The Arabidopsis thaliana ABC Protein Superfamily, a Complete Inventory

Sánchez-Fernández, Rocío; Davies, T.G. Emyr; Coleman, J. O. D.; Rea, Philip A.

doi:10.1074/jbc.m103104200

Cited by 481 publications

(430 citation statements)

References 63 publications

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“…Arsenite is then directly pumped out of most cells (Kuroda et al, 1996) or chelated by g-EC-containing thiol peptides to form As(III)-peptide complexes (Scott et al, 1993;Ha et al, 1999;Pickering et al, 2000;Schmoger et al, 2000;Hartley-Whitaker et al, 2001). As(III)-GSH complexes are thought to be pumped into vacuoles through the action of a family of GSH conjugate pumps (Lu et al, 1997;Tommasini et al, 1998;Sanchez-Fernandez et al, 2001). Therefore, we anticipate that GS overexpression or GS overexpression combined with overexpression of ECS might dramatically enhance arsenic tolerance and perhaps above-ground accumulation in plant vacuoles.…”

Section: Discussionmentioning

confidence: 99%

Enhanced tolerance to and accumulation of mercury, but not arsenic, in plants overexpressing two enzymes required for thiol peptide synthesis

Heaton

Carreira³

et al. 2006

Physiologia Plantarum

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Arsenic and mercury are among the most toxic elemental pollutants in the environment, endangering human health and ecological integrity. Both elements are found in highly thiol‐reactive forms, arsenite and Hg(II), respectively, in plant tissues. Overexpression of Escherichia coliγ‐glutamylcysteine synthetase (ECS) or glutathione synthetase (GS) in Arabidopsis thaliana plants provided significant increases in the thiol peptides glutathione (GSH) and γ‐glutamylcysteine (γ‐EC), and/or phytochelatins (PCs), and some resistance to arsenic and mercury, but no substantial increases in the levels of these elements in above‐ground tissues. In contrast, the co‐expression of ECS and GS in ECS × GS lines produced significant increases in tolerance to toxic levels of mercury. The ECS × GS co‐expression line accumulated 35‐fold more biomass and three‐fold more mercury aboveground than the wild type (WT) when grown on Hg(II). No increases in arsenic accumulation were detected in the ECS × GS line. Increased resistance to and accumulation of mercury apparently resulted from enhanced root concentrations of PCs in ECS × GS co‐expression lines not seen in the wild type or lines expressing ECS or GS alone. Correlations between the levels of arsenic and mercury resistance and accumulation and increases in the accumulation of the various thiol peptides in the ECS, GS and ECS × GS transgenic plant lines are discussed.

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Section: Discussionmentioning

confidence: 99%

Enhanced tolerance to and accumulation of mercury, but not arsenic, in plants overexpressing two enzymes required for thiol peptide synthesis

Heaton

Carreira³

et al. 2006

Physiologia Plantarum

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“…The PDR genes encode a subfamily of ABC transporter in plants (Sanchez-Fernandez et al, 2001;Martinoia et al, 2002). The data presented in this paper provide a definitive annotation of the genomic sequences encoding this family of transporter in potato and indicate that potato contains 76 gene encoding PDR proteins.…”

Section: Discussionmentioning

confidence: 78%

Genome-wide identification, functional analysis and expression profiling of pleiotropic drug resistance (PDR) sub-family in potato

Ou¹,

Huang²,

Zhao³

et al. 2013

Afr. J. Biotechnol.

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The plant pleiotropic drug resistance (PDR) family of ATP-binding cassette (ABC) transporters has comprehensively been researched in relation to transport of antifungal agents and resistant pathogens. In our study, analyses of the whole family of PDR genes present in the potato genome were provided. This analysis resolves discrepancies of potato PDR proteins and provides an expression analysis of all annotated potato PDR genes based on RNA-seq data. The results indicate that the potato genome contains 76 encoding PDR proteins and that these genes show a specific expression patterns, both at the organ level and in response to various hormonal treatment. These data provide some clues for future molecular genetic analysis of this important subfamily of ABC transporters. In addition, potato PDR genes may also play some important roles in the transportation of antifungal agents and resistant pathogens.

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“…It has also been used as a measure of similarity by programs such as XDOM (Gouzy et al 1997), and in the creation of HOVERGEN (Duret et al 1994), HOBACGEN (Perriere et al 2000) databases. Figure 8 shows an instance where our usage of match as the basic unit of similarity helps distinguish members of two different families in the ABC superfamily (Sanchez-Fernandez et al 2001). In this particular case, all hits have very similar identities (≈30%), cover (≈40%) and score.…”

Section: Discussion Match As Unit Of Similaritymentioning

confidence: 99%

“…One should also keep in mind that the same family is sometimes independently listed by several groups. For instance, the PDR family appears three times-as a member of the ABC superfamily (Sanchez-Fernandez et al 2001), as a member of the ABC Transporters superfamily, and yet again, independently as the ABC transporter PDR subfamily (van den Brule and Smart 2002). Only the final version, which is a superset of the other two is fully clusterable.…”

Section: Genome Research 1165mentioning

confidence: 99%

“…Idiosyncracies in the family: One example is the structure of the two members of the PMP family (ABC superfamily) shown in Figure 11. The PMP proteins are supposed to be half-molecule ABC transporters (Sanchez-Fernandez et al 2001), however, Q94FB9 is a full-molecule transporter with each half being PMP like. This causes the cover of the match between the two proteins to reduce by 50%.…”

Section: Genome Research 1165mentioning

confidence: 99%

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Visualizing Sequence Similarity of Protein Families

Veeramachaneni

Makałowski²

2004

Genome Res.

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Classification of proteins into families is one of the main goals of functional analysis. Proteins are usually assigned to a family on the basis of the presence of family-specific patterns, domains, or structural elements. Whereas proteins belonging to the same family are generally similar to each other, the extent of similarity varies widely across families. Some families are characterized by short, well-defined motifs, whereas others contain longer, less-specific motifs. We present a simple method for visualizing such differences. We applied our method to the Arabidopsis thaliana families listed at The Arabidopsis Information Resource (TAIR) Web site and for 76% of the nontrivial families (families with more than one member), our method identifies simple similarity measures that are necessary and sufficient to cluster members of the family together. Our visualization method can be used as part of an annotation pipeline to identify potentially incorrectly defined families. We also describe how our method can be extended to identify novel families and to assign unclassified proteins into known families.Genome projects (Bernal et al. 2001) are generating sequence data at a much faster rate than can be effectively analyzed. The goal of functional genomics is to determine the function of proteins predicted by these sequencing projects (Bork et al. 1998;Eisenberg et al. 2000;Tsoka and Ouzounis 2000). Because experimental evidence about individual proteins is difficult to obtain, a common strategy is to classify proteins into families on the basis of the presence of shared features or by clustering using some similarity measure. The underlying assumption is that members of the same family may possess similar or identical biochemical functions (Hegyi and Gerstein 1999) and that one can assign the functions of well-characterized members of a family to other members whose functions are not known or not well understood (Heger and Holm 2000).The simplest methods for clustering proteins into families rely on sequence-similarity measures, such as those obtained by BLAST (Altschul et al. 1990). More sophisticated approaches detect domains using domain databases (Bateman et al. 2002;Servant et al. 2002;Mulder et al. 2003), optionally use the order of domains as a fingerprint for the protein, and classify proteins into families on the basis of the presence of shared domains or similar domain architecture (Geer et al. 2002). Classification of proteins into families using structural similarities (Holm and Sander 1996) is, at present, limited by the relatively small number of structures available in PDB ( Similarity-based clustering is a two-step process-one first needs to determine pairwise similarities between all pairs of proteins and then apply a clustering method that uses the similarity matrix to group proteins into clusters. However, methods that quantify similarity by using some attribute of the best BLAST hit and use single-linkage clustering are not always successful. One problem such methods face is the detection of th...

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The Arabidopsis thaliana ABC Protein Superfamily, a Complete Inventory

Cited by 481 publications

References 63 publications

Enhanced tolerance to and accumulation of mercury, but not arsenic, in plants overexpressing two enzymes required for thiol peptide synthesis

Enhanced tolerance to and accumulation of mercury, but not arsenic, in plants overexpressing two enzymes required for thiol peptide synthesis

Genome-wide identification, functional analysis and expression profiling of pleiotropic drug resistance (PDR) sub-family in potato

Visualizing Sequence Similarity of Protein Families

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