Relevant bibliographies by topics / Gene-For-Gene interaction

Academic literature on the topic 'Gene-For-Gene interaction'

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Published: 8 June 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Gene-For-Gene interaction":

1

Wang, Yaping, Donghui Li, and Peng Wei. "Powerful Tukey's One Degree-of-Freedom Test for Detecting Gene-Gene and Gene-Environment Interactions." Cancer Informatics 14s2 (January 2015): CIN.S17305. http://dx.doi.org/10.4137/cin.s17305.

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Genome-wide association studies (GWASs) have identified thousands of single nucleotide polymorphisms (SNPs) robustly associated with hundreds of complex human diseases including cancers. However, the large number of G WAS-identified genetic loci only explains a small proportion of the disease heritability. This “missing heritability” problem has been partly attributed to the yet-to-be-identified gene-gene (G × G) and gene-environment (G × E) interactions. In spite of the important roles of G × G and G × E interactions in understanding disease mechanisms and filling in the missing heritability, straightforward GWAS scanning for such interactions has very limited statistical power, leading to few successes. Here we propose a two-step statistical approach to test G × G/G × E interactions: the first step is to perform principal component analysis (PCA) on the multiple SNPs within a gene region, and the second step is to perform Tukey's one degree-of-freedom (1-df) test on the leading PCs. We derive a score test that is computationally fast and numerically stable for the proposed Tukey's 1-df interaction test. Using extensive simulations we show that the proposed approach, which combines the two parsimonious models, namely, the PCA and Tukey's 1-df form of interaction, outperforms other state-of-the-art methods. We also demonstrate the utility and efficiency gains of the proposed method with applications to testing G × G interactions for Crohn's disease using the Wellcome Trust Case Control Consortium (WTCCC) GWAS data and testing G × E interaction using data from a case-control study of pancreatic cancer.
2

Zhang, Jigang, Jian Li, and Hong-Wen Deng. "Identifying Gene Interaction Enrichment for Gene Expression Data." PLoS ONE 4, no. 11 (November 30, 2009): e8064. http://dx.doi.org/10.1371/journal.pone.0008064.

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Mechanic, Leah E., Brian T. Luke, Julie E. Goodman, Stephen J. Chanock, and Curtis C. Harris. "Polymorphism Interaction Analysis (PIA): a method for investigating complex gene-gene interactions." BMC Bioinformatics 9, no. 1 (2008): 146. http://dx.doi.org/10.1186/1471-2105-9-146.

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Zhou, R., M. Wang, W. Li, S. Wang, Z. Zhou, J. Li, T. Wu, H. Zhu, and T. H. Beaty. "Gene-Gene Interactions among SPRYs for Nonsyndromic Cleft Lip/Palate." Journal of Dental Research 98, no. 2 (October 1, 2018): 180–85. http://dx.doi.org/10.1177/0022034518801537.

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Nonsyndromic cleft lip with or without cleft palate (NSCL/P) is a common birth defect with a complex genetic architecture. Gene-gene interactions have been increasingly regarded as contributing to the etiology of NSCL/P. A recent genome-wide association study revealed that a novel single-nucleotide polymorphism at SPRY1 in 4q28.1 showed a significant association with NSCL/P. In the current study, we explored the role of 3 SPRY genes in the etiology of NSCL/P by detecting gene-gene interactions: SPRY1, SPRY2, and SPRY4—with SPRY3 excluded due to its special location on the X chromosome. We selected markers in 3 SPRY genes to test for gene-gene interactions using 1,908 case-parent trios recruited from an international consortium established for a genome-wide association study of nonsyndromic oral clefts. As the trios came from populations with different ancestries, subgroup analyses were conducted among Europeans and Asians. Cordell’s method based on conditional logistic regression models was applied to test for potential gene-gene interactions via the statistical package TRIO in R software. Gene-gene interaction analyses yielded 10 pairs of SNPs in Europeans and 6 pairs in Asians that achieved significance after Bonferroni correction. The significant interactions were confirmed in the 10,000-permutation tests (empirical P = 0.003 for the most significant interaction). The study identified gene-gene interactions among SPRY genes among 1,908 NSCL/P trios, which revealed the importance of potential gene-gene interactions for understanding the genetic architecture of NSCL/P. The evidence of gene-gene interactions in this study also provided clues for future biological studies to further investigate the mechanism of how SPRY genes participate in the development of NSCL/P.
5

Zhou, Xiangdong, Keith C. C. Chan, Zhihua Huang, and Jingbin Wang. "Determining dependency and redundancy for identifying gene–gene interaction associated with complex disease." Journal of Bioinformatics and Computational Biology 18, no. 05 (October 2020): 2050035. http://dx.doi.org/10.1142/s0219720020500353.

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As interactions among genetic variants in different genes can be an important factor for predicting complex diseases, many computational methods have been proposed to detect if a particular set of genes has interaction with a particular complex disease. However, even though many such methods have been shown to be useful, they can be made more effective if the properties of gene–gene interactions can be better understood. Towards this goal, we have attempted to uncover patterns in gene–gene interactions and the patterns reveal an interesting property that can be reflected in an inequality that describes the relationship between two genotype variables and a disease-status variable. We show, in this paper, that this inequality can be generalized to [Formula: see text] genotype variables. Based on this inequality, we establish a conditional independence and redundancy (CIR)-based definition of gene–gene interaction and the concept of an interaction group. From these new definitions, a novel measure of gene–gene interaction is then derived. We discuss the properties of these concepts and explain how they can be used in a novel algorithm to detect high-order gene–gene interactions. Experimental results using both simulated and real datasets show that the proposed method can be very promising.
6

Sa, Jian, Xu Liu, Tao He, Guifen Liu, and Yuehua Cui. "A Nonlinear Model for Gene-Based Gene-Environment Interaction." International Journal of Molecular Sciences 17, no. 6 (June 4, 2016): 882. http://dx.doi.org/10.3390/ijms17060882.

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Chen, Zhongxue. "Testing for gene-gene interaction in case-control GWAS." Statistics and Its Interface 10, no. 2 (2017): 267–77. http://dx.doi.org/10.4310/sii.2017.v10.n2.a10.

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Corvol, Harriet, Anthony De Giacomo, Celeste Eng, Max Seibold, Elad Ziv, Rocio Chapela, Jose R. Rodriguez-Santana, et al. "Genetic ancestry modifies pharmacogenetic gene–gene interaction for asthma." Pharmacogenetics and Genomics 19, no. 7 (July 2009): 489–96. http://dx.doi.org/10.1097/fpc.0b013e32832c440e.

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Song, Minsun, and Dan L. Nicolae. "Restricted parameter space models for testing gene-gene interaction." Genetic Epidemiology 33, no. 5 (July 2009): 386–93. http://dx.doi.org/10.1002/gepi.20392.

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Li, Qing, Yoonhee Kim, Bhoom Suktitipat, Jacqueline B. Hetmanski, Mary L. Marazita, Priya Duggal, Terri H. Beaty, and Joan E. Bailey-Wilson. "Gene-Gene Interaction AmongWNTGenes for Oral Cleft in Trios." Genetic Epidemiology 39, no. 5 (February 6, 2015): 385–94. http://dx.doi.org/10.1002/gepi.21888.

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Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Gene-For-Gene interaction":

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Assareh, Amin. "OPTIMIZING DECISION TREE ENSEMBLES FOR GENE-GENE INTERACTION DETECTION." Kent State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=kent1353971575.

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Bendahmane, Abdelhafid. "Analysis of a gene-for-gene interaction associated with Rx-mediated resistance to potato virus X." Thesis, University of East Anglia, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389350.

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Aleknonytė-Resch, Milda [Verfasser], Astrid [Akademischer Betreuer] Dempfle, and Hinrich [Gutachter] Schulenburg. "The Validity and Statistical Power of the Case-Only Study Design for Interaction Analysis : Gene-Gene Interaction and the Role of Genotype Imputation in Gene-Environment Interaction / Milda Aleknonytė-Resch ; Gutachter: Hinrich Schulenburg ; Betreuer: Astrid Dempfle." Kiel : Universitätsbibliothek Kiel, 2021. http://d-nb.info/1229916962/34.

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Elisson, Hanna. "Uncertainty in Genetic Mapping and Gene Interaction for Diabetes in Rats." Thesis, Uppsala University, Department of Mathematics, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-122559.

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MacGowan, Alice Laura. "Embryonic retinoid deficiency and congenital malformation : evidence for gene-environment interaction." Thesis, University of Sheffield, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398657.

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Thi, Cam Thach Doan. "A GRAPHICAL USER INTERFACE FOR LARGE-SCALE GENE EXPRESSION ANALYSIS." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-20870.

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Recently, the whole-genome expression analysis – which is analyzing most or all of the genes in biological systems, and is a rich and powerful way to discover gene pathway - has become increasingly affordable because of the increasing amount of microarray data available in public databases. Additionally, due to the enormously available information content in these repositories, researchers have to spend large amount of time to decide on the right information to proceed. There should be an application to assist biological researchers reducing the time in finding good data sets to analyze. In this project, a thorough study in HCI, Information Visualization, interaction design and development methodologies are carried out in order to build a web-based user interface that enables searching and browsing gene expression data and their correlation (web-based). Findings from literature review are applied to create a web-based user interface in large-gene expression analysis. Then, a survey is carried out to collect and analyze pilot users‟ feedback. The questionnaire shows that the users are very interested in using the system and they would like to spend more time interacting with it. They give positive feedbacks about interactive data visualization in the website help them to save time on viewing, navigating and interpreting complicated data. Besides, it is easy to navigate and learn how to use the system to achieve interesting findings in biology. The questionnaire shows that the author is successful in applying findings from literature review to build the website. Besides, from the results there are suggestions for improvement such as the flexibility in the website by automatically recognizing the alias gene names from different databases, filling-in gene symbols using first few characters, narrowing down a search to a particular species such as human or rat, etc.
Program: Masterutbildning i Informatik
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Korkmaz, Gulberal Kircicegi Yoksul. "Mining Microarray Data For Biologically Important Gene Sets." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614266/index.pdf.

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Microarray technology enables researchers to measure the expression levels of thousands of genes simultaneously to understand relationships between genes, extract pathways, and in general understand a diverse amount of biological processes such as diseases and cell cycles. While microarrays provide the great opportunity of revealing information about biological processes, it is a challenging task to mine the huge amount of information contained in the microarray datasets. Generally, since an accurate model for the data is missing, first a clustering algorithm is applied and then the resulting clusters are examined manually to find genes that are related with the biological process under inspection. We need automated methods for this analysis which can be used to eliminate unrelated genes from data and mine for biologically important genes. Here, we introduce a general methodology which makes use of traditional clustering algorithms and involves integration of the two main sources of biological information, Gene Ontology and interaction networks, with microarray data for eliminating unrelated information and find a clustering result containing only genes related with a given biological process. We applied our methodology successfully on a number of different cases and on different organisms. We assessed the results with Gene Set Enrichment Analysis method and showed that our final clusters are highly enriched. We also analyzed the results manually and found that most of the genes that are in the final clusters are actually related with the biological process under inspection.
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Piqueras, Matias. "HERITABILITY FOR SOCIAL TRUST ACROSS SOCIOECONOMIC STATUS: : Is There a Gene-Environment Interaction?" Thesis, Uppsala universitet, Statsvetenskapliga institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-394876.

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In political science literature, the development of social trust is often explained in terms of the influence of different environmental factors, socioeconomic status (SES) being one of the most important. Yet, even though there is empirical support of a genetic component in the expression of social trust, less is known about its interaction with environmental factors. The present study aims to explore heritability of social trust across socioeconomic status using a twin-design that tests potential gene-environment (GxE) interactions. Moreover, the study explicitly tests the hypothesis that different levels of SES may moderate the influence of genetic and environmental effects on social trust. Data comes from the Swedish Twin Registry and consist of 1535 twin pairs born between 1943–1959. Social trust was measured through self-report on a scale of 1–10. Socioeconomic status was assessed as a dichotomized variable of high/low SES, determined on the basis of the father’s occupation during the twin’s childhood or adolescence. To test whether SES interacted with genetic and environmental effects for social trust, I used structural equation modeling (SEM). Results from the best fitting model show that social trust has a significant genetic component, with an estimated heritability of 0.41 in low SES and 0.33 in high SES. Results showed no evidence for a significant difference in heritability between low and high SES. Accordingly, it can be concluded that the results of the study do not support the hypothesis that SES moderate the influence of genetic effects on social trust.
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Cai, Yu. "Molecular Characterization of the mop2, a Gene Required for Epigenetic Silencing." Diss., The University of Arizona, 2006. http://hdl.handle.net/10150/195361.

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The mop2 gene is required for epigenetic silencing; it was originally defined as a mutation, Mop2-1, which when dominant prevented paramutation at b1. Paramutation is an allele communication that causes a mitotically and meiotically heritable change in gene expression. Mop2-1 was subsequently shown to be involved in maintaining the silenced paramutant state and to prevent dsRNA-mediated transcriptional gene silencing (activities revealed only when the mutation is homozygous). Understanding the product encoded by mop2 will help dissect the underlying mechanisms involved in paramutation and dsRNA-mediated transcriptional silencing. This dissertation describes map-based cloning and candidate gene approaches directed toward the eventual goal of identification of mop2.Initial mapping of mop2 placed it within a region delineated by the markers umc1823 and eks1. On the maize physical map this region contains 21 BAC (Bacteria Artificial Chromosome) clones, representing 2.9 Mb. Skim sequencing identified additional markers for mapping and revealed the gene content. Extensive candidate gene examinations, including gene sequencing, expression profiling with microarrays and RT-PCR, and complementation tests with mutant alleles did not identify any of the four chromatin and RNAi-related genes as mop2.The new markers developed from the skim sequence enabled further mapping and molecular genotyping, which revealed that the Mop2-1 mutation was unstable. Approxi¬mately 10% of phenotypic heterozygous plants were actually genotypic homozygous. Further mapping using only Mop2-1 homozygous plants reduced the mop2 interval to a region of nine BACs, containing 57 genes.The mop2 region is highly syntenic to a rice region of 1.25 Mb on chromosome 4. The gene alignment and repetitive sequence analyses between the syntenic regions in these two species revealed both syntenic and non-syntenic blocks of sequences. Analyses suggested several potential mechanisms for the collinearity breakage, including, but not limited to, tandem duplications of genes in one species but not the other and the presence of gene fragments in maize, but not in rice.The research described herein provides the basis for continued efforts to clone mop2. Fine-structure mapping with new markers and a larger population, as well as candidate gene sequencing in the Mop2-1 BAC library, should be pursued to clone mop2.
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Mietzsch, Mario [Verfasser]. "Adeno-Associated Virus Vectors for Gene Therapy : From Scalable Production Systems via Serotype-Specific Glycan Interaction Patterns to Gene Transfer Applications / Mario Mietzsch." Berlin : Freie Universität Berlin, 2014. http://d-nb.info/1055942203/34.

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Books on the topic "Gene-For-Gene interaction":

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R, Crute I., Holub E. B, Burdon J. J, and British Society for Plant Pathology., eds. The gene-for-gene relationship in plant-parasite interactions. Wallington, UK: CAB International, 1997.

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Håkansssson, Gunilla. Nuclear-mitochondrial interactions and its relevance for male sterility in Nicotiana: Analysis of mitochondrial genome organization, gene expression and respiration in male-fertile and alloplasmic male-sterile materials. Uppsala, Sweden: Swedish University of Agricultural Sciences, Dept. of Plant Breeding, 1992.

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3

Sasaki, Joni Y., Jessica LeClair, Alexandria West, and Heejung S. Kim. The Gene–Culture Interaction Framework and Implications for Health. Edited by Joan Y. Chiao, Shu-Chen Li, Rebecca Seligman, and Robert Turner. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199357376.013.20.

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Razzoli, Maria, Alessandro Bartolomucci, and Valeria Carola. Gene-by-Environment Mouse Models for Mood Disorders. Edited by Turhan Canli. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199753888.013.013.

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Much of the impact of genes on mood disorders likely depends on interactions between genes and the environment. Recent studies demonstrating an interaction between specific genes and life stressful events (early and/or adult) in the modulation of several mood disorders (e.g., serotonin transporter and brain-derived neurotrophic factor genes) have compelled researchers to incorporate information about adverse environmental experiences into the study of genetic risk factors; these same gene-by-environment (G×E) interactions have been identified in mouse models. Notably, G×E not yet described in humans (e.g., serotonin 1A receptor gene) have been uncovered, providing helpful indications to discover similar interactions in humans. Accurate knowledge of the modality of expression of gene-by-stress interaction may help design prevention protocols aimed at identifying susceptibility to mood disorders on the basis of genetic predisposition and exposure to environmental stressful conditions, thus providing patients with appropriate pharmacological and psychological support.
5

He, Zihuai, Michael Windle, James Y. Dai, and Caroline Y. Doyle. Statistical Approaches to Gene X Environment Interactions for Complex Phenotypes. MIT Press, 2016.

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Windle, Michael, Charles Kooperberg, James Y. Dai, Li Yang Hsu, and Jung-Ying Tzeng. Statistical Approaches to Gene X Environment Interactions for Complex Phenotypes. MIT Press, 2016.

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7

Klengel, Torsten, Lauren A. M. Lebois, Sheila Gaynor, and Guia Guffanti. Genetics and Gene–Environment Interaction. Edited by Frederick J. Stoddard, David M. Benedek, Mohammed R. Milad, and Robert J. Ursano. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190457136.003.0017.

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Trauma and stress-related disorders make an excellent case for gene-environment interactions because although exposure to trauma and stress is a well-established risk factors toward their development, such factors alone are not sufficient to explain etiopathogenesis. Exposure to traumatic events is a prerequisite of posttraumatic stress disorder (PTSD) diagnosis, but the majority of individuals who are exposed to even a severe traumatic event do not develop PTSD. Why some individuals are vulnerable and others are resilient remains an open question. While genetic factors may play a significant role, it is conceivable that the complex interplay between genetic and environmental factors contribute to the observed interindividual variability.
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Silvers, W. K. Coat Colors of Mice: A Model for Mammalian Gene Action and Interaction. Springer London, Limited, 2012.

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Han, Shihui. Gene-culture interaction on human behavior and the brain. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198743194.003.0007.

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Chapter 7 reviews empirical findings that allow consideration of biological and environmental influences on human behavior from an evolutionary perspective (e.g., gene-culture coevolution) and from a perspective of individual development (e.g., gene-culture interaction). It also reviews imaging genetic studies that link genes with brain functional organization. It introduces a cultural neuroscience paradigm for investigating genetic influences on the coupling of brain activity and culture by presenting two studies that examined how serotonin transporter functional polymorphism and oxytocin receptor gene moderate the association between interdependence and brain activities involved in self-reflection and empathy. These studies illustrate a new approach to understanding the manner with which culture interacts with gene to shape human brain activity.
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Silvers, W. K. The Coat Colors of Mice: A Model For Mammalian Gene Action And Interaction. Springer, 2011.

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Book chapters on the topic "Gene-For-Gene interaction":

1

Cregan, P. B., M. J. Sadowsky, and H. H. Keyser. "Gene-for-gene interaction in the legume-Rhizobium symbiosis." In The Rhizosphere and Plant Growth, 163–71. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3336-4_32.

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Manohar, S. K., M. P. Gowrav, and H. V. Gangadharappa. "Materials for Gene Delivery Systems." In Interaction of Nanomaterials With Living Cells, 411–37. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2119-5_14.

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Xiong, Momiao, and Xuesen Wu. "Statistics for Testing Gene–Environment Interaction." In Environmental Factors, Genes, and the Development of Human Cancers, 53–95. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6752-7_3.

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Turner, Stephen D., Scott M. Dudek, and Marylyn D. Ritchie. "Incorporating Domain Knowledge into Evolutionary Computing for Discovering Gene-Gene Interaction." In Parallel Problem Solving from Nature, PPSN XI, 394–403. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15844-5_40.

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Kokošar, Jaka, Martin Špendl, and Blaž Zupan. "Gene Interactions in Survival Data Analysis: A Data-Driven Approach Using Restricted Mean Survival Time and Literature Mining." In Discovery Science, 293–307. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45275-8_20.

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AbstractUnveiling gene interactions is crucial for comprehending biological processes, particularly their combined impact on phenotypes. Computational methodologies for gene interaction discovery have been extensively studied, but their application to censored data has yet to be thoroughly explored. Our work introduces a data-driven approach to identifying gene interactions that profoundly influence survival rates through the use of survival analysis. Our approach calculates the restricted mean survival time (RMST) for gene pairs and compares it against their individual expressions. If the interaction’s RMST exceeds that of the individual gene expressions, it suggests a potential functional association. We focused on L1000 landmark genes using TCGA na METABRIC data sets. Our findings demonstrate numerous additive and competing interactions and a scarcity of XOR-type interactions. We substantiated our results by cross-referencing with existing interactions in STRING and BioGRID databases and using large language models to summarize complex biological data. Although many potential gene interactions were hypothesized, only a fraction have been experimentally explored. This novel approach enables biologists to initiate a further investigation based on our ranked gene pairs and the generated literature summaries, thus offering a comprehensive, data-driven approach to understanding gene interactions affecting survival rates.
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Katagiri, Fumiaki, and R. Todd Leister. "Use of transient expression in plants for the study of the “gene-for-gene” interaction." In Cellular Integration of Signalling Pathways in Plant Development, 311–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72117-5_27.

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Bhalla, Parinishtha, Anukriti Verma, Bhawna Rathi, Shivani Sharda, and Pallavi Somvanshi. "Exploring Molecular Signatures in Spondyloarthritis: A Step Towards Early Diagnosis." In Proceedings of the Conference BioSangam 2022: Emerging Trends in Biotechnology (BIOSANGAM 2022), 142–55. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-020-6_15.

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AbstractSpondyloarthritis is an acute inflammatory disorder of the musculoskeletal system often accompanied by pain, stiffness, bone and tissue damage. It majorly consists of ankylosing spondylitis, psoriatic arthritis and reactive arthritis. It follows a differential diagnosis pattern for demarcation between the spondyloarthritis subtypes and other arthritic subtypes such as rheumatoid arthritis, juvenile arthritis and osteoarthritis due to the heterogeneity causing gradual chronicity and complications. Presence of definite molecular markers can not only improve diagnosis efficiency but also aid in their prognosis and therapy. This study is an attempt to compose a refined list of such unique and common molecular signatures of the considered subtypes, by employing a reductionist approach amalgamating gene retrieval, protein-protein interaction network, functional, pathway, micro-RNA-gene and transcription factor-gene regulatory network analysis. Gene retrieval and protein-protein interaction network analysis resulted in unique and common interacting genes of arthritis subtypes. Functional annotation and pathway analysis found vital functions and pathways unique and common in arthritis subtypes. Furthermore, miRNA-gene and transcription factor-gene interaction networks retrieved unique and common miRNA’s and transcription factors in arthritis subtypes. Furthermore, the study identified important signatures of arthritis subtypes that can serve as markers assisting in prognosis, early diagnosis and personalized treatment of arthritis patients requiring validation via prospective experimental studies.
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Miyao, Akio, and Hirohiko Hirochika. "Transposon-Insertion Lines of Rice for Analysis of Gene Function." In Rice Blast: Interaction with Rice and Control, 107–12. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-0-306-48582-4_12.

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Da, Bingshui, Abhishek Gupta, Yew Soon Ong, and Liang Feng. "The Boon of Gene-Culture Interaction for Effective Evolutionary Multitasking." In Lecture Notes in Computer Science, 54–65. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28270-1_5.

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Tiwari, Aneeta, Elhan Khan, Sonam Dwivedi, Haram Sarfraz, and Iffat Zareen Ahmad. "Gene Regulation for Drought, Cold, Heavy Metal and Environmental Responses." In Genomics of Plant–Pathogen Interaction and the Stress Response, 171–84. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003153481-9.

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Conference papers on the topic "Gene-For-Gene interaction":

1

Diaz-Diaz, Norberto, Francisco Gomez-Vela, Jesus Aguilar-Ruiz, and Jorge Garcia-Gutierrez. "Gene-gene interaction based clustering method for microarray data." In 2011 11th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2011. http://dx.doi.org/10.1109/isda.2011.6121800.

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Assareh, Amin, L. Gwenn Volkert, and Jing Li. "Interaction Trees: Optimizing Ensembles of Decision Trees for Gene-Gene Interaction Detections." In 2012 Eleventh International Conference on Machine Learning and Applications (ICMLA). IEEE, 2012. http://dx.doi.org/10.1109/icmla.2012.114.

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Tong, Dong Ling, and Christine Siew Ken Lee. "A Systems Biology Approach to Model Gene-Gene Interaction for Childhood Sarcomas." In 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2018. http://dx.doi.org/10.1109/bibe.2018.00074.

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Li, Xin, Zhu Zhang, Hsinchun Chen, and Jiexun Li. "Graph Kernel-Based Learning for Gene Function Prediction from Gene Interaction Network." In 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007). IEEE, 2007. http://dx.doi.org/10.1109/bibm.2007.25.

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Huh, Iksoo, and Taesung Park. "Multifactor dimendionality reduction analysis for gene-gene interaction of multiple binary traits." In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2014. http://dx.doi.org/10.1109/bibm.2014.6999382.

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Su, Ming-Wei, Kuan-Yen Tung, Ching-Hui Tsai, Nai-Wei Kuo, Pi-Hui Liang, and Yungling L. Lee. "GSTP1 Is A Hub Gene For Gene By Air Pollution Interaction On Childhood Asthma." In American Thoracic Society 2012 International Conference, May 18-23, 2012 • San Francisco, California. American Thoracic Society, 2012. http://dx.doi.org/10.1164/ajrccm-conference.2012.185.1_meetingabstracts.a2502.

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Alam, Md Ashad, Osamu Komori, Vince Calhoun, and Yu-Ping Wang. "Robust Kernel Canonical Correlation Analysis to Detect Gene-Gene Interaction for Imaging Genetics Data." In BCB '16: ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2975167.2975196.

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Sungyoung Lee, Min-Seok Kwon, Ik-Soo Huh, and Taesung Park. "CUDA-LR: CUDA-accelerated logistic regression analysis tool for gene-gene interaction for genome-wide association study." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112454.

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Rao, Jiahua, Shuangjia Zheng, Sijie Mai, and Yuedong Yang. "Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/544.

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Illuminating the interconnections between drugs and genes is an important topic in drug development and precision medicine. Currently, computational predictions of drug-gene interactions mainly focus on the binding interactions without considering other relation types like agonist, antagonist, etc. In addition, existing methods either heavily rely on high-quality domain features or are intrinsically transductive, which limits the capacity of models to generalize to drugs/genes that lack external information or are unseen during the training process. To address these problems, we propose a novel Communicative Subgraph representation learning for Multi-relational Inductive drug-Gene interactions prediction (CoSMIG), where the predictions of drug-gene relations are made through subgraph patterns, and thus are naturally inductive for unseen drugs/genes without retraining or utilizing external domain features. Moreover, the model strengthened the relations on the drug-gene graph through a communicative message passing mechanism. To evaluate our method, we compiled two new benchmark datasets from DrugBank and DGIdb. The comprehensive experiments on the two datasets showed that our method outperformed state-of-the-art baselines in the transductive scenarios and achieved superior performance in the inductive ones. Further experimental analysis including LINCS experimental validation and literature verification also demonstrated the value of our model.
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Liang, Yulan, and Arpad Kelemen. "Bayesian Dynamic Multivariate Models for Inferring Gene Interaction Networks." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.260091.

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Reports on the topic "Gene-For-Gene interaction":

1

Sun, Jielin, Jianfeng Xu, and Siqun L. Zheng. Systematic Search for Gene-Gene Interaction Effect on Prostate Cancer Risk. Fort Belvoir, VA: Defense Technical Information Center, July 2012. http://dx.doi.org/10.21236/ada564269.

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Sun, Jielin, Jianfeng Xu, and Siqun L. Zheng. Systematic Search for Gene-Gene Interaction Effect on Prostate Cancer Risk. Fort Belvoir, VA: Defense Technical Information Center, July 2013. http://dx.doi.org/10.21236/ada593732.

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Tan, Qihua, Anatoli I. Yashin, Else M. Bladbjerg, Moniek De Maat, Karen Andersen-Ranberg, Bernard Jeune, Kaare Christensen, and James W. Vaupel. A case-only approach for assessing gene-sex interaction in human longevity. Rostock: Max Planck Institute for Demographic Research, May 2001. http://dx.doi.org/10.4054/mpidr-wp-2001-012.

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Lifschitz, Eliezer, and Elliot Meyerowitz. The Relations between Cell Division and Cell Type Specification in Floral and Vegetative Meristems of Tomato and Arabidopsis. United States Department of Agriculture, February 1996. http://dx.doi.org/10.32747/1996.7613032.bard.

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Meristems were the central issue of our project. Genes that are required for cell division, cell elongation, cell proliferation and cell fate were studied in the tomato system. The analysis of the dUTPase and threonine deaminase genes, along with the dissection of their regulatory regions is completed, while that of the RNR2 and PPO genes is at an advanced stage. All these genes were isolated in our laboratory. In addition, 8 different MADS box genes were studied in transgenic plants and their genetic relevances discovered. We have also shown that a given MADS box gene can modify the polarity of cell division without affecting the fate of the organ. In vivo interaction between two MADS box genes was demonstrated and the functional dependency of the tomato agamous gene on the TM5 gene product established. We have exploited the Knotted1 meristematic gene in conjunction with tomato leaf meristematic genes to show that simple and compound leaves and, for that matter, sepals and compound leaves, are formed by two different developmental programs. In this context we have also isolated and characterized the tomato Knotted1 gene (TKnl) and studied its expression pattern. A new program in which eight different meristematic genes in tomato will be studied emerged as a result of these studies. In essence, we have shown that it is possible to study and manipulate plant developmental systems using reverse genetic techniques and have provided a wealth of new molecular tools to interested colleagues working with tomato. Similarly, genes responsible for cell division, cell proliferation and cell fate were studied in Arabidopsis floral meristems. Among these genes are the TSO1, TSO2, HANABA TARANU and UNUSUAL FLORAL ORGANS genes, each affecting in its own way the number of pattern of cell divisions, and cell fate, in developing Arabodopsis flowers. In addition, new methods have been established for the assessment of the function of regulatory gene action in the different clonal layers of developing floral meristems.
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Horwitz, Benjamin A., and Barbara Gillian Turgeon. Fungal Iron Acquisition, Oxidative Stress and Virulence in the Cochliobolus-maize Interaction. United States Department of Agriculture, March 2012. http://dx.doi.org/10.32747/2012.7709885.bard.

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Our project focused on genes for high affinity iron acquisition in Cochliobolus heterostrophus, a necrotrophic pathogen of maize, and their intertwined relationship to oxidative stress status and virulence of the fungus on the host. An intriguing question was why mutants lacking the nonribosomal peptide synthetase (NRPS) gene (NPS6) responsible for synthesis of the extracellular siderophore, coprogen, are sensitive to oxidative stress. Our overall objective was to understand the mechanistic connection between iron stress and oxidative stress as related to virulence of a plant pathogen to its host. The first objective was to examine the interface where small molecule peptide and reactive oxygen species (ROS) mechanisms overlap. The second objective was to determine if the molecular explanation for common function is common signal transduction pathways. These pathways, built around sensor kinases, response regulators, and transcription factors may link sequestering of iron, production of antioxidants, resistance to oxidative stress, and virulence. We tested these hypotheses by genetic manipulation of the pathogen, virulence assays on the host plant, and by following the expression of key fungal genes. An addition to the original program, made in the first year, was to develop, for fungi, a genetically encoded indicator of redox state based on the commercially available Gfp-based probe pHyper, designed for animal cell biology. We implemented several tools including a genetically encoded indicator of redox state, a procedure to grow iron-depleted plants, and constructed a number of new mutants in regulatory genes. Lack of the major Fe acquisition pathways results in an almost completely avirulent phenotype, showing how critical Fe acquisition is for the pathogen to cause disease. Mutants in conserved signaling pathways have normal ability to regulate NPS6 in response to Fe levels, as do mutants in Lae1 and Vel1, two master regulators of gene expression. Vel1 mutants are sensitive to oxidative stress, and the reason may be underexpression of a catalase gene. In nps6 mutants, CAT3 is also underexpressed, perhaps explaining the sensitivity to oxidative stress. We constructed a deletion mutant for the Fe sensor-regulator SreA and found that it is required for down regulation of NPS6 under Fe-replete conditions. Lack of SreA, though, did not make the fungus over-sensitive to ROS, though the mutant had a slow growth rate. This suggests that overproduction of siderophore under Fe-replete conditions is not very damaging. On the other hand, increasing Fe levels protected nps6 mutants from inhibition by ROS, implying that Fe-catalyzed Fenton reactions are not the main factor in its sensitivity to ROS. We have made some progress in understanding why siderophore mutants are sensitive to oxidative stress, and in doing so, defined some novel regulatory relationships. Catalase genes, which are not directly related to siderophore biosynthesis, are underexpressed in nps6 mutants, suggesting that the siderophore product (with or without bound Fe) may act as a signal. Siderophores, therefore, could be a target for intervention in the field, either by supplying an incorrect signal or blocking a signal normally provided during infection. We already know that nps6 mutants cause smaller lesions and have difficulty establishing invasive growth in the host. Lae1 and Vel1 are the first factors shown to regulate both super virulence conferred by T-toxin, and basic pathogenicity, due to unknown factors. The mutants are also altered in oxidative stress responses, key to success in the infection court, asexual and sexual development, essential for fungal dissemination in the field, aerial hyphal growth, and pigment biosynthesis, essential for survival in the field. Mutants in genes encoding NADPH oxidase (Nox) are compromised in development and virulence. Indeed the triple mutant, which should lack all Nox activity, was nearly avirulent. Again, gene expression experiments provided us with initial evidence that superoxide produced by the fungus may be most important as a signal. Blocking oxidant production by the pathogen may be a way to protect the plant host, in interactions with necrotrophs such as C. heterostrophus which seem to thrive in an oxidant environment.
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Levisohn, Sharon, Mark Jackwood, and Stanley Kleven. New Approaches for Detection of Mycoplasma iowae Infection in Turkeys. United States Department of Agriculture, February 1995. http://dx.doi.org/10.32747/1995.7612834.bard.

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Mycoplasma iowae (Mi) is a pathogenic avian mycoplasma which causes mortality in turkey embryos and as such has clinical and economic significance for the turkey breeder industry. Control of Mi infection is severely hampered by lack of adequate diagnostic tests, together with resistance to most antibiotics and resilience to environment. A markedly high degree of intra-species antigenic variation also contributes to difficulties in detection and control of infection. In this project we have designed an innovative gene-based diagnostic test based on specific amplification of the 16S rRNA gene of Mi. This reaction, designed Multi-species PCR-RFLP test, also amplifies the DNA of the pathogenic avian mycoplasmas M. gallisepticum (Mg) and M. synoviae (Ms). This test detects DNA equivalent to about 300 cfu Mi or either of the other two target mycoplasmas, individually or in mixed infection. It is a quick test, applicable to a wide variety of clinical samples, such as allantoic fluid or tracheal or cloacal swab suspensions. Differential diagnosis is carried out by gel electro-phoresis of the PCR amplicon digested with selected restriction enzymes (Restriction Fragment Length Polymorphism). This can also be readily accomplished by using a simple Dot-Blot hybridization assay with digoxigenin-labeled oligonucleotide probes reacting specifically with unique Mi, Mg or Ms sequences in the PCR amplicon. The PCR/OLIGO test increased sensitivity by at least 10-fold with a capacity for rapid testing of large numbers of samples. Experimental infection trials were carried out to evaluate the diagnostic tools and to study pathogenesis of Mi infection. Field studies and experimental infection of embryonated eggs indicated both synergistic and competitive interaction of mycoplasma pathogens in mixed infection. The value of the PCR diagnostic tests for following the time course of egg transmission was shown. A workable serological test (Dot Immunobinding Assay) was also developed but there was no clear-cut evidence that infected turkeys develop an immune response. Typing of a wide spectrum of Mi field isolates by a variety of gene-based molecular techniques indicated a higher degree of genetic homogeneity than predicted on the basis of the phenotypic variability. All known strains of Mi were detected by the method developed. Together with an M. meleagridis-PCR test based on the same gene, the Multi-species PCR test is a highly valuable tool for diagnosis of pathogenic mycoplasmas in single or mixed infection. The further application of this rapid and specific test as a part of Mi and overall mycoplasma control programs will be dependent on developments in the turkey industry.
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Barash, Itamar, J. Mina Bissell, Alexander Faerman, and Moshe Shani. Modification of Milk Composition via Transgenesis: The Role of the Extracellular Matrix in Regulating Transgene Expression. United States Department of Agriculture, July 1995. http://dx.doi.org/10.32747/1995.7570558.bard.

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Altering milk composition via transgenesis depends on three main factors. (1) The availability of an efficient regulatory sequences for targeting transgene(s) to the mammary gland; (2) a reliable in vitro model to test the expression of transgenes prior to their introduction to the animal genome; and (3) better understanding of the major factors which determine the rate of gene expression and protein synthesis. The current studies provide the necessary means and knowledge to alter milk protein composition via transgenesis. The following specific goals were achieved: a: Identifying regulatory regions in the b-lactoglobulin (BLG) gene and the cross-talk between elements which enabled us to construct an efficient vector for the expression of desirable cDNA's in the mammary gland. b: The establishment of a sheep mammary cell line that serves as a model for the analysis of endogenous and exogenous milk protein synthesis in the mammary gland of livestock. c: An accurate comparison of the potency of the 5' regulatory sequences from the BLG and whey acidic protein (WAP) promoters in directing the expression of human serum albumin (HSA) to the mammary gland in vitro and in vivo. In this study we have also shown that sequences within the coding region may determine a specific pattern of expression for the transgene, distinct from that of the native milk protein genes. d: Characterizing the dominant role of ECM in transgene expression in mammary epithelial cells. e: Further characterization of the BCE-1 enhancer element in the promoter of the b-casein gene as a binding site for the c/EBP-b and Stat5. Identifying its interaction with chromatin and its up regulation by inhibitors of histone deacetylation. f: Identifying a mechanism of translational control as a mediator for the synergistic effect of insulin and prolactin on protein synthesis in the mammary gland.
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Funkenstein, Bruria, and Shaojun (Jim) Du. Interactions Between the GH-IGF axis and Myostatin in Regulating Muscle Growth in Sparus aurata. United States Department of Agriculture, March 2009. http://dx.doi.org/10.32747/2009.7696530.bard.

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Growth rate of cultured fish from hatching to commercial size is a major factor in the success of aquaculture. The normal stimulus for muscle growth in growing fish is not well understood and understanding the regulation of muscle growth in fish is of particular importance for aquaculture. Fish meat constitutes mostly of skeletal muscles and provides high value proteins in most people's diet. Unlike mammals, fish continue to grow throughout their lives, although the size fish attain, as adults, is species specific. Evidence indicates that muscle growth is regulated positively and negatively by a variety of growth and transcription factors that control both muscle cell proliferation and differentiation. In particular, growth hormone (GH), fibroblast growth factors (FGFs), insulin-like growth factors (IGFs) and transforming growth factor-13 (TGF-13) play critical roles in myogenesis during animal growth. An important advance in our understanding of muscle growth was provided by the recent discovery of the crucial functions of myostatin (MSTN) in controlling muscle growth. MSTN is a member of the TGF-13 superfamily and functions as a negative regulator of skeletal muscle growth in mammals. Studies in mammals also provided evidence for possible interactions between GH, IGFs, MSTN and the musclespecific transcription factor My oD with regards to muscle development and growth. The goal of our project was to try to clarify the role of MSTNs in Sparus aurata muscle growth and in particular determine the possible interaction between the GH-IGFaxis and MSTN in regulating muscle growth in fish. The steps to achieve this goal included: i) Determining possible relationship between changes in the expression of growth-related genes, MSTN and MyoD in muscle from slow and fast growing sea bream progeny of full-sib families and that of growth rate; ii) Testing the possible effect of over-expressing GH, IGF-I and IGF-Il on the expression of MSTN and MyoD in skeletal muscle both in vivo and in vitro; iii) Studying the regulation of the two S. aurata MSTN promoters and investigating the possible role of MyoD in this regulation. The major findings of our research can be summarized as follows: 1) Two MSTN promoters (saMSTN-1 and saMSTN-2) were isolated and characterized from S. aurata and were found to direct reporter gene activity in A204 cells. Studies were initiated to decipher the regulation of fish MSTN expression in vitro using the cloned promoters; 2) The gene coding for saMSTN-2 was cloned. Both the promoter and the first intron were found to be polymorphic. The first intron zygosity appears to be associated with growth rate; 3) Full length cDNA coding for S. aurata growth differentiation factor-l I (GDF-II), a closely related growth factor to MSTN, was cloned from S. aurata brain, and the mature peptide (C-terminal) was found to be highly conserved throughout evolution. GDF-II transcript was detected by RT -PCR analysis throughout development in S. aurata embryos and larvae, suggesting that this mRNA is the product of the embryonic genome. Transcripts for GDF-Il were detected by RT-PCR in brain, eye and spleen with highest level found in brain; 4) A novel member of the TGF-Bsuperfamily was partially cloned from S. aurata. It is highly homologous to an unidentified protein (TGF-B-like) from Tetraodon nigroviridisand is expressed in various tissues, including muscle; 5) Recombinant S. aurata GH was produced in bacteria, refolded and purified and was used in in vitro and in vivo experiments. Generally, the results of gene expression in response to GH administration in vivo depended on the nutritional state (starvation or feeding) and the time at which the fish were sacrificed after GH administration. In vitro, recombinantsaGH activated signal transduction in two fish cell lines: RTHI49 and SAFI; 6) A fibroblastic-like cell line from S. aurata (SAF-I) was characterized for its gene expression and was found to be a suitable experimental system for studies on GH-IGF and MSTN interactions; 7) The gene of the muscle-specific transcription factor Myogenin was cloned from S. aurata, its expression and promoter activity were characterized; 8) Three genes important to myofibrillogenesis were cloned from zebrafish: SmyDl, Hsp90al and skNAC. Our data suggests the existence of an interaction between the GH-IGFaxis and MSTN. This project yielded a great number of experimental tools, both DNA constructs and in vitro systems that will enable further studies on the regulation of MSTN expression and on the interactions between members of the GHIGFaxis and MSTN in regulating muscle growth in S. aurata.
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Or, Etti, Tai-Ping Sun, Amnon Lichter, and Avichai Perl. Characterization and Manipulation of the Primary Components in Gibberellin Signaling in the Grape Berry. United States Department of Agriculture, January 2010. http://dx.doi.org/10.32747/2010.7592649.bard.

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Seedless cultivars dominate the table grape industry. In these cultivars it is mandatory to apply gibberellin (GA) to stimulate berry development to a commercially acceptable size. These cultivars differ in their sensitivity to GA application, and it frequently results in adverse effects such as decreased bud fertility and increased fruit drop. Our long term goals are to (1) understand the molecular basis for the differential sensitivity and identify markers for selection of sensitive cultivars (2) to develop new strategies for targeted manipulation of the grape berry response to GA that will eliminate the need in GA application and the undesirable effects of GA on the vine, while maintaining its desirable effects on the berry. Both strategies are expected to reduce production cost and meet growing consumer demand for reduced use of chemicals. This approach relies on a comprehensive characterization of the central components in the GA signaling cascade in the berry. Several key components in the GA signaling pathway were identified in Arabidopsis and rice, including the GA receptors, GID1s, and a family of DELLA proteins that are the major negative regulators of the GA response. GA activates its response pathway by binding to GID1s, which then target DELLAs for degradation via interaction with SLY, a DELLA specific F-box protein. In grape, only one DELLA gene was characterized prior to this study, which plays a major role in inhibiting GA-promoted stem growth and GA-repressed floral induction but it does not regulate fruit growth. Therefore, we speculated that other DELLA family member(s) may control GA responses in berry, and their identification and manipulation may result in GA-independent berry growth. In the current study we isolated two additional VvDELLA family members, two VvGID1 genes and two VvSLY genes. Arabidopsis anti-AtRGA polyclonal antibodies recognized all three purified VvDELLA proteins, but its interaction with VvDELLA3 was weaker. Overexpression of the VvDELLAs, the VvGID1s, and the VvSLYs in the Arabidopsis mutants ga1-3/rga-24, gid1a-2/1c-2 and sly1-10, respectively, rescued the various mutant phenotypes. In vitro GAdependent physical interaction was shown between the VvDELLAs and the VvGID1s, and GAindependent interaction was shown between the VvDELLAs and VvSLYs. Interestingly, VvDELLA3 did not interact with VvGID1b. Together, the results indicate that the identified grape homologs serve as functional DELLA repressors, receptors and DELLA-interacting F-box proteins. Expression analyses revealed that (1) VvDELLA2 was expressed in all the analyzed tissues and was the most abundant (2) VvDELLA1 was low expressed in berries, confirming former study (3) Except in carpels and very young berries, VvDELLA3 levels were the lowest in most tissues. (4) Expression of both VvGID1s was detected in all the grape tissues, but VvGID1b transcript levels were significantly higher than VvGID1a. (5) In general, both VvDELLAs and VvGID1s transcripts levels increased as tissues aged. Unfertilized and recently fertilized carpels did not follow this trend, suggesting different regulatory mechanism of GA signaling in these stages. Characterization of the response to GA of various organs in three seedless cultivars revealed differential response of the berries and rachis. Interestingly, VvDELLA3 transcript levels in the GA-unresponsive berries of cv. Spring blush were significantly higher compared to their levels in the highly responsive berries of cv. Black finger. Assuming that VvDELLA2 and VvDELLA3 are regulating berry size, constructs carrying potential dominant mutations in each gene were created. Furthermore, constitutive silencing of these genes by mIR is underway, to reveal the effect of each gene on the berry phenotype.
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Schaffer, Arthur, Jack Preiss, Marina Petreikov, and Ilan Levin. Increasing Starch Accumulation via Genetic Modification of the ADP-glucose Pyrophosphorylase. United States Department of Agriculture, October 2009. http://dx.doi.org/10.32747/2009.7591740.bard.

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The overall objective of the research project was to utilize biochemical insights together with both classical and molecular genetic strategies to improve tomato starch accumulation. The proposal was based on the observation that the transient starch accumulation in the immature fruit serves as a reservoir for carbohydrate and soluble sugar content in the mature fruit, thereby impacting on fruit quality. The general objectives were to optimize AGPase function and activity in developing fruit in order to increase its transient starch levels. The specific research objectives were to: a) perform directed molecular evolution of the limiting enzyme of starch synthesis, AGPase, focussing on the interaction of its regulatory and catalytic subunits; b) determine the mode of action of the recently identified allelic variant for the regulatory subunit in tomato fruit that leads to increased AGPase activity and hence starch content. During the course of the research project major advances were made in understanding the interaction of the small and large subunits of AGPase, in particular the regulatory roles of the different large subunits, in determining starch synthesis. The research was performed using various experimental systems, including bacteria and Arabidopsis, potato and tomato, allowing for broad and meaningful conclusions to be drawn. A novel discovery was that one of the large subunits of tomato AGPase is functional as a monomer. A dozen publications describing the research were published in leading biochemical and horticultural journals. The research results clearly indicated that increasing AGPase activity temporally in the developing fruit increase the starch reservoir and, subsequently, the fruit sugar content. This was shown by a comparison of the carbohydrate balance in near-isogenic tomato lines differing in a gene encoding for the fruit-specific large subunit (LS1). The research also revealed that the increase in AGPase activity is due to a temporal extension of LS1 gene expression in the developing fruit which in turn stabilizes the limiting heterotetrameric enzyme, leading to sustained starch synthesis. This genetic variation can successfully be utilized in the breeding of high quality tomatoes.

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