Cell genomicsPub Date : 2024-04-10Epub Date: 2024-04-01DOI: 10.1016/j.xgen.2024.100538
Lili Wang, Nikita Babushkin, Zhonghua Liu, Xuanyao Liu
{"title":"Trans-eQTL mapping in gene sets identifies network effects of genetic variants.","authors":"Lili Wang, Nikita Babushkin, Zhonghua Liu, Xuanyao Liu","doi":"10.1016/j.xgen.2024.100538","DOIUrl":"https://doi.org/10.1016/j.xgen.2024.100538","url":null,"abstract":"<p><p>Nearly all trait-associated variants identified in genome-wide association studies (GWASs) are noncoding. The cis regulatory effects of these variants have been extensively characterized, but how they affect gene regulation in trans has been the subject of fewer studies because of the difficulty in detecting trans-expression quantitative loci (eQTLs). We developed trans-PCO for detecting trans effects of genetic variants on gene networks. Our simulations demonstrate that trans-PCO substantially outperforms existing trans-eQTL mapping methods. We applied trans-PCO to two gene expression datasets from whole blood, DGN (N = 913) and eQTLGen (N = 31,684), and identified 14,985 high-quality trans-eSNP-module pairs associated with 197 co-expression gene modules and biological processes. We performed colocalization analyses between GWAS loci of 46 complex traits and the trans-eQTLs. We demonstrated that the identified trans effects can help us understand how trait-associated variants affect gene regulatory networks and biological pathways.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11019359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140861063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-04-10Epub Date: 2024-03-08DOI: 10.1016/j.xgen.2024.100522
Bahar Zirak, Mohsen Naghipourfar, Ali Saberi, Delaram Pouyabahar, Amirhossein Zarezadeh, Lixi Luo, Lisa Fish, Doowon Huh, Albertas Navickas, Ali Sharifi-Zarchi, Hani Goodarzi
{"title":"Revealing the grammar of small RNA secretion using interpretable machine learning.","authors":"Bahar Zirak, Mohsen Naghipourfar, Ali Saberi, Delaram Pouyabahar, Amirhossein Zarezadeh, Lixi Luo, Lisa Fish, Doowon Huh, Albertas Navickas, Ali Sharifi-Zarchi, Hani Goodarzi","doi":"10.1016/j.xgen.2024.100522","DOIUrl":"10.1016/j.xgen.2024.100522","url":null,"abstract":"<p><p>Small non-coding RNAs can be secreted through a variety of mechanisms, including exosomal sorting, in small extracellular vesicles, and within lipoprotein complexes. However, the mechanisms that govern their sorting and secretion are not well understood. Here, we present ExoGRU, a machine learning model that predicts small RNA secretion probabilities from primary RNA sequences. We experimentally validated the performance of this model through ExoGRU-guided mutagenesis and synthetic RNA sequence analysis. Additionally, we used ExoGRU to reveal cis and trans factors that underlie small RNA secretion, including known and novel RNA-binding proteins (RBPs), e.g., YBX1, HNRNPA2B1, and RBM24. We also developed a novel technique called exoCLIP, which reveals the RNA interactome of RBPs within the cell-free space. Together, our results demonstrate the power of machine learning in revealing novel biological mechanisms. In addition to providing deeper insight into small RNA secretion, this knowledge can be leveraged in therapeutic and synthetic biology applications.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11019361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140068977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-04-10DOI: 10.1016/j.xgen.2024.100536
Tyler J Hansen, Sarah L Fong, Jessica K Day, John A Capra, Emily Hodges
{"title":"Human gene regulatory evolution is driven by the divergence of regulatory element function in both cis and trans.","authors":"Tyler J Hansen, Sarah L Fong, Jessica K Day, John A Capra, Emily Hodges","doi":"10.1016/j.xgen.2024.100536","DOIUrl":"https://doi.org/10.1016/j.xgen.2024.100536","url":null,"abstract":"<p><p>Gene regulatory divergence between species can result from cis-acting local changes to regulatory element DNA sequences or global trans-acting changes to the regulatory environment. Understanding how these mechanisms drive regulatory evolution has been limited by challenges in identifying trans-acting changes. We present a comprehensive approach to directly identify cis- and trans-divergent regulatory elements between human and rhesus macaque lymphoblastoid cells using assay for transposase-accessible chromatin coupled to self-transcribing active regulatory region (ATAC-STARR) sequencing. In addition to thousands of cis changes, we discover an unexpected number (∼10,000) of trans changes and show that cis and trans elements exhibit distinct patterns of sequence divergence and function. We further identify differentially expressed transcription factors that underlie ∼37% of trans differences and trace how cis changes can produce cascades of trans changes. Overall, we find that most divergent elements (67%) experienced changes in both cis and trans, revealing a substantial role for trans divergence-alone and together with cis changes-in regulatory differences between species.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11019363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140861062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-03-13DOI: 10.1016/j.xgen.2024.100525
Shondra M Pruett-Miller
{"title":"Fragmid: A toolkit for rapid assembly and assessment of CRISPR technologies.","authors":"Shondra M Pruett-Miller","doi":"10.1016/j.xgen.2024.100525","DOIUrl":"10.1016/j.xgen.2024.100525","url":null,"abstract":"<p><p>The CRISPR toolbox continues to expand at a rapid pace, leaving researchers scrambling to assess the latest tools in their systems of interest. McGee et al.<sup>1</sup> have developed a modular assembly platform with standardized and interchangeable components for rapid construction and deployment of novel CRISPR constructs.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10943574/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140133406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-03-13Epub Date: 2024-02-22DOI: 10.1016/j.xgen.2024.100505
Hugh D Goold, Jeffrey L Moseley, Kyle J Lauersen
{"title":"The synthetic future of algal genomes.","authors":"Hugh D Goold, Jeffrey L Moseley, Kyle J Lauersen","doi":"10.1016/j.xgen.2024.100505","DOIUrl":"10.1016/j.xgen.2024.100505","url":null,"abstract":"<p><p>Algae are diverse organisms with significant biotechnological potential for resource circularity. Taking inspiration from fermentative microbes, engineering algal genomes holds promise to broadly expand their application ranges. Advances in genome sequencing with improvements in DNA synthesis and delivery techniques are enabling customized molecular tool development to confer advanced traits to algae. Efforts to redesign and rebuild entire genomes to create fit-for-purpose organisms currently being explored in heterotrophic prokaryotes and eukaryotic microbes could also be applied to photosynthetic algae. Future algal genome engineering will enhance yields of native products and permit the expression of complex biochemical pathways to produce novel metabolites from sustainable inputs. We present a historical perspective on advances in engineering algae, discuss the requisite genetic traits to enable algal genome optimization, take inspiration from whole-genome engineering efforts in other microbes for algal systems, and present candidate algal species in the context of these engineering goals.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10943592/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139941356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"In vivo CRISPR screening directly targeting testicular cells.","authors":"Yuki Noguchi, Yasuhito Onodera, Tatsuo Miyamoto, Masahiro Maruoka, Hidetaka Kosako, Jun Suzuki","doi":"10.1016/j.xgen.2024.100510","DOIUrl":"10.1016/j.xgen.2024.100510","url":null,"abstract":"<p><p>CRISPR-Cas9 short guide RNA (sgRNA) library screening is a powerful approach to understand the molecular mechanisms of biological phenomena. However, its in vivo application is currently limited. Here, we developed our previously established in vitro revival screening method into an in vivo one to identify factors involved in spermatogenesis integrity by utilizing sperm capacitation as an indicator. By introducing an sgRNA library into testicular cells, we successfully pinpointed the retinal degeneration 3 (Rd3) gene as a significant factor in spermatogenesis. Single-cell RNA sequencing (scRNA-seq) analysis highlighted the high expression of Rd3 in round spermatids, and proteomics analysis indicated that Rd3 interacts with mitochondria. To search for cell-type-specific signaling pathways based on scRNA-seq and proteomics analyses, we developed a computational tool, Hub-Explorer. Through this, we discovered that Rd3 modulates oxidative stress by regulating mitochondrial distribution upon ciliogenesis induction. Collectively, our screening system provides a valuable in vivo approach to decipher molecular mechanisms in biological processes.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":11.1,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10943590/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140051234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-03-13Epub Date: 2024-02-26DOI: 10.1016/j.xgen.2024.100506
Karsten Suhre
{"title":"Genetic associations with ratios between protein levels detect new pQTLs and reveal protein-protein interactions.","authors":"Karsten Suhre","doi":"10.1016/j.xgen.2024.100506","DOIUrl":"10.1016/j.xgen.2024.100506","url":null,"abstract":"<p><p>Protein quantitative trait loci (pQTLs) are an invaluable source of information for drug target development because they provide genetic evidence to support protein function, suggest relationships between cis- and trans-associated proteins, and link proteins to disease endpoints. Using Olink proteomics data for 1,463 proteins measured in over 54,000 samples of the UK Biobank, we identified 4,248 associations with 2,821 ratios between protein levels (rQTLs). rQTLs were 7.6-fold enriched in known protein-protein interactions, suggesting that their ratios reflect biological links between the implicated proteins. Conducting a GWAS on ratios increased the number of discovered genetic signals by 24.7%. The approach can identify novel loci of clinical relevance, support causal gene identification, and reveal complex networks of interacting proteins. Taken together, our study adds significant value to the genetic insights that can be derived from the UKB proteomics data and motivates the wider use of ratios in large-scale GWAS.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10943581/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139984761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-03-13DOI: 10.1016/j.xgen.2024.100524
Rachel M Petersen, Amanda J Lea
{"title":"Diet composition impacts eQTL discovery across multiple tissues in baboons.","authors":"Rachel M Petersen, Amanda J Lea","doi":"10.1016/j.xgen.2024.100524","DOIUrl":"10.1016/j.xgen.2024.100524","url":null,"abstract":"<p><p>Understanding how genetic variation impacts gene expression is a major goal of genomics; however, only a fraction of disease-associated loci have been demonstrated to impact gene expression when cells are in an unperturbed \"steady state.\" In this issue of Cell Genomics, Lin et al.<sup>1</sup> investigate how exposure to a particular cellular context (i.e., a high-cholesterol, high-fat diet) can enhance our ability to identify new regulatory variants through longitudinal sampling of three tissue types in the baboon.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10943575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140133405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-03-13DOI: 10.1016/j.xgen.2024.100519
Abby V McGee, Yanjing V Liu, Audrey L Griffith, Zsofia M Szegletes, Bronte Wen, Carolyn Kraus, Nathan W Miller, Ryan J Steger, Berta Escude Velasco, Justin A Bosch, Jonathan D Zirin, Raghuvir Viswanatha, Erik J Sontheimer, Amy Goodale, Matthew A Greene, Thomas M Green, John G Doench
{"title":"Modular vector assembly enables rapid assessment of emerging CRISPR technologies.","authors":"Abby V McGee, Yanjing V Liu, Audrey L Griffith, Zsofia M Szegletes, Bronte Wen, Carolyn Kraus, Nathan W Miller, Ryan J Steger, Berta Escude Velasco, Justin A Bosch, Jonathan D Zirin, Raghuvir Viswanatha, Erik J Sontheimer, Amy Goodale, Matthew A Greene, Thomas M Green, John G Doench","doi":"10.1016/j.xgen.2024.100519","DOIUrl":"10.1016/j.xgen.2024.100519","url":null,"abstract":"<p><p>The diversity of CRISPR systems, coupled with scientific ingenuity, has led to an explosion of applications; however, to test newly described innovations in their model systems, researchers typically embark on cumbersome, one-off cloning projects to generate custom reagents that are optimized for their biological questions. Here, we leverage Golden Gate cloning to create the Fragmid toolkit, a modular set of CRISPR cassettes and delivery technologies, along with a web portal, resulting in a combinatorial platform that enables scalable vector assembly within days. We further demonstrate that multiple CRISPR technologies can be assessed in parallel in a pooled screening format using this resource, enabling the rapid optimization of both novel technologies and cellular models. These results establish Fragmid as a robust system for the rapid design of CRISPR vectors, and we anticipate that this assembly approach will be broadly useful for systematic development, comparison, and dissemination of CRISPR technologies.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":11.1,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10943585/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140133408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-03-13Epub Date: 2024-02-29DOI: 10.1016/j.xgen.2024.100511
Dan J Woodcock, Atef Sahli, Ruxandra Teslo, Vinayak Bhandari, Andreas J Gruber, Aleksandra Ziubroniewicz, Gunes Gundem, Yaobo Xu, Adam Butler, Ezequiel Anokian, Bernard J Pope, Chol-Hee Jung, Maxime Tarabichi, Stefan C Dentro, J Henry R Farmery, Peter Van Loo, Anne Y Warren, Vincent Gnanapragasam, Freddie C Hamdy, G Steven Bova, Christopher S Foster, David E Neal, Yong-Jie Lu, Zsofia Kote-Jarai, Michael Fraser, Robert G Bristow, Paul C Boutros, Anthony J Costello, Niall M Corcoran, Christopher M Hovens, Charlie E Massie, Andy G Lynch, Daniel S Brewer, Rosalind A Eeles, Colin S Cooper, David C Wedge
{"title":"Genomic evolution shapes prostate cancer disease type.","authors":"Dan J Woodcock, Atef Sahli, Ruxandra Teslo, Vinayak Bhandari, Andreas J Gruber, Aleksandra Ziubroniewicz, Gunes Gundem, Yaobo Xu, Adam Butler, Ezequiel Anokian, Bernard J Pope, Chol-Hee Jung, Maxime Tarabichi, Stefan C Dentro, J Henry R Farmery, Peter Van Loo, Anne Y Warren, Vincent Gnanapragasam, Freddie C Hamdy, G Steven Bova, Christopher S Foster, David E Neal, Yong-Jie Lu, Zsofia Kote-Jarai, Michael Fraser, Robert G Bristow, Paul C Boutros, Anthony J Costello, Niall M Corcoran, Christopher M Hovens, Charlie E Massie, Andy G Lynch, Daniel S Brewer, Rosalind A Eeles, Colin S Cooper, David C Wedge","doi":"10.1016/j.xgen.2024.100511","DOIUrl":"10.1016/j.xgen.2024.100511","url":null,"abstract":"<p><p>The development of cancer is an evolutionary process involving the sequential acquisition of genetic alterations that disrupt normal biological processes, enabling tumor cells to rapidly proliferate and eventually invade and metastasize to other tissues. We investigated the genomic evolution of prostate cancer through the application of three separate classification methods, each designed to investigate a different aspect of tumor evolution. Integrating the results revealed the existence of two distinct types of prostate cancer that arise from divergent evolutionary trajectories, designated as the Canonical and Alternative evolutionary disease types. We therefore propose the evotype model for prostate cancer evolution wherein Alternative-evotype tumors diverge from those of the Canonical-evotype through the stochastic accumulation of genetic alterations associated with disruptions to androgen receptor DNA binding. Our model unifies many previous molecular observations, providing a powerful new framework to investigate prostate cancer disease progression.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10943594/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140013825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}