Advanced genetics (Hoboken, N.J.)最新文献

{"title":"(Advanced Genetics 4/12)","authors":"","doi":"10.1002/ggn2.202170041","DOIUrl":"https://doi.org/10.1002/ggn2.202170041","url":null,"abstract":"<p>\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ggn2.202170041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91859673","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":"Community Consensus Guidelines to Support FAIR Data Standards in Clinical Research Studies in Primary Mitochondrial Disease","authors":"Amel Karaa,&nbsp;Laura E. MacMullen,&nbsp;John C. Campbell,&nbsp;John Christodoulou,&nbsp;Bruce H. Cohen,&nbsp;Thomas Klopstock,&nbsp;Yasutoshi Koga,&nbsp;Costanza Lamperti,&nbsp;Rob van Maanen,&nbsp;Robert McFarland,&nbsp;Sumit Parikh,&nbsp;Shamima Rahman,&nbsp;Fernando Scaglia,&nbsp;Alexander V. Sherman,&nbsp;Philip Yeske,&nbsp;Marni J. Falk","doi":"10.1002/ggn2.202100047","DOIUrl":"10.1002/ggn2.202100047","url":null,"abstract":"<p>Primary mitochondrial diseases (PMD) are genetic disorders with extensive clinical and molecular heterogeneity where therapeutic development efforts have faced multiple challenges. Clinical trial design, outcome measure selection, lack of reliable biomarkers, and deficiencies in long-term natural history data sets remain substantial challenges in the increasingly active PMD therapeutic development space. Developing “FAIR” (findable, accessible, interoperable, reusable) data standards to make data sharable and building a more transparent community data sharing paradigm to access clinical research metadata are the first steps to address these challenges. This collaborative community effort describes the current landscape of PMD clinical research data resources available for sharing, obstacles, and opportunities, including ways to incentivize and encourage data sharing among diverse stakeholders. This work highlights the importance of, and challenges to, developing a unified system that enables clinical research structured data sharing and supports harmonized data deposition standards across clinical consortia and research groups. The goal of these efforts is to improve the efficiency and effectiveness of drug development and improve understanding of the natural history of PMD. This initiative aims to maximize the benefit for PMD patients, research, industry, and other stakeholders while acknowledging challenges related to differing needs and international policies on data privacy, security, management, and oversight.</p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8936395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10498531","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 Development of Mitochondrial Gene Editing Tools and Their Possible Roles in Crop Improvement for Future Agriculture","authors":"Jinghua Yang,&nbsp;Xiaodong Yang,&nbsp;Tongbing Su,&nbsp;Zhongyuan Hu,&nbsp;Mingfang Zhang","doi":"10.1002/ggn2.202100019","DOIUrl":"10.1002/ggn2.202100019","url":null,"abstract":"<p>We are living in the era of genome editing. Nowadays, targeted editing of the plant nuclear DNA is prevalent in basic biological research and crop improvement since its first establishment a decade ago. However, achieving the same accomplishment for the plant mitochondrial genome has long been deemed impossible. Recently, the pioneer studies on editing plant mitogenome have been done using the mitochondria-targeted transcription activator-like effector nucleases (mitoTALENs) in rice, rapeseed, and Arabidopsis. It is well documented that mitochondria play essential roles in plant development and stress tolerance, particularly, in cytoplasmic male sterility widely used in production of hybrids. The success of mitochondrial genome editing enables studying the fundamentals of mitochondrial genome. Furthermore, mitochondrial RNA editing (mostly by nuclear-encoded pentatricopeptide repeat (PPR) proteins) in a sequence-specific manner can simultaneously change the production of translatable mitochondrial mRNA. Moreover, direct editing of the nuclear-encoding mitochondria-targeted factors required for plant mitochondrial genome dynamics and recombination may facilitate genetic manipulation of plant mitochondria. Here, the present state of knowledge on editing the plant mitochondrial genome is reviewed.</p>","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9744482/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10564860","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":"Transposable Element Populations Shed Light on the Evolutionary History of Wheat and the Complex Co-Evolution of Autonomous and Non-Autonomous Retrotransposons","authors":"Thomas Wicker,&nbsp;Christoph Stritt,&nbsp;Alexandros G. Sotiropoulos,&nbsp;Manuel Poretti,&nbsp;Curtis Pozniak,&nbsp;Sean Walkowiak,&nbsp;Heidrun Gundlach,&nbsp;Nils Stein","doi":"10.1002/ggn2.202100022","DOIUrl":"10.1002/ggn2.202100022","url":null,"abstract":"Wheat has one of the largest and most repetitive genomes among major crop plants, containing over 85% transposable elements (TEs). TEs populate genomes much in the way that individuals populate ecosystems, diversifying into different lineages, sub‐families and sub‐populations. The recent availability of high‐quality, chromosome‐scale genome sequences from ten wheat lines enables a detailed analysis how TEs evolved in allohexaploid wheat, its diploids progenitors, and in various chromosomal haplotype segments. LTR retrotransposon families evolved into distinct sub‐populations and sub‐families that were active in waves lasting several hundred thousand years. Furthermore, It is shown that different retrotransposon sub‐families were active in the three wheat sub‐genomes, making them useful markers to study and date polyploidization events and chromosomal rearrangements. Additionally, haplotype‐specific TE sub‐families are used to characterize chromosomal introgressions in different wheat lines. Additionally, populations of non‐autonomous TEs co‐evolved over millions of years with their autonomous partners, leading to complex systems with multiple types of autonomous, semi‐autonomous and non‐autonomous elements. Phylogenetic and TE population analyses revealed the relationships between non‐autonomous elements and their mobilizing autonomous partners. TE population analysis provided insights into genome evolution of allohexaploid wheat and genetic diversity of species, and may have implication for future crop breeding.","PeriodicalId":72071,"journal":{"name":"Advanced genetics (Hoboken, N.J.)","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9744471/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10564856","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":"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}