Briefings in Functional Genomics最新文献_第2页

STAT3-dependent long non-coding RNA Lncenc1 contributes to mouse ES cells pluripotency via stabilizing Klf4 mRNA. STAT3依赖的长非编码RNA Lncenc1通过稳定KmRNA促进小鼠ES细胞的多能性。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-09-27 DOI: 10.1093/bfgp/elad045

Emanuele Monteleone, Paola Corrieri, Paolo Provero, Daniele Viavattene, Lorenzo Pulvirenti, Laura Raggi, Elena Carbognin, Marco E Bianchi, Graziano Martello, Salvatore Oliviero, Pier Paolo Pandolfi, Valeria Poli

{"title":"STAT3-dependent long non-coding RNA Lncenc1 contributes to mouse ES cells pluripotency via stabilizing Klf4 mRNA.","authors":"Emanuele Monteleone, Paola Corrieri, Paolo Provero, Daniele Viavattene, Lorenzo Pulvirenti, Laura Raggi, Elena Carbognin, Marco E Bianchi, Graziano Martello, Salvatore Oliviero, Pier Paolo Pandolfi, Valeria Poli","doi":"10.1093/bfgp/elad045","DOIUrl":"10.1093/bfgp/elad045","url":null,"abstract":"Embryonic stem cells (ESCs) preserve the unique ability to differentiate into any somatic cell lineage while maintaining their self-renewal potential, relying on a complex interplay of extracellular signals regulating the expression/activity of pluripotency transcription factors and their targets. Leukemia inhibitory factor (LIF)-activated STAT3 drives ESCs' stemness by a number of mechanisms, including the transcriptional induction of pluripotency factors such as Klf4 and the maintenance of a stem-like epigenetic landscape. However, it is unknown if STAT3 directly controls stem-cell specific non-coding RNAs, crucial to balance pluripotency and differentiation. Applying a bioinformatic pipeline, here we identify Lncenc1 in mouse ESCs as an STAT3-dependent long non-coding RNA that supports pluripotency. Lncenc1 acts in the cytoplasm as a positive feedback regulator of the LIF-STAT3 axis by competing for the binding of microRNA-128 to the 3'UTR of the Klf4 core pluripotency factor mRNA, enhancing its expression. Our results unveil a novel non-coding RNA-based mechanism for LIF-STAT3-mediated pluripotency.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11428181/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41160189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Microscale marvels: unveiling the macroscopic significance of micropeptides in human health. 微观奇迹：揭示微肽对人类健康的宏观意义。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-09-27 DOI: 10.1093/bfgp/elae018

Deepyaman Das, Soumita Podder

引用次数: 0

Correction to: STAT3-dependent long non-coding RNA Lncenc1 contributes to mouse ES cells pluripotency via stabilizing Klf4 mRNA. 更正：STAT3依赖性长非编码RNA Lncenc1通过稳定Klf4 mRNA促进小鼠ES细胞的多能性。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-09-27 DOI: 10.1093/bfgp/elad047

引用次数: 0

Interpretation of SNP combination effects on schizophrenia etiology based on stepwise deep learning with multi-precision data. 基于多精度数据的逐步深度学习解释SNP组合对精神分裂症病因的影响。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-09-27 DOI: 10.1093/bfgp/elad041

Yousang Jo, Maree J Webster, Sanghyeon Kim, Doheon Lee

{"title":"Interpretation of SNP combination effects on schizophrenia etiology based on stepwise deep learning with multi-precision data.","authors":"Yousang Jo, Maree J Webster, Sanghyeon Kim, Doheon Lee","doi":"10.1093/bfgp/elad041","DOIUrl":"10.1093/bfgp/elad041","url":null,"abstract":"Schizophrenia genome-wide association studies (GWAS) have reported many genomic risk loci, but it is unclear how they affect schizophrenia susceptibility through interactions of multiple SNPs. We propose a stepwise deep learning technique with multi-precision data (SLEM) to explore the SNP combination effects on schizophrenia through intermediate molecular and cellular functions. The SLEM technique utilizes two levels of precision data for learning. It constructs initial backbone networks with more precise but small amount of multilevel assay data. Then, it learns strengths of intermediate interactions with the less precise but massive amount of GWAS data. The learned networks facilitate identifying effective SNP interactions from the intractably large space of all possible SNP combinations. We have shown that the extracted SNP combinations show higher accuracy than any single SNPs and preserve the accuracy in an independent dataset. The learned networks also provide interpretations of molecular and cellular interactions of SNP combinations toward schizophrenia etiology.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11428150/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41123956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Unveiling aging dynamics in the hematopoietic system insights from single-cell technologies. 单细胞技术揭示造血系统的衰老动态

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-09-27 DOI: 10.1093/bfgp/elae019

Xinrong Jin, Ruohan Zhang, Yunqi Fu, Qiunan Zhu, Liquan Hong, Aiwei Wu, Hu Wang

{"title":"Unveiling aging dynamics in the hematopoietic system insights from single-cell technologies.","authors":"Xinrong Jin, Ruohan Zhang, Yunqi Fu, Qiunan Zhu, Liquan Hong, Aiwei Wu, Hu Wang","doi":"10.1093/bfgp/elae019","DOIUrl":"10.1093/bfgp/elae019","url":null,"abstract":"As the demographic structure shifts towards an aging society, strategies aimed at slowing down or reversing the aging process become increasingly essential. Aging is a major predisposing factor for many chronic diseases in humans. The hematopoietic system, comprising blood cells and their associated bone marrow microenvironment, intricately participates in hematopoiesis, coagulation, immune regulation and other physiological phenomena. The aging process triggers various alterations within the hematopoietic system, serving as a spectrum of risk factors for hematopoietic disorders, including clonal hematopoiesis, immune senescence, myeloproliferative neoplasms and leukemia. The emerging single-cell technologies provide novel insights into age-related changes in the hematopoietic system. In this review, we summarize recent studies dissecting hematopoietic system aging using single-cell technologies. We discuss cellular changes occurring during aging in the hematopoietic system at the levels of the genomics, transcriptomics, epigenomics, proteomics, metabolomics and spatial multi-omics. Finally, we contemplate the future prospects of single-cell technologies, emphasizing the impact they may bring to the field of hematopoietic system aging research.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140861721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction. 系统分析基因组数据的不同生物信息学管道及其对染色质环路预测深度学习模型的影响。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-09-27 DOI: 10.1093/bfgp/elae009

Anup Kumar Halder, Abhishek Agarwal, Karolina Jodkowska, Dariusz Plewczynski

{"title":"A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction.","authors":"Anup Kumar Halder, Abhishek Agarwal, Karolina Jodkowska, Dariusz Plewczynski","doi":"10.1093/bfgp/elae009","DOIUrl":"10.1093/bfgp/elae009","url":null,"abstract":"Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genome organization. This systematic investigation explores the realm of specialized bioinformatics pipelines designed specifically for the analysis of chromatin loops and structures. Our investigation incorporates two protein (CTCF and Cohesin) factor-specific loop interaction datasets from six distinct pipelines, amassing a comprehensive collection of 36 diverse datasets. Through a meticulous review of existing literature, we offer a holistic perspective on the methodologies, tools and algorithms underpinning the analysis of this multifaceted genomic feature. We illuminate the vast array of approaches deployed, encompassing pivotal aspects such as data preparation pipeline, preprocessing, statistical features and modelling techniques. Beyond this, we rigorously assess the strengths and limitations inherent in these bioinformatics pipelines, shedding light on the interplay between data quality and the performance of deep learning models, ultimately advancing our comprehension of genomic intricacies.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140330336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding large scale sequencing datasets through changes to protein folding. 通过蛋白质折叠的变化理解大规模测序数据集。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-09-27 DOI: 10.1093/bfgp/elae007

David Shorthouse, Harris Lister, Gemma S Freeman, Benjamin A Hall

引用次数: 0

Computational drug repurposing for viral infectious diseases: a case study on monkeypox. 病毒性传染病的计算药物再利用：猴痘案例研究。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-09-27 DOI: 10.1093/bfgp/elad058

Sovan Saha, Piyali Chatterjee, Mita Nasipuri, Subhadip Basu, Tapabrata Chakraborti

引用次数: 0

Advancements in genetic techniques and functional genomics for enhancing crop traits and agricultural sustainability. 基因技术和功能基因组学在提高作物性状和农业可持续性方面的进步。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-09-27 DOI: 10.1093/bfgp/elae017

Surender Kumar, Anupama Singh, Chander Mohan Singh Bist, Munish Sharma

{"title":"Advancements in genetic techniques and functional genomics for enhancing crop traits and agricultural sustainability.","authors":"Surender Kumar, Anupama Singh, Chander Mohan Singh Bist, Munish Sharma","doi":"10.1093/bfgp/elae017","DOIUrl":"10.1093/bfgp/elae017","url":null,"abstract":"Genetic variability is essential for the development of new crop varieties with economically beneficial traits. The traits can be inherited from wild relatives or induced through mutagenesis. Novel genetic elements can then be identified and new gene functions can be predicted. In this study, forward and reverse genetics approaches were described, in addition to their applications in modern crop improvement programs and functional genomics. By using heritable phenotypes and linked genetic markers, forward genetics searches for genes by using traditional genetic mapping and allele frequency estimation. Despite recent advances in sequencing technology, omics and computation, genetic redundancy remains a major challenge in forward genetics. By analyzing close-related genes, we will be able to dissect their functional redundancy and predict possible traits and gene activity patterns. In addition to these predictions, sophisticated reverse gene editing tools can be used to verify them, including TILLING, targeted insertional mutagenesis, gene silencing, gene targeting and genome editing. By using gene knock-down, knock-up and knock-out strategies, these tools are able to detect genetic changes in cells. In addition, epigenome analysis and editing enable the development of novel traits in existing crop cultivars without affecting their genetic makeup by increasing epiallelic variants. Our understanding of gene functions and molecular dynamics of various biological phenomena has been revised by all of these findings. The study also identifies novel genetic targets in crop species to improve yields and stress tolerances through conventional and non-conventional methods. In this article, genetic techniques and functional genomics are specifically discussed and assessed for their potential in crop improvement.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140873722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial for BFG special issue: Computational genomics for precision medicine and personalized healthcare. BFG 特刊编辑：精准医学和个性化医疗的计算基因组学。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-09-27 DOI: 10.1093/bfgp/elae021

Tapabrata Chakraborti, Subhadip Basu

引用次数: 0