GigaScience最新文献_第5页

Mechanisms of hepatic steatosis in chickens: integrated analysis of the host genome, molecular phenomics and gut microbiome. 鸡肝脏脂肪变性的机制：宿主基因组、分子表型组学和肠道微生物组的综合分析。

IF 11.8 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae023

Congjiao Sun, Fangren Lan, Qianqian Zhou, Xiaoli Guo, Jiaming Jin, Chaoliang Wen, Yanxin Guo, Zhuocheng Hou, Jiangxia Zheng, Guiqin Wu, Guangqi Li, Yiyuan Yan, Junying Li, Qiugang Ma, Ning Yang

{"title":"Mechanisms of hepatic steatosis in chickens: integrated analysis of the host genome, molecular phenomics and gut microbiome.","authors":"Congjiao Sun, Fangren Lan, Qianqian Zhou, Xiaoli Guo, Jiaming Jin, Chaoliang Wen, Yanxin Guo, Zhuocheng Hou, Jiangxia Zheng, Guiqin Wu, Guangqi Li, Yiyuan Yan, Junying Li, Qiugang Ma, Ning Yang","doi":"10.1093/gigascience/giae023","DOIUrl":"10.1093/gigascience/giae023","url":null,"abstract":"Hepatic steatosis is the initial manifestation of abnormal liver functions and often leads to liver diseases such as nonalcoholic fatty liver disease in humans and fatty liver syndrome in animals. In this study, we conducted a comprehensive analysis of a large chicken population consisting of 705 adult hens by combining host genome resequencing; liver transcriptome, proteome, and metabolome analysis; and microbial 16S ribosomal RNA gene sequencing of each gut segment. The results showed the heritability (h2 = 0.25) and duodenal microbiability (m2 = 0.26) of hepatic steatosis were relatively high, indicating a large effect of host genetics and duodenal microbiota on chicken hepatic steatosis. Individuals with hepatic steatosis had low microbiota diversity and a decreased genetic potential to process triglyceride output from hepatocytes, fatty acid β-oxidation activity, and resistance to fatty acid peroxidation. Furthermore, we revealed a molecular network linking host genomic variants (GGA6: 5.59-5.69 Mb), hepatic gene/protein expression (PEMT, phosphatidyl-ethanolamine N-methyltransferase), metabolite abundances (folate, S-adenosylmethionine, homocysteine, phosphatidyl-ethanolamine, and phosphatidylcholine), and duodenal microbes (genus Lactobacillus) to hepatic steatosis, which could provide new insights into the regulatory mechanism of fatty liver development.","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11152177/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141261606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma. 全蛋白质组关联研究和功能验证确定了胰腺导管腺癌的新型蛋白质标记物。

IF 11.8 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae012

Jingjing Zhu, Ke Wu, Shuai Liu, Alexandra Masca, Hua Zhong, Tai Yang, Dalia H Ghoneim, Praveen Surendran, Tanxin Liu, Qizhi Yao, Tao Liu, Sarah Fahle, Adam Butterworth, Md Ashad Alam, Jaydutt V Vadgama, Youping Deng, Hong-Wen Deng, Chong Wu, Yong Wu, Lang Wu

{"title":"Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma.","authors":"Jingjing Zhu, Ke Wu, Shuai Liu, Alexandra Masca, Hua Zhong, Tai Yang, Dalia H Ghoneim, Praveen Surendran, Tanxin Liu, Qizhi Yao, Tao Liu, Sarah Fahle, Adam Butterworth, Md Ashad Alam, Jaydutt V Vadgama, Youping Deng, Hong-Wen Deng, Chong Wu, Yong Wu, Lang Wu","doi":"10.1093/gigascience/giae012","DOIUrl":"10.1093/gigascience/giae012","url":null,"abstract":"Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC.Significance: PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. This dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions.","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11010651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140856176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chromosome-level genome assemblies of two littorinid marine snails indicate genetic basis of intertidal adaptation and ancient karyotype evolved from bilaterian ancestors. 两种海蜗牛的染色体级基因组组装表明了潮间带适应性的遗传基础以及从两栖类祖先演化而来的古老核型。

IF 11.8 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae072

Yan-Shu Wang, Meng-Yu Li, Yu-Long Li, Yu-Qiang Li, Dong-Xiu Xue, Jin-Xian Liu

{"title":"Chromosome-level genome assemblies of two littorinid marine snails indicate genetic basis of intertidal adaptation and ancient karyotype evolved from bilaterian ancestors.","authors":"Yan-Shu Wang, Meng-Yu Li, Yu-Long Li, Yu-Qiang Li, Dong-Xiu Xue, Jin-Xian Liu","doi":"10.1093/gigascience/giae072","DOIUrl":"https://doi.org/10.1093/gigascience/giae072","url":null,"abstract":"Living in the intertidal environment, littorinid snails are excellent models for understanding genetic mechanisms underlying adaptation to harsh fluctuating environments. Furthermore, the karyotypes of littorinid snails, with the same chromosome number as the presumed bilaterian ancestor, make them valuable for investigating karyotype evolution from the bilaterian ancestor to mollusks. Here, we generated high-quality, chromosome-scale genome assemblies for 2 littorinid marine snails, Littorina brevicula (927.94 Mb) and Littoraria sinensis (882.51 Mb), with contig N50 of 3.43 Mb and 2.31 Mb, respectively. Comparative genomic analyses identified 92 expanded gene families and 85 positively selected genes as potential candidates possibly associated with intertidal adaptation in the littorinid lineage, which were functionally enriched in stimulus responses, innate immunity, and apoptosis process regulation and might be involved in cellular homeostasis maintenance in stressful intertidal environments. Genome macrosynteny analyses indicated that 4 fissions and 4 fusions led to the evolution from the 17 presumed bilaterian ancestral chromosomes to the 17 littorinid chromosomes, implying that the littorinid snails have a highly conserved karyotype with the bilaterian ancestor. Based on the most parsimonious reconstruction of the common ancestral karyotype of scallops and littorinid snails, 3 chromosomal fissions and 1 chromosomal fusion from the bilaterian ancient linkage groups were shared by the bivalve scallop and gastropoda littorinid snails, indicating that the chromosome-scale ancient gene linkages were generally preserved in the mollusk genomes for over 500 million years. The highly conserved karyotype makes the littorinid snail genomes valuable resources for understanding early bilaterian evolution and biology.","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11423352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142344885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

V-pipe 3.0: a sustainable pipeline for within-sample viral genetic diversity estimation. V-pipe 3.0：用于样本内病毒遗传多样性估计的可持续管道。

IF 11.8 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae065

Lara Fuhrmann, Kim Philipp Jablonski, Ivan Topolsky, Aashil A Batavia, Nico Borgsmüller, Pelin Icer Baykal, Matteo Carrara, Chaoran Chen, Arthur Dondi, Monica Dragan, David Dreifuss, Anika John, Benjamin Langer, Michal Okoniewski, Louis du Plessis, Uwe Schmitt, Franziska Singer, Tanja Stadler, Niko Beerenwinkel

引用次数: 0

Correction to: A graph clustering algorithm for detection and genotyping of structural variants from long reads. Correction to：从长读数中检测结构变异并进行基因分型的图聚类算法。

IF 11.8 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae077

引用次数: 0

FAIR data retrieval for sensitive clinical research data in Galaxy. 银河系统中敏感临床研究数据的 FAIR 数据检索。

IF 11.8 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giad099

Jasper Ouwerkerk, Helena Rasche, John D Spalding, Saskia Hiltemann, Andrew P Stubbs

{"title":"FAIR data retrieval for sensitive clinical research data in Galaxy.","authors":"Jasper Ouwerkerk, Helena Rasche, John D Spalding, Saskia Hiltemann, Andrew P Stubbs","doi":"10.1093/gigascience/giad099","DOIUrl":"10.1093/gigascience/giad099","url":null,"abstract":"Background: In clinical research, data have to be accessible and reproducible, but the generated data are becoming larger and analysis complex. Here we propose a platform for Findable, Accessible, Interoperable, and Reusable (FAIR) data access and creating reproducible findings. Standardized access to a major genomic repository, the European Genome-Phenome Archive (EGA), has been achieved with API services like PyEGA3. We aim to provide a FAIR data analysis service in Galaxy by retrieving genomic data from the EGA and provide a generalized \"omics\" platform for FAIR data analysis.Results: To demonstrate this, we implemented an end-to-end Galaxy workflow to replicate the findings from an RD-Connect synthetic dataset Beyond the 1 Million Genomes (synB1MG) available from the EGA. We developed the PyEGA3 connector within Galaxy to easily download multiple datasets from the EGA. We added the gene.iobio tool, a diagnostic environment for precision genomics, to Galaxy and demonstrate that it provides a more dynamic and interpretable view for trio analysis results. We developed a Galaxy trio analysis workflow to determine the pathogenic variants from the synB1MG trios using the GEMINI and gene.iobio tool. The complete workflow is available at WorkflowHub, and an associated tutorial was created in the Galaxy Training Network, which helps researchers unfamiliar with Galaxy to run the workflow.Conclusions: We showed the feasibility of reusing data from the EGA in Galaxy via PyEGA3 and validated the workflow by rediscovering spiked-in variants in synthetic data. Finally, we improved existing tools in Galaxy and created a workflow for trio analysis to demonstrate the value of FAIR genomics analysis in Galaxy.","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10821763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139570419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward genome assemblies for all marine vertebrates: current landscape and challenges. 实现所有海洋脊椎动物的基因组组装：现状与挑战。

IF 11.8 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giad119

Emma de Jong, Lara Parata, Philipp E Bayer, Shannon Corrigan, Richard J Edwards

引用次数: 0

On the variability of dynamic functional connectivity assessment methods. 动态功能连通性评估方法的变异性。

IF 11.8 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae009

Mohammad Torabi, Georgios D Mitsis, Jean-Baptiste Poline

{"title":"On the variability of dynamic functional connectivity assessment methods.","authors":"Mohammad Torabi, Georgios D Mitsis, Jean-Baptiste Poline","doi":"10.1093/gigascience/giae009","DOIUrl":"10.1093/gigascience/giae009","url":null,"abstract":"Background: Dynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker. However, various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this work, we aimed to study the results variability of commonly used dFC methods.Methods: We implemented 7 dFC assessment methods in Python and used them to analyze the functional magnetic resonance imaging data of 395 subjects from the Human Connectome Project. We measured the similarity of dFC results yielded by different methods using several metrics to quantify overall, temporal, spatial, and intersubject similarity.Results: Our results showed a range of weak to strong similarity between the results of different methods, indicating considerable overall variability. Somewhat surprisingly, the observed variability in dFC estimates was found to be comparable to the expected functional connectivity variation over time, emphasizing the impact of methodological choices on the final results. Our findings revealed 3 distinct groups of methods with significant intergroup variability, each exhibiting distinct assumptions and advantages.Conclusions: Overall, our findings shed light on the impact of dFC assessment analytical flexibility and highlight the need for multianalysis approaches and careful method selection to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-driven dFC variations from physiological confounds and developing validation frameworks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox, PydFC, which facilitates multianalysis dFC assessment, with the goal of enhancing the reliability and interpretability of dFC studies.","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11000510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140863530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

spatiAlign: an unsupervised contrastive learning model for data integration of spatially resolved transcriptomics. spatiAlign：用于空间解析转录组学数据整合的无监督对比学习模型。

IF 11.8 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae042

Chao Zhang, Lin Liu, Ying Zhang, Mei Li, Shuangsang Fang, Qiang Kang, Ao Chen, Xun Xu, Yong Zhang, Yuxiang Li

{"title":"spatiAlign: an unsupervised contrastive learning model for data integration of spatially resolved transcriptomics.","authors":"Chao Zhang, Lin Liu, Ying Zhang, Mei Li, Shuangsang Fang, Qiang Kang, Ao Chen, Xun Xu, Yong Zhang, Yuxiang Li","doi":"10.1093/gigascience/giae042","DOIUrl":"10.1093/gigascience/giae042","url":null,"abstract":"Background: Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times.Findings: We propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space.Conclusions: In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11258913/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141727100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

StereoSiTE: a framework to spatially and quantitatively profile the cellular neighborhood organized iTME. StereoSiTE：从空间上定量分析细胞邻近组织 iTME 的框架。

IF 11.8 2区生物学

GigaScience Pub Date : 2024-01-02 DOI: 10.1093/gigascience/giae078

Xing Liu, Chi Qu, Chuandong Liu, Na Zhu, Huaqiang Huang, Fei Teng, Caili Huang, Bingying Luo, Xuanzhu Liu, Min Xie, Feng Xi, Mei Li, Liang Wu, Yuxiang Li, Ao Chen, Xun Xu, Sha Liao, Jiajun Zhang

{"title":"StereoSiTE: a framework to spatially and quantitatively profile the cellular neighborhood organized iTME.","authors":"Xing Liu, Chi Qu, Chuandong Liu, Na Zhu, Huaqiang Huang, Fei Teng, Caili Huang, Bingying Luo, Xuanzhu Liu, Min Xie, Feng Xi, Mei Li, Liang Wu, Yuxiang Li, Ao Chen, Xun Xu, Sha Liao, Jiajun Zhang","doi":"10.1093/gigascience/giae078","DOIUrl":"https://doi.org/10.1093/gigascience/giae078","url":null,"abstract":"Background: Spatial transcriptome (ST) technologies are emerging as powerful tools for studying tumor biology. However, existing tools for analyzing ST data are limited, as they mainly rely on algorithms developed for single-cell RNA sequencing data and do not fully utilize the spatial information. While some algorithms have been developed for ST data, they are often designed for specific tasks, lacking a comprehensive analytical framework for leveraging spatial information.Results: In this study, we present StereoSiTE, an analytical framework that combines open-source bioinformatics tools with custom algorithms to accurately infer the functional spatial cell interaction intensity (SCII) within the cellular neighborhood (CN) of interest. We applied StereoSiTE to decode ST datasets from xenograft models and found that the CN efficiently distinguished different cellular contexts, while the SCII analysis provided more precise insights into intercellular interactions by incorporating spatial information. By applying StereoSiTE to multiple samples, we successfully identified a CN region dominated by neutrophils, suggesting their potential role in remodeling the immune tumor microenvironment (iTME) after treatment. Moreover, the SCII analysis within the CN region revealed neutrophil-mediated communication, supported by pathway enrichment, transcription factor regulon activities, and protein-protein interactions.Conclusions: StereoSiTE represents a promising framework for unraveling the mechanisms underlying treatment response within the iTME by leveraging CN-based tissue domain identification and SCII-inferred spatial intercellular interactions. The software is designed to be scalable, modular, and user-friendly, making it accessible to a wide range of researchers.","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11503478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142498592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0