Advanced genetics (Hoboken, N.J.)最新文献

{"title":"Genes for Yield Stability in Tomatoes","authors":"Josef Fisher,&nbsp;Dani Zamir","doi":"10.1002/ggn2.202100049","DOIUrl":"10.1002/ggn2.202100049","url":null,"abstract":"<p>Breeding plant varieties with adaptation to unstable environments requires some knowledge about the genetic control of yield stability. To further this goal, a meta-analysis of 12 years of field harvest data of 76 <i>Solanum pennellii</i> introgression lines (ILs) is conducted. Five quantitative trait loci (QTL) affecting yield stability are mapped; IL10-2-2 is unique as this introgression improved yield stability without affecting mean yield both in the historic data and in four years of field validations. Another dimension of the stability question is which genes when perturbed affect yield stability. For this the authors tested in the field 48 morphological mutants and found one ‘canalization’ mutant (<i>canal-1</i>) with a consistent effect of reducing the stability of a bouquet of traits including leaf variegation, plant size and yield. <i>canal-1</i> mapped to a DNAJ chaperone gene (Solyc01g108200) whose homologues in <i>C. elegans</i> regulate phenotypic canalization. Additional alleles of <i>canal-1</i> are generated using CRISPR/CAS9 and the resulting seedlings have uniform variegation suggesting that only specific changes in <i>canal-1</i> can lead to unstable variegation and yield instability. The identification of IL10-2-2 demonstrates the value of historical phenotypic data for discovering genes for stability. It is also shown that a green-fruited wild species is a source of QTL to improve tomato yield stability.</p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9744526/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10499910","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":"Gut Microbiota Linked with Reduced Fear of Humans in Red Junglefowl Has Implications for Early Domestication","authors":"Lara C. Puetz,&nbsp;Tom O. Delmont,&nbsp;Ostaizka Aizpurua,&nbsp;Chunxue Guo,&nbsp;Guojie Zhang,&nbsp;Rebecca Katajamaa,&nbsp;Per Jensen,&nbsp;M. Thomas P. Gilbert","doi":"10.1002/ggn2.202100018","DOIUrl":"10.1002/ggn2.202100018","url":null,"abstract":"<p>Domestication of animals can lead to profound phenotypic modifications within short evolutionary time periods, and for many species behavioral selection is likely at the forefront of this process. Animal studies have strongly implicated that the gut microbiome plays a major role in host behavior and cognition through the microbiome–gut–brain axis. Consequently, herein, it is hypothesized that host gut microbiota may be one of the earliest phenotypes to change as wild animals were domesticated. Here, the gut microbiome community in two selected lines of red junglefowl that are selected for either high or low fear of humans up to eight generations is examined. Microbiota profiles reveal taxonomic differences in gut bacteria known to produce neuroactive compounds between the two selection lines. Gut–brain module analysis by means of genome-resolved metagenomics identifies enrichment in the microbial synthesis and degradation potential of metabolites associated with fear extinction and reduces anxiety-like behaviors in low fear fowls. In contrast, high fear fowls are enriched in gut–brain modules from the butyrate and glutamate pathways, metabolites associated with fear conditioning. Overall, the results identify differences in the composition and functional potential of the gut microbiota across selection lines that may provide insights into the mechanistic explanations of the domestication process.</p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9744516/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10499912","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":"Facilitating Machine Learning-Guided Protein Engineering with Smart Library Design and Massively Parallel Assays","authors":"Hoi Yee Chu,&nbsp;Alan S. L. Wong","doi":"10.1002/ggn2.202100038","DOIUrl":"10.1002/ggn2.202100038","url":null,"abstract":"<p>Protein design plays an important role in recent medical advances from antibody therapy to vaccine design. Typically, exhaustive mutational screens or directed evolution experiments are used for the identification of the best design or for improvements to the wild-type variant. Even with a high-throughput screening on pooled libraries and Next-Generation Sequencing to boost the scale of read-outs, surveying all the variants with combinatorial mutations for their empirical fitness scores is still of magnitudes beyond the capacity of existing experimental settings. To tackle this challenge, in-silico approaches using machine learning to predict the fitness of novel variants based on a subset of empirical measurements are now employed. These machine learning models turn out to be useful in many cases, with the premise that the experimentally determined fitness scores and the amino-acid descriptors of the models are informative. The machine learning models can guide the search for the highest fitness variants, resolve complex epistatic relationships, and highlight bio-physical rules for protein folding. Using machine learning-guided approaches, researchers can build more focused libraries, thus relieving themselves from labor-intensive screens and fast-tracking the optimization process. Here, we describe the current advances in massive-scale variant screens, and how machine learning and mutagenesis strategies can be integrated to accelerate protein engineering. More specifically, we examine strategies to make screens more economical, informative, and effective in discovery of useful variants.</p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9744531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10506858","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":"(Advanced Genetics 4/12)","authors":"","doi":"10.1002/ggn2.202170041","DOIUrl":"https://doi.org/10.1002/ggn2.202170041","url":null,"abstract":"","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80211765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Masthead: (Advanced Genetics 4/12)","authors":"N. Barzilai, A. Einstein, J. Batley","doi":"10.1002/ggn2.202170042","DOIUrl":"https://doi.org/10.1002/ggn2.202170042","url":null,"abstract":"Nadav Ahituv, University of California, San Francisco, San Francisco, CA USA Nir Barzilai, Albert Einstein College of Medicine, Bronx, NY USA Jacqueline Batley, University of Western Australia, Perth, Australia Touati Benoukraf,Memorial University of Newfoundland, St. John’s, NL, Canada Ewan Birney, EMBL-EBI, Cambridge, UK Catherine A. Brownstein, Boston Children’s Hospital, Boston, MA USA Stephen J. Chanock, National Cancer Institute, Bethesda, MD USA George Church, Harvard Medical School, Boston, MA USA Francesco Cucca, University of Sassari, Sassari, Sardinia, Italy Marcella Devoto, Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA USA Roland Eils, Berlin Institue of Health, Berlin, Germany Jeanette Erdmann, Institute for Cardiogenetics, University of Lubeck, Lubeck, Germany Andrew Feinberg, Johns Hopkins University, Baltimore, MD USA Claudio Franceschi, University of Bologna, Bologna, Italy Paul W. Franks, Lund University, Malmö, Sweden Rachel Freathy, University of Exeter, Exeter, UK Jingyuan Fu, University Medical Center Groningen, Groningen, The Netherlands Eileen Furlong, European Molecular Biology Laboratory, Heidelberg, Germany Tom Gilbert, University of Copenhagen, The Globe Institute, Copenhagen, Denmark Joseph G. Gleeson, University of California, San Diego, Howard Hughes Medical Institute for Genomic Medicine, La Jolla, CA USA Erica Golemis, Fox Chase Cancer Center, Philadelphia, PA USA Sarah Hearne, International Maize and Wheat Improvement Centre (CIMMYT), Texcoco, Mexico Agnar Helgason, deCODE Genetics, Reykjavik, Iceland Kristina Hettne, Leiden University Libraries, Leiden, The Netherlands John Hickey, The Roslin Institute, Edinburgh, UK Sanwen Huang, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China Youssef Idaghdour, New York University, Abu Dhabi, Abu Dhabi, UAE Rosalind John, Cardiff University, Cardiff, UK Astrid Junker, Leibniz Institute of Plant Genetics, Crop Plant Research (IPK) Gatersleben, Stadt Seeland, OT Gatersleben, Germany Moien Kanaan, Bethlehem University, Bethlehem, Palestine Beat Keller, University of Zurich, Zurich, Switzerland Tuuli Lappalainen, New York Genome Center, Columbia University, New York, NY USA Luis F. Larrondo, Pontifica Universidad Catolica de Chile, Santiago, Chile Suet-Yi Leung, The University of Hong Kong, Hong Kong, China Ryan Lister, The University of Western Australia, Perth, Australia Jianjun Liu, Genome Institute Singapore, Singapore Naomichi Matsumoto, Yokohama City University, Yokohama, Japan Rachel S. Meyer, University of California, Los Angeles, Los Angeles, CA USA Nicola Mulder, University of Cape Town, Cape Town, South Africa Huck-Hui Ng, Genome Institute of Singapore, Singapore John Novembre, University of Chicago, Chicago, IL USA Seishi Ogawa, Kyoto University, Kyoto, Japan Guilherme Oliveira, Vale Institute of Technology, Belem, Brazil Qiang Pan-Hammarstrom, Karolinska Institute, Stockholm, Sw","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88390713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breeding custom-designed crops for improved drought adaptation","authors":"Rajeev K. Varshney,&nbsp;Rutwik Barmukh,&nbsp;Manish Roorkiwal,&nbsp;Yiping Qi,&nbsp;Jana Kholova,&nbsp;Roberto Tuberosa,&nbsp;Matthew P. Reynolds,&nbsp;Francois Tardieu,&nbsp;Kadambot H. M. Siddique","doi":"10.1002/ggn2.202100017","DOIUrl":"10.1002/ggn2.202100017","url":null,"abstract":"<p>The current pace of crop improvement is inadequate to feed the burgeoning human population by 2050. Higher, more stable, and sustainable crop production is required against a backdrop of drought stress, which causes significant losses in crop yields. Tailoring crops for drought adaptation may hold the key to address these challenges and provide resilient production systems for future harvests. Understanding the genetic and molecular landscape of the functionality of alleles associated with adaptive traits will make designer crop breeding the prospective approach for crop improvement. Here, we highlight the potential of genomics technologies combined with crop physiology for high-throughput identification of the genetic architecture of key drought-adaptive traits and explore innovative genomic breeding strategies for designing future crops.</p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9744523/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10509207","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":"Optogenetic-mediated cardiovascular differentiation and patterning of human pluripotent stem cells","authors":"Peter B. Hellwarth,&nbsp;Yun Chang,&nbsp;Arundhati Das,&nbsp;Po-Yu Liang,&nbsp;Xiaojun Lian,&nbsp;Nicole A. Repina,&nbsp;Xiaoping Bao","doi":"10.1002/ggn2.202100011","DOIUrl":"10.1002/ggn2.202100011","url":null,"abstract":"<p>Precise spatial and temporal regulation of dynamic morphogen signals during human development governs the processes of cell proliferation, migration, and differentiation to form organized tissues and organs. Tissue patterns spontaneously emerge in various human pluripotent stem cell (hPSC) models. However, the lack of molecular methods for precise control over signal dynamics limits the reproducible production of tissue patterns and a mechanistic understanding of self-organization. We recently implemented an optogenetic-based OptoWnt platform for light-controllable regulation of Wnt/β-catenin signaling in hPSCs for <i>in vitro</i> studies. Using engineered illumination devices to generate light patterns and thus precise spatiotemporal control over Wnt activation, here we triggered spatially organized transcriptional changes and mesoderm differentiation of hPSCs. In this way, the OptoWnt system enabled robust endothelial cell differentiation and cardiac tissue patterning <i>in vitro</i>. Our results demonstrate that spatiotemporal regulation of signaling pathways via synthetic OptoWnt enables instructive stem cell fate engineering and tissue patterning.</p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/ggn2.202100011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10509210","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":"A phylogenetic analysis of the wild Tulipa species (Liliaceae) of Kosovo based on plastid and nuclear DNA sequence","authors":"Avni Hajdari,&nbsp;Bledar Pulaj,&nbsp;Corinna Schmiderer,&nbsp;Xhavit Mala,&nbsp;Brett Wilson,&nbsp;Kimete Lluga-Rizani,&nbsp;Behxhet Mustafa","doi":"10.1002/ggn2.202100016","DOIUrl":"10.1002/ggn2.202100016","url":null,"abstract":"<p>In Kosovo, the genus <i>Tulipa</i> is represented by eight taxa, most of which form a species complex surrounding <i>Tulipa scardica</i>. To investigate the phylogenetic relationship of these <i>Tulipa</i> species a Bayesian analysis was undertaken using the ITS nuclear marker and <i>trnL-trnF</i>, <i>rbcL</i> and <i>psbA-trnH</i> plastid markers. The resulting phylogenetic trees show that Kosovarian <i>Tulipa</i> species consistently group into two main clades, the subgenera <i>Eriostemones</i> and <i>Tulipa</i>. Furthermore, our analyses provide some evidence that the subspecies of <i>Tulipa sylvestris</i> are genetically distinguishable, however not significantly enough to support their reclassification as species. In contrast, the markers provide some novel information to reassess the species concepts of the <i>T</i>. <i>scardica</i> complex. Our data provide support for the synonymisation of <i>Tulipa luanica</i> and <i>Tulipa kosovarica</i> under the species <i>Tulipa serbica</i>. Resolution and sampling limitations hinder any concrete conclusion about whether <i>Tulipa albanica</i> and <i>T</i>. <i>scardica</i> are true species, yet our data do provide some support that these are unique taxa and therefore should continue to be treated as such until further clarification. Overall, our work shows that genetic data will be important in determining species concepts in this genus, however, even with a molecular perspective pulling apart closely related taxa can be extremely challenging.</p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/ggn2.202100016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10509209","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":"The genomic origin of Zana of Abkhazia","authors":"Ashot Margaryan,&nbsp;Mikkel-Holger S. Sinding,&nbsp;Christian Carøe,&nbsp;Vladimir Yamshchikov,&nbsp;Igor Burtsev,&nbsp;M. Thomas P. Gilbert","doi":"10.1002/ggn2.10051","DOIUrl":"10.1002/ggn2.10051","url":null,"abstract":"<p>Enigmatic phenomena have sparked the imagination of people around the globe into creating folkloric creatures. One prime example is Zana of Abkhazia (South Caucasus), a well-documented 19th century female who was captured living wild in the forest. Zana's appearance was sufficiently unusual, that she was referred to by locals as an Almasty—the analog of Bigfoot in the Caucasus. Although the exact location of Zana's burial site was unknown, the grave of her son, Khwit, was identified in 1971. The genomes of Khwit and the alleged Zana skeleton were sequenced to an average depth of ca. 3× using ancient DNA techniques. The identical mtDNA and parent-offspring relationship between the two indicated that the unknown woman was indeed Zana. Population genomic analyses demonstrated that Zana's immediate genetic ancestry can likely be traced to present-day East-African populations. We speculate that Zana might have had a genetic disorder such as congenital generalized hypertrichosis which could partially explain her strange behavior, lack of speech, and long body hair. Our findings elucidate Zana's unfortunate story and provide a clear example of how prejudices of the time led to notions of cryptic hominids that are still held and transmitted by some today.</p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/ggn2.10051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10857452","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":"Design of a FAIR digital data health infrastructure in Africa for COVID-19 reporting and research","authors":"Mirjam van Reisen,&nbsp;Francisca Oladipo,&nbsp;Mia Stokmans,&nbsp;Mouhamed Mpezamihgo,&nbsp;Sakinat Folorunso,&nbsp;Erik Schultes,&nbsp;Mariam Basajja,&nbsp;Aliya Aktau,&nbsp;Samson Yohannes Amare,&nbsp;Getu Tadele Taye,&nbsp;Putu Hadi Purnama Jati,&nbsp;Kudakwashe Chindoza,&nbsp;Morgane Wirtz,&nbsp;Meriem Ghardallou,&nbsp;Gertjan van Stam,&nbsp;Wondimu Ayele,&nbsp;Reginald Nalugala,&nbsp;Ibrahim Abdullahi,&nbsp;Obinna Osigwe,&nbsp;John Graybeal,&nbsp;Araya Abrha Medhanyie,&nbsp;Abdullahi Abubakar Kawu,&nbsp;Fenghong Liu,&nbsp;Katy Wolstencroft,&nbsp;Erik Flikkenschild,&nbsp;Yi Lin,&nbsp;Joëlle Stocker,&nbsp;Mark A. Musen","doi":"10.1002/ggn2.10050","DOIUrl":"10.1002/ggn2.10050","url":null,"abstract":"<p>The limited volume of COVID-19 data from Africa raises concerns for global genome research, which requires a diversity of genotypes for accurate disease prediction, including on the provenance of the new SARS-CoV-2 mutations. The Virus Outbreak Data Network (VODAN)-Africa studied the possibility of increasing the production of clinical data, finding concerns about data ownership, and the limited use of health data for quality treatment at point of care. To address this, VODAN Africa developed an architecture to record clinical health data and research data collected on the incidence of COVID-19, producing these as human- and machine-readable data objects in a distributed architecture of locally governed, linked, human- and machine-readable data. This architecture supports analytics at the point of care and—through data visiting, across facilities—for generic analytics. An algorithm was run across FAIR Data Points to visit the distributed data and produce aggregate findings. The FAIR data architecture is deployed in Uganda, Ethiopia, Liberia, Nigeria, Kenya, Somalia, Tanzania, Zimbabwe, and Tunisia.</p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/ggn2.10050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39412993","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}