as a metabolic signal to dampen fatty acid overload and facilitate glucose utilization. ConclusionsOur study identifies a critical "liver-heart axis" where hepatic ketogenesis serves as an essential regulator of myocardial metabolic resilience. Impaired hepatic ketogenesis creates a metabolic mismatch that drives pathological cardiac remodeling. These findings highlight the liver as a therapeutic target within the CKM syndrome framework, suggesting that restoring the hepato-cardiac metabolic bridge may ameliorate HFpEF progression. What is New?O_LIThis study identifies a novel liver-adipose-heart axis that governs myocardial metabolic resilience during the development of heart failure with preserved ejection fraction (HFpEF). C_LIO_LIWe demonstrate that while both the liver and heart upregulate ketogenesis under metabolic stress, only hepatic ketogenesis--and not cardiac-intrinsic ketogenesis--is essential for mitigating pathological cardiac remodeling. C_LIO_LIMechanistically, liver-derived {beta} -hydroxybutyrate acts as a critical C_LIO_LIendocrine signal that dampens fatty acid oxidation and facilitates myocardial glucose utilization. C_LI What Are the Clinical Implications?O_LIOur findings highlight the liver as a central therapeutic target within the cardiovascular-kidney-metabolic (CKM) syndrome framework, where hepatic metabolic failure directly drives cardiac dysfunction. C_LIO_LIRestoring the hepato-cardiac metabolic bridge, through either hepatic-targeted therapies or ketone body supplementation, represents a promising strategy to enhance myocardial metabolic flexibility and ameliorate HfpEF in patients with multi-organ metabolic disorders. C_LI

biochemistry↗

bioRxiv2025

Unconscious switching of dorsal and medial pathways for plasticity and stability during NREM and REM sleep

While visual perceptual learning improves during non-REM sleep and stabilizes during REM sleep via excitatory-inhibitory neurotransmitter (E/I) balance in early visual areas (EVA), the role of prefrontal regions remains unclear. Here, we show that contributions of the dorsolateral prefrontal cortex (DLPFC) and medial prefrontal cortex (mPFC) differ by sleep stage in human adults. During non-REM sleep, plasticity increased in DLPFC--indexed by elevated E/I balance measured with magnetic resonance spectroscopy and polysomnography--in correlation with performance gains. During REM sleep, stability increased in mPFC--indexed by reduced E/I balance--in correlation with resilience to retrograde interference from new learning. E/I balance changes and their effects on learning paralleled those in EVA. Connectivity weights between EVA and DLPFC, and between EVA and mPFC, switched with sleep stage. These findings suggest the presence of dorsal and medial pathways that unconsciously alternate between non-REM sleep and REM sleep to improve and stabilize learning.

neuroscience↗

bioRxiv2025

A dual genetic constraint underlies the conservation of early brains in vertebrates.

As the body plan, the embryonic brain bauplan reflects the shared features of vertebrate brains. Yet, disagreements among sparse histogenetic frameworks have undermined the bauplans power to trace homologies. Here, we generate and integrate five vertebrate single-cell multi-omic atlases of early embryonic brains, revealing a conserved cellular blueprint that defines equivalent progenitor domains across species. Our cellular neuromeric model provides an unified and unbiased framework for the vertebrate brain bauplan and revises the regionalisation of the prosencephalon, refining its molecular boundaries and developmental relationships. Furthermore, cross-species gene-network analyses expose regulatory complexity beyond classical neuromeric patterning, resolving networks into modules aligned with regional cell types or cell class (progenitor/neuron). Evolutionarily, two main developmental constraints emerge: brain bauplan genes, early essential for regional identity, and pleiotropic stemness gene modules, indispensable across all proliferating cells. In turn, later development displays tissue-specific and less essential modules, explaining its rapid divergence into species-specific features. Together, these findings reveal a dual evolutionary constraint--neuromeric identity and pleiotropic stemness--that underlies the conservation of early vertebrate brains. This duality explains how deeply conserved regulatory architectures coexist with evolutionary flexibility to develop into the immense diversity of vertebrate nervous systems. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/684766v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@a337a2org.highwire.dtl.DTLVardef@19987adorg.highwire.dtl.DTLVardef@9a9394org.highwire.dtl.DTLVardef@4ef279_HPS_FORMAT_FIGEXP M_FIG C_FIG

evolutionary biology↗

bioRxiv2025

Gene regulatory innovations from transposable elements in mammalian cerebellum development

Transposable elements (TEs) are hypothesized to have driven gene regulatory innovation, yet their contributions to primate brain development at the cell type level remain largely unexplored. Using single-cell multiomics data for the cerebellum from human, macaque, marmoset, and mouse, we revealed that TE contributions to different cell types are shaped by differences in evolutionary constraint across cell types, as well as the preferential co-option of certain TEs in specific cell states. Using a sequence-based deep-learning model predicting cell-type-specific chromatin accessibility, we systematically assessed the co-option potential of TEs into cerebellar gene regulatory networks, identifying seventeen TE fragments with complex regulatory sequences in their ancestral states that facilitate their co-option as cell-type-specific cis-regulatory elements. Primate-specific HERVL (human endogenous retrovirus L) is predominantly accessible in differentiating granule cells and other rhombic-lip-derived neuroblasts during hindbrain development, and contributes to species-specific gene expression patterns. Our study uncovers a pivotal role for TEs in the evolution of the cerebellum. More generally, it demonstrates how TEs can be directly co-opted into the gene regulatory networks of specific cell types and introduces a generalizable analytical framework for dissecting their contribution to mammalian regulatory evolution.

genetics↗

bioRxiv2025

DeepRES: Deep learning enables reaction-based comprehensive enzyme screening

BackgroundEnzymes accelerate biochemical reactions in living organisms, thus playing an important role in metabolism. Although metabolic pathway databases are growing, many metabolic reactions, termed orphan enzymes, have not been annotated to gene sequences, which hinders functional annotation in genomic analysis. Moreover, protein databases contain many proteins of unknown function. Owing to this gap between known proteins and enzymatic reactions, various proteins of unknown function may be orphan enzymes; however, available tools cannot adequately predict these links. ResultsIn this study, we developed DeepRES, an AI-based framework for comprehensive enzyme screening, to explore novel enzyme candidates from proteins of unknown function for reactions of interest. DeepRES implements enzyme screening via two steps: classification of enzymes and non-enzymes and prediction of catalytic capabilities for enzyme-reaction pairs. The two deep learning models comprising DeepRES showed comparable or superior performance to that of existing software. We performed screening of 1,255 orphan enzymes involved in the microbiome using DeepRES and successfully identified candidate proteins for 897 orphan enzymes. We then used those candidates as references for genomic analysis and explored novel biosynthetic gene clusters from microbial genomes to obtain promising candidate gene clusters, including those related to anthocyanin degradation. ConclusionsComprehensive enzyme screening via DeepRES, which is the first computational tool designed to associate orphan enzymes with proteins of unknown function, is expected to facilitate high-throughput identification of orphan enzyme-encoding genes. Furthermore, DeepRES can be easily integrated into the current genomic analysis pipeline to extend the functional annotation.

bioinformatics↗

bioRxiv2025

Sleep is associated with reduction of excitatory signaling in medial prefrontal cortex

Although many sleep medications enhance inhibitory signaling, it remains unclear whether inhibitory or excitatory neurotransmitters contribute to the natural transition from wakefulness to sleep in humans. Here, we show that changes in excitatory, rather than inhibitory, neurotransmitter levels are associated with this transition. Young, healthy participants underwent two nap sessions with polysomnography, during which glutamate and GABA concentrations in the medial prefrontal cortex were measured using magnetic resonance spectroscopy. Glutamate gradually decreased during deeper sleep stages compared to wakefulness in the second session, with better sleep quality. No such change occurred in the first session with poorer sleep, likely due to the first-night effect. Furthermore, reduced glutamate significantly mediated sleep-onset latency in both sessions. Conversely, GABA concentration did not change from wakefulness to sleep in either session. These findings provide the first evidence that reduced excitatory signaling is a key feature of natural good sleep onset in the human brain.

neuroscience↗

bioRxiv2025

Synergistic effects of Cyp51 isozyme-specific azole antifungal agents on fungi with multiple cyp51 isozyme genes

Pathogenic fungi pose significant societal challenges. The limited availability of therapeutic targets due to the eukaryotic nature of fungi emphasizes the importance of available drug targets such as Cyp51, a crucial enzyme in ergosterol biosynthesis, inhibited by azole antifungals. This study explored the susceptibility patterns of azole antifungals against Cyp51 isozyme deletion strains ({Delta}cyp51A and {Delta}cyp51B) in Trichophyton rubrum, the predominant dermatophyte species. Distinct susceptible patterns were observed among azole antifungals for {Delta}cyp51A and {Delta}cyp51B. Although most azole antifungal agents exhibited increased antifungal activity against {Delta}cyp51A, select agents demonstrated increased antifungal activity against {Delta}cyp51B. Remarkably, fluconazole, sulconazole, and imazalil exhibited relatively increased activity against {Delta}cyp51A, whereas prochloraz demonstrated increased activity against {Delta}cyp51B. Combining these isozyme-selective agents exerted synergistic effects against the wild-type strain and the parent ku80-knockout strain but not against individual Cyp51 knockout mutants. Hence, the two Cyp51 isozymes, Cyp51A and Cyp51B, may be inhibited by distinct azole antifungals, exerting a synergistic effect with the dual azole antifungal combination. This synergistic effect was also observed on another fungal species, Aspergillus welwitschiae, which also has two Cyp51 isozymes. These data demonstrate that combining azole antifungals with different Cyp51 isozyme selectivities exerts synergistic effects against fungi possessing multiple Cyp51 isozymes. This study proposes a novel therapeutic approach for addressing fungal infections through the combination of antifungal drugs that inhibit the same enzymatic activity but exhibit different isozyme selectivity. It also emphasizes the potential for developing drugs targeting specific isozymes, a previously underutilized approach in the realm of antifungal drug development.

microbiology↗

bioRxiv2025

The evolution of gene regulation in mammalian cerebellum development

Gene regulatory changes are considered major drivers of evolutionary innovations, including the cerebellums expansion during human evolution, yet they remain largely unexplored. In this study, we combined single-nucleus measurements of gene expression and chromatin accessibility from six mammals (human, bonobo, macaque, marmoset, mouse, and opossum) to uncover conserved and diverged regulatory networks in cerebellum development. We identified core regulators of cell identity and developed sequence-based models that revealed conserved regulatory codes. By predicting chromatin accessibility across 240 mammalian species, we reconstructed the evolutionary histories of human cis-regulatory elements, identifying sets associated with positive selection and gene expression changes, including the recent gain of THRB expression in cerebellar progenitor cells. Collectively, our work reveals the shared and mammalian lineage-specific regulatory programs governing cerebellum development.

evolutionary biology↗