Maloof Lab:Jose M. Jimenez-Gomez

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I am a Postdoctoral fellow in [[Maloof_Lab |Julin Maloof's lab]] in the [http://www-plb.ucdavis.edu/ Section of Plant Biology] at the [http://www.ucdavis.edu University of California Davis].<br>
I am a Postdoctoral fellow in [[Maloof_Lab |Julin Maloof's lab]] in the [http://www-plb.ucdavis.edu/ Section of Plant Biology] at the [http://www.ucdavis.edu University of California Davis].<br>
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In 2005, I completed my PhD. in JM Martinez-Zapater's lab at the [http://www.cnb.uam.es CNB] (National Center for Biotechnology) in Madrid, Spain, where I performed a quantitative genetic analysis of flowering time in tomato.
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In 2005, I completed my PhD. in JM Martinez-Zapater's lab at the [http://www.cnb.uam.es CNB] (National Center for Biotechnology) in Madrid, Spain, where I performed a quantitative genetic analysis of flowering time in tomato <cite>Jimenez-Gomez07</cite>.
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Plants form different environments exhibit different degrees of responsiveness to the same light stimulus. For example, when plants accommodated to sunny environments detect foliar shade from neighboring vegetation they respond increasing petiole and stem elongation and reducing the time to reproduction, a phenomenon called the "shade avoidance response". On the other hand, plants surrounded by tall vegetation, used to the shade and do not present this response.  
Plants form different environments exhibit different degrees of responsiveness to the same light stimulus. For example, when plants accommodated to sunny environments detect foliar shade from neighboring vegetation they respond increasing petiole and stem elongation and reducing the time to reproduction, a phenomenon called the "shade avoidance response". On the other hand, plants surrounded by tall vegetation, used to the shade and do not present this response.  
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To identify the molecular mechanisms underlying this differences we are performing QTL analysis using a previously developed, well characterized Recombinant Inbred Line set descent from two different natural populations of <i>Arabidopsis thaliana</i>: Bayreuth, originary from the German low altitude fallow lands, and Shahdara, from the high mountains of Tadjikistan (Loudet et al. 02).<br>
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To identify the molecular mechanisms underlying this differences we are performing QTL analysis using a previously developed, well characterized Recombinant Inbred Line set descent from two different natural populations of <i>Arabidopsis thaliana</i>: Bayreuth, originary from the German low altitude fallow lands, and Shahdara, from the high mountains of Tadjikistan <cite>Loudet02</cite>.<br>
I grew replicated individual RILs in environments simulating shade and sun conditions and measured them for a number of traits characteristic of the shade avoidance response syndrome. For the QTL analysis I modeled this phenotipic data to calculate a shade avoidance response index and used an available map that includes more than 500 markers.<br>
I grew replicated individual RILs in environments simulating shade and sun conditions and measured them for a number of traits characteristic of the shade avoidance response syndrome. For the QTL analysis I modeled this phenotipic data to calculate a shade avoidance response index and used an available map that includes more than 500 markers.<br>
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I am focusing now in a chromosomal region containing about 200 genes to fine map and identify the gene  responsible for the differential response to shade between the two natural populations. To do this I employ traditional genetic approaches as well as genomic and network analysis. I am developing a protocol to construct gene networks that will help me consider candidate genes based on coexpression with other genes across microarray experiments, colocalization with expression QTLs (West at el. 07), functional categorization and presence of polymorphisms (Clark et al. 07).
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I am focusing now in a chromosomal region containing about 200 genes to fine map and identify the gene  responsible for the differential response to shade between the two natural populations. To do this I employ traditional genetic approaches as well as genomic and network analysis. I am developing a protocol to construct gene networks that will help me consider candidate genes based on coexpression with other genes across microarray experiments <cite>Riken</cite>, colocalization with expression QTLs <cite>West07</cite>, functional categorization <cite>GO_Classification</cite> and presence of polymorphisms <cite>Clark07</cite>.
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<h3><font style="color:#F8B603;">Single Nuncleotide Polymorphism discovery between wild Tomato species</font></h3>
<h3><font style="color:#F8B603;">Single Nuncleotide Polymorphism discovery between wild Tomato species</font></h3>
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<h3><font style="color:#F8B603;">Molecular evolution of PHYTOCHROME B</font></h3>
<h3><font style="color:#F8B603;">Molecular evolution of PHYTOCHROME B</font></h3>
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PHYTOCHROME B (PHYB) is the main plant photoreceptor involved in the shade avoidance response. This gene has been reported to be under selective pressure, suggesting that plants with different shade avoidance responses may have different functional alleles of PHYB. Under these presumptions I am sequencing and cloning PHYB genes from a number of species with diverse shade avoidance behaviours. I will soon test if the variation in light responses between these plants are due to particular amino-acid changes in this photoreceptor.
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PHYTOCHROME B (PHYB) is the main plant photoreceptor involved in the shade avoidance response. This gene has been reported to be under selective pressure, suggesting that plants with different shade avoidance responses may have different functional alleles of PHYB. Under these presumptions I am sequencing and cloning PHYB genes from a number of species with diverse shade avoidance behaviors. I will soon test if the variation in light responses between these plants are due to particular amino-acid changes in this photoreceptor.
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<h3><font style="color:#F8B603;">Proteomics of light perception</font></h3>
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When plants are exposed to light a number of changes occur that are controlled by complex signaling processes. Light perception includes interaction with flowering time pathways, the circadian clock and hormone pathways between others. Genetics and genomic analysis have so far allowed us to identify and understand part of how this signals occur at the gene expression level, but very little is known about the changes produced in the plant at protein level. The new advances in Proteomics make possible to identify small protein changes with high precision. In collaboration with the Proteomics Facility at the UC Davis Genome Center we are preparing a set of experiments that will allow us to determine the accuracy and power of the newest techniques in protein quantification and to better understand how the proteome is regulated by light.   
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<h3><font style="color:#F8B603;">References</font></h3>
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<biblio>
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#Jimenez-Gomez07 pmid=17502904
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#Loudet02 pmid=12582628
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#West07 pmid=16702412
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#Clark07 pmid=17641193
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#Riken [http://prime.psc.riken.jp/ Platform for Riken Metabolomics]
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#GO_Classification pmid=10802651
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Revision as of 12:36, 26 November 2007

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Room 2115
Section of Plant Biology
1002 Life Sciences, One Shields Ave.
University of California Davis
Davis, CA 95616

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Jose M Jimenez-Gomez, PhD.

Contact


I am a Postdoctoral fellow in Julin Maloof's lab in the Section of Plant Biology at the University of California Davis.

In 2005, I completed my PhD. in JM Martinez-Zapater's lab at the CNB (National Center for Biotechnology) in Madrid, Spain, where I performed a quantitative genetic analysis of flowering time in tomato [1].

QTL analysis of the shade avoidance response in Arabidopsis


Plants exhibit phenotypic plasticity in response to different environmental light cues. For example, shade from neighboring plants sensed by the phytochrome photoreceptors causes increased petiole and stem elongation and early reproduction, collectively called the Shade Avoidance Response. Interestingly, the degree of plasticity varies among strains and species, and this variation can have adaptive value.

Plants form different environments exhibit different degrees of responsiveness to the same light stimulus. For example, when plants accommodated to sunny environments detect foliar shade from neighboring vegetation they respond increasing petiole and stem elongation and reducing the time to reproduction, a phenomenon called the "shade avoidance response". On the other hand, plants surrounded by tall vegetation, used to the shade and do not present this response. To identify the molecular mechanisms underlying this differences we are performing QTL analysis using a previously developed, well characterized Recombinant Inbred Line set descent from two different natural populations of Arabidopsis thaliana: Bayreuth, originary from the German low altitude fallow lands, and Shahdara, from the high mountains of Tadjikistan [2].
I grew replicated individual RILs in environments simulating shade and sun conditions and measured them for a number of traits characteristic of the shade avoidance response syndrome. For the QTL analysis I modeled this phenotipic data to calculate a shade avoidance response index and used an available map that includes more than 500 markers.


LOD score graph for several of the traits measured



I am focusing now in a chromosomal region containing about 200 genes to fine map and identify the gene responsible for the differential response to shade between the two natural populations. To do this I employ traditional genetic approaches as well as genomic and network analysis. I am developing a protocol to construct gene networks that will help me consider candidate genes based on coexpression with other genes across microarray experiments [3], colocalization with expression QTLs [4], functional categorization [5] and presence of polymorphisms [6].

Fragment of a gene network


Single Nuncleotide Polymorphism discovery between wild Tomato species


I use a bioinformatic approach to scrutinize the available tomato EST sequences and detect Single Nucleotide Polymorphisms. This will allow me to estimate the divergence between wild and cultivated tomato species, and will serve to have an idea of the effectiveness of the high throughput genomic methods that are and will be available soon for these species.

Molecular evolution of PHYTOCHROME B


PHYTOCHROME B (PHYB) is the main plant photoreceptor involved in the shade avoidance response. This gene has been reported to be under selective pressure, suggesting that plants with different shade avoidance responses may have different functional alleles of PHYB. Under these presumptions I am sequencing and cloning PHYB genes from a number of species with diverse shade avoidance behaviors. I will soon test if the variation in light responses between these plants are due to particular amino-acid changes in this photoreceptor.

amino-acid changes in a fragment of the PHYB gene in 8 speceis, red and black bars indicate non conserverd/ conserved amino-acid changes respectively



Proteomics of light perception


When plants are exposed to light a number of changes occur that are controlled by complex signaling processes. Light perception includes interaction with flowering time pathways, the circadian clock and hormone pathways between others. Genetics and genomic analysis have so far allowed us to identify and understand part of how this signals occur at the gene expression level, but very little is known about the changes produced in the plant at protein level. The new advances in Proteomics make possible to identify small protein changes with high precision. In collaboration with the Proteomics Facility at the UC Davis Genome Center we are preparing a set of experiments that will allow us to determine the accuracy and power of the newest techniques in protein quantification and to better understand how the proteome is regulated by light.



References


  1. Jiménez-Gómez JM, Alonso-Blanco C, Borja A, Anastasio G, Angosto T, Lozano R, and Martínez-Zapater JM. . pmid:17502904. PubMed HubMed [Jimenez-Gomez07]
  2. Loudet O, Chaillou S, Camilleri C, Bouchez D, and Daniel-Vedele F. . pmid:12582628. PubMed HubMed [Loudet02]
  3. Platform for Riken Metabolomics [Riken]
  4. West MA, van Leeuwen H, Kozik A, Kliebenstein DJ, Doerge RW, St Clair DA, and Michelmore RW. . pmid:16702412. PubMed HubMed [West07]
  5. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, and Sherlock G. . pmid:10802651. PubMed HubMed [GO_Classification]
  6. Clark RM, Schweikert G, Toomajian C, Ossowski S, Zeller G, Shinn P, Warthmann N, Hu TT, Fu G, Hinds DA, Chen H, Frazer KA, Huson DH, Schölkopf B, Nordborg M, Rätsch G, Ecker JR, and Weigel D. . pmid:17641193. PubMed HubMed [Clark07]
All Medline abstracts: PubMed HubMed


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