Difference between revisions of "Maloof Lab:Jose M. Jimenez-Gomez"

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<h2>Jose M Jimenez-Gomez, PhD.</h2>
<h2>Jose M Jimenez-Gomez, PhD.</h2>
[mailto:jmjimenez@ucdavis.edu Contact]
[mailto:jmjimenez@ucdavis.edu Contact]

Revision as of 09:23, 17 March 2009

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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.


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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].

My main interests are based on the application of modern genetic and bioinformatic techniques to the study of plant evolution. To do this I survey different species and natural populations of plants presenting variation in interesting characteristics, and analyze the responsible molecular mechanism. Here is an small description of some of my work:

QTL analysis of the shade avoidance response in Arabidopsis

It is well known that plants from different light environments exhibit different degrees of responsiveness to similar light stimulus. For example, plants accommodated to sunny environments detect foliar shade from neighboring vegetation and respond increasing their petioles/stems and reducing the time to reproduction, a phenomenon called the "shade avoidance response". On the other hand, plants adapted to live under dense canopies are less sensitive to the shade and present a reduced shade avoidance 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].
We grew replicated individual RILs in environments simulating shade and sun conditions and characterized them on a number of traits associated with the shade avoidance response syndrome. For the QTL analysis we calculated a shade avoidance response index fitting fixed effect models to the phenotipic data, and used an available genetic map for the population that includes more than 500 Single Feature Polymorphism (SFP) markers [3].

QTL analysis.jpg

LOD score graph for several of the traits measured

We are now focusing 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 we employ traditional genetic approaches as well as genomic and network analysis. We are developing a protocol to construct gene networks that will help us consider candidate genes based on coexpression with other genes across microarray experiments [4], colocalization with expression QTLs [5], functional categorization [6] and presence of polymorphisms between the parental lines [7].

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Fragment of a gene network

Single Nuncleotide Polymorphism discovery between wild Tomato species

We use bioinformatics to detect Single Nucleotide Polymorphisms among the numerous tomato EST sequences available in the databases. We aim to estimate divergence rates over large regions of the genome of selected species, and obtain a new set of molecular markers useful in natural variation studies. We use this data to perform functional and evolutionary pre-genomic analyses on this dataset, which should give us an idea of which gene families evolve more rapidly/slowly and have been important during tomato domestication.

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 we are sequencing and cloning PHYB genes from a number of species with diverse shade avoidance behaviors. We will soon test if the variation in light responses between these plants are due to particular amino-acid changes in this photoreceptor.

PHYB alignment.jpg

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


  1. Jiménez-Gómez JM, Alonso-Blanco C, Borja A, Anastasio G, Angosto T, Lozano R, and Martínez-Zapater JM. Quantitative genetic analysis of flowering time in tomato. Genome. 2007 Mar;50(3):303-15. DOI:10.1139/g07-009 | PubMed ID:17502904 | HubMed [Jimenez-Gomez07]
  2. Loudet O, Chaillou S, Camilleri C, Bouchez D, and Daniel-Vedele F. Bay-0 x Shahdara recombinant inbred line population: a powerful tool for the genetic dissection of complex traits in Arabidopsis. Theor Appl Genet. 2002 May;104(6-7):1173-1184. DOI:10.1007/s00122-001-0825-9 | PubMed ID:12582628 | HubMed [Loudet02]
  3. West MA, van Leeuwen H, Kozik A, Kliebenstein DJ, Doerge RW, St Clair DA, and Michelmore RW. High-density haplotyping with microarray-based expression and single feature polymorphism markers in Arabidopsis. Genome Res. 2006 Jun;16(6):787-95. DOI:10.1101/gr.5011206 | PubMed ID:16702412 | HubMed [West06]
  4. Riken [Riken]
  5. West MA, Kim K, Kliebenstein DJ, van Leeuwen H, Michelmore RW, Doerge RW, and St Clair DA. Global eQTL mapping reveals the complex genetic architecture of transcript-level variation in Arabidopsis. Genetics. 2007 Mar;175(3):1441-50. DOI:10.1534/genetics.106.064972 | PubMed ID:17179097 | HubMed [West07]
  6. 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. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000 May;25(1):25-9. DOI:10.1038/75556 | PubMed ID:10802651 | HubMed [GO_Classification]
  7. 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. Common sequence polymorphisms shaping genetic diversity in Arabidopsis thaliana. Science. 2007 Jul 20;317(5836):338-42. DOI:10.1126/science.1138632 | PubMed ID:17641193 | HubMed [Clark07]
All Medline abstracts: PubMed | HubMed