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Yeon J, Porwal C, McGrath PT, Sengupta P. Identification of a spontaneously arising variant affecting thermotaxis behavior in a recombinant inbred Caenorhabditis elegans line. G3 (BETHESDA, MD.) 2023; 13:jkad186. [PMID: 37572357 PMCID: PMC10542565 DOI: 10.1093/g3journal/jkad186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 06/26/2023] [Accepted: 08/02/2023] [Indexed: 08/14/2023]
Abstract
Analyses of the contributions of genetic variants in wild strains to phenotypic differences have led to a more complete description of the pathways underlying cellular functions. Causal loci are typically identified via interbreeding of strains with distinct phenotypes in order to establish recombinant inbred lines (RILs). Since the generation of RILs requires growth for multiple generations, their genomes may contain not only different combinations of parental alleles but also genetic changes that arose de novo during the establishment of these lines. Here, we report that in the course of generating RILs between Caenorhabditis elegans strains that exhibit distinct thermotaxis behavioral phenotypes, we identified spontaneously arising variants in the ttx-1 locus. ttx-1 encodes the terminal selector factor for the AFD thermosensory neurons, and loss-of-function mutations in ttx-1 abolish thermotaxis behaviors. The identified genetic changes in ttx-1 in the RIL are predicted to decrease ttx-1 function in part via specifically affecting a subset of AFD-expressed ttx-1 isoforms. Introduction of the relevant missense mutation in the laboratory C. elegans strain via gene editing recapitulates the thermotaxis behavioral defects of the RIL. Our results suggest that spontaneously occurring genomic changes in RILs may complicate identification of loci contributing to phenotypic variation, but that these mutations may nevertheless lead to the identification of important causal molecules and mechanisms.
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Affiliation(s)
- Jihye Yeon
- Department of Biology, Brandeis University, Waltham, MA 02454, USA
| | - Charmi Porwal
- Department of Biology, Brandeis University, Waltham, MA 02454, USA
| | - Patrick T McGrath
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Piali Sengupta
- Department of Biology, Brandeis University, Waltham, MA 02454, USA
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Soucy JR, Aguzzi EA, Cho J, Gilhooley MJ, Keuthan C, Luo Z, Monavarfeshani A, Saleem MA, Wang XW, Wohlschlegel J, Baranov P, Di Polo A, Fortune B, Gokoffski KK, Goldberg JL, Guido W, Kolodkin AL, Mason CA, Ou Y, Reh TA, Ross AG, Samuels BC, Welsbie D, Zack DJ, Johnson TV. Retinal ganglion cell repopulation for vision restoration in optic neuropathy: a roadmap from the RReSTORe Consortium. Mol Neurodegener 2023; 18:64. [PMID: 37735444 PMCID: PMC10514988 DOI: 10.1186/s13024-023-00655-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 09/07/2023] [Indexed: 09/23/2023] Open
Abstract
Retinal ganglion cell (RGC) death in glaucoma and other optic neuropathies results in irreversible vision loss due to the mammalian central nervous system's limited regenerative capacity. RGC repopulation is a promising therapeutic approach to reverse vision loss from optic neuropathies if the newly introduced neurons can reestablish functional retinal and thalamic circuits. In theory, RGCs might be repopulated through the transplantation of stem cell-derived neurons or via the induction of endogenous transdifferentiation. The RGC Repopulation, Stem Cell Transplantation, and Optic Nerve Regeneration (RReSTORe) Consortium was established to address the challenges associated with the therapeutic repair of the visual pathway in optic neuropathy. In 2022, the RReSTORe Consortium initiated ongoing international collaborative discussions to advance the RGC repopulation field and has identified five critical areas of focus: (1) RGC development and differentiation, (2) Transplantation methods and models, (3) RGC survival, maturation, and host interactions, (4) Inner retinal wiring, and (5) Eye-to-brain connectivity. Here, we discuss the most pertinent questions and challenges that exist on the path to clinical translation and suggest experimental directions to propel this work going forward. Using these five subtopic discussion groups (SDGs) as a framework, we suggest multidisciplinary approaches to restore the diseased visual pathway by leveraging groundbreaking insights from developmental neuroscience, stem cell biology, molecular biology, optical imaging, animal models of optic neuropathy, immunology & immunotolerance, neuropathology & neuroprotection, materials science & biomedical engineering, and regenerative neuroscience. While significant hurdles remain, the RReSTORe Consortium's efforts provide a comprehensive roadmap for advancing the RGC repopulation field and hold potential for transformative progress in restoring vision in patients suffering from optic neuropathies.
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Affiliation(s)
- Jonathan R Soucy
- Department of Ophthalmology, Schepens Eye Research Institute of Mass. Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Erika A Aguzzi
- The Institute of Ophthalmology, University College London, London, England, UK
| | - Julie Cho
- Spencer Center for Vision Research, Byers Eye Institute, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Michael James Gilhooley
- The Institute of Ophthalmology, University College London, London, England, UK
- Moorfields Eye Hospital, London, England, UK
| | - Casey Keuthan
- Department of Ophthalmology, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ziming Luo
- Spencer Center for Vision Research, Byers Eye Institute, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Aboozar Monavarfeshani
- Center for Brain Science and Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
- Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
| | - Meher A Saleem
- Bascom Palmer Eye Institute, University of Miami Health System, Miami, FL, USA
| | - Xue-Wei Wang
- Department of Orthopaedic Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Petr Baranov
- Department of Ophthalmology, Schepens Eye Research Institute of Mass. Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Adriana Di Polo
- Department of Neuroscience, University of Montreal, Montreal, QC, Canada
- University of Montreal Hospital Research Centre, Montreal, QC, Canada
| | - Brad Fortune
- Discoveries in Sight Research Laboratories, Devers Eye Institute and Legacy Research Institute, Legacy Health, Portland, OR, USA
| | - Kimberly K Gokoffski
- Department of Ophthalmology, Roski Eye Institute, University of Southern California, Los Angeles, CA, USA
| | - Jeffrey L Goldberg
- Spencer Center for Vision Research, Byers Eye Institute, Stanford University School of Medicine, Palo Alto, CA, USA
| | - William Guido
- Department of Anatomical Sciences and Neurobiology, School of Medicine, University of Louisville, Louisville, KY, USA
| | - Alex L Kolodkin
- The Solomon H Snyder, Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Carol A Mason
- Departments of Pathology and Cell Biology, Neuroscience, and Ophthalmology, College of Physicians and Surgeons, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Yvonne Ou
- Department of Ophthalmology, University of California, San Francisco, CA, USA
| | - Thomas A Reh
- Department of Biological Structure, University of Washington, Seattle, WA, USA
| | - Ahmara G Ross
- Departments of Ophthalmology and Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Brian C Samuels
- Department of Ophthalmology and Visual Sciences, Callahan Eye Hospital, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Derek Welsbie
- Shiley Eye Institute and Viterbi Family Department of Ophthalmology, University of California, San Diego, CA, USA
| | - Donald J Zack
- Glaucoma Center of Excellence, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, 21287 MD, USA
- Departments of Neuroscience, Molecular Biology & Genetics, and Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas V Johnson
- Departments of Neuroscience, Molecular Biology & Genetics, and Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Cellular & Molecular Medicine Program, Johns Hopkins University School of Medicine, Baltimore, 21287 MD, USA.
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Zhou J, Gali KK, Jha AB, Tar’an B, Warkentin TD. Identification of Quantitative Trait Loci Associated with Seed Protein Concentration in a Pea Recombinant Inbred Line Population. Genes (Basel) 2022; 13:1531. [PMID: 36140699 PMCID: PMC9498679 DOI: 10.3390/genes13091531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/23/2022] [Accepted: 08/23/2022] [Indexed: 11/16/2022] Open
Abstract
This research aimed to identify quantitative trait loci (QTLs) associated with seed protein concentration in a recombinant inbred line (RIL) population of pea and aimed to validate the identified QTLs using chromosome segment-introgressed lines developed by recurrent backcrossing. PR-25, an RIL population consisting of 108 F7 bulked lines derived from a cross between CDC Amarillo (yellow cotyledon) and CDC Limerick (green cotyledon), was used in this research. The RIL population was genotyped using an Axiom 90K SNP array. A total of 10,553 polymorphic markers were used for linkage map construction, after filtering for segregation distortion and missing values. The linkage map represents 901 unique loci on 11 linkage groups which covered a map distance of 855.3 Centimorgans. Protein concentration was assessed using near-infrared (NIR) spectroscopy of seeds harvested from field trials in seven station-years in Saskatchewan, Canada, during the 2019-2021 field seasons. Three QTLs located on chromosomes 2, 3 and 5 were identified to be associated with seed protein concentration. These QTLs explained 22%, 11% and 17% of the variation for protein concentration, respectively. The identified QTLs were validated by introgression lines, developed by marker-assisted selection of backcross lines for introgression of corresponding chromosome segments (~1/4 chromosome) harboring the QTL regions. Introgression line PR-28-7, not carrying any protein-related QTLs identified in this study, was 4.7% lower in protein concentration than CDC Amarillo, the lower protein parent of PR-25 which carried one identified protein-related QTL. The SNP markers located at the peak of the three identified QTLs will be converted into breeder-friendly KASP assays, which will be used for the selection of high-protein lines from segregating populations.
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Affiliation(s)
| | | | | | | | - Thomas D. Warkentin
- Crop Development Centre, Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK S7N 5A8, Canada
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Cunicelli M, Olukolu BA, Sams C, Schneider L, West D, Pantalone V. Mapping and identification of QTL in 5601T × U99-310255 RIL population using SNP genotyping: soybean seed quality traits. Mol Biol Rep 2022; 49:6623-6632. [PMID: 35618938 DOI: 10.1007/s11033-022-07505-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 04/22/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Molecular markers have played and will continue to play a major role in the genetic characterization and improvement of soybeans. They have helped identify major loci for tolerance to abiotic stressors, disease resistance, herbicide resistance, soybean seed quality traits, and yield. However, most yield quantitative trait loci (QTL) are specific to a certain population, and the genetic variation found in the specific bi-parental population is not always shared in other populations. A major objective in soybean breeding is to develop high yielding cultivars. Unfortunately, soybean seed yield, as well as protein and oil content, are complex quantitative traits to characterize from the phenotypic and genotypic perspectives. The objectives of this study are to detect soybean genomic regions that increase protein content, while maintaining oil content and seed yield and to successfully identify soybean QTL associated with these seed quality traits. METHODS AND RESULTS To achieve these objectives, data from the 138 recombinant inbred lines grown in six environments were used to perform QTL detection analyses in search of significant genomic regions affecting soybean seed protein, oil, and yield. CONCLUSIONS A total of 21 QTL were successfully identified for yield, protein, oil, methionine, threonine, lodging, maturity, and meal. Knowledge of their locations and flanking markers will aid in marker assisted selection for plant breeders. This will lead to a more valuable soybean for farmers, processors, and animal nutritionists.
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Affiliation(s)
| | - Bode A Olukolu
- Department of Entomology and Plant Pathology, University of Tennessee, 352 Plant Biotechnology Bldg. 2505 EJ Chapman Dr., Knoxville, TN, 37996, USA
| | - Carl Sams
- Department of Plant Sciences, University of Tennessee, 112 Plant Biotechnology Bldg. 2505 EJ Chapman Dr., Knoxville, TN, 37996, USA
| | - Liesel Schneider
- Department of Animal Sciences, University of Tennessee, 232 Brehm Animal Science Bldg. 2506 River Dr., Knoxville, TN, 37996, USA
| | - Dennis West
- Department of Plant Sciences, University of Tennessee, 112 Plant Biotechnology Bldg. 2505 EJ Chapman Dr., Knoxville, TN, 37996, USA
| | - Vincent Pantalone
- Department of Plant Sciences, University of Tennessee, 112 Plant Biotechnology Bldg. 2505 EJ Chapman Dr., Knoxville, TN, 37996, USA
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Doldur-Balli F, Imamura T, Veatch OJ, Gong NN, Lim DC, Hart MP, Abel T, Kayser MS, Brodkin ES, Pack AI. Synaptic dysfunction connects autism spectrum disorder and sleep disturbances: A perspective from studies in model organisms. Sleep Med Rev 2022; 62:101595. [PMID: 35158305 PMCID: PMC9064929 DOI: 10.1016/j.smrv.2022.101595] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 12/24/2021] [Accepted: 01/19/2022] [Indexed: 01/03/2023]
Abstract
Sleep disturbances (SD) accompany many neurodevelopmental disorders, suggesting SD is a transdiagnostic process that can account for behavioral deficits and influence underlying neuropathogenesis. Autism Spectrum Disorder (ASD) comprises a complex set of neurodevelopmental conditions characterized by challenges in social interaction, communication, and restricted, repetitive behaviors. Diagnosis of ASD is based primarily on behavioral criteria, and there are no drugs that target core symptoms. Among the co-occurring conditions associated with ASD, SD are one of the most prevalent. SD often arises before the onset of other ASD symptoms. Sleep interventions improve not only sleep but also daytime behaviors in children with ASD. Here, we examine sleep phenotypes in multiple model systems relevant to ASD, e.g., mice, zebrafish, fruit flies and worms. Given the functions of sleep in promoting brain connectivity, neural plasticity, emotional regulation and social behavior, all of which are of critical importance in ASD pathogenesis, we propose that synaptic dysfunction is a major mechanism that connects ASD and SD. Common molecular targets in this interplay that are involved in synaptic function might be a novel avenue for therapy of individuals with ASD experiencing SD. Such therapy would be expected to improve not only sleep but also other ASD symptoms.
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Affiliation(s)
- Fusun Doldur-Balli
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
| | - Toshihiro Imamura
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA; Division of Pulmonary and Sleep Medicine, Children's Hospital of Philadelphia, Philadelphia, USA
| | - Olivia J Veatch
- Department of Psychiatry and Behavioral Sciences, School of Medicine, The University of Kansas Medical Center, Kansas City, USA
| | - Naihua N Gong
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Diane C Lim
- Pulmonary, Allergy, Critical Care and Sleep Medicine Division, Department of Medicine, Miller School of Medicine, University of Miami, Miami, USA
| | - Michael P Hart
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Ted Abel
- Iowa Neuroscience Institute and Department of Neuroscience & Pharmacology, University of Iowa, Iowa City, USA
| | - Matthew S Kayser
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA; Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA; Chronobiology and Sleep Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Edward S Brodkin
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Allan I Pack
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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Three Quantitative Trait Loci Explain More than 60% of Variation for Chill Coma Recovery Time in a Natural Population of Drosophila ananassae. G3-GENES GENOMES GENETICS 2019; 9:3715-3725. [PMID: 31690597 PMCID: PMC6829138 DOI: 10.1534/g3.119.400453] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Ectothermic species such as insects are particularly vulnerable to climatic fluctuations. Nevertheless, many insects that evolved and diversified in the tropics have successfully colonized temperate regions all over the globe. To shed light on the genetic basis of cold tolerance in such species, we conducted a quantitative trait locus (QTL) mapping experiment for chill coma recovery time (CCRT) in Drosophila ananassae, a cosmopolitan species that has expanded its range from tropical to temperate regions. We created a mapping population of recombinant inbred advanced intercross lines (RIAILs) from two founder strains with diverging CCRT phenotypes. The RIAILs were phenotyped for their CCRT and, together with the founder strains, genotyped for polymorphic markers with double-digest restriction site-associated DNA (ddRAD) sequencing. Using a hierarchical mapping approach that combined standard interval mapping and a multiple-QTL model, we mapped three QTL which altogether explained 64% of the phenotypic variance. For two of the identified QTL, we found evidence of epistasis. To narrow down the list of cold tolerance candidate genes, we cross-referenced the QTL intervals with genes that we previously identified as differentially expressed in response to cold in D. ananassae, and with thermotolerance candidate genes of D. melanogaster. Among the 58 differentially expressed genes that were contained within the QTL, GF15058 showed a significant interaction of the CCRT phenotype and gene expression. Further, we identified the orthologs of four D. melanogaster thermotolerance candidate genes, MtnA, klarsicht, CG5246 (D.ana/GF17132) and CG10383 (D.ana/GF14829) as candidates for cold tolerance in D. ananassae.
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Nybom H, Weising K, Rotter B. DNA fingerprinting in botany: past, present, future. INVESTIGATIVE GENETICS 2014; 5:1. [PMID: 24386986 PMCID: PMC3880010 DOI: 10.1186/2041-2223-5-1] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2013] [Accepted: 12/02/2013] [Indexed: 12/20/2022]
Abstract
Almost three decades ago Alec Jeffreys published his seminal Nature papers on the use of minisatellite probes for DNA fingerprinting of humans (Jeffreys and colleagues Nature 1985, 314:67-73 and Nature 1985, 316:76-79). The new technology was soon adopted for many other organisms including plants, and when Hilde Nybom, Kurt Weising and Alec Jeffreys first met at the very First International Conference on DNA Fingerprinting in Berne, Switzerland, in 1990, everybody was enthusiastic about the novel method that allowed us for the first time to discriminate between humans, animals, plants and fungi on the individual level using DNA markers. A newsletter coined "Fingerprint News" was launched, T-shirts were sold, and the proceedings of the Berne conference filled a first book on "DNA fingerprinting: approaches and applications". Four more conferences were about to follow, one on each continent, and Alec Jeffreys of course was invited to all of them. Since these early days, methodologies have undergone a rapid evolution and diversification. A multitude of techniques have been developed, optimized, and eventually abandoned when novel and more efficient and/or more reliable methods appeared. Despite some overlap between the lifetimes of the different technologies, three phases can be defined that coincide with major technological advances. Whereas the first phase of DNA fingerprinting ("the past") was dominated by restriction fragment analysis in conjunction with Southern blot hybridization, the advent of the PCR in the late 1980s gave way to the development of PCR-based single- or multi-locus profiling techniques in the second phase. Given that many routine applications of plant DNA fingerprinting still rely on PCR-based markers, we here refer to these methods as "DNA fingerprinting in the present", and include numerous examples in the present review. The beginning of the third phase actually dates back to 2005, when several novel, highly parallel DNA sequencing strategies were developed that increased the throughput over current Sanger sequencing technology 1000-fold and more. High-speed DNA sequencing was soon also exploited for DNA fingerprinting in plants, either in terms of facilitated marker development, or directly in the sense of "genotyping-by-sequencing". Whereas these novel approaches are applied at an ever increasing rate also in non-model species, they are still far from routine, and we therefore treat them here as "DNA fingerprinting in the future".
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Affiliation(s)
- Hilde Nybom
- Department of Plant Breeding–Balsgård, Swedish University for Agricultural Sciences, Fjälkestadsvägen 459, Kristianstad 29194, Sweden
| | - Kurt Weising
- Plant Molecular Systematics, Institute of Biology, University of Kassel, Kassel 34109, Germany
| | - Björn Rotter
- GenXPro GmbH, Altenhöferallee 3, Frankfurt 60438, Germany
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Ashbrook DG, Hager R. Empirical testing of hypotheses about the evolution of genomic imprinting in mammals. Front Neuroanat 2013; 7:6. [PMID: 23641202 PMCID: PMC3639422 DOI: 10.3389/fnana.2013.00006] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2013] [Accepted: 04/10/2013] [Indexed: 01/01/2023] Open
Abstract
The close interaction between mother and offspring in mammals is thought to contribute to the evolution of genomic imprinting or parent-of-origin dependent gene expression. Empirical tests of theories about the evolution of imprinting have been scant for several reasons. Models make different assumptions about the traits affected by imprinted genes and the scenarios in which imprinting is predicted to have been selected for. Thus, competing hypotheses cannot readily be tested against each other. Further, it is far from clear how predictions about expression patterns of genes with specific phenotypic effects can be tested given current methodology of assaying gene expression levels, be it in the brain or in other tissues. We first set out a scenario for testing competing hypotheses and delineate the different assumptions and predictions of models. We then outline how predictions may be tested using mouse models such as intercrosses or recombinant inbred (RI) systems that can be phenotyped for traits relevant to imprinting theories. Further, we briefly discuss different molecular approaches that may be used in conjunction with experiments to ascertain expression patterns of imprinted genes and thus the testing of predictions.
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Affiliation(s)
- David G Ashbrook
- Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester Manchester, UK
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