T-cell recognition of chemicals, protein allergens and drugs: towards the development of in vitro assays

Chemicals can elicit T-cell-mediated diseases such as allergic contact dermatitis and adverse drug reactions. Therefore, testing of chemicals, drugs and protein allergens for hazard identification and risk assessment is essential in regulatory toxicology. The seventh amendment of the EU Cosmetics Directive now prohibits the testing of cosmetic ingredients in mice, guinea pigs and other animal species to assess their sensitizing potential. In addition, the EU Chemicals Directive REACh requires the retesting of more than 30,000 chemicals for different toxicological endpoints, including sensitization, requiring vast numbers of animals. Therefore, alternative methods are urgently needed to eventually replace animal testing. Here, we summarize the outcome of an expert meeting in Rome on 7 November 2009 on the development of T-cell-based in vitro assays as tools in immunotoxicology to identify hazardous chemicals and drugs. In addition, we provide an overview of the development of the field over the last two decades.

Proposed minimal standards for the description of genera, species and subspecies of the Pasteurellaceae

Principles and guidelines are presented to ensure a solid scientific standard of papers dealing with the taxonomy of taxa of Pasteurellaceae Pohl 1981. The classification of the Pasteurellaceae is in principle based on a polyphasic approach. DNA sequencing of certain genes is very important for defining the borders of a taxon. However, the characteristics that are common to all members of the taxon and which might be helpful for separating it from related taxa must also be identified. Descriptions have to be based on as many strains as possible (inclusion of at least five strains is highly desirable), representing different sources with respect to geography and ecology, to allow proper characterization both phenotypically and genotypically, to establish the extent of diversity of the cluster to be named. A genus must be monophyletic based on 16S rRNA gene sequence-based phylogenetic analysis. Only in very rare cases is it acceptable that monophyly can not be achieved by 16S rRNA gene sequence comparison. Recently, the monophyly of genera has been confirmed by sequence comparison of housekeeping genes. In principle, a new genus should be recognized by a distinct phenotype, and characters that separate the new genus from its neighbours should be given clearly. Due to the overall importance of accurate classification of species, at least two genotypic methods are needed to show coherence and for separation at the species level. The main criterion for the classification of a novel species is that it forms a monophyletic group based on 16S rRNA gene sequence-based phylogenetic analysis. However, some groups might also include closely related species. In these cases, more sensitive tools for genetic recognition of species should be applied, such as DNA-DNA hybridizations. The comparison of housekeeping gene sequences has recently been used for genotypic definition of species. In order to separate species, phenotypic characters must also be identified to recognize them, and at least two phenotypic differences from existing species should be identified if possible. We recommend the use of the subspecies category only for subgroups associated with disease or similar biological characteristics. At the subspecies level, the genotypic groups must always be nested within the boundaries of an existing species. Phenotypic cohesion must be documented at the subspecies level and separation between subspecies and related species must be fully documented, as well as association with particular disease and host. An overview of methods previously used to characterize isolates of the Pasteurellaceae has been given. Genotypic and phenotypic methods are separated in relation to tests for investigating diversity and cohesion and to separate taxa at the level of genus as well as species and subspecies.

Detection and pattern recognition applied to leaves and chromosomes

The Project you are about to see it is based on the technologies used on object detection and recognition, especially on leaves and chromosomes. To do so, this document contains the typical parts of a scientific paper, as it is what it is. It is composed by an Abstract, an Introduction, points that have to do with the investigation area, future work, conclusions and references used for the elaboration of the document. The Abstract talks about what are we going to find in this paper, which is technologies employed on pattern detection and recognition for leaves and chromosomes and the jobs that are already made for cataloguing these objects. In the introduction detection and recognition meanings are explained. This is necessary as many papers get confused with these terms, specially the ones talking about chromosomes. Detecting an object is gathering the parts of the image that are useful and eliminating the useless parts. Summarizing, detection would be recognizing the objects borders. When talking about recognition, we are talking about the computers or the machines process, which says what kind of object we are handling. Afterwards we face a compilation of the most used technologies in object detection in general. There are two main groups on this category: Based on derivatives of images and based on ASIFT points. The ones that are based on derivatives of images have in common that convolving them with a previously created matrix does the treatment of them. This is done for detecting borders on the images, which are changes on the intensity of the pixels. Within these technologies we face two groups: Gradian based, which search for maximums and minimums on the pixels intensity as they only use the first derivative. The Laplacian based methods search for zeros on the pixels intensity as they use the second derivative. Depending on the level of details that we want to use on the final result, we will choose one option or the other, because, as its logic, if we used Gradian based methods, the computer will consume less resources and less time as there are less operations, but the quality will be worse. On the other hand, if we use the Laplacian based methods we will need more time and resources as they require more operations, but we will have a much better quality result. After explaining all the derivative based methods, we take a look on the different algorithms that are available for both groups. The other big group of technologies for object recognition is the one based on ASIFT points, which are based on 6 image parameters and compare them with another image taking under consideration these parameters. These methods disadvantage, for our future purposes, is that it is only valid for one single object. So if we are going to recognize two different leaves, even though if they refer to the same specie, we are not going to be able to recognize them with this method. It is important to mention these types of technologies as we are talking about recognition methods in general. At the end of the chapter we can see a comparison with pros and cons of all technologies that are employed. Firstly comparing them separately and then comparing them all together, based on our purposes. Recognition techniques, which are the next chapter, are not really vast as, even though there are general steps for doing object recognition, every single object that has to be recognized has its own method as the are different. This is why there is not a general method that we can specify on this chapter. We now move on into leaf detection techniques on computers. Now we will use the technique explained above based on the image derivatives. Next step will be to turn the leaf into several parameters. Depending on the document that you are referring to, there will be more or less parameters. Some papers recommend to divide the leaf into 3 main features (shape, dent and vein] and doing mathematical operations with them we can get up to 16 secondary features. Next proposition is dividing the leaf into 5 main features (Diameter, physiological length, physiological width, area and perimeter] and from those, extract 12 secondary features. This second alternative is the most used so it is the one that is going to be the reference. Following in to leaf recognition, we are based on a paper that provides a source code that, clicking on both leaf ends, it automatically tells to which specie belongs the leaf that we are trying to recognize. To do so, it only requires having a database. On the tests that have been made by the document, they assure us a 90.312% of accuracy over 320 total tests (32 plants on the database and 10 tests per specie]. Next chapter talks about chromosome detection, where we shall pass the metaphasis plate, where the chromosomes are disorganized, into the karyotype plate, which is the usual view of the 23 chromosomes ordered by number. There are two types of techniques to do this step: the skeletonization process and swiping angles. Skeletonization progress consists on suppressing the inside pixels of the chromosome to just stay with the silhouette. This method is really similar to the ones based on the derivatives of the image but the difference is that it doesnt detect the borders but the interior of the chromosome. Second technique consists of swiping angles from the beginning of the chromosome and, taking under consideration, that on a single chromosome we cannot have more than an X angle, it detects the various regions of the chromosomes. Once the karyotype plate is defined, we continue with chromosome recognition. To do so, there is a technique based on the banding that chromosomes have (grey scale bands] that make them unique. The program then detects the longitudinal axis of the chromosome and reconstructs the band profiles. Then the computer is able to recognize this chromosome. Concerning the future work, we generally have to independent techniques that dont reunite detection and recognition, so our main focus would be to prepare a program that gathers both techniques. On the leaf matter we have seen that, detection and recognition, have a link as both share the option of dividing the leaf into 5 main features. The work that would have to be done is to create an algorithm that linked both methods, as in the program, which recognizes leaves, it has to be clicked both leaf ends so it is not an automatic algorithm. On the chromosome side, we should create an algorithm that searches for the beginning of the chromosome and then start to swipe angles, to later give the parameters to the program that searches for the band profiles. Finally, on the summary, we explain why this type of investigation is needed, and that is because with global warming, lots of species (animals and plants] are beginning to extinguish. That is the reason why a big database, which gathers all the possible species, is needed. For recognizing animal species, we just only have to have the 23 chromosomes. While recognizing a plant, there are several ways of doing it, but the easiest way to input a computer is to scan the leaf of the plant. RESUMEN. El proyecto que se puede ver a continuación trata sobre las tecnologías empleadas en la detección y reconocimiento de objetos, especialmente de hojas y cromosomas. Para ello, este documento contiene las partes típicas de un paper de investigación, puesto que es de lo que se trata. Así, estará compuesto de Abstract, Introducción, diversos puntos que tengan que ver con el área a investigar, trabajo futuro, conclusiones y biografía utilizada para la realización del documento. Así, el Abstract nos cuenta qué vamos a poder encontrar en este paper, que no es ni más ni menos que las tecnologías empleadas en el reconocimiento y detección de patrones en hojas y cromosomas y qué trabajos hay existentes para catalogar a estos objetos. En la introducción se explican los conceptos de qué es la detección y qué es el reconocimiento. Esto es necesario ya que muchos papers científicos, especialmente los que hablan de cromosomas, confunden estos dos términos que no podían ser más sencillos. Por un lado tendríamos la detección del objeto, que sería simplemente coger las partes que nos interesasen de la imagen y eliminar aquellas partes que no nos fueran útiles para un futuro. Resumiendo, sería reconocer los bordes del objeto de estudio. Cuando hablamos de reconocimiento, estamos refiriéndonos al proceso que tiene el ordenador, o la máquina, para decir qué clase de objeto estamos tratando. Seguidamente nos encontramos con un recopilatorio de las tecnologías más utilizadas para la detección de objetos, en general. Aquí nos encontraríamos con dos grandes grupos de tecnologías: Las basadas en las derivadas de imágenes y las basadas en los puntos ASIFT. El grupo de tecnologías basadas en derivadas de imágenes tienen en común que hay que tratar a las imágenes mediante una convolución con una matriz creada previamente. Esto se hace para detectar bordes en las imágenes que son básicamente cambios en la intensidad de los píxeles. Dentro de estas tecnologías nos encontramos con dos grupos: Los basados en gradientes, los cuales buscan máximos y mínimos de intensidad en la imagen puesto que sólo utilizan la primera derivada; y los Laplacianos, los cuales buscan ceros en la intensidad de los píxeles puesto que estos utilizan la segunda derivada de la imagen. Dependiendo del nivel de detalles que queramos utilizar en el resultado final nos decantaremos por un método u otro puesto que, como es lógico, si utilizamos los basados en el gradiente habrá menos operaciones por lo que consumirá más tiempo y recursos pero por la contra tendremos menos calidad de imagen. Y al revés pasa con los Laplacianos, puesto que necesitan más operaciones y recursos pero tendrán un resultado final con mejor calidad. Después de explicar los tipos de operadores que hay, se hace un recorrido explicando los distintos tipos de algoritmos que hay en cada uno de los grupos. El otro gran grupo de tecnologías para el reconocimiento de objetos son los basados en puntos ASIFT, los cuales se basan en 6 parámetros de la imagen y la comparan con otra imagen teniendo en cuenta dichos parámetros. La desventaja de este método, para nuestros propósitos futuros, es que sólo es valido para un objeto en concreto. Por lo que si vamos a reconocer dos hojas diferentes, aunque sean de la misma especie, no vamos a poder reconocerlas mediante este método. Aún así es importante explicar este tipo de tecnologías puesto que estamos hablando de técnicas de reconocimiento en general. Al final del capítulo podremos ver una comparación con los pros y las contras de todas las tecnologías empleadas. Primeramente comparándolas de forma separada y, finalmente, compararemos todos los métodos existentes en base a nuestros propósitos. Las técnicas de reconocimiento, el siguiente apartado, no es muy extenso puesto que, aunque haya pasos generales para el reconocimiento de objetos, cada objeto a reconocer es distinto por lo que no hay un método específico que se pueda generalizar. Pasamos ahora a las técnicas de detección de hojas mediante ordenador. Aquí usaremos la técnica explicada previamente explicada basada en las derivadas de las imágenes. La continuación de este paso sería diseccionar la hoja en diversos parámetros. Dependiendo de la fuente a la que se consulte pueden haber más o menos parámetros. Unos documentos aconsejan dividir la morfología de la hoja en 3 parámetros principales (Forma, Dentina y ramificación] y derivando de dichos parámetros convertirlos a 16 parámetros secundarios. La otra propuesta es dividir la morfología de la hoja en 5 parámetros principales (Diámetro, longitud fisiológica, anchura fisiológica, área y perímetro] y de ahí extraer 12 parámetros secundarios. Esta segunda propuesta es la más utilizada de todas por lo que es la que se utilizará. Pasamos al reconocimiento de hojas, en la cual nos hemos basado en un documento que provee un código fuente que cucando en los dos extremos de la hoja automáticamente nos dice a qué especie pertenece la hoja que estamos intentando reconocer. Para ello sólo hay que formar una base de datos. En los test realizados por el citado documento, nos aseguran que tiene un índice de acierto del 90.312% en 320 test en total (32 plantas insertadas en la base de datos por 10 test que se han realizado por cada una de las especies]. El siguiente apartado trata de la detección de cromosomas, en el cual se debe de pasar de la célula metafásica, donde los cromosomas están desorganizados, al cariotipo, que es como solemos ver los 23 cromosomas de forma ordenada. Hay dos tipos de técnicas para realizar este paso: Por el proceso de esquelotonización y barriendo ángulos. El proceso de esqueletonización consiste en eliminar los píxeles del interior del cromosoma para quedarse con su silueta; Este proceso es similar a los métodos de derivación de los píxeles pero se diferencia en que no detecta bordes si no que detecta el interior de los cromosomas. La segunda técnica consiste en ir barriendo ángulos desde el principio del cromosoma y teniendo en cuenta que un cromosoma no puede doblarse más de X grados detecta las diversas regiones de los cromosomas. Una vez tengamos el cariotipo, se continua con el reconocimiento de cromosomas. Para ello existe una técnica basada en las bandas de blancos y negros que tienen los cromosomas y que son las que los hacen únicos. Para ello el programa detecta los ejes longitudinales del cromosoma y reconstruye los perfiles de las bandas que posee el cromosoma y que lo identifican como único. En cuanto al trabajo que se podría desempeñar en el futuro, tenemos por lo general dos técnicas independientes que no unen la detección con el reconocimiento por lo que se habría de preparar un programa que uniese estas dos técnicas. Respecto a las hojas hemos visto que ambos métodos, detección y reconocimiento, están vinculados debido a que ambos comparten la opinión de dividir las hojas en 5 parámetros principales. El trabajo que habría que realizar sería el de crear un algoritmo que conectase a ambos ya que en el programa de reconocimiento se debe clicar a los dos extremos de la hoja por lo que no es una tarea automática. En cuanto a los cromosomas, se debería de crear un algoritmo que busque el inicio del cromosoma y entonces empiece a barrer ángulos para después poder dárselo al programa que busca los perfiles de bandas de los cromosomas. Finalmente, en el resumen se explica el por qué hace falta este tipo de investigación, esto es que con el calentamiento global, muchas de las especies (tanto animales como plantas] se están empezando a extinguir. Es por ello que se necesitará una base de datos que contemple todas las posibles especies tanto del reino animal como del reino vegetal. Para reconocer a una especie animal, simplemente bastará con tener sus 23 cromosomas; mientras que para reconocer a una especie vegetal, existen diversas formas. Aunque la más sencilla de todas es contar con la hoja de la especie puesto que es el elemento más fácil de escanear e introducir en el ordenador.

Diversification, expression, and γδ T cell recognition of evolutionarily distant members of the MIC family of major histocompatibility complex class I-related molecules

Distant relatives of major histocompatibility complex (MHC) class I molecules, human MICA and MICB, function as stress-induced antigens that are broadly recognized by intestinal epithelial γδ T cells. They may thus play a central role in the immune surveillance of damaged, infected, or otherwise stressed intestinal epithelial cells. However, the generality of this system in evolution and the mode of recognition of MICA and MICB are undefined. Analysis of cDNA sequences from various primate species defined translation products that are homologous to MICA and MICB. All of the MIC polypeptides have common characteristics, although they are extraordinarily diverse. The most notable alterations are several deletions and frequent amino acid substitutions in the putative α-helical regions of the α1α2 domains. However, the primate MIC molecules were expressed on the surfaces of normal and transfected cells. Moreover, despite their sharing of relatively few identical amino acids in potentially accessible regions of their α1α2 domains, they were recognized by diverse human intestinal epithelial γδ T cells that are restricted by MICA and MICB. Thus, MIC molecules represent a family of MHC proteins that are structurally diverse yet appear to be functionally conserved. The promiscuous mode of γδ T cell recognition of these antigens may be explained by their sharing of a single conserved interaction site.

All males are not created equal: Fertility differences depend on gamete recognition polymorphisms in sea urchins

Behaviors, morphologies, and genetic loci directly involved in reproduction have been increasingly shown to be polymorphic within populations. Explaining how such variants are maintained by selection is crucial to understanding the genetic basis of fertility differences, but direct tests of how alleles at reproductive loci affect fertility are rare. In the sea urchin genus Echinometra, the protein bindin mediates sperm attachment to eggs, evolves quickly, and is polymorphic within species. Eggs exposed to experimental sperm mixtures show strong discrimination on the basis of the males’ bindin genotype. Different females produce eggs that nonrandomly select sperm from different males, showing that variable egg–sperm interactions determine fertility. Eggs select sperm with a bindin genotype similar to their own, suggesting strong linkage between female choice and male trait loci. These experiments demonstrate that alleles at a single locus can have a strong effect on fertilization and that reproductive loci may retain functional polymorphisms through epistatic interactions between male and female traits. They also suggest that positive selection at gamete recognition loci like bindin involves strong selection within species on mate choice interactions.

Reciprocal secretion of proteins by the bacterial type III machines of plant and animal pathogens suggests universal recognition of mRNA targeting signals

Bacterial pathogens of both animals and plants use type III secretion machines to inject virulence proteins into host cells. Although many components of the secretion machinery are conserved among different bacterial species, the substrates for their type III pathways are not. The Yersinia type III machinery recognizes some secretion substrates via a signal that is encoded within the first 15 codons of yop mRNA. These signals can be altered by frameshift mutations without affecting secretion of the encoded polypeptides, suggesting a mechanism whereby translation of yop mRNA is coupled to the translocation of newly synthesized polypeptide. We report that the type III machinery of Erwinia chrysanthemi cloned in Escherichia coli recognizes the secretion signals of yopE and yopQ. Pseudomonas syringae AvrB and AvrPto, two proteins exported by the recombinant Erwinia machine, can also be secreted by the Yersinia type III pathway. Mapping AvrPto sequences sufficient for the secretion of reporter fusions in Yersinia revealed the presence of an mRNA secretion signal. We propose that 11 conserved components of type III secretion machines may recognize signals that couple mRNA translation to polypeptide secretion.

Prion protein NMR structure and species barrier for prion diseases

The structural basis of species specificity of transmissible spongiform encephalopathies, such as bovine spongiform encephalopathy or “mad cow disease” and Creutzfeldt–Jakob disease in humans, has been investigated using the refined NMR structure of the C-terminal domain of the mouse prion protein with residues 121–231. A database search for mammalian prion proteins yielded 23 different sequences for the fragment 124–226, which display a high degree of sequence identity and show relevant amino acid substitutions in only 18 of the 103 positions. Except for a unique isolated negative surface charge in the bovine protein, the amino acid differences are clustered in three distinct regions of the three-dimensional structure of the cellular form of the prion protein. Two of these regions represent potential species-dependent surface recognition sites for protein–protein interactions, which have independently been implicated from in vitro and in vivo studies of prion protein transformation. The third region consists of a cluster of interior hydrophobic side chains that may affect prion protein transformation at later stages, after initial conformational changes in the cellular protein.

Phylogeny of rice genomes with emphasis on origins of allotetraploid species

The rice genus, Oryza, which comprises 23 species and 9 recognized genome types, represents an enormous gene pool for genetic improvement of rice cultivars. Clarification of phylogenetic relationships of rice genomes is critical for effective utilization of the wild rice germ plasm. By generating and comparing two nuclear gene (Adh1 and Adh2) trees and a chloroplast gene (matK) tree of all rice species, phylogenetic relationships among the rice genomes were inferred. Origins of the allotetraploid species, which constitute more than one-third of rice species diversity, were reconstructed based on the Adh gene phylogenies. Genome types of the maternal parents of allotetraploid species were determined based on the matK gene tree. The phylogenetic reconstruction largely supports the previous recognition of rice genomes. It further revealed that the EE genome species is most closely related to the DD genome progenitor that gave rise to the CCDD genome. Three species of the CCDD genome may have originated through a single hybridization event, and their maternal parent had the CC genome. The BBCC genome species had different origins, and their maternal parents had either a BB or CC genome. An additional genome type, HHKK, was recognized for Oryza schlechteri and Porteresia coarctata, suggesting that P. coarctata is an Oryza species. The AA genome lineage, which contains cultivated rice, is a recently diverged and rapidly radiated lineage within the rice genus.

Acoustic and neural bases for innate recognition of song

In behavior reminiscent of the responsiveness of human infants to speech, young songbirds innately recognize and prefer to learn the songs of their own species. The acoustic and physiological bases for innate recognition were investigated in fledgling white-crowned sparrows lacking song experience. A behavioral test revealed that the complete conspecific song was not essential for innate recognition: songs composed of single white-crowned sparrow phrases and songs played in reverse elicited vocal responses as strongly as did normal song. In all cases, these responses surpassed those to other species’ songs. Although auditory neurons in the song nucleus HVc and the underlying neostriatum of fledglings did not prefer conspecific song over foreign song, some neurons responded strongly to particular phrase types characteristic of white-crowned sparrows and, thus, could contribute to innate song recognition.

Similar DNA recognition properties of alternatively spliced Drosophila POU factors

The POU-IV or “Brn-3” class of POU-domain transcription factors is represented in Drosophila by I-POU and twin-of-I-POU, alternative splice products of the I-POU gene. I-POU has been previously reported to inhibit DNA binding by the POU-III class factor drifter/Cf1a via the formation of heterodimeric complexes. Here we report that expression of the I-POU/tI-POU message is maximal late in the embryonic phase of Drosophila development, and I-POU is the preferred splice variant. Although I-POU lacks two basic amino acid residues in the POU-homeodomain found in tI-POU and Brn-3.0, these three POU-IV class proteins exhibit very similar DNA-binding specificity. In contrast to previously published reports, the results presented here show no effect of I-POU on DNA binding by drifter, and no evidence for I-POU/drifter dimerization. These results suggest that the I-POU/tI-POU gene products function by transcriptional mechanisms similar to those of the homologous POU-IV class factors expressed in other species, not by a unique inhibitory mechanism.

Parent–progeny recognition as a function of MHC odortype identity

The several linked polymorphic genes of the MHC, which has been proposed as a prime determinant of sensed genetic individuality within species, is known to operate in mice by olfactory recognition in aspects of reproductive behavior that concern mate selection, thereby favoring outbreeding and heterozygosity, and also concern the maintenance of pregnancy. A single base-change can alter an individual MHC odortype, and the potential range of combinatorial MHC-determined odortypes is clearly vast. Following our findings that newborn mice already express their MHC odortype (which is detectable at 9 days of gestational age), we sought to determine whether MHC is involved in behavioral aspects of early development, such as rearing. In the studies presented herein, we report the ability and proclivity of mothers to recognize and preferentially retrieve syngeneic (genetically identical) pups from other pups differing only for MHC. Reciprocally, we report the ability of pups to recognize their familial environment, regardless of whether they had been nursed by their biological mothers or by foster mothers. Early learning experiences of the MHC environment are apparently a key element in survival, assuring maternal protection and promoting outbreeding.

Resected RNA pseudoknots and their recognition by histidyl-tRNA synthetase

Duplexes constituted by closed or open RNA circles paired to single-stranded oligonucleotides terminating with 3′-CCAOH form resected pseudoknots that are substrates of yeast histidyl-tRNA synthetase. Design of this RNA fold is linked to the mimicry of the pseudoknotted amino acid accepting branch of the tRNA-like domain from brome mosaic virus, known to be charged by tyrosyl-tRNA synthetases, with RNA minihelices recapitulating accepting branches of canonical tRNAs. Prediction of the histidylation function of the new family of minimalist tRNA-like structures relates to the geometry of resected pseudoknots that allows proper presentation to histidyl-tRNA synthetase of analogues of the histidine identity determinants N-1 and N73 present in tRNAs. This geometry is such that the analogue of the major N-1 histidine determinant in the RNA circles faces the analogue of the discriminator N73 nucleotide in the accepting oligonucleotides. The combination of identity elements found in tRNAHis species from archaea, eubacteria, and organelles (G-1/C73) is the most efficient for determining histidylation of the duplexes. The inverse combination (C-1/G73) leads to the worst histidine acceptors with charging efficiencies reduced by 2–3 orders of magnitude. Altogether, these findings open new perspectives for understanding evolution of tRNA identity and serendipitous RNA functions.

Ancient origin of pathogen recognition specificity conferred by the tomato disease resistance gene Pto

We have investigated the origin of the Pto disease resistance (R) gene that was previously identified in the wild tomato species Lycopersicon pimpinellifolium and isolated by map-based cloning. Pto encodes a serine-threonine protein kinase that specifically recognizes strains of Pseudomonas syringae pv. tomato (Pst) that express the avirulence gene avrPto. We examined an accession of the distantly related wild species Lycopersicon hirsutum var. glabratum that exhibits avrPto-specific resistance to Pst. The Pst resistance of L. hirsutum was introgressed into a susceptible Lycopersicon esculentum background to create the near-isogenic line 96T133-3. Resistance to Pst(avrPto) in 96T133-3 was inherited as a single dominant locus and cosegregated with a restriction fragment length polymorphism detected by the Pto gene. This observation suggested that a member of the Pto gene family confers Pst(avrPto) resistance in this L. hirsutum line. Here we report the cloning and characterization of four members of the Pto family from 96T133-3. One gene (LhirPto) is 97% identical to Pto and encodes a catalytically active protein kinase that elicits a hypersensitive response when coexpressed with avrPto in leaves of Nicotiana benthamiana. In common with the Pto kinase, the LhirPto protein physically interacts with AvrPto and downstream members of the Pto signaling pathway. Our studies indicate that R genes of the protein kinase class may not evolve rapidly in response to pathogen pressure and rather that their ability to recognize specific Avr proteins can be highly conserved.

Arabidopsis MBP1 gene encodes a conserved ubiquitin recognition component of the 26S proteasome.

Multiubiquitin chain attachment is a key step leading to the selective degradation of abnormal polypeptides and many important regulatory proteins by the eukaryotic 26S proteasome. However, the mechanism by which the 26S complex recognizes this posttranslational modification is unknown. Using synthetic multiubiquitin chains to probe an expression library for interacting proteins, we have isolated an Arabidopsis cDNA, designated MBP1, that encodes a 41-kDa acidic protein exhibiting high affinity for chains, especially those containing four or more ubiquitins. Based on similar physical and immunological properties, multiubiquitin binding affinities, and peptide sequence, MBP1 is homologous to subunit 5a of the human 26S proteasome. Structurally related proteins also exist in yeast, Caenorhabditis, and other plant species. Given their binding properties, association with the 26S proteasome, and widespread distribution, MBP1, S5a, and related proteins likely function as essential ubiquitin recognition components of the 26S proteasome.

Differential recognition of the type I interferon receptor by interferons tau and alpha is responsible for their disparate cytotoxicities.

Interferon tau (IFN tau), originally identified as a pregnancy recognition hormone, is a type I interferon that is related to the various IFN alpha species (IFN alpha s). Ovine IFN tau has antiviral activity similar to that of human IFN alpha A on the Madin-Darby bovine kidney (MDBK) cell line and is equally effective in inhibiting cell proliferation. In this study, IFN tau was found to differ from IFN alpha A in that is was > 30-fold less toxic to MDBK cells at high concentrations. Excess IFN tau did not block the cytotoxicity of IFN alpha A on MDBK cells, suggesting that these two type I IFNs recognize the type I IFN receptor differently on these cells. In direct binding studies, 125I-IFN tau had a Kd of 3.90 x 10(-10) M for receptor on MDBK cells, whereas that of 125I-IFN alpha A was 4.45 x 10(-11) M. Consistent with the higher binding affinity, IFN alpha A was severalfold more effective than IFN tau in competitive binding against 125I-IFN tau to receptor on MDBK cells. Paradoxically, the two IFNs had similar specific antiviral activities on MDBK cells. However, maximal IFN antiviral activity required only fractional occupancy of receptors, whereas toxicity was associated with maximal receptor occupancy. Hence, IFN alpha A, with the higher binding affinity, was more toxic than IFN tau. The IFNs were similar in inducing the specific phosphorylation of the type I receptor-associated tyrosine kinase Tyk2, and the transcription factors Stat1 alpha and Stat2, suggesting that phosphorylation of these signal transduction proteins is not involved in the cellular toxicity associated with type I IFNs. Experiments using synthetic peptides suggest that differences in the interaction at the N terminal of IFN tau and IFN alpha with the type I receptor complex contribute significantly to differences in high-affinity equilibrium binding of these molecules. It is postulated that such a differential recognition of the receptor is responsible for the similar antiviral but different cytotoxic effects of these IFNs. Moreover, these data imply that receptors are "spare'' with respect to certain biological properties, and we speculate that IFNs may induce a concentration-dependent selective association of receptor subunits.