The Mammalian Phenotype (MP) Ontology enables robust annotation of mammalian phenotypes in the context of mutations, quantitative trait loci and strains that are used as models of human biology and disease.

Background Mammalian phenotypes are complex and the term itself is imprecise. Generally, we use the word phenotype in referring to the appearance or manifestation of a set of traits in an individual that result from the combined action and interaction of genotype and environment. Because mouse is the premier model organism for the study of human biology and disease, the goal of comparative phenotyping and building new animal models through genetic engineering holds great promise. The mouse has distinct advantages for studies that translate to humans. It is a small, short-lived mammal with a fully sequenced genome in which all life stages can be accessed, and for which myriad tools are available for precisely experimentally manipulating its genome. Further, the large collection of inbred strains of mice and the controlled environment in which the animals live provides the ability to confirm phenotype observations and to systematically perturb environmental factors and genetic input to measure effects under defined conditions. Current international efforts to 'make a mutation' for every gene through mutagenesis [ 1 ] and genetic engineering [ 2 , 3 ] make it imperative for phenotype data to be represented in standard descriptive formats to enable computational analysis and comparison. Mammalian phenotypes are frequently genetically complex. Mutation of even a single gene almost always produces pleiotropic effects. Conversely, non-allelic mutations can produce indistinguishable phenotypes. Modifier genes and epistatic interactions can markedly alter the phenotype. Combining different allelic combinations of different genes can produce unique phenotypes not found in the single-gene mutation genotype. Imprinting of genes can dramatically affect phenotype. Mutations expressed in different inbred strains of mice can manifest as an increase or decrease of severity or penetrance of the corresponding phenotype. Quantitative trait loci (QTL) can contribute in complex nonlinear ways to the phenotype. In addition, mutations that are 'genomic' in nature, either disrupting or deleting multiple genes or occurring in intergenic regions, can produce distinct phenotypes and challenge us to think beyond gene effects to genomic effects. The outcome of these complex interactions can be dissected and reproducibly examined by characterizing inbred strains that represent the combined phenotype of the 'whole-genome' genotype in its environmental context. The Mouse Genome Database (MGD) at the Mouse Genome Informatics website [ 4 , 5 ] serves as the model organism database for mouse, representing the genetics, genomics and biology of the mouse and as a community resource for mammalian studies. Significant reorganization and modeling of phenotypes is now underway to support these data robustly, to represent phenotypes in ways that are computationally accessible, and to provide human interfaces to these data that will enable knowledge building and hypothesis generation. One component of this work is the development of the Mammalian Phenotype (MP) Ontology, a structured vocabulary that will aid in standardizing annotations and, with its concepts definitions, unambiguously describe phenotypic observations.
Background
Mammalian phenotypes are complex and the term itself is imprecise. Generally, we use the word phenotype in referring to the appearance or manifestation of a set of traits in an individual that result from the combined action and interaction of genotype and environment.Because mouse is the premier model organism for the study of human biology and disease, the goal of comparative phenotyping and building new animal models through genetic engineering holds great promise. The mouse has distinct advantages for studies that translate to humans. It is a small, short-lived mammal with a fully sequenced genome in which all life stages can be accessed, and for which myriad tools are available for precisely experimentally manipulating its genome. Further, the large collection of inbred strains of mice and the controlled environment in which the animals live provides the ability to confirm phenotype observations and to systematically perturb environmental factors and genetic input to measure effects under defined conditions. Current international efforts to 'make a mutation' for every gene through mutagenesis [ 1 ] and genetic engineering [ 2 , 3 ] make it imperative for phenotype data to be represented in standard descriptive formats to enable computational analysis and comparison.Mammalian phenotypes are frequently genetically complex. Mutation of even a single gene almost always produces pleiotropic effects. Conversely, non-allelic mutations can produce indistinguishable phenotypes. Modifier genes and epistatic interactions can markedly alter the phenotype. Combining different allelic combinations of different genes can produce unique phenotypes not found in the single-gene mutation genotype. Imprinting of genes can dramatically affect phenotype. Mutations expressed in different inbred strains of mice can manifest as an increase or decrease of severity or penetrance of the corresponding phenotype. Quantitative trait loci (QTL) can contribute in complex nonlinear ways to the phenotype. In addition, mutations that are 'genomic' in nature, either disrupting or deleting multiple genes or occurring in intergenic regions, can produce distinct phenotypes and challenge us to think beyond gene effects to genomic effects. The outcome of these complex interactions can be dissected and reproducibly examined by characterizing inbred strains that represent the combined phenotype of the 'whole-genome' genotype in its environmental context.The Mouse Genome Database (MGD) at the Mouse Genome Informatics website [ 4 , 5 ] serves as the model organism database for mouse, representing the genetics, genomics and biology of the mouse and as a community resource for mammalian studies. Significant reorganization and modeling of phenotypes is now underway to support these data robustly, to represent phenotypes in ways that are computationally accessible, and to provide human interfaces to these data that will enable knowledge building and hypothesis generation. One component of this work is the development of the Mammalian Phenotype (MP) Ontology, a structured vocabulary that will aid in standardizing annotations and, with its concepts definitions, unambiguously describe phenotypic observations.Results and discussion The problems of text Written descriptions of phenotypes in higher organisms reflect the complexity of the subject, the richness of language, and the phenomenal diversity that these data represent. While text descriptions are commonly used in publications describing phenotype, and have been the basis of electronically accessible phenotypic descriptions (for example, Online Mendelian Inheritance in Man (OMIM) [ 6 ] and the Mouse Locus Catalog (MLC) [ 7 ], text is unreliable for searching, either manually or computationally. From the user's perspective, even the best full-text search including Boolean operators will miss appropriate records (false negatives) and return unwanted records (false positives). Consider the example in Table  1  where searches were done to find spontaneous mutations in which mice were entirely or partially devoid of hair/fur. To obtain a complete result, the user would need to use a number of search terms and synonyms. The wording within the text depends upon the author of the record and his/her particular word usage and editorial style. A minimum of four search terms is needed to recover the 27 relevant mutations displayed in this table and it cannot be ascertained if this is a complete set of mutations for this phenotype. Conversely, the user is returned with 23 irrelevant results. Irrelevant results can be returned for many reasons including, but not limited to, the following: the author of the record is contrasting the phenotype of a mutation in one gene with a mutation in another gene; the author is making a statement that includes the negation of the trait; the match is based on gene name rather than phenotype; the mutant was used as a linkage marker to genetically map another gene. A further detriment to database text records is their difficulty to update and maintain. As new information is learned about a phenotypic mutant, the record must be continually rewritten. Although this practice might be sustained for a small number of records, it does not scale when thousands of mutant records are considered. The alternative of simply adding on another paragraph to existing text records becomes confusing, with potentially conflicting information and different writing styles appearing in one textual description, and unwieldy, with more and more text that may no longer represent a logical synthesis. Nomenclatures, vocabularies and ontologies Formal nomenclatures for genes, mutant alleles and inbred strains of mice have existed since the 1940s [ 8 , 9 ]. The MGD [ 4 ] serves as the authoritative source for the names and symbols associated with mouse genes, alleles and strains. The advantage of applying such nomenclatures has been increasingly recognized as genomes become better defined and the realized power of comparative genomics allows homologous and orthologous gene relationships to be explicitly defined. At present, human, mouse and rat gene nomenclatures operate in parallel, using coordinated symbols for all three species' genes. In addition, mouse and rat strain nomenclatures were merged to one standard strain nomenclature recently, making strain identity and nomenclature conventions consistent. Nomenclature guides for mouse and rat genes, mutant alleles, and strains are available online and regularly revised based on international nomenclature committees' reviews [ 10 ]. Beyond nomenclatures, which are key to object identities and relationships, are vocabularies that can be used to describe broader concepts and categorizations. Vocabularies can take many forms, including simple lists of controlled terms, such as the cytogenetic band designations used to name the bands defined by chromosome staining or the classes of genetic markers, such as gene, pseudogene, expressed sequence tag (EST), and so forth. The annotation of complex biological data and concepts requires more than lists and simple vocabularies. Ontologies, or 'descriptions of what there is', contain both concepts, with precise meanings, and relationships among those concepts. As such ontologies are able to support descriptions of complex biology and are useful in making these data more amenable to computational analyses. The first widely used ontology developed and adopted in the biological domain is the Gene Ontology (GO) [ 11 - 13 ] which contains concepts of molecular function, cellular localization and biological process for annotating the functional aspects of genes. The GO is structured as a directed acyclic graph (DAG), where each vocabulary term (node) may have both multiple parent term and multiple child term relationships. MGD uses GO extensively for gene annotation [ 14 ]. In addition, MGD has adopted the Mouse Embryo Anatomy Nomenclature Database [ 15 ] and the Anatomical Dictionary for the Adult Mouse [ 16 ] for annotating data that include anatomical attributes, such as tissue sources for clones and phenotypes. The Gene Expression Database (GXD) [ 17 ], integrated with MGD through the Mouse Genome Informatics (MGI) system [ 4 ], applies these anatomical ontologies as a central concept in the description of expression data. Mammalian Phenotype Ontology Although the need for vocabularies as key components to consistent phenotype annotations for mammals has been recognized for some time [ 18 ], and many smaller controlled vocabularies have been implemented to describe various aspects of phenotype in MGD (for example, class of mutation, embryonic stem (ES) cell lines used for generating targeted mutations, type of inheritance), much of the data has remained in text form. Over the past two years, the Mammalian Phenotype (MP) Ontology has emerged to more precisely describe phenotypes, and to allow easier access to phenotype-sequence interactions. Our goal is to describe the richness of phenotypes as precisely as they are known, recognizing that phenotype data are by nature complex and usually incomplete. Taking advantage of structural properties of a DAG, we have the ability to annotate phenotypes to the level of data resolution available, whether general or very specific and the ability to query with a high-level term, returning all phenotypes containing annotations to that term or to terms more specific than the query term. Thus, one can query for 'respiratory signs/symptoms' and retrieve all phenotypes annotated to this term and its hierarchical 'children' (abnormal breathing, abnormal respiratory sounds, anoxia, apnea, dyspnea, hypercapnia, and so on), or specifically request annotations to any of these sub-terms. The top level terms of the MP Ontology include physiological systems, behavior, developmental phenotypes and survival/aging. Physiological systems branch into morphological and physiological phenotypes at the level immediately below. A browser to view the ontology is available at [ 19 ] (Figure  1 ). In this browser the DAG structure is flattened into a hierarchy, with multiple hierarchies representing unique paths to a term displayed sequentially. MP terms and synonyms can be searched or users can browse the ontology starting from the high-level terms and open levels continuously to increasingly granular terms. Each MP ontology term has a unique identifier, a definition and synonyms. In the term detail pages, these data and the number of hierarchical paths of the vocabulary where the terms appear are displayed. A plus sign following the term indicates that children of this term exist. In this figure, displayed next to the term, is a link indicating the number of annotation instances in MGD using this term or children of this term. This feature, due to be publicly available in early 2005, will greatly improve phenotype-centric searching in MGD. Developing the MP vocabulary To initiate the vocabulary, we first developed a high-level categorization of phenotypes consisting of approximately 100 terms, such as heart/cardiovascular dysmorphology and skeletal axial defects. As we used this list for annotations, terms were refined and general organizing principles for the MP vocabulary were developed. An important component of our approach has been to address two practical implementation questions. From the biologist's perspective, the question is what term would be used to describe a specific phenotypic trait. From the curation perspective, we ask what terms reflect biological reality and maximize curator productivity. From a purely ontological perspective, every trait could be broken down into a core object, such as 'cornea' or 'gastrulation', defined by anatomical, behavioral or physiological terms, and a series of attribute vocabularies that describe the quality, quantity and character of a trait. For the practical reason of needing robust terms to describe phenotypes up-front to speed curation and the problem of losing biological meaning, particularly for clinical or dysmorphology terms, when terms are completely deconstructed (that is, the sum of the parts is less than the term itself), we have chosen to use compound terms in the MP Ontology. A few examples of terms where it is difficult to preserve the full biological meaning once they are deconstructed are shown in Table  2 . In addition, it should be noted that each of these terms requires multiple annotations to recover all aspects that the single term provides. Use of complex terms in the MP Ontology, however, does not preclude also storing the decomposed version should this later prove desirable (see PATO model discussed in [ 20 ]). More important, the MP Ontology can currently hold, for each term, database cross-references to other ontologies. This is a common practice in GO when compound terms are developed. For the MP Ontology, these cross-references include anatomical terms from the Mouse Anatomy ontologies [ 15 , 16 ] and the GO process terms [ 21 ]. Three major strategies are being pursued to further develop the vocabulary itself. First and most important is through the ongoing process of curating phenotype data. As new phenotypic traits are described and published, the need for new terms is recognized. New terms added in this way may be a simple addition to an existing hierarchical path or may result in the addition of entire new branches in the hierarchy. Second, collaborative efforts between the MGD phenotype curators, the mouse mutagenesis centers and the rat genetics community identify new specific terms and suggest improved organization of terms within particular hierarchical branches. Third, we are recruiting individuals with expertise in specific biological domains to review and evaluate sections of the vocabulary for accuracy, completeness and systematic arrangement. The MP Ontology is a work in progress and remains incomplete in some areas. We welcome the participation of the mammalian research community so that the most useful, definitive and universally applicable terms will be included. Information can be obtained by sending e-mail to  pheno@informatics.jax.org . While common pathological and clinical terms are used in the MP Ontology, considerations for term placement within the structure and for precise terminology is often derived from comparison with other open biological ontologies (OBO) [ 22 ]. Recently, a cell-type ontology has become available [ 23 ] and a comparison of terminology to this ontology has not yet been completed. We are working with the mutant mouse pathology database Pathbase [ 24 , 25 ] to map and cross-reference terms from their Pathology Ontology. Vocabulary tools The MP Ontology was built as a DAG using the DAG-Edit software written by John Richter and Suzanna Lewis [ 26 ]. The MP Ontology is updated daily and can be browsed or searched online at [ 19 ]. MP files also are available in flat file format and OBO format from our ftp site [ 27 ] and are posted at the OBO site [ 28 ]. Phenotype data annotation Phenotypes are described in the MGD relative to the genotype of the individual. Genotype objects specifically consist of one or more allele pairs describing mutations or QTLs and the genetic background strain(s) where the phenotype was observed. Each phenotype annotation associates a MP Ontology term with a genotype/strain and the reference or data source supporting this assertion. Additional modifying text may be annotated to describe detail that is not easily standardized. Examples include experimental conditions, age of onset and incidence, and trait penetrance, among others. The annotation note may also include specifics of the phenotype where such details are deemed to be too case specific to be a MP term. In addition, genotypes are associated with OMIM where a particular mouse genotype is a model for human diseases and syndromes. Figure  2  shows the portion of one phenotype record that uses the MP Ontology.
Results and discussion
The problems of text Written descriptions of phenotypes in higher organisms reflect the complexity of the subject, the richness of language, and the phenomenal diversity that these data represent. While text descriptions are commonly used in publications describing phenotype, and have been the basis of electronically accessible phenotypic descriptions (for example, Online Mendelian Inheritance in Man (OMIM) [ 6 ] and the Mouse Locus Catalog (MLC) [ 7 ], text is unreliable for searching, either manually or computationally. From the user's perspective, even the best full-text search including Boolean operators will miss appropriate records (false negatives) and return unwanted records (false positives). Consider the example in Table  1  where searches were done to find spontaneous mutations in which mice were entirely or partially devoid of hair/fur. To obtain a complete result, the user would need to use a number of search terms and synonyms. The wording within the text depends upon the author of the record and his/her particular word usage and editorial style. A minimum of four search terms is needed to recover the 27 relevant mutations displayed in this table and it cannot be ascertained if this is a complete set of mutations for this phenotype. Conversely, the user is returned with 23 irrelevant results. Irrelevant results can be returned for many reasons including, but not limited to, the following: the author of the record is contrasting the phenotype of a mutation in one gene with a mutation in another gene; the author is making a statement that includes the negation of the trait; the match is based on gene name rather than phenotype; the mutant was used as a linkage marker to genetically map another gene. A further detriment to database text records is their difficulty to update and maintain. As new information is learned about a phenotypic mutant, the record must be continually rewritten. Although this practice might be sustained for a small number of records, it does not scale when thousands of mutant records are considered. The alternative of simply adding on another paragraph to existing text records becomes confusing, with potentially conflicting information and different writing styles appearing in one textual description, and unwieldy, with more and more text that may no longer represent a logical synthesis.
The problems of text
Written descriptions of phenotypes in higher organisms reflect the complexity of the subject, the richness of language, and the phenomenal diversity that these data represent. While text descriptions are commonly used in publications describing phenotype, and have been the basis of electronically accessible phenotypic descriptions (for example, Online Mendelian Inheritance in Man (OMIM) [ 6 ] and the Mouse Locus Catalog (MLC) [ 7 ], text is unreliable for searching, either manually or computationally. From the user's perspective, even the best full-text search including Boolean operators will miss appropriate records (false negatives) and return unwanted records (false positives).Consider the example in Table  1  where searches were done to find spontaneous mutations in which mice were entirely or partially devoid of hair/fur. To obtain a complete result, the user would need to use a number of search terms and synonyms. The wording within the text depends upon the author of the record and his/her particular word usage and editorial style. A minimum of four search terms is needed to recover the 27 relevant mutations displayed in this table and it cannot be ascertained if this is a complete set of mutations for this phenotype. Conversely, the user is returned with 23 irrelevant results. Irrelevant results can be returned for many reasons including, but not limited to, the following: the author of the record is contrasting the phenotype of a mutation in one gene with a mutation in another gene; the author is making a statement that includes the negation of the trait; the match is based on gene name rather than phenotype; the mutant was used as a linkage marker to genetically map another gene.A further detriment to database text records is their difficulty to update and maintain. As new information is learned about a phenotypic mutant, the record must be continually rewritten. Although this practice might be sustained for a small number of records, it does not scale when thousands of mutant records are considered. The alternative of simply adding on another paragraph to existing text records becomes confusing, with potentially conflicting information and different writing styles appearing in one textual description, and unwieldy, with more and more text that may no longer represent a logical synthesis.Nomenclatures, vocabularies and ontologies Formal nomenclatures for genes, mutant alleles and inbred strains of mice have existed since the 1940s [ 8 , 9 ]. The MGD [ 4 ] serves as the authoritative source for the names and symbols associated with mouse genes, alleles and strains. The advantage of applying such nomenclatures has been increasingly recognized as genomes become better defined and the realized power of comparative genomics allows homologous and orthologous gene relationships to be explicitly defined. At present, human, mouse and rat gene nomenclatures operate in parallel, using coordinated symbols for all three species' genes. In addition, mouse and rat strain nomenclatures were merged to one standard strain nomenclature recently, making strain identity and nomenclature conventions consistent. Nomenclature guides for mouse and rat genes, mutant alleles, and strains are available online and regularly revised based on international nomenclature committees' reviews [ 10 ]. Beyond nomenclatures, which are key to object identities and relationships, are vocabularies that can be used to describe broader concepts and categorizations. Vocabularies can take many forms, including simple lists of controlled terms, such as the cytogenetic band designations used to name the bands defined by chromosome staining or the classes of genetic markers, such as gene, pseudogene, expressed sequence tag (EST), and so forth. The annotation of complex biological data and concepts requires more than lists and simple vocabularies. Ontologies, or 'descriptions of what there is', contain both concepts, with precise meanings, and relationships among those concepts. As such ontologies are able to support descriptions of complex biology and are useful in making these data more amenable to computational analyses. The first widely used ontology developed and adopted in the biological domain is the Gene Ontology (GO) [ 11 - 13 ] which contains concepts of molecular function, cellular localization and biological process for annotating the functional aspects of genes. The GO is structured as a directed acyclic graph (DAG), where each vocabulary term (node) may have both multiple parent term and multiple child term relationships. MGD uses GO extensively for gene annotation [ 14 ]. In addition, MGD has adopted the Mouse Embryo Anatomy Nomenclature Database [ 15 ] and the Anatomical Dictionary for the Adult Mouse [ 16 ] for annotating data that include anatomical attributes, such as tissue sources for clones and phenotypes. The Gene Expression Database (GXD) [ 17 ], integrated with MGD through the Mouse Genome Informatics (MGI) system [ 4 ], applies these anatomical ontologies as a central concept in the description of expression data.
Nomenclatures, vocabularies and ontologies
Formal nomenclatures for genes, mutant alleles and inbred strains of mice have existed since the 1940s [ 8 , 9 ]. The MGD [ 4 ] serves as the authoritative source for the names and symbols associated with mouse genes, alleles and strains. The advantage of applying such nomenclatures has been increasingly recognized as genomes become better defined and the realized power of comparative genomics allows homologous and orthologous gene relationships to be explicitly defined. At present, human, mouse and rat gene nomenclatures operate in parallel, using coordinated symbols for all three species' genes. In addition, mouse and rat strain nomenclatures were merged to one standard strain nomenclature recently, making strain identity and nomenclature conventions consistent. Nomenclature guides for mouse and rat genes, mutant alleles, and strains are available online and regularly revised based on international nomenclature committees' reviews [ 10 ].Beyond nomenclatures, which are key to object identities and relationships, are vocabularies that can be used to describe broader concepts and categorizations. Vocabularies can take many forms, including simple lists of controlled terms, such as the cytogenetic band designations used to name the bands defined by chromosome staining or the classes of genetic markers, such as gene, pseudogene, expressed sequence tag (EST), and so forth.The annotation of complex biological data and concepts requires more than lists and simple vocabularies. Ontologies, or 'descriptions of what there is', contain both concepts, with precise meanings, and relationships among those concepts. As such ontologies are able to support descriptions of complex biology and are useful in making these data more amenable to computational analyses. The first widely used ontology developed and adopted in the biological domain is the Gene Ontology (GO) [ 11 - 13 ] which contains concepts of molecular function, cellular localization and biological process for annotating the functional aspects of genes. The GO is structured as a directed acyclic graph (DAG), where each vocabulary term (node) may have both multiple parent term and multiple child term relationships. MGD uses GO extensively for gene annotation [ 14 ]. In addition, MGD has adopted the Mouse Embryo Anatomy Nomenclature Database [ 15 ] and the Anatomical Dictionary for the Adult Mouse [ 16 ] for annotating data that include anatomical attributes, such as tissue sources for clones and phenotypes. The Gene Expression Database (GXD) [ 17 ], integrated with MGD through the Mouse Genome Informatics (MGI) system [ 4 ], applies these anatomical ontologies as a central concept in the description of expression data.Mammalian Phenotype Ontology Although the need for vocabularies as key components to consistent phenotype annotations for mammals has been recognized for some time [ 18 ], and many smaller controlled vocabularies have been implemented to describe various aspects of phenotype in MGD (for example, class of mutation, embryonic stem (ES) cell lines used for generating targeted mutations, type of inheritance), much of the data has remained in text form. Over the past two years, the Mammalian Phenotype (MP) Ontology has emerged to more precisely describe phenotypes, and to allow easier access to phenotype-sequence interactions. Our goal is to describe the richness of phenotypes as precisely as they are known, recognizing that phenotype data are by nature complex and usually incomplete. Taking advantage of structural properties of a DAG, we have the ability to annotate phenotypes to the level of data resolution available, whether general or very specific and the ability to query with a high-level term, returning all phenotypes containing annotations to that term or to terms more specific than the query term. Thus, one can query for 'respiratory signs/symptoms' and retrieve all phenotypes annotated to this term and its hierarchical 'children' (abnormal breathing, abnormal respiratory sounds, anoxia, apnea, dyspnea, hypercapnia, and so on), or specifically request annotations to any of these sub-terms. The top level terms of the MP Ontology include physiological systems, behavior, developmental phenotypes and survival/aging. Physiological systems branch into morphological and physiological phenotypes at the level immediately below. A browser to view the ontology is available at [ 19 ] (Figure  1 ). In this browser the DAG structure is flattened into a hierarchy, with multiple hierarchies representing unique paths to a term displayed sequentially. MP terms and synonyms can be searched or users can browse the ontology starting from the high-level terms and open levels continuously to increasingly granular terms. Each MP ontology term has a unique identifier, a definition and synonyms. In the term detail pages, these data and the number of hierarchical paths of the vocabulary where the terms appear are displayed. A plus sign following the term indicates that children of this term exist. In this figure, displayed next to the term, is a link indicating the number of annotation instances in MGD using this term or children of this term. This feature, due to be publicly available in early 2005, will greatly improve phenotype-centric searching in MGD.
Mammalian Phenotype Ontology
Although the need for vocabularies as key components to consistent phenotype annotations for mammals has been recognized for some time [ 18 ], and many smaller controlled vocabularies have been implemented to describe various aspects of phenotype in MGD (for example, class of mutation, embryonic stem (ES) cell lines used for generating targeted mutations, type of inheritance), much of the data has remained in text form. Over the past two years, the Mammalian Phenotype (MP) Ontology has emerged to more precisely describe phenotypes, and to allow easier access to phenotype-sequence interactions.Our goal is to describe the richness of phenotypes as precisely as they are known, recognizing that phenotype data are by nature complex and usually incomplete. Taking advantage of structural properties of a DAG, we have the ability to annotate phenotypes to the level of data resolution available, whether general or very specific and the ability to query with a high-level term, returning all phenotypes containing annotations to that term or to terms more specific than the query term. Thus, one can query for 'respiratory signs/symptoms' and retrieve all phenotypes annotated to this term and its hierarchical 'children' (abnormal breathing, abnormal respiratory sounds, anoxia, apnea, dyspnea, hypercapnia, and so on), or specifically request annotations to any of these sub-terms.The top level terms of the MP Ontology include physiological systems, behavior, developmental phenotypes and survival/aging. Physiological systems branch into morphological and physiological phenotypes at the level immediately below. A browser to view the ontology is available at [ 19 ] (Figure  1 ). In this browser the DAG structure is flattened into a hierarchy, with multiple hierarchies representing unique paths to a term displayed sequentially. MP terms and synonyms can be searched or users can browse the ontology starting from the high-level terms and open levels continuously to increasingly granular terms.Each MP ontology term has a unique identifier, a definition and synonyms. In the term detail pages, these data and the number of hierarchical paths of the vocabulary where the terms appear are displayed. A plus sign following the term indicates that children of this term exist. In this figure, displayed next to the term, is a link indicating the number of annotation instances in MGD using this term or children of this term. This feature, due to be publicly available in early 2005, will greatly improve phenotype-centric searching in MGD.Developing the MP vocabulary To initiate the vocabulary, we first developed a high-level categorization of phenotypes consisting of approximately 100 terms, such as heart/cardiovascular dysmorphology and skeletal axial defects. As we used this list for annotations, terms were refined and general organizing principles for the MP vocabulary were developed. An important component of our approach has been to address two practical implementation questions. From the biologist's perspective, the question is what term would be used to describe a specific phenotypic trait. From the curation perspective, we ask what terms reflect biological reality and maximize curator productivity. From a purely ontological perspective, every trait could be broken down into a core object, such as 'cornea' or 'gastrulation', defined by anatomical, behavioral or physiological terms, and a series of attribute vocabularies that describe the quality, quantity and character of a trait. For the practical reason of needing robust terms to describe phenotypes up-front to speed curation and the problem of losing biological meaning, particularly for clinical or dysmorphology terms, when terms are completely deconstructed (that is, the sum of the parts is less than the term itself), we have chosen to use compound terms in the MP Ontology. A few examples of terms where it is difficult to preserve the full biological meaning once they are deconstructed are shown in Table  2 . In addition, it should be noted that each of these terms requires multiple annotations to recover all aspects that the single term provides. Use of complex terms in the MP Ontology, however, does not preclude also storing the decomposed version should this later prove desirable (see PATO model discussed in [ 20 ]). More important, the MP Ontology can currently hold, for each term, database cross-references to other ontologies. This is a common practice in GO when compound terms are developed. For the MP Ontology, these cross-references include anatomical terms from the Mouse Anatomy ontologies [ 15 , 16 ] and the GO process terms [ 21 ]. Three major strategies are being pursued to further develop the vocabulary itself. First and most important is through the ongoing process of curating phenotype data. As new phenotypic traits are described and published, the need for new terms is recognized. New terms added in this way may be a simple addition to an existing hierarchical path or may result in the addition of entire new branches in the hierarchy. Second, collaborative efforts between the MGD phenotype curators, the mouse mutagenesis centers and the rat genetics community identify new specific terms and suggest improved organization of terms within particular hierarchical branches. Third, we are recruiting individuals with expertise in specific biological domains to review and evaluate sections of the vocabulary for accuracy, completeness and systematic arrangement. The MP Ontology is a work in progress and remains incomplete in some areas. We welcome the participation of the mammalian research community so that the most useful, definitive and universally applicable terms will be included. Information can be obtained by sending e-mail to  pheno@informatics.jax.org . While common pathological and clinical terms are used in the MP Ontology, considerations for term placement within the structure and for precise terminology is often derived from comparison with other open biological ontologies (OBO) [ 22 ]. Recently, a cell-type ontology has become available [ 23 ] and a comparison of terminology to this ontology has not yet been completed. We are working with the mutant mouse pathology database Pathbase [ 24 , 25 ] to map and cross-reference terms from their Pathology Ontology.
Developing the MP vocabulary
To initiate the vocabulary, we first developed a high-level categorization of phenotypes consisting of approximately 100 terms, such as heart/cardiovascular dysmorphology and skeletal axial defects. As we used this list for annotations, terms were refined and general organizing principles for the MP vocabulary were developed.An important component of our approach has been to address two practical implementation questions. From the biologist's perspective, the question is what term would be used to describe a specific phenotypic trait. From the curation perspective, we ask what terms reflect biological reality and maximize curator productivity.From a purely ontological perspective, every trait could be broken down into a core object, such as 'cornea' or 'gastrulation', defined by anatomical, behavioral or physiological terms, and a series of attribute vocabularies that describe the quality, quantity and character of a trait. For the practical reason of needing robust terms to describe phenotypes up-front to speed curation and the problem of losing biological meaning, particularly for clinical or dysmorphology terms, when terms are completely deconstructed (that is, the sum of the parts is less than the term itself), we have chosen to use compound terms in the MP Ontology. A few examples of terms where it is difficult to preserve the full biological meaning once they are deconstructed are shown in Table  2 . In addition, it should be noted that each of these terms requires multiple annotations to recover all aspects that the single term provides. Use of complex terms in the MP Ontology, however, does not preclude also storing the decomposed version should this later prove desirable (see PATO model discussed in [ 20 ]). More important, the MP Ontology can currently hold, for each term, database cross-references to other ontologies. This is a common practice in GO when compound terms are developed. For the MP Ontology, these cross-references include anatomical terms from the Mouse Anatomy ontologies [ 15 , 16 ] and the GO process terms [ 21 ].Three major strategies are being pursued to further develop the vocabulary itself. First and most important is through the ongoing process of curating phenotype data. As new phenotypic traits are described and published, the need for new terms is recognized. New terms added in this way may be a simple addition to an existing hierarchical path or may result in the addition of entire new branches in the hierarchy. Second, collaborative efforts between the MGD phenotype curators, the mouse mutagenesis centers and the rat genetics community identify new specific terms and suggest improved organization of terms within particular hierarchical branches. Third, we are recruiting individuals with expertise in specific biological domains to review and evaluate sections of the vocabulary for accuracy, completeness and systematic arrangement. The MP Ontology is a work in progress and remains incomplete in some areas. We welcome the participation of the mammalian research community so that the most useful, definitive and universally applicable terms will be included. Information can be obtained by sending e-mail to  pheno@informatics.jax.org .While common pathological and clinical terms are used in the MP Ontology, considerations for term placement within the structure and for precise terminology is often derived from comparison with other open biological ontologies (OBO) [ 22 ]. Recently, a cell-type ontology has become available [ 23 ] and a comparison of terminology to this ontology has not yet been completed. We are working with the mutant mouse pathology database Pathbase [ 24 , 25 ] to map and cross-reference terms from their Pathology Ontology.Vocabulary tools The MP Ontology was built as a DAG using the DAG-Edit software written by John Richter and Suzanna Lewis [ 26 ]. The MP Ontology is updated daily and can be browsed or searched online at [ 19 ]. MP files also are available in flat file format and OBO format from our ftp site [ 27 ] and are posted at the OBO site [ 28 ].
Vocabulary tools
The MP Ontology was built as a DAG using the DAG-Edit software written by John Richter and Suzanna Lewis [ 26 ]. The MP Ontology is updated daily and can be browsed or searched online at [ 19 ]. MP files also are available in flat file format and OBO format from our ftp site [ 27 ] and are posted at the OBO site [ 28 ].Phenotype data annotation Phenotypes are described in the MGD relative to the genotype of the individual. Genotype objects specifically consist of one or more allele pairs describing mutations or QTLs and the genetic background strain(s) where the phenotype was observed. Each phenotype annotation associates a MP Ontology term with a genotype/strain and the reference or data source supporting this assertion. Additional modifying text may be annotated to describe detail that is not easily standardized. Examples include experimental conditions, age of onset and incidence, and trait penetrance, among others. The annotation note may also include specifics of the phenotype where such details are deemed to be too case specific to be a MP term. In addition, genotypes are associated with OMIM where a particular mouse genotype is a model for human diseases and syndromes. Figure  2  shows the portion of one phenotype record that uses the MP Ontology.
Phenotype data annotation
Phenotypes are described in the MGD relative to the genotype of the individual. Genotype objects specifically consist of one or more allele pairs describing mutations or QTLs and the genetic background strain(s) where the phenotype was observed. Each phenotype annotation associates a MP Ontology term with a genotype/strain and the reference or data source supporting this assertion. Additional modifying text may be annotated to describe detail that is not easily standardized. Examples include experimental conditions, age of onset and incidence, and trait penetrance, among others. The annotation note may also include specifics of the phenotype where such details are deemed to be too case specific to be a MP term. In addition, genotypes are associated with OMIM where a particular mouse genotype is a model for human diseases and syndromes. Figure  2  shows the portion of one phenotype record that uses the MP Ontology.Conclusions The MP Ontology and annotation schema was designed to minimize curatorial time, yet remain precise enough to describe phenotypic data. It supports robust phenotypic annotations and querying capabilities for mouse phenotype data. While this vocabulary is far from complete, we have designed strategies for its continued development as a collaborative effort for supporting the representation of existing mutations and those that continue to be created. As of 1 November 2004, over 11,150 phenotypic alleles representing mutations in 5,214 unique genes had been catalogued in MGD. For these alleles, 9,696 genotype records exist, with 21,556 phenotypic annotation instances. The MP Ontology is also used in phenotypic data annotations at the RGD [ 29 ]. As our database groups continue to accumulate annotations, it will be possible to mine these data to ask interesting questions about similarities and differences in comparable allele effects between the species, as well as within species. Comparative phenotype data will potentially uncover new modifier effects and point to new pathway relationships and genetic networks tied to disease processes. The MP Ontology will be critical for enabling computational analyses and providing a framework for improved web views and other human-comprehensible displays for the research community.
Conclusions
The MP Ontology and annotation schema was designed to minimize curatorial time, yet remain precise enough to describe phenotypic data. It supports robust phenotypic annotations and querying capabilities for mouse phenotype data. While this vocabulary is far from complete, we have designed strategies for its continued development as a collaborative effort for supporting the representation of existing mutations and those that continue to be created.As of 1 November 2004, over 11,150 phenotypic alleles representing mutations in 5,214 unique genes had been catalogued in MGD. For these alleles, 9,696 genotype records exist, with 21,556 phenotypic annotation instances. The MP Ontology is also used in phenotypic data annotations at the RGD [ 29 ]. As our database groups continue to accumulate annotations, it will be possible to mine these data to ask interesting questions about similarities and differences in comparable allele effects between the species, as well as within species. Comparative phenotype data will potentially uncover new modifier effects and point to new pathway relationships and genetic networks tied to disease processes. The MP Ontology will be critical for enabling computational analyses and providing a framework for improved web views and other human-comprehensible displays for the research community.


In the recent years, several cytokines have been associated with Alzheimer's disease (AD) development and progression and many studies have correlated this risk with polymorphisms in the genes encoding these molecules. Also the type 1 cytokine interferon (IFN)-γ belongs to a cytokine class that affects the immune function; in fact it plays a major role in defence against viruses and intracellular pathogens but also in the induction of the immune-mediated inflammatory response. The aim of this study was to evaluate the role of IFN-γ in AD by studying the association of +874T→A IFN-γ gene polymorphism with AD. We included in this study 115 AD patients (70 women, 45 men, mean age 80) and 90 sex and age-matched healthy controls (HC, 51 women, 39 men, mean age 82) from northern Italy. Genomic DNA was extracted with the salting-out method from whole blood of all subjects; the genotyping at IFN-γ loci was assessed with ARMS-PCR. The data obtained from the +874T→A IFN-γ gene polymorphism analysis of AD patients and HC lack of any statistically significant differences also when stratified according to gender. In conclusion these results confirm the previous shown lack of association between +874T→A IFN-γ gene polymorphism and the risk of AD. However, other polymorphisms have been demonstrated to influence IFN-γ transcription and since natural killer cells of AD patients show higher production of the cytokine, further analysis will be necessary to clarify the role of this gene in the pathogenesis of the disease.

In the human brain several cell types are responsible for initiating and amplifying a specific inflammatory response. In Alzheimer disease (AD) signs of an inflammatory activation of microglia and astroglia are present both inside and outside amyloid deposits. Cell cultures and animal models suggest an interactive relationship between inflammatory response activation, reduced neuronal functioning and amyloid deposition. Furthermore cells associated with extracellular plaques within AD brains can produce a variety of cytokines, chemokines and other related proteins that influence plaque and tangle formation [ 1 ]. For these reasons cytokines could play a critical role in the pathogenesis of AD. In the recent years, several cytokines have been associated with AD development and progression and many studies have correlated this risk with polymorphisms in the genes encoding these molecules [ 2 - 4 ]. Inside this research area we described that single nucleotide polymorphisms (SNP)s of the intereleukin(IL)-10 and IL-6 genes were associated with highest risk of AD with apparent interaction between these two genes [ 5 ]; these results supported the theory that the overall risk of developing AD may be governed by a 'susceptibility profile' and reflected the combined influence of inheriting multiple high-risk alleles. Also the type 1 cytokine interferon (IFN)-γ belongs to a cytokine class that affects the immune function; in fact it plays a major role in defence against viruses and intracellular pathogens but also in the induction of the immune-mediated inflammatory response [ 6 ]. It has been reported that the polymorphism +874(T→A) of the gene encoding IFN-γ is associated with a different production of this molecule, in particular the T allele correlates with increased levels of the cytokine [ 7 ]. In regards to the hypothesis that IFN-γ SNP may represent a genetic risk factor for AD, a recent study did not support this possibility [ 8 ]. Therefore with reference to this paper we try to confirm their hypothesis in a sample of patients, that were already genotyped for apolipoprotein E (ApoE), IL-10 and IL-6 [ 5 ]. We included in this study 115 AD patients (70 women, 45 men, mean age 80 ± 2) and 90 sex and age-matched healthy controls (HC, 51 women, 39 men, mean age 82 ± 2) from northern Italy. The clinical diagnosis of AD fulfilled the international criteria of the DMS IV and NINCDS-ADRDA; every patient had a recent brain magnetic resonance imaging (MRI)/computed tomography (CT) scan available. Cognitive performances were assessed according to the Mini-Mental State Evaluation (MMSE). Only AD and HC without clinical signs of inflammation (e.g. normal body temperature, no concomitant inflammatory condition) were eligible in order to minimize the risk of clinical or sub-clinical inflammatory processes. Blood chemistry tests were done and subjects with an abnormal red blood cell sedimentation rate or altered albumin and transferrin plasma levels were excluded. AD patients were further selected according to their C reactive protein (CRP) plasma levels and anyone with CRP above 5 mg/L (mean + 2 standard deviations of control values) were not eligible. Informed consent was obtained from all the subjects or their relatives. Genomic DNA was extracted with the salting-out method from whole blood of all subjects; the genotyping at IFN-γ loci was assessed with the same amplification method (ARMS-PCR) described by Scola et al. [ 8 ]. The genotype frequencies in the study groups were compared by the chi-square (χ) test in order to calculate significant different SNP distribution between AD patients and controls (Table  1 ). The data obtained from the +874T→A IFN-γ gene polymorphism analysis of AD patients and HC lack of any statistically significant differences also when stratified according to gender (data not shown). The percentage of the different genotypes was similar in AD compared with HC (T/T: 17.4% vs. 16.8% ; T/A: 50.4% vs. 55.1% ; A/A 32.2% vs. 28.1%) and consequently also the allele distribution shows no differences (T: 42.6% vs. 44.4% ; A: 57.4% vs. 55.6%). Our genotype distribution looks very similar to that found by Scola et al. [ 8 ]. In conclusion these results confirm the lake of association between +874T→A IFN-γ gene polymorphism and the risk of AD. Nevertheless other polymorphisms have been demonstrated to influence IFN-γ transcription and since natural killer cells of AD patients show higher production of the cytokine, further analysis will be necessary to clarify the role of this gene in the pathogenesis of the disease [ 9 , 10 ].IFN-γ genotype and allele distributionGenotypeAlleleT/T (H)T/A (I)A/A (L)TAADHCGenotype χ 2  = 0.488, df = 2, p = 0.783Allele χ 2  = 0.232, df = 1, p = 0.63In brackets there are the corresponding phenotype high (H), intermediate (I) and low (L).


Background The TM4SF10 gene encodes a putative four-transmembrane domains protein of unknown function termed Brain Cell Membrane Protein 1 (BCMP1), and is abundantly expressed in the brain. This gene is located on the short arm of human chromosome X at p21.1. The hypothesis that mutations in the TM4SF10 gene are associated with impaired brain function was investigated by sequencing the gene in individuals with hereditary X-linked mental retardation (XLMR).
Background
The TM4SF10 gene encodes a putative four-transmembrane domains protein of unknown function termed Brain Cell Membrane Protein 1 (BCMP1), and is abundantly expressed in the brain. This gene is located on the short arm of human chromosome X at p21.1. The hypothesis that mutations in the TM4SF10 gene are associated with impaired brain function was investigated by sequencing the gene in individuals with hereditary X-linked mental retardation (XLMR).Methods The coding region (543 bp) of TM4SF10, including intronic junctions, and the long 3' untranslated region (3 233 bp), that has been conserved during evolution, were sequenced in 16 male XLMR patients from 14 unrelated families with definite, or suggestive, linkage to the TM4SF10 gene locus, and in 5 normal males.
Methods
The coding region (543 bp) of TM4SF10, including intronic junctions, and the long 3' untranslated region (3 233 bp), that has been conserved during evolution, were sequenced in 16 male XLMR patients from 14 unrelated families with definite, or suggestive, linkage to the TM4SF10 gene locus, and in 5 normal males.Results Five sequence changes were identified but none was found to be associated with the disease. Two of these changes correspond to previously known SNPs, while three other were novel SNPs in the TM4SF10 gene.
Results
Five sequence changes were identified but none was found to be associated with the disease. Two of these changes correspond to previously known SNPs, while three other were novel SNPs in the TM4SF10 gene.Conclusion We have investigated the majority of the known MRX families linked to the TM4SF10 gene region. In the absence of mutations detected, our study indicates that alterations of TM4SF10 are not a frequent cause of XLMR.
Conclusion
We have investigated the majority of the known MRX families linked to the TM4SF10 gene region. In the absence of mutations detected, our study indicates that alterations of TM4SF10 are not a frequent cause of XLMR.

Background Brain Cell Membrane Protein 1 (BCMP1) cDNA was fortuitously isolated from a thyroid cDNA library [ 1 ]. It encodes a 181aa-long putative four-transmembrane domain protein which appears related to both Peripheral Myelin Protein 22 / Epithelial Membrane Proteins and Claudins protein families, and exhibits significant similarities to the  Caenorhabditis elegans  VAB-9 protein, a protein that has recently been shown to be involved in the control of cell adhesion and epidermal morphology [ 2 ]. The protein sequence itself has been extremely well conserved during evolution, as exemplified by the observation that human and canine sequences are identical and differ from the mouse sequence at only 2 positions. The corresponding gene has now been renamed TM4SF10 in man and mouse, and is located on the X chromosome in both species, as well as in the other mammalian species investigated to date [ 1 ]. Initial Northern blot analysis of TM4SF10/BCMP1 gene transcripts distribution in adult dog tissues revealed very high expression in the brain, and lower but clearly detectable levels of expression in most of the tissues examined [ 1 ]. Data mining in the SAGEmap database [ 3 ] confirmed this observation in man, as elevated tag counts have been reported in brain astrocytoma (SAGE H127 library), brain ependymoma (SAGE ependymoma 353 and 582 libraries) and normal spinal cord (SAGE normal spinal cord library) as compared to other tissues. Together with its localization on the X chromosome, the high expression level detected in the brain and the putative role of the encoded protein in specific cell contacts raised the possibility that the TM4SF10 gene may be involved in X-linked mental retardation (XLMR) in man. Initially, the TM4SF10 gene was assigned to Xp11.4 [ 1 ]. As the integration between human cytogenetic and DNA sequence-based maps is still evolving, the gene has been reassigned to band p21.1. It is noteworthy that TM4SF2, another gene encoding a four-transmembrane domain protein, is located at the p11.4-p21.1 border on human chromosome X, in the very close vicinity of TM4SF10, and constitutes a known XLMR gene [ 4 , 5 ]. Recent compilations of XLMR families [ 6 - 8 ] mention several conditions mapped to the Xp11.4-p21.1 region. We report the result of mutation screening of TM4SF10 in a cohort of XLMR patients whose gene was mapped to this region of the X chromosome and does not correspond to TM4SF2.
Background
Brain Cell Membrane Protein 1 (BCMP1) cDNA was fortuitously isolated from a thyroid cDNA library [ 1 ]. It encodes a 181aa-long putative four-transmembrane domain protein which appears related to both Peripheral Myelin Protein 22 / Epithelial Membrane Proteins and Claudins protein families, and exhibits significant similarities to the  Caenorhabditis elegans  VAB-9 protein, a protein that has recently been shown to be involved in the control of cell adhesion and epidermal morphology [ 2 ]. The protein sequence itself has been extremely well conserved during evolution, as exemplified by the observation that human and canine sequences are identical and differ from the mouse sequence at only 2 positions. The corresponding gene has now been renamed TM4SF10 in man and mouse, and is located on the X chromosome in both species, as well as in the other mammalian species investigated to date [ 1 ].Caenorhabditis elegansInitial Northern blot analysis of TM4SF10/BCMP1 gene transcripts distribution in adult dog tissues revealed very high expression in the brain, and lower but clearly detectable levels of expression in most of the tissues examined [ 1 ]. Data mining in the SAGEmap database [ 3 ] confirmed this observation in man, as elevated tag counts have been reported in brain astrocytoma (SAGE H127 library), brain ependymoma (SAGE ependymoma 353 and 582 libraries) and normal spinal cord (SAGE normal spinal cord library) as compared to other tissues. Together with its localization on the X chromosome, the high expression level detected in the brain and the putative role of the encoded protein in specific cell contacts raised the possibility that the TM4SF10 gene may be involved in X-linked mental retardation (XLMR) in man.Initially, the TM4SF10 gene was assigned to Xp11.4 [ 1 ]. As the integration between human cytogenetic and DNA sequence-based maps is still evolving, the gene has been reassigned to band p21.1. It is noteworthy that TM4SF2, another gene encoding a four-transmembrane domain protein, is located at the p11.4-p21.1 border on human chromosome X, in the very close vicinity of TM4SF10, and constitutes a known XLMR gene [ 4 , 5 ]. Recent compilations of XLMR families [ 6 - 8 ] mention several conditions mapped to the Xp11.4-p21.1 region. We report the result of mutation screening of TM4SF10 in a cohort of XLMR patients whose gene was mapped to this region of the X chromosome and does not correspond to TM4SF2.Methods Blood genomic DNA was collected from 16 patients (14 unrelated) and 5 unrelated healthy volunteers using a standard procedure [ 9 ]. The patients were affected males from families with definite, or possible, linkage to the region at Xp11.4-p21.1. Patients belonged to the following published MRX(S) families: MRX9 [ 10 ], MRX10 [ 11 ], MRX11 [ 11 ], MRX12 [ 11 ], MRX18 [ 12 ], MRX56 [ 6 ] and MRXS10 [ 13 ]. Additional patients were from an XLMR family with epilepsy [ 14 ] and 4 other XLMR families (C.S., F.K., J.G., unpublished), a MRXS family with macrocephaly and large ears (C.S., unpublished), and another MRXS family (J.G., unpublished). Chromosomal linkage data and major phenotypic traits are described in table  1 . All samples were studied anonymously and all procedures met the standards of our institutional ethics committee. Table 1 Description of the patients included in the study Patient Family Linkage data Phenotype P1, P2 XLMR family (F.K., now MRX79) chromosome X X-linked mental retardation P3, P4 MRXS10 Xp11.21-Xp11.4 Mental retardation, choreoathetosis, abnormal behavior P5 XLMR family (F.K.) Chromosome X, pericentromeric Non-syndromic mental retardation P6 MRX9 Xp11.22-Xp11.4 Non-syndromic mental retardation P7 MRX10 Xp11.3-Xp21.2 Non-syndromic mental retardation P8 MRX11 Xp11.3-Xp21.2 Non-syndromic mental retardation P9 MRX12 Xp11.21-Xp21.2 Non-syndromic mental retardation P10 MRX18 Xp11.3-Xp21.2 Non-syndromic mental retardation P11 XLMR family with epilepsy Xp11.23-Xp22.22 Non-syndromic mental retardation, epilepsy P12 MRXS family (J.G.) Xp21.3-Xq21.3 Non-syndromic mental retardation P13 XLMR family with macrocephaly (J.G.) Xp11.4-Xq13.1 Macrocephaly, moderate to profound mental retardation P14 XLMR family (C.S.) Xp11.3-Xp21.1 Seizures, ataxia, aggressive and hyperactive behavior, speech delay, mild to moderate mental retardation P15 MRXS (C.S.) Xp22.22-Xq24 Macrocephaly, prominent ears and moderate to severe mental retardation P16 MRX56 Xp11.21-Xp21.1 Non-syndromic mental retardation PCR reactions were performed in a final volume of 100 μl containing 200 ng of genomic DNA, 1 μg of each primer (see table  2  for primer sequences), 200 μM of each dNTP, 1X PCR buffer (QIAGEN) and 2.5 units of Taq polymerase (QIAGEN). Additionally, 10% DMSO or 20% Q solution (QIAGEN) were also included in some reactions (see table  2 ). After an initial denaturation step (93°C, 45 sec.), 35 cycles were conducted as follows: 93°C, 45 sec.; annealing temp. (see table  2 ), 45 sec.; 72°C, 45 sec. (amplicons Ex1–Ex3) or 1 min. (amplicons 3'UTR F1–F4). A final extension step (72°C, 3 min.) was done at the end of the reaction. PCR products were purified using the QIAquick PCR purification kit (QIAGEN) before sequencing. Purified PCR fragments were roughly quantitated by agarose gel electrophoresis using SmartLadder molecular weight marker (EUROGENTEC) as a quantitative reference. Table 2 PCR conditions and primer sequences used to amplify TM4SF10 gene fragments from total genomic DNA Amplicon Size Primer sequence PCR conditions Ex1 442 bp fw: AGAGCCCCGAGGGAGCGA, rev: GGGGACAGGCGGTGACTG T anneal  = 55°C, 10% DMSO Ex2 447 bp fw: AAATCCTAGCAAACCCCTGG, rev: TCTGCATAGGAAAGGAAGATGG T anneal  = 50°C Ex3 447 bp fw: CCATCTAGAACAAGCCATCTTTAA, rev: TAAATCAACTGAGCAAACTGCTTG T anneal  = 50°C 3'UTR F1 959 bp fw: GGCCTGGGGTGCAACTATAT, rev: TAGGCAAATGTATGTGGAGGGT T anneal  = 55°C, 10% DMSO 3'UTR F2 1101 bp fw: ATTGGTGCCTCAGCCCTATCTA, rev: GCAACCATTCTTAAGACAAGCT T anneal  = 57°C, 20% Q solution 3'UTR F3 1130 bp fw : CAGTATGTTCTGGTTTTGGCCC, rev : TATCTAACAATGGGTTTGTGGC T anneal  = 57°C, 20% Q solution 3'UTR F4 1097 bp fw : CCTTCTCAGCAAAGAGCCCTAC, rev : AAGGATCTTGGGAGATAATTTG T anneal  = 57°C, 20% Q solution About 50–100 ng of purified PCR fragment was used in a DNA sequencing reaction using a nested, internal primer. DNA sequencing was performed using ABI PRISM BigDye Terminator Cycle Sequencing Ready Reaction kit (Applied Biosystems) on an Applied Biosystems 3100 automatic DNA sequencer. The sequences of the primers are given in table  3 . Table 3 Sequences of the primers used for sequencing of TM4SF10 gene fragments Amplicon Primer sequence Ex1 fw: CCGAGGGAGCGAGTCCCC Ex2 fw: CACATCTGTTGAGCCACTGC Ex3 rev: GATGCTCCACAAGTGTTTTAGA 3'UTR F1 fw : TGCCTGAACCCTAAGAACTATG, rev : GGAGGGTTAGGGAACAACTTAT 3'UTR F2 fw : CTGCATGAGTTGCTTTTGTACC, rev : GCAACCATTCTTAAGACAAGCT 3'UTR F3 fw : TCTGTTAAGAGCAGGACCACAT, rev1: ACTCGAGATGTGATGATATTGG, rev2 : TATCTAACAATGGGTTTGTGGC 3'UTR F4 fw : AACATGAAAATTGTTGCTTCTC, rev : AAGGATCTTGGGAGATAATTTG
Methods
Blood genomic DNA was collected from 16 patients (14 unrelated) and 5 unrelated healthy volunteers using a standard procedure [ 9 ]. The patients were affected males from families with definite, or possible, linkage to the region at Xp11.4-p21.1. Patients belonged to the following published MRX(S) families: MRX9 [ 10 ], MRX10 [ 11 ], MRX11 [ 11 ], MRX12 [ 11 ], MRX18 [ 12 ], MRX56 [ 6 ] and MRXS10 [ 13 ]. Additional patients were from an XLMR family with epilepsy [ 14 ] and 4 other XLMR families (C.S., F.K., J.G., unpublished), a MRXS family with macrocephaly and large ears (C.S., unpublished), and another MRXS family (J.G., unpublished). Chromosomal linkage data and major phenotypic traits are described in table  1 . All samples were studied anonymously and all procedures met the standards of our institutional ethics committee.Description of the patients included in the studyPCR reactions were performed in a final volume of 100 μl containing 200 ng of genomic DNA, 1 μg of each primer (see table  2  for primer sequences), 200 μM of each dNTP, 1X PCR buffer (QIAGEN) and 2.5 units of Taq polymerase (QIAGEN). Additionally, 10% DMSO or 20% Q solution (QIAGEN) were also included in some reactions (see table  2 ). After an initial denaturation step (93°C, 45 sec.), 35 cycles were conducted as follows: 93°C, 45 sec.; annealing temp. (see table  2 ), 45 sec.; 72°C, 45 sec. (amplicons Ex1–Ex3) or 1 min. (amplicons 3'UTR F1–F4). A final extension step (72°C, 3 min.) was done at the end of the reaction. PCR products were purified using the QIAquick PCR purification kit (QIAGEN) before sequencing. Purified PCR fragments were roughly quantitated by agarose gel electrophoresis using SmartLadder molecular weight marker (EUROGENTEC) as a quantitative reference.PCR conditions and primer sequences used to amplify TM4SF10 gene fragments from total genomic DNAAbout 50–100 ng of purified PCR fragment was used in a DNA sequencing reaction using a nested, internal primer. DNA sequencing was performed using ABI PRISM BigDye Terminator Cycle Sequencing Ready Reaction kit (Applied Biosystems) on an Applied Biosystems 3100 automatic DNA sequencer. The sequences of the primers are given in table  3 .Sequences of the primers used for sequencing of TM4SF10 gene fragmentsResults and discussion Sequencing of TM4SF10 coding region The human TM4SF10 gene is composed of 3 exons and produces a 4 kb-long mRNA. The short coding region (543 bp) is interrupted by 2 introns and the last and the largest exon also contains a 3 233 bp-long 3'UTR (see Figure  1 ). Initially, we sequenced the coding region and exon-intron junctions of the gene in the DNAs from 16 XLMR patients and from one normal male (amplicons Ex1–Ex3, Figure  1 ). No mutations were identified. One silent polymorphism was observed at position 186 in the cDNA sequence (clone DKFZp761J17121; GenBank accession number AL136550), corresponding to the 3 rd  base of the codon Arg59, where a C residue was present in half of the sequences and a G residue in the other half. Individual single nucleotide polymorphism (SNP) data are shown in table  4 . It is noteworthy that patients P3 and P4 who belong to the same family exhibit a difference in their TM4SF10 gene sequence at this level. This observation argues against a causal role of the gene in this family. The 186C>G polymorphism in the TM4SF10 gene had been previously reported [ 15 ]. Figure 1 Schematic of the TM4SF10 gene and single nucleotide polymorphisms identified in the study.  The structure of the gene is outlined with exons represented as light-blue boxes and the coding region as dark-blue areas within the boxes. The 3' end of the gene is also enlarged (bottom). Amplified regions are delineated and locations of sequence primers (arrows) used in this study are depicted. Identified SNPs are indicated (dotted lines) with reference to genomic contig (GenBank accession number NT_011757) sequence coordinates and cDNA (clone DKFZp761J17121; GenBank accession numberAL136550) sequence coordinates (italics) when relevant. Numbers in parentheses indicate the occurrences of the nucleotide in the individual sequences. The 3 novel SNPs identified in the work appear in red. Table 4 Description of the individual TM4SF10 gene SNP haplotypes determined in this study Exon 1: 186 (Arg59), [21589434] 3' UTR: 705, [21562747] 3' UTR: 2870, [21560583] 3' UTR: 2907, [21560546] 3' intergenic: [21559611] N1 C C G T C N2 (n.d.) C C C C N3 (n.d.) C C T C N4 (n.d.) T C T T N5 (n.d.) C C T C P1 C T C T T P2 C T C T T P3 G C C T C P4 C C C T C P5 G C C T T P6 G C C T T P7 G C C T C P8 G C C T C P9 G C C T C P10 C C C T T P11 C T C T T P12 G C C T C P13 G C C T C P14 C C C T T P15 C T C T T P16 G C C T C The first row identifies the source of the DNA, either a normal individual (Nx) or a patient (Px; see also table 1 for the detailed description of the patients). The individual bases found at each polymorphic position in the DNA sequence (identified by the nucleotide position in the cDNA sequence and/or in the genomic contig sequence [in square brackets]; see also figure 1) are given in the following rows. (n.d.): not determined. Sequencing of the 3'UTR The long 3'UTR sequence of the TM4SF10/BCMP1 transcript is highly conserved, with an overall score of 72% when human, dog, mouse and rat sequences are compared. As the 3'UTR of mRNAs may contain regulatory sequences that participate in the control of gene expression, we decided to screen this part of the gene as well. The entire region, including the sequences around the polyadenylation site, was subdivided into four overlapping fragments of approximately 1 kb in length (3'UTR F1–F4, Figure  1 ) and sequenced from both ends. In fragment 3' UTR F3 the presence of a stretch of 12 consecutive A residues on the sense strand resulted in difficulties in proper reading of the sequences located downstream of this motif. In order to overcome this problem, an additional sequence primer (rev2) was used to obtain overlaps between the 3 separate sequences for each individual fragment. In the cDNA sequence AL136550 the motif is composed of 13 A residues, which is a likely sequence artefact. TM4SF10 sequence was obtained from 16 patients and 5 controls. Four SNPs were identified in the non-coding part of the gene: 3 of them were located in the 3'UTR of the mRNA while the fourth one was located downstream of the polyadenylation site (Figure  1 ). Only this last one (C21559611T) had been previously reported in the SNP database [ 15 ], the other three representing novel SNPs in the TM4SF10 gene. The individual SNP haplotypes determined here are described in table  4 . It is also noteworthy that during the course of our investigation, the genetic defect of one of the unpublished XLMR family included in the study (see top of table  1 ) has been identified and mapped to Xq28, within the MECP2 gene [ 16 ].
Results and discussion
Sequencing of TM4SF10 coding region The human TM4SF10 gene is composed of 3 exons and produces a 4 kb-long mRNA. The short coding region (543 bp) is interrupted by 2 introns and the last and the largest exon also contains a 3 233 bp-long 3'UTR (see Figure  1 ). Initially, we sequenced the coding region and exon-intron junctions of the gene in the DNAs from 16 XLMR patients and from one normal male (amplicons Ex1–Ex3, Figure  1 ). No mutations were identified. One silent polymorphism was observed at position 186 in the cDNA sequence (clone DKFZp761J17121; GenBank accession number AL136550), corresponding to the 3 rd  base of the codon Arg59, where a C residue was present in half of the sequences and a G residue in the other half. Individual single nucleotide polymorphism (SNP) data are shown in table  4 . It is noteworthy that patients P3 and P4 who belong to the same family exhibit a difference in their TM4SF10 gene sequence at this level. This observation argues against a causal role of the gene in this family. The 186C>G polymorphism in the TM4SF10 gene had been previously reported [ 15 ]. Figure 1 Schematic of the TM4SF10 gene and single nucleotide polymorphisms identified in the study.  The structure of the gene is outlined with exons represented as light-blue boxes and the coding region as dark-blue areas within the boxes. The 3' end of the gene is also enlarged (bottom). Amplified regions are delineated and locations of sequence primers (arrows) used in this study are depicted. Identified SNPs are indicated (dotted lines) with reference to genomic contig (GenBank accession number NT_011757) sequence coordinates and cDNA (clone DKFZp761J17121; GenBank accession numberAL136550) sequence coordinates (italics) when relevant. Numbers in parentheses indicate the occurrences of the nucleotide in the individual sequences. The 3 novel SNPs identified in the work appear in red. Table 4 Description of the individual TM4SF10 gene SNP haplotypes determined in this study Exon 1: 186 (Arg59), [21589434] 3' UTR: 705, [21562747] 3' UTR: 2870, [21560583] 3' UTR: 2907, [21560546] 3' intergenic: [21559611] N1 C C G T C N2 (n.d.) C C C C N3 (n.d.) C C T C N4 (n.d.) T C T T N5 (n.d.) C C T C P1 C T C T T P2 C T C T T P3 G C C T C P4 C C C T C P5 G C C T T P6 G C C T T P7 G C C T C P8 G C C T C P9 G C C T C P10 C C C T T P11 C T C T T P12 G C C T C P13 G C C T C P14 C C C T T P15 C T C T T P16 G C C T C The first row identifies the source of the DNA, either a normal individual (Nx) or a patient (Px; see also table 1 for the detailed description of the patients). The individual bases found at each polymorphic position in the DNA sequence (identified by the nucleotide position in the cDNA sequence and/or in the genomic contig sequence [in square brackets]; see also figure 1) are given in the following rows. (n.d.): not determined.
Sequencing of TM4SF10 coding region
The human TM4SF10 gene is composed of 3 exons and produces a 4 kb-long mRNA. The short coding region (543 bp) is interrupted by 2 introns and the last and the largest exon also contains a 3 233 bp-long 3'UTR (see Figure  1 ). Initially, we sequenced the coding region and exon-intron junctions of the gene in the DNAs from 16 XLMR patients and from one normal male (amplicons Ex1–Ex3, Figure  1 ). No mutations were identified. One silent polymorphism was observed at position 186 in the cDNA sequence (clone DKFZp761J17121; GenBank accession number AL136550), corresponding to the 3 rd  base of the codon Arg59, where a C residue was present in half of the sequences and a G residue in the other half. Individual single nucleotide polymorphism (SNP) data are shown in table  4 . It is noteworthy that patients P3 and P4 who belong to the same family exhibit a difference in their TM4SF10 gene sequence at this level. This observation argues against a causal role of the gene in this family. The 186C>G polymorphism in the TM4SF10 gene had been previously reported [ 15 ].Description of the individual TM4SF10 gene SNP haplotypes determined in this studyThe first row identifies the source of the DNA, either a normal individual (Nx) or a patient (Px; see also table 1 for the detailed description of the patients). The individual bases found at each polymorphic position in the DNA sequence (identified by the nucleotide position in the cDNA sequence and/or in the genomic contig sequence [in square brackets]; see also figure 1) are given in the following rows. (n.d.): not determined.Sequencing of the 3'UTR The long 3'UTR sequence of the TM4SF10/BCMP1 transcript is highly conserved, with an overall score of 72% when human, dog, mouse and rat sequences are compared. As the 3'UTR of mRNAs may contain regulatory sequences that participate in the control of gene expression, we decided to screen this part of the gene as well. The entire region, including the sequences around the polyadenylation site, was subdivided into four overlapping fragments of approximately 1 kb in length (3'UTR F1–F4, Figure  1 ) and sequenced from both ends. In fragment 3' UTR F3 the presence of a stretch of 12 consecutive A residues on the sense strand resulted in difficulties in proper reading of the sequences located downstream of this motif. In order to overcome this problem, an additional sequence primer (rev2) was used to obtain overlaps between the 3 separate sequences for each individual fragment. In the cDNA sequence AL136550 the motif is composed of 13 A residues, which is a likely sequence artefact. TM4SF10 sequence was obtained from 16 patients and 5 controls. Four SNPs were identified in the non-coding part of the gene: 3 of them were located in the 3'UTR of the mRNA while the fourth one was located downstream of the polyadenylation site (Figure  1 ). Only this last one (C21559611T) had been previously reported in the SNP database [ 15 ], the other three representing novel SNPs in the TM4SF10 gene. The individual SNP haplotypes determined here are described in table  4 . It is also noteworthy that during the course of our investigation, the genetic defect of one of the unpublished XLMR family included in the study (see top of table  1 ) has been identified and mapped to Xq28, within the MECP2 gene [ 16 ].
Sequencing of the 3'UTR
The long 3'UTR sequence of the TM4SF10/BCMP1 transcript is highly conserved, with an overall score of 72% when human, dog, mouse and rat sequences are compared. As the 3'UTR of mRNAs may contain regulatory sequences that participate in the control of gene expression, we decided to screen this part of the gene as well. The entire region, including the sequences around the polyadenylation site, was subdivided into four overlapping fragments of approximately 1 kb in length (3'UTR F1–F4, Figure  1 ) and sequenced from both ends. In fragment 3' UTR F3 the presence of a stretch of 12 consecutive A residues on the sense strand resulted in difficulties in proper reading of the sequences located downstream of this motif. In order to overcome this problem, an additional sequence primer (rev2) was used to obtain overlaps between the 3 separate sequences for each individual fragment. In the cDNA sequence AL136550 the motif is composed of 13 A residues, which is a likely sequence artefact.TM4SF10 sequence was obtained from 16 patients and 5 controls. Four SNPs were identified in the non-coding part of the gene: 3 of them were located in the 3'UTR of the mRNA while the fourth one was located downstream of the polyadenylation site (Figure  1 ). Only this last one (C21559611T) had been previously reported in the SNP database [ 15 ], the other three representing novel SNPs in the TM4SF10 gene. The individual SNP haplotypes determined here are described in table  4 . It is also noteworthy that during the course of our investigation, the genetic defect of one of the unpublished XLMR family included in the study (see top of table  1 ) has been identified and mapped to Xq28, within the MECP2 gene [ 16 ].Conclusions In this study, we have investigated the majority of the known MRX families linked to the TM4SF10 gene region. In the absence of mutations detected, our results indicate that alterations in the transcribed region of TM4SF10 are not a frequent cause of XLMR. Although the gene promoter has not been identified and screened yet, it appears very unlikely that all mutations would be there. This work also identified three novel SNPs in the TM4SF10 gene, which adds to our knowledge of SNP occurrence within this gene.
Conclusions
In this study, we have investigated the majority of the known MRX families linked to the TM4SF10 gene region. In the absence of mutations detected, our results indicate that alterations in the transcribed region of TM4SF10 are not a frequent cause of XLMR. Although the gene promoter has not been identified and screened yet, it appears very unlikely that all mutations would be there.This work also identified three novel SNPs in the TM4SF10 gene, which adds to our knowledge of SNP occurrence within this gene.Competing interests None declared.
Competing interests
None declared.Authors' contributions CCH performed/managed the PCR and sequencing reactions, and analyzed the DNA sequences. FK, JG, MJA, EHF and CS provided the DNA samples. These authors also participated in the writing of the manuscript. DC conceived and supervised the study, and drafted the manuscript. All authors read and approved the final manuscript.
Authors' contributions
CCH performed/managed the PCR and sequencing reactions, and analyzed the DNA sequences. FK, JG, MJA, EHF and CS provided the DNA samples. These authors also participated in the writing of the manuscript. DC conceived and supervised the study, and drafted the manuscript. All authors read and approved the final manuscript.Pre-publication history The pre-publication history for this paper can be accessed here:
Pre-publication history
The pre-publication history for this paper can be accessed here:


Chemical reactions lie at the heart of many biological processes, from photosynthesis and respiration to cell signaling and drug metabolism. Thanks to an atmosphere rich in oxygen, many organisms use oxygen to carry out these life processes. But oxygen metabolism produces highly toxic by-products called reactive oxygen species. When oxidation outpaces detoxifying reactions, oxidative stress occurs, and accumulating reactive oxygen species are free to wreak havoc on cellular machinery.Cysteine, one of the 20 different amino acids that make up proteins, contains a thiol group, which can be modified upon oxidation. A thiol group can stabilize protein structures by forming covalent disulfide bonds and can mediate cysteine-regulated redox reactions. At the same time, however, the high reactivity of thiol groups makes them also particularly vulnerable to nonspecific reactions during conditions of oxidative stress. Over the past few years, an increasing number of proteins have been discovered that use oxidative thiol chemistry to regulate their protein activity. In  PLoS Biology,  Lars Leichert and Ursula Jakob describe a novel method to monitor thiol modifications in proteins subjected to varying redox conditions in a living organism, the bacteria  Escherichia coli . This technique is capable of providing a global snapshot of the redox state of protein cysteines during normal and oxidative stress conditions in the cell.PLoS Biology,To detect proteins that have the ability to undergo stress-induced thiol modifications, Leichert and Jakob differentially labeled the thiol groups of thiol-modified and non-thiol-modified proteins. The proteins were then separated on two-dimensional gels based on their charge and molecular weight. If the technique worked, most thiol-modified proteins should be detected in the oxidizing environment of the  E. coli  periplasm (the region between the cell's membrane layers), and they were.After proving the method's ability to detect proteins whose thiol groups were oxidized, the next logical step was to determine what proteins DsbA—the enzyme that catalyzes disulfide bond formation in the  E. coli  periplasm—was targeting. In  E. coli  mutant strains that lack DsbA, Leichert and Jakob identified a number of proteins with either substantially less or no thiol modification as compared to wild-type (non-mutant) strains, suggesting that these proteins are indeed DsbA substrates.In contrast to the periplasm, the  E. coli  cytoplasm contains several reducing systems. When the researchers tested a mutant strain that lacked the gene for the reducing enzyme thioredoxin, they found that a large number of proteins accumulated in an oxidized state. Many of these proteins have cysteines and require a reduced thiol status for their activity. These results demonstrated that under normal growing conditions, many proteins contain cysteine residues that are vulnerable to even small amounts of reactive oxygen species and so require the constant attention of detoxifying enzymes.In a final set of experiments, Leichert and Jakob discovered a number of proteins whose thiol groups get specifically modified in the presence of reactive oxygen species. These results start to explain some of the many metabolic changes that occur in oxidatively stressed cells.Leichert and Jakob's technique should be applicable to many different cell types and organisms and can be used to investigate the in vivo thiol status of cellular proteins exposed to virtually any physiological or pathological condition that is accompanied by oxidative stress. The next step will be to investigate just how thiol modifications mediate the various functions of redox-regulated proteins.


Four years ago in  Science , Stuart Kim, a Stanford developmental biologist, made the case for laying down the broad strokes of a complex physiological process before defining its mechanisms. “A powerful, top-down, holistic approach,” he wrote, “is to identify all of the components of a particular cellular process, so that one can define the global picture first and then use it as a framework to understand how the individual components of the process fit together.” To get a broad view of gene expression in the aging nematode, Kim's lab turned to DNA microarrays and functional genomics. In a new study, Kim and colleagues apply this same approach to the decidedly more complex problem of human aging and “present a molecular portrait” of the aging kidney.ScienceScientists have identified a wide range of molecular pathways and mechanisms associated with aging. Many have been found in evolutionarily distant organisms, suggesting they have been conserved and could shed light on human aging. Yet other studies suggest that since few animals reach old age in the wild, any aging-related physiological changes aren't likely to impact the fitness of a population and so aren't likely to be conserved. Consequently, aging pathways in worms, for example, would have little bearing on humans. To investigate the molecular pathways associated with human aging, the authors focused on human tissue—in this case, the kidney.Kidneys came from 74 patients, ranging in age from 27 to 92. Samples were extracted from donated kidneys or “meticulously harvested” from kidneys with localized disease to ensure only normal tissue was taken. Two structures that are critical to kidney function (removing toxins from blood) were removed from each sample: the renal cortex, which filters plasma, and the medulla, which alters urine composition to maintain fluid balance. Both deteriorate with age. An extensive clinical history was noted for each sample to account for any potentially confounding medical factors.Kim and colleagues then isolated RNA transcripts from the samples to determine the activity of every gene, broken down by age and kidney section, through microarray analysis. Looking for differences in gene expression across the genome, they identified genes that showed a statistically significant change in expression as a function of age. Of 33,000 known human genes on the microarray, 985 showed age-related changes, most showing increased activity. These changes are truly age-regulated, the authors conclude, since none of the medical factors impacted the observed changes in gene expression.Although cortex and medulla have different cell types and perform different functions, their genetic aging profile was very similar, suggesting a common aging mechanism operates in both structures. In fact, these mechanisms may function broadly, as most of the age-regulated kidney genes were also active in a wide range of human tissues. Other organisms appear to lack these changes, however, prompting the authors to argue that understanding aging in humans will require human subjects.Most importantly, the genetic profile of the tissue samples correlated with the physiological and morphological decline of an aging kidney. An 81-year-old patient with an unusually healthy kidney had a molecular profile typical of someone much younger, while a 78-year-old with a damaged kidney had the profile of a much older person. Using the power of functional genomics, this study has identified a set of genes that can serve as molecular markers for various stages of a deteriorating kidney and predict the relative health of a patient compared to their age group. These gene sets can also serve as probes to shed light on the molecular pathways at work in the aging kidney, and possibly on the process of aging itself.