BMC Biology最新文献

{"title":"Domain-specific AI segmentation of IMPDH2 rod/ring structures in mouse embryonic stem cells.","authors":"Samuel T M Ball, Meagan J Hennessy, Yuhan Tan, Kai F Hoettges, Neil D Perkins, David J Wilkinson, Michael R H White, Yalin Zheng, David A Turner","doi":"10.1186/s12915-025-02226-7","DOIUrl":"https://doi.org/10.1186/s12915-025-02226-7","url":null,"abstract":"<p><strong>Background: </strong>Inosine monophosphate dehydrogenase 2 (IMPDH2) is an enzyme that catalyses the rate-limiting step of guanine nucleotides. In mouse embryonic stem cells (ESCs), IMPDH2 forms large multi-protein complexes known as rod-ring (RR) structures that dissociate when ESCs differentiate. Manual analysis of RR structures from confocal microscopy images, although possible, is not feasible on a large scale due to the quantity of RR structures present in each field of view. To address this analysis bottleneck, we have created a fully automatic RR image classification pipeline to segment, characterise and measure feature distributions of these structures in ESCs.</p><p><strong>Results: </strong>We find that this model can automatically segment images with a Dice score of over 80% for both rods and rings for in-domain images compared to expert annotation, with a slight drop to 70% for datasets out of domain. Important feature measurements derived from these segmentations show high agreement with the measurements derived from expert annotation, achieving an R² score of over 90% for counting the number of RRs over the dataset.</p><p><strong>Conclusions: </strong>We have established for the first time a quantitative baseline for RR distribution in pluripotent ESCs and have made a pipeline available for training to be applied to other models in which RR remain an open topic of study.</p>","PeriodicalId":9339,"journal":{"name":"BMC Biology","volume":"23 1","pages":"126"},"PeriodicalIF":4.4,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12067766/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143954514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcriptome analysis reveals PTBP1 as a key regulator of circRNA biogenesis.","authors":"Mohan Wang, Shanshan Zheng, Yan Zhang, Jingwen Zhang, Fuming Lai, Cong Zhou, Qiangwei Zhou, Xingwang Li, Guoliang Li","doi":"10.1186/s12915-025-02233-8","DOIUrl":"https://doi.org/10.1186/s12915-025-02233-8","url":null,"abstract":"<p><strong>Background: </strong>Circular RNAs (circRNAs) are a class of non-coding RNAs generated through back splicing. High expression of circRNAs is often associated with numerous abnormal cellular biological processes. However, the regulatory factors of circRNAs are not fully understood.</p><p><strong>Results: </strong>In this study, we identified PTBP1 as a crucial regulator of circRNA biogenesis through a comprehensive analysis of the whole transcriptome profiles across 10 diverse cell lines. Knockdown of PTBP1 led to a significant decrease in circRNA expression, concomitant with a distinct reduction in cell proliferation. To investigate the regulatory mechanism of PTBP1 on circRNA biogenesis, we constructed a minigene reporter based on SPPL3 gene. The results showed that PTBP1 can bind to the flanking introns of circSPPL3, and the mutation of PTBP1 motif impedes the back splicing of circSPPL3. Subsequently, to demonstrate that this observation is not an exception, the comprehensive regulatory effects of PTBP1 on circRNAs were confirmed by miniGFP, reflecting the necessity of the binding site in the flanking introns. Analysis of data from clinical samples showed that both PTBP1 and circRNAs exhibited substantial upregulation in acute myeloid leukemia, further demonstrating a potential role for PTBP1 in promoting circRNA biogenesis under in vivo conditions. Competitive endogenous RNA (ceRNA) network revealed that PTBP1-associated circRNAs participated in biological processes associated with cell proliferation.</p><p><strong>Conclusions: </strong>In summary, our study is the first to identify the regulatory effect of PTBP1 on circRNA biogenesis and indicates a possible link between PTBP1 and circRNA expression in leukemia.</p>","PeriodicalId":9339,"journal":{"name":"BMC Biology","volume":"23 1","pages":"127"},"PeriodicalIF":4.4,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12067716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143970091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A single-cell transcriptomic atlas of all cell types in the brain of 5xFAD Alzheimer mice in response to dietary inulin supplementation.","authors":"Xiaoyan Wang, Houyu Zhang, Zhou Wan, Xuetong Li, Carlos F Ibáñez, Meng Xie","doi":"10.1186/s12915-025-02230-x","DOIUrl":"https://doi.org/10.1186/s12915-025-02230-x","url":null,"abstract":"<p><strong>Background: </strong>Alzheimer's disease (AD) is a progressive neurodegenerative disease that is a major threat to the aging population. Due to lack of effective therapy, preventive treatments are important strategies to limit AD onset and progression, of which dietary regimes have been implicated as a key factor. Diet with high fiber content is known to have beneficial effects on cognitive decline in AD. However, a global survey on microbiome and brain cell dynamics in response to high fiber intake at single-cell resolution in AD mouse models is still missing.</p><p><strong>Results: </strong>Here, we show that dietary inulin supplementation synergized with AD progression to specifically increase the abundance of Akkermansia muciniphila in gut microbiome of 5 × Familial AD (FAD) mice. By performing single-nucleus RNA sequencing on different regions of the whole brain with three independent biological replicates, we reveal region-specific changes in the proportion of neuron, astrocyte, and granule cell subpopulations upon inulin supplementation in 5xFAD mice. In addition, we find that astrocytes have more pronounced region-specific diversity than microglia. Intriguingly, such dietary change reduces amyloid-β plaque burden and alleviates microgliosis in the forebrain region, without affecting the spatial learning and memory.</p><p><strong>Conclusions: </strong>These results provide a comprehensive overview on the transcriptomic changes in individual cells of the entire mouse brain in response to high fiber intake and a resourceful foundation for future mechanistic studies on the influence of diet and gut microbiome on the brain during neurodegeneration.</p>","PeriodicalId":9339,"journal":{"name":"BMC Biology","volume":"23 1","pages":"124"},"PeriodicalIF":4.4,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065180/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143960518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning.","authors":"Pengpai Li, Bowen Shao, Guoqing Zhao, Zhi-Ping Liu","doi":"10.1186/s12915-025-02231-w","DOIUrl":"https://doi.org/10.1186/s12915-025-02231-w","url":null,"abstract":"<p><strong>Background: </strong>Understanding protein-molecular interaction is crucial for unraveling the mechanisms underlying diverse biological processes. Machine learning (ML) techniques have been extensively employed in predicting these interactions and have garnered substantial research focus. Previous studies have predominantly centered on improving model performance through novel and efficient ML approaches, often resulting in overoptimistic predictive estimates. However, these advancements frequently neglect the inherent biases stemming from network properties, particularly in biological contexts.</p><p><strong>Results: </strong>In this study, we examined the biases inherent in ML models during the learning and prediction of protein-molecular interactions, particularly those arising from the scale-free property of biological networks-a characteristic where in a few nodes have many connections while most have very few. Our comprehensive analysis across diverse tasks, datasets, and ML methods provides compelling evidence of these biases. We discovered that the training and evaluation of ML models are profoundly influenced by network topology, potentially distorting model performance assessments. To mitigate this issue, we propose the degree distribution balanced (DDB) sampling strategy, a straightforward yet potent approach that alleviates biases stemming from network properties. This method further underscores the limitations of certain ML models in learning protein-molecular interactions solely from intrinsic molecular features.</p><p><strong>Conclusions: </strong>Our findings present a novel perspective for assessing the performance of ML models in inferring protein-molecular interactions with greater fairness. By addressing biases introduced by network properties, the DDB sampling approach provides a more balanced and precise assessment of model capabilities. These insights hold the potential to bolster the reliability of ML models in bioinformatics, fostering a more stringent evaluation framework for predicting protein-molecular interactions.</p>","PeriodicalId":9339,"journal":{"name":"BMC Biology","volume":"23 1","pages":"123"},"PeriodicalIF":4.4,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143953925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging explainable multi-scale features for fine-grained circRNA-miRNA interaction prediction.","authors":"Li Peng, Wang Wang, Zongyi Yang, Xiangzheng Fu, Wei Liang, Dongsheng Cao","doi":"10.1186/s12915-025-02227-6","DOIUrl":"https://doi.org/10.1186/s12915-025-02227-6","url":null,"abstract":"<p><strong>Background: </strong>Circular RNAs (circRNAs) and microRNAs (miRNAs) interactions have essential implications in various biological processes and diseases. Computational science approaches have emerged as powerful tools for studying and predicting these intricate molecular interactions, garnering considerable attention. Current methods face two significant limitations: the lack of precise interpretable models and insufficient representation of homogeneous and heterogeneous molecules.</p><p><strong>Results: </strong>We propose a novel method, MFERL, that addresses both limitations through multi-scale representation learning and an explainable fine-grained model for predicting circRNA-miRNA interactions (CMI). MFERL learns multi-scale representations by aggregating homogeneous node features and interacting with heterogeneous node features, as well as through novel dual-convolution attention mechanisms and contrastive learning to enhance features.</p><p><strong>Conclusions: </strong>We utilize a manifold-based method to examine model performance in detail, revealing that MFERL exhibits robust generalization, robustness, and interpretability. Extensive experiments show that MFERL outperforms state-of-the-art models and offers a promising direction for understanding CMI intrinsic mechanisms.</p>","PeriodicalId":9339,"journal":{"name":"BMC Biology","volume":"23 1","pages":"121"},"PeriodicalIF":4.4,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065178/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144062044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations.","authors":"Hang Wei, Jialu Hou, Yumeng Liu, Alexey K Shaytan, Bin Liu, Hao Wu","doi":"10.1186/s12915-025-02221-y","DOIUrl":"10.1186/s12915-025-02221-y","url":null,"abstract":"<p><strong>Background: </strong>Exploring piRNA-disease associations can help discover candidate diagnostic or prognostic biomarkers and therapeutic targets. Several computational methods have been presented for identifying associations between piRNAs and diseases. However, the existing methods encounter challenges such as over-smoothing in feature learning and overlooking specific local proximity relationships, resulting in limited representation of piRNA-disease pairs and insufficient detection of association patterns.</p><p><strong>Results: </strong>In this study, we propose a novel computational method called iPiDA-LGE for piRNA-disease association identification. iPiDA-LGE comprises two graph convolutional neural network modules based on local and global piRNA-disease graphs, aimed at capturing specific and general features of piRNA-disease pairs. Additionally, it integrates their refined and macroscopic inferences to derive the final prediction result.</p><p><strong>Conclusions: </strong>The experimental results show that iPiDA-LGE effectively leverages the advantages of both local and global graph learning, thereby achieving more discriminative pair representation and superior predictive performance.</p>","PeriodicalId":9339,"journal":{"name":"BMC Biology","volume":"23 1","pages":"119"},"PeriodicalIF":4.4,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065364/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143953971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Done deal-cohabiting dominant and subordinate dogs differently rely on familiar demonstrators in a detour task.","authors":"Péter Pongrácz, Petra Dobos, Fruzsina Prónik, Kata Vékony","doi":"10.1186/s12915-025-02232-9","DOIUrl":"https://doi.org/10.1186/s12915-025-02232-9","url":null,"abstract":"<p><strong>Background: </strong>Companion dogs live in a mixed-species environment, where they can successfully learn from both humans and dogs. Breed type, the demonstrator's behavior, and in multi-dog households, the dogs' hierarchy are known influencing factors of the efficiency of dogs' social learning. In previous studies, always an unfamiliar dog or experimenter was the demonstrator of the given task. Now we tested social learning in a setting more relevant to the everyday life of dogs, where the demonstrator was either the owner or a cohabiting dog. We used the validated dog-rank assessment questionnaire (DRA-Q) and the well-established detour paradigm. We hypothesized that beyond the previously found associations between social learning and rank, we would find stronger differences between high- and low-ranking cohabiting dogs due to the subjects' everyday experience and different relationships with the demonstrators.</p><p><strong>Results: </strong>We found that dominant dogs learn more effectively from the owner than from their subordinate dog companion. Subordinate dogs increased their success rate only when their dominant counterpart demonstrated the task, but did not improve when the owner was the demonstrator. Dogs with higher agonistic rank could improve their detour speed more often than the lower-ranked individuals in the Owner demonstration group, but we found no effect of the subranks in the Dog demonstration group.</p><p><strong>Conclusions: </strong>These results warrant the intricate effect of within-group hierarchy of dogs even in non-competitive contexts. The strong difference between the subordinate and dominant dogs' learning performance in the Owner-demonstration group aligns with the \"owner as the main resource for dogs\" hypothesis.</p>","PeriodicalId":9339,"journal":{"name":"BMC Biology","volume":"23 1","pages":"125"},"PeriodicalIF":4.4,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143966304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity.","authors":"Xun Wang, Zhijun Xia, Runqiu Feng, Tongyu Han, Hanyu Wang, Wenqian Yu, Xingguang Wang","doi":"10.1186/s12915-025-02222-x","DOIUrl":"https://doi.org/10.1186/s12915-025-02222-x","url":null,"abstract":"<p><strong>Background: </strong>Drug-target binding affinity (DTA) prediction can accelerate the drug screening process, and deep learning techniques have been used in all facets of drug research. Affinity prediction based on deep learning methods has proven crucial to drug discovery, design, and reuse. Among these, the sequence-based approach using 1D sequences of drugs and targets as inputs typically results in the loss of structural information, whereas the structure-based method frequently results in increased computing costs due to the intricate structure of the molecule graph.</p><p><strong>Results: </strong>We propose a sequential multifeature fusion method (SMFF-DTA) to achieve efficient and accurate prediction. SMFF-DTA uses sequential methods to represent the structural information and physicochemical properties of drugs and targets and introduces multiple attention blocks to capture interaction features closely.</p><p><strong>Conclusions: </strong>As demonstrated by our extensive studies, SMFF-DTA outperforms the other methods in terms of various metrics, showing its advantages and effectiveness as a drug-target binding affinity predictor.</p>","PeriodicalId":9339,"journal":{"name":"BMC Biology","volume":"23 1","pages":"120"},"PeriodicalIF":4.4,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065342/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143970495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two independent regulatory mechanisms synergistically contribute to P450-mediated insecticide resistance in a lepidopteran pest, Spodoptera exigua.","authors":"Bo Hu, Yuting Zhang, Zhiping Xing, Xiangzhu Chen, Cong Rao, Kuitun Liu, Anjiang Tan, Jianya Su","doi":"10.1186/s12915-025-02228-5","DOIUrl":"https://doi.org/10.1186/s12915-025-02228-5","url":null,"abstract":"<p><strong>Background: </strong>Cytochrome P450 enzymes play a pivotal role in the detoxification of plant allelochemicals and insecticides. Overexpression of P450 genes has been proven to be involved in insecticide resistance in insects. However, the molecular mechanisms underlying the regulation of P450 genes in insects are poorly understood.</p><p><strong>Results: </strong>Here, we determine that upregulation of CYP321B1 confers resistance to organophosphate (chlorpyrifos) and pyrethroid (cypermethrin and deltamethrin) insecticides in the resistant Spodoptera exigua strain. Enhanced expression of transcription factors CncC/Maf contributes to the increase in the expression of CYP321B1 in the resistant strain. Reporter gene assays and site-directed mutagenesis analyses confirm that a specific binding site is crucial for binding CncC/Maf to activate the expression of CYP321B1. In addition, creation of a new binding site resulting from the cis-mutations in the promoter region of CYP321B1 in the resistant strain facilitates the binding of the POU/homeodomain transcription factor Nubbin, and further enhances the expression of this P450 gene. Furthermore, we authenticate that changes in both trans- and cis-regulatory elements in the promoter region of CYP321B1 act in combination to modulate the promoter activity in a synergistic manner.</p><p><strong>Conclusions: </strong>Collectively, these results demonstrate that two distinct but synergistic mechanisms coordinately result in the overexpression of CYP321B1 involved in insecticide resistance in an agriculturally important insect pest, S. exigua. The information on mechanisms of metabolic resistance could help to understand the development of resistance to insecticides by other pests and contribute to designing effective integrated pest management strategies for the pest control.</p>","PeriodicalId":9339,"journal":{"name":"BMC Biology","volume":"23 1","pages":"122"},"PeriodicalIF":4.4,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143976313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Current perspectives in drug targeting intrinsically disordered proteins and biomolecular condensates.","authors":"Caolitao Qin, Yun-Long Wang, Jian Zheng, Xiang-Bo Wan, Xin-Juan Fan","doi":"10.1186/s12915-025-02214-x","DOIUrl":"https://doi.org/10.1186/s12915-025-02214-x","url":null,"abstract":"<p><p>Intrinsically disordered proteins (IDPs) and biomolecular condensates are critical for cellular processes and physiological functions. Abnormal biomolecular condensates can cause diseases such as cancer and neurodegenerative disorders. IDPs, including intrinsically disordered regions (IDRs), were previously considered undruggable due to their lack of stable binding pockets. However, recent evidence indicates that targeting them can influence cellular processes. This review explores current strategies to target IDPs and biomolecular condensates, potential improvements, and the challenges and opportunities in this evolving field.</p>","PeriodicalId":9339,"journal":{"name":"BMC Biology","volume":"23 1","pages":"118"},"PeriodicalIF":4.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12054275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143976839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}