Framework for a protein ontology

Philosophy

BioMed CentralBMC Bioinformatics ssOpen AcceProceedings Framework for a Protein Ontology Darren A Natale*†1, Cecilia N Arighi†1, Winona C Barker1, Judith Blake2, TiCheng Chang1, Zhangzhi Hu1, Hongfang Liu3, Barry Smith4 and Cathy H Wu1 Address: 1Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven St., NW, Washington, DC, 20007, USA, 2The Jackson Laboratory, 600 Main St., Bar Harbor, ME, 04609, USA, 3Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University, Room 176, Building D, Washington, DC, 20057, USA and 4Department of Philosophy, State University of New York at Buffalo, Park Hall, Buffalo, NY, 12460, USA Email: Darren A Natale* firstname.lastname@example.com; Cecilia N Arighi email@example.com; Winona C Barker email@example.com; Judith Blake email@example.com; Ti-Cheng Chang firstname.lastname@example.com; Zhangzhi Hu email@example.com; Hongfang Liu email@example.com; Barry Smith firstname.lastname@example.com; Cathy H Wu email@example.com * Corresponding author †Equal contributors Abstract Biomedical ontologies are emerging as critical tools in genomic and proteomic research, where complex data in disparate resources need to be integrated. A number of ontologies describe properties that can be attributed to proteins. For example, protein functions are described by the Gene Ontology (GO) and human diseases by SNOMED CT or ICD10. There is, however, a gap in the current set of ontologies – one that describes the protein entities themselves and their relationships. We have designed the PRotein Ontology (PRO) to facilitate protein annotation and to guide new experiments. The components of PRO extend from the classification of proteins on the basis of evolutionary relationships to the representation of the multiple protein forms of a gene (products generated by genetic variation, alternative splicing, proteolytic cleavage, and other posttranslational modifications). PRO will allow the specification of relationships between PRO, GO and other ontologies in the OBO Foundry. Here we describe the initial development of PRO, illustrated using human and mouse proteins involved in the transforming growth factor-beta and bone morphogenetic protein signaling pathways. Background Ontology-based methodologies for data integration promote precise communication between scientists, enable information retrieval across multiple resources, and extend the power of computational approaches to perform data exploration, inference and mining [1-3]. The Open Biomedical Ontologies (OBO) library [4] is an umbrella for ontologies shared across different biological and medical domains. There is, however, a gap in the current OBO library of ontologies – a protein ontology that defines proteins, protein classes, and their relationships. Filling this gap will support machine reasoning over the shared features of related proteins and will foster the interconnection between ontologies that describe protein from First International Workshop on Text Mining in Bioinformatics (TMBio) 2006 Arlington, VA, USA. 10 November 2006 Published: 27 November 2007 BMC Bioinformatics 2007, 8(Suppl 9):S1 doi:10.1186/1471-2105-8-S9-S1 <supplement> <title>
First International Workshop on Text Mining in Bioinformatics (TMBio) 2006
 </title> <editor>Min Song and Zoran Obradovic</editor> <note>Proceedings</note> </supplement> This article is available from: http://www.biomedcentral.com/1471-2105/8/S9/S1 © 2007 Natale et al; licensee BioMed Central Ltd. Page 1 of 12 (page number not for citation purposes) BMC Bioinformatics 2007, 8(Suppl 9):S1 http://www.biomedcentral.com/1471-2105/8/S9/S1attributes. Here we present the framework and initial development of the Protein Ontology (PRO) [5] that describes types of proteins and the relations between them. PRO will concentrate on (a) an ontology of proteins based on evolutionary relatedness (ontology for protein evolution) and (b) an ontology of the multiple protein forms produced from a given gene locus (ontology for protein forms). Biomedical ontologies Like standardized measures and rules of syntax, ontologies bring the benefits of synchronization. Thus, for an ontology to be of maximal value, it is crucial to ensure that for each domain of inquiry there is community convergence on a single ontology. The Open Biomedical Ontologies (OBO) provides a resource where biomedical ontologies are made available in a standard format that allows systematic updating and versioning on the basis of community feedback. Currently, there are nearly 60 ontologies distributed through the OBO web site, spanning domains from anatomy (e.g., Mouse adult anatomy) to ethology (Loggerhead nesting), and from gene and gene product features (Sequence Ontology and Gene Ontology) to phenotypic qualities knowledge (Disease Ontology). The OBO Foundry [6], a consortium formed by a subset of developers of OBO ontologies, has outlined a set of principles specifying best practices in ontology development that are designed to foster interoperability and ensure a gradual improvement of quality and formal rigor. Ontologies in the OBO Foundry are required to be well-documented, to adopt a common formal language, and to be developed in a collaborative manner. The following summarizes several candidate OBO Foundry ontologies that are related to PRO. Gene Ontology (GO) The Gene Ontology (GO) is by far the most widely used ontology in any discipline [7]. It aims to formalize the expression of information about biological processes, molecular functions, and cellular components through a controlled vocabulary structured in three mutually independent hierarchies. The GO has been used to annotate the genes of humans and a variety of model organisms, and it has facilitated both in-depth understanding of biology within a single organism and comparison of biological processes across multiple organisms. Sequence Ontology (SO) The Sequence Ontology (SO) provides a rich set of terms, relations, and definitions for genome and chromosome annotation [8]. A subset of SO terms addresses the consequences of gene mutation on protein products; for example, whether the mutation decreases or eliminates protein activity. Disease Ontology (DO) Still a work in progress, DO [9] is designed to be a controlled medical vocabulary to facilitate the mapping of diseases and associated conditions to medical coding systems such as the ICD10 [10] and SNOMED CT [11], and to other vocabularies within the Unified Medical Language System (UMLS) [12]. Other protein-related ontologies The PSI-MOD ontology [13] has a comprehensive collection of terms for annotations that describe various types of protein modifications, including cross-links and pre-, coand post-translational modifications. PSI-MOD is partly constructed using RESID [14] terms, a controlled vocabulary for defining modification features of protein entries in the UniProt Knowledgebase (UniProtKB) [15]. PSI-MI [16] and the INOH Event Ontology (EO) [17] are ontologies that describe some of the types of protein interaction events. Finally, both the INOH Molecule Role Ontology [18] and Reactome [19], contain molecular functional group names, abstract molecule names and concrete molecule names manually collected from literature. The structure of each of the other protein-related resources described above aligns well with PRO. Two other ontologies have been designed for database integration or annotation. Protein Ontology (PO) [20] includes terms and relationships to describe attributes of individual protein forms (such as physicochemical properties), while the Proteomics Process Ontology (ProPreO) [21] enables a detailed description of proteomics experimental processes and data. Neither ontology includes the protein forms themselves. Protein Ontology development The development of PRO will proceed by taking a pragmatically motivated approach to populating classes, relations, and annotations. We start with an initial set of types using existing, complementary, curated protein classification resources. Relations between these types are defined following the methodology of the OBO Relations Ontology [22]. Connections to other ontologies are used to formulate annotations of PRO classes. Finally, the results are subjected to manual validation by experts. An overview of PRO is provided in Figure 1. For brevity, we refer to the protein evolution component as ProEvo and the protein forms component as ProForm. Protein Evolution component (ProEvo) The diverse proteins we find today in living organisms can be grouped into protein families, each member of whichPage 2 of 12 (page number not for citation purposes) BMC Bioinformatics 2007, 8(Suppl 9):S1 http://www.biomedcentral.com/1471-2105/8/S9/S1derives from a common ancestor. Families have built up over time by copying events (speciation or gene duplication), followed by divergence of the copies from each other. This expansion of a protein family can be represented as a bifurcating tree: each bifurcating node represents the copying of an ancestral sequence. These ancestral sequences are now extinct, but they are inferred from the sequences we observe today. Despite the passage of millions of years of divergence, members of each family still share recognizable similarities. It is therefore often possible to infer certain properties of the ancestral protein, such as function, based on the recognizable similarities of its modern descendants. During the process of protein evolution, there are portions of proteins – called domains – that are usually copied in their entirety, presumably because they represent a minimal functional unit. A protein comprises one or more domains, usually with additional sequences connecting and surrounding them. Note that using our definition of domain, some domains have never combined as modules with another domain (at least as observed thus far). Proteins with similar domain architecture (that is, the same combination of domains in the same order) are said to be homeomorphic. In the case of single-domain proteins, the evolutionary history is identical to (or is a subtree of) that of the domain itself. However, the evolutionary history of multi-domain proteins is more complex: it can only be represented by a single tree as far back as the earliest ancestor that contained the same architecture. Prior to that, one must look to the histories of the constituent domains. The relationship between a protein and each of its constituent domains can be modeled using the has_part relationship already defined in the OBO Relation Ontology. The relationship is most obvious for multi-domain proteins, but it also holds for single-domain proteins. One complication is that domains within a multi-domain protein can be lost in one or more lineages (e.g., [23,24]). This means that a has_part relationship to this domain PRO protein ontology overviewFigure 1 PRO protein ontology overview. The figure shows the current (partial) working model and a subset of the possible connections to other ontologies. Blue text boxes:ProEvo component; lavender text boxes: ProForm component.Page 3 of 12 (page number not for citation purposes) BMC Bioinformatics 2007, 8(Suppl 9):S1 http://www.biomedcentral.com/1471-2105/8/S9/S1that obtains for the parent class will not obtain for the child class. Therefore, we will use a lacks relationship type to describe evolutionary loss in the child lineage [25]. The GO molecular function ontology organizes function classes from the general (at the top of the hierarchy) to the specific (at the leaf nodes). In contrast, the hierarchy of ProEvo classes is based on evolutionary relatedness, organized from the distantly-related (at the top of the hierarchy) to the more closely related (at the leaf nodes). In many cases the functional and evolutionary classes will overlap. However, consider the case of erythrocyte membrane protein band 4.2 (EPB4.2). This protein is a major component of the red blood cell membrane skeleton [26] that was co-opted from an ancestral protein-glutamine gamma-glutamyltransferase [27], but subsequently lost the ancestral function [28]. In the GO molecular function ontology, the appropriate association for EPB4.2 is "constituent of cytoskeleton" (GO:0005200). For PRO, its parent is "protein-glutamine gamma-glutamyltransferase." The evolutionary relationship between the human and mouse versions of EPB4.2 and protein-glutamine gammaglutamyltransferase is represented schematically in Figure 2. The difference in function is not due to gain or loss of specific sections of protein (domains), since all four proteins share end-to-end similarity and common domain architecture. However, two of the residues of the catalytic triad of protein-glutamine gamma-glutamyltransferase [29] are changed in EPB4.2 (data not shown). Populating ProEvo classes: Resources Several resources exist that group proteins according to function, sequence or structure-based relatedness. We use four of these resources to guide the initial construction of PRO. Together, these resources represent all of the basic elements of a protein evolutionary ontology outlined above. They provide the set of classes that are most important for one of the primary tasks we wish to accomplish with the evolution component: reliably using experimental data from other organisms to understand human genes. Moreover, each of these four resources has been curated by expert biologists to ensure quality. For clarity, in the description of these resources, we refer to the sets of proteins as "groups" or "families" or "clusters," and the name given to the set as the "class." The section below lists each resource according to the evolutionary relationships for which each approach is most appropriate, from the most distant to the closest. Structure-based clusters with remote domain homology: SCOP SCOP (Structural Classification of Proteins) [30] is arranged hierarchically into four levels: class, fold, superfamily and family. Homology (common ancestry) can be asserted for proteins in the same family on the basis of sequence data alone and for proteins in the same superfamily on the basis of three-dimensional (3D) structure data. Proteins in different superfamilies in the same fold group or class have similarities in 3D topology but do not necessarily have a common ancestor. Therefore, only the SCOP superfamily and family data are relevant for the purposes of PRO, with the former defining remote homology (shared ancestry that diverged in the distant past) and the latter defining close homology (shared ancestry that diverged in the more recent past). Sequence-based clusters with close domain homology: Pfam Pfam domain families [31] are comparable to SCOP families. However, Pfam contains domain definitions even in the absence of structure information; thus, Pfam represents a superset of SCOP families. Accordingly, we will use Pfam domain families in place of SCOP families to represent the "close" level of evolutionary relatedness for domains. Clusters of homologous proteins: PIRSF The PIRSF family classification system provides protein classification from superfamily to subfamily levels in a network structure to reflect the evolutionary relationship between sets of whole proteins and between whole proteins and domains [32]. The primary PIRSF classification unit is the homeomorphic family, whose members are homologous (evolved from a common ancestor) and homeomorphic (sharing full-length sequence similarity and a common domain architecture). Basing classification on whole proteins allows annotation of family-specific biological functions, biochemical activities, and sequence features, while an understanding of the domain architecture of a protein provides insight into its general functional and structural properties as well as into complex evolutionary mechanisms. Functionally-diverged subfamilies: PANTHER A PANTHER subfamily [33] is defined as a monophyletic group of proteins that have distinct functions as compared to other monophyletic groups in the same protein family. These functional differences can derive from gain and loss of additional domains or from changes in the protein sequence. Populating ProEvo classes: Mechanism The initial ProEvo classes will derive from the curated protein clustering resources described above. How one class relates to another consequently resolves to how each cluster relates to another, and the problem condenses to a simple mapping exercise. The relationships between SCOP clusters and Pfam clusters already exist, as do the relationships between Pfam, PIRSF, and PANTHER. To facilitate updates and tracking between these initial resources and ProEvo classes, we will use both PRO accessions and IDs, similar to the system used by UniProt [15].Page 4 of 12 (page number not for citation purposes) BMC Bioinformatics 2007, 8(Suppl 9):S1 http://www.biomedcentral.com/1471-2105/8/S9/S1Thus, whenever possible, each PRO class will have an incremented number as its accession (e.g., PRO:00000001) and a source-database cross-reference (e.g., PRO:PIRSF000001). Updating ProEvo classes Once the initial mapping is done – and the classes and relationships are verified – the composition of the underlying clusters and how they interrelate will not be of consequence to PRO except as a source of additional nodes. That is, source database changes need not be reflected in the ontology. Consider the example of the hexokinase family of proteins, which includes xylulokinase [34]. Suppose the initial population of PRO classes yields xylulokinase is_a hexokinase, and the source database is subsequently modified such that the original xylulokinase Schematic representation of the evolutionary relationship between human and mouse versions EPB42 and TGM3Figure 2 Schematic representation of the evolutionary relationship between human and mouse versions EPB42 and TGM3. Top panel: protein-glutamine gamma-glutamyltransferase (TGM3) and erythrocyte membrane protein band 4.2 (EPB42) are descended from a common ancestor with glutamyltransferase activity. Middle panel: All four proteins share a common domain arrangement. Bottom panel: PRO and GO connections. TGM3 and EBP42 are descended from an ancestral transglutaminase (TGase) represented by the large circle. This protein evolved into the ancestral forms of TGM3 and EPB42, represented by the ovals. All descendants of the ancestral TGase comprise the parts indicated by blue boxes. Except where indicated, all descendants of the ancestral TGase have transglutaminase activity and are involved in protein modification. However, the ancestral EPB42 lacks these attributes, and instead acquired new attributes not shared by the ancestral TGM3.Page 5 of 12 (page number not for citation purposes) BMC Bioinformatics 2007, 8(Suppl 9):S1 http://www.biomedcentral.com/1471-2105/8/S9/S1family is renamed to ketopentose kinase after adding ribulokinases. The original relation still holds even though the xylulokinase family no longer exists, so the PRO xylulokinase class does not get deleted. Instead, a new level – ketopentose kinase – could be inserted between xylulokinase and hexokinase. Protein Forms component (ProForm) A number of different protein forms can be derived from a single gene. Protein databases typically represent only one reference sequence for a gene product, and do not have separate entries for mutations that can give rise to disease, for different forms that arise through variations in splicing, or for post-translational modifications. For example, cleavage of a signal peptide is needed for protein secretion. Also, specific residues can be covalently modified with a variety of chemical moieties. Some proteins engage in cyclic processes that involve, for example, phosphorylation and dephosphorylation. These various modified forms of a given gene product are critical to making precise annotation. For example, many diseases are not caused by the "normal" protein, but by a genetic variant. Also, a protein can activate a process when in its phosphorylated form, but inhibit that same process when not. Such nuances are not possible with the existing ontologies. Therefore, PRO allows for the definition of sequence forms arising from genetic, splice, and translational variation, and from post-translational cleavage and modification. Relationships between protein forms will be simple and direct and make use of existing relations whenever possible. We will use OBO's Relations Ontology as a source of well-defined relationships, adding further relationships on the basis of need. For example, it is biologically reasonable to say that the product of a post-translational modification is modified_from the initial protein. However, using such a relationship adds complexity to the system and hinders the possible interconnections with other ontologies. In fact, modified_from is just a more specific way of asserting that "new entity created_from old entity" or "new entity derives_from old entity." The three relations convey identical ideas, but the latter is already part of the core set of relations [22]. Note, however, that this existing relation does not accurately describe the relationship between two variations of the same gene product, nor does the is_a relation. Therefore, we use a new relation variant_of for this situation. Populating ProForm classes: Resources Both the richness and the usefulness of an ontology stem from the diversity and comprehensiveness of its classes. Accordingly, we intend to capture the diverse forms that a protein can take. UniProtKB/Swiss-Prot contains information on sequence variants due to mutation, alternative splicing, or protein cleavage, and on post-translational modification. These data, found within the controlled vocabulary of FT (feature) lines or free text of CC (comment) lines, have been used to populate the appropriate classes. Other sources of data from which information can be computationally extracted include MGI [35] and iProClass [36]. Populating ProForm classes: Mechanism We have developed a parser to transform information from the sources indicated above into OBO format nodes and relationships. The parser captures experimentally verified biological entities, ignoring those labeled as "by similarity," "potential," or "probable." There are three kinds of entities considered by the parser: isoforms, variants, and cleavage and modification products. For example, post-translational modification nodes are automatically populated from UniProtKB based on the FT field and from iProClass based on the PIR Feature and Post Translational Modification fields. Automatically-populated ProForm terms are verified by a curator and edited using OBO-EDIT. Additional terms are added as necessary after curator review of the literature. Connecting to and between other ontologies Several ontologies – notably, GO and DO – are pertinent to protein annotation in that the goal is to annotate gene products. However, the current representation of proteins in databases is not amenable to direct connection of annotation to the appropriate protein forms themselves. The development of PRO provides this necessary intermediary for connections between these other ontologies. For example, the logical connection between the process term X and the disease term Y is the protein form Z. ProEvo connections Though it is most logical and accurate to connect the attributes available from other ontologies to specific terms in the ProForm component, it is nonetheless useful to make connections to terms of the ProEvo component as well. Doing so provides the ability to reason across species – for example, to apply knowledge obtained from a mouse model to the human protein in the same class. Pfam, PIRSF, and PANTHER all associate GO classes (which include the class terms and definitions) with a homologous group of proteins or domains. We will use these associations to provide an initial set of relations between ProEvo domain and protein classes and GO classes. Given the possibility of functional shift within a homologous group of proteins, we propose that the appropriate relationship between a ProEvo class and a GO class will be has_ancestral_property, meaning that all instances of thePage 6 of 12 (page number not for citation purposes) BMC Bioinformatics 2007, 8(Suppl 9):S1 http://www.biomedcentral.com/1471-2105/8/S9/S1class descend from a common ancestor, and that, unless otherwise specified (see below), the properties of the ancestor are inherited by all instances of the class, i.e. by all descendants of the ancestor. However, a subset of proteins in a larger family might, under the influence of natural selection, diverge so greatly from their ancestors that they can be considered to form a class of their own. In such cases, the new class can lose attributes of its ancestor; that is, the attributes of the ancestral class are not conserved in all of its descendant classes, as has happened with the homologous proteins protein-glutamine gamma-glutamyltransferase and erythrocyte membrane protein band 4.2. We can handle such situations by introducing the relation lacks_ancestral_property to represent this process. Thus, the ancestral erythrocyte membrane protein band 4.2 lacks_ancestral_property of involvement in protein modification, but has_ancestral_property structural constituent of cytoskeleton. ProForm connections To support functional annotation and disease understanding, relations are defined between ProForm component classes and other appropriate ontologies and controlled vocabularies (Figure 1). Connection of protein forms to GO classes using appropriate relations will support accurate functional annotation. Relations defined between ProForm classes and the Disease Ontology (DO) will facilitate disease understanding. The Sequence Ontology (SO) will provide a structured controlled vocabulary to describe the consequences of gene mutations on the protein sequence. For attributes not yet defined in OBO Foundry ontologies, well-accepted controlled vocabularies will be adopted. Using the parser described above, cross-references to other ontologies or knowledgebases (e.g., HUGO, GO, OMIM, RESID) were extracted from UniProtKB/Swiss-Prot and MGI entries and mapped to appropriate ontological/controlled vocabulary terms for selected human and mouse proteins of known disease phenotypes. GO annotations with experimental evidence were extracted from iProClass. The results were converted into annotations of relationships between PRO classes and those from other ontologies and were subjected to verification and literature-based curation. An example is given below. A PRO example Smad proteins are essential to serine/threonine kinase receptor signaling pathways regulated by phosphorylation. Smad2 undergoes phosphorylation at serines 465 and 467 upon activation of the transforming growth factor-beta (TGF-beta) type I receptor [37] (Figure 3). The phosphorylations permit association of Smad2 with Smad4, nuclear translocation of the complex, and regulation of transcription [38]. Therefore, the TGF-beta receptor-phosphorylated form is the active Smad2 form. Other forms of Smad2 are possible (Figure 4). We have curated a prototype PRO using Smad proteins from the human and mouse TGF-beta and bone morphogenetic signaling pathways [5]. Later versions will be expanded to additional proteins from these and other pathways. Figure 5 illustrates the PRO structure for the Smad2 protein. Smad2 belongs to the "smad protein" family (source: PIRSF037286) and, more specifically, to the subfamily "receptor-regulated Smad protein, Smad2/Smad3 type" (source: PIRSF500455). Smad family proteins contain MH1 and MH2 domains (source: PF03165 and PF03166, respectively). The former is found in Smad-related proteins and nuclear factor 1 family proteins, whereas the MH2 domain is exclusively found in Smad proteins. Smad2 component of the TGF-beta signaling pathwayFigure 3 Smad2 component of the TGF-beta signaling pathway. Not all protein forms and pathway branches are indicated. The steps shown are preceded by phosphorylation of Smad4, TGF-beta binding to the receptor, and receptor phosphorylation. Step 1: Phosphorylation of Smad2 by TGF beta receptor I. Step 2: Complex formation of Smad2 and Smad4. Step 3: Nuclear import of Smad2:Smad4. Step 4: Binding of Smad2:Smad4 complex coactivator to responsive element.Page 7 of 12 (page number not for citation purposes) BMC Bioinformatics 2007, 8(Suppl 9):S1 http://www.biomedcentral.com/1471-2105/8/S9/S1Each gene may give rise to more than one PRO node, including a wild-type canonical protein plus any described splice and genetic variants. Relationships to GO, PSI-MOD and UMLS are listed under the corresponding node with the use of controlled vocabulary (information currently annotated using UMLS will eventually be replaced by DO). The terms for has_function, has_modification, participates_in and located_in are applied only to the appropriate forms based on experimental verification. The active phosphorylated form of Smad2 (PRO:00000013) located_in nucleus, derives from Smad2 sequence 1 (PRO:00000011) (designated by the derives_from symbol ">" preceding the PRO accession number), which is located_in cytoplasm. Also, the phosphorylated form acquires the function-related terms "transforming growth factor beta receptor, pathway-specific cytoplasmic mediator activity," "Smad binding," and "transcription coactivator activity." Two other forms are derived from further phosphorylation of the active form, and represent the product obtained after regulation by other kinases. Phosphorylation by ERK-1 yields a form (PRO:00000014) with increased transcription coactivator activity [39] (there is currently no "modulation" ontology that provides this type of annotation), while phosphorylation by CAMK2 yields a form (PRO:00000015) that does not localize to the nucleus and thus is unable to coactivate transcription. Smad2 has one splice form that lacks exon 3 (PRO:00000016). This form still maintains the characteristic functions of the TGF-beta receptor-activated form of Smad2, but can now bind directly to DNA (as can the closely-related Smad3 and other so-called R-Smads), and its transcription activity is further enhanced [40]. Finally, genetic variants related to disease are listed. Mutations in Smad2 have been found in colorectal carcinoma. TGF-beta signaling occurring during human colorectal carcinogenesis involves a shift in TGF-beta function, reducing the cytokine's anti-proliferative effect, while increasing actions that promote invasion and metastasis [41]. In the case of the variant with histidine-445 (PRO:00000019), signaling through the TGF-beta pathway is disrupted. The protein is expressed but is not phosphorylated. Need for a protein ontology A protein ontology must fill two distinct needs: 1) a structure to support formal, computer-based inferences of shared attributes among homologous proteins; and 2) an explicit representation of the various forms of a given gene product. Need for a protein evolution component Protein sequence homology (i.e., descent from a common ancestral sequence) is the most widely used approach for annotating the putative functions of genes. While homology with a protein of known, experimentally characterized function is a critical tool for inferring the function of an uncharacterized protein, these inferences must be made carefully. Because there are no simple rules that can be applied consistently for all attributes of all proteins, homology-based inference methods can lead to errors. In a study of annotation consistency in the FlyBase, the primary cause (60%) for errors in GO annotations was determined to be incorrect homology-based inference [42]. However, all of the homology-based errors that were detected could be corrected using more rigorous wholeprotein family/subfamily-based rules for functional inference, such as is done in the protein classification databases PANTHER and PIRSF. An ontology of protein evolution that explicitly models both whole proteins and parts of proteins (domains) will support formal, computer-based inferences of shared attributes among homologous proteins, and will enable more consistent, accurate and precise computational annotation. Furthermore, such an ontology will allow for a richer expression of relationships (including negatives such as "lacks" [25]) than is possible using conventional protein classification databases. This formalization will ensure rigorous application of experimental data to understanding protein-coding genes derived from high-throughput genome, cDNA, EST, or environmental sequencing projects. In addition, it will allow the transfer of described function/phenotypes of proteins from model organisms to human orthologs and may highlight potential candidates to explain a human disease (see below). Need for a protein forms component Alternative splicing and post-translational modifications give rise to the multiple products of a single gene, each of which can have different activities and different expression patterns. Nonetheless, the annotation information in most model organism and sequence databases is organized within a single entry, often without indicating which possible form is the correct object for annotation. Thus, annotation is associated with protein X when in fact it is specific to peptide Y derived from protein X, or to isoform Xa, or to a phosphorylated form of protein X; disease associations are more accurately ascribed to mutant forms of protein X. These various specific entities are natural components of pathway ontologies or databases such as INOH Event Ontology [17] or Reactome [19] (the latter does contain the relevant entities, but as accessions only that are not formed into an ontology structure). As biomedical data expand, it will be increasingly important to explicitly represent these protein forms so attributes are attached to the appropriate entity. For example, the GOPage 8 of 12 (page number not for citation purposes) BMC Bioinformatics 2007, 8(Suppl 9):S1 http://www.biomedcentral.com/1471-2105/8/S9/S1terms that annotate the UniProtKB entry for human Smad2 indicate that the protein is a complex-forming nuclear transcription upregulator involved in the TGFbeta signaling cascade. While this does indeed describe the major function of Smad2, the annotation is only accurate for two out of the seven forms shown in Figure 4. The remaining forms either only partly fit this description or don't fit it at all. PRO facilitates understanding of human disease Mouse models can give valuable insight into human biology. As indicated in Figure 5, the human and mouse Smad2 have many common sequence forms. Alternative splicing of Smad2 exon 3 gives rise to a second distinct protein isoform. The phosphorylated short Smad2 isoform (PRO:00000018), unlike the full-length phosphorylated Smad2 (PRO:00000013), retains the direct DNAbinding activity (GO:0003677) common to every other receptor-regulated Smad (R-Smad; including Smads 1, 3, 5, and 8) [43]. Importantly, PRO shows that this form is common to mouse and human. Knockout mouse experiments indicate that Smad2 plays an essential role in patterning the embryonic axis and specification of definitive endoderm [44]. Mice that exclusively express the short isoform correctly specify the anterior-posterior axis and definitive endoderm and are viable and fertile, suggesting that the short form activates all essential target genes downstream of TGF-beta-related ligands early in development [45]. The direct comparison between specific mouse and human sequence forms facilitated by PRO can guide scientific inquiry. For example, experiments designed to elucidate the specific role of each human Smad2 isoform at different developmental stages are suggested by the information uncovered in the mouse counterparts. The activities of specific isoforms derived from variants that are agents of colorectal carcinomas could also be a factor to investigate. Conclusion PRO is designed to be a formal, well-principled and extensible OBO Foundry ontology for proteins, with a basic set of well-defined relations to support semantic integration and machine reasoning. PRO development has begun by including classes relevant to human and mouse proteins in UniProtKB/Swiss-Prot and MGI, with a focus on disease-related proteins, but will be expanded on a systemby-system basis using pathways covered by the INOH pathway and Reactome databases. The framework described here can be adopted by scientists interested in curating PRO classes from other areas of interest. The development of PRO is expected to create a cycle of improvement for both the ontology and the protein knowledgebases from which the initial information is extracted. For example, literature-based curation revealed that two of the modifications noted in the UniProtKB entry SMAD2_HUMAN occur in a single molecule (PRO:00000014). Such information can be fed back into the UniProtKB entry, along with the PRO node. Similar annotations throughout the database will, in turn, provide a richer information source for PRO. PRO will have an impact beyond the knowledge contained therein. For example, essentially all homologous proteins in families referenced by PRO – irrespective of source organism – can be annotated using PRO terms, including the attributes from connected ontologies. In addition, by providing the means to reference specific protein forms, PRO will foster a precise interconnection between several ontologies. Comparison of information among related organisms and related ontologies is indispensable to human disease research. List of abbreviations used ICD10, International Classification of Diseases, Tenth Revision; SNOMED CT, Systemized Nomenclature of Medicine Clinical Terms; PSI-MOD, Proteomics Standards Initiative Protein Modification; PSI-MI, Proteomics Standards Initiative Molecular Interactions; INOH, InteMultiple possible Smad2 formsFigure 4 Multiple possible Smad2 forms. Not all possibilities are indicated. The third column indicates the known properties for each form. Italicized text indicates those properties that are accurately reflected by the GO terms currently used to annotate human Smad2 in UniProt KB (SMAD2_HUMAN; accession Q15796).Page 9 of 12 (page number not for citation purposes) BMC Bioinformatics 2007, 8(Suppl 9):S1 http://www.biomedcentral.com/1471-2105/8/S9/S1 Page 10 of 12 (page number not for citation purposes) A PRO example using nodes and relationships illustrated by Smad2 proteinFigure 5 A PRO example using nodes and relationships illustrated by Smad2 protein. Not all possibilities are indicated. Cross-references to source of information (in curly braces) and description of sequence forms (in parentheses) are given for clarity. The symbols preceding each PRO accession are as follows: $: root; >: has_part (for domains) or derives_from (for proteins); <: variant_of. BMC Bioinformatics 2007, 8(Suppl 9):S1 http://www.biomedcentral.com/1471-2105/8/S9/S1grating Network Objects with Hierarchies; PANTHER, Protein ANalysis THrough Evolutionary Relationships. Competing interests The authors declare that they have no competing interests. Authors' contributions DAN, CNA, WCB, ZH, HL, BS, CHW participated in the production of the manuscript. HL performed the data mining to produce an automatically-generated "prePRO." CNA curated the Protein Ontology for Smad2 (and related proteins of the TGF-B signaling pathway). TCC and CNA curated the PIRSF protein families for Smad2 (and related proteins of the TGF-B signaling pathway). DAN, CNA, WCB, JB, ZH, BS, and CHW formulated the Protein Ontology framework. All authors read and approved the final manuscript. Acknowledgements Paul Thomas (SRI) provided critical input on the ProEvo component. Funding for PRO development is provided by NIH grant 1 R01 GM080646-01. Support for B. Smith's contribution was provided by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant 1 U 54 HG004028. Support for J. Blake's contribution was provided by NIH P41 grants HG 002273 and HG 003300. This article has been published as part of BMC Bioinformatics Volume 8 Supplement 9, 2007: First International Workshop on Text Mining in Bioinformatics (TMBio) 2006. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/8?issue=S9. References 1. Bard J: Ontologies: Formalising biological knowledge for bioinformatics. Bioessays 2003, 25:501-506. 2. Blake JA: Bio-ontologies – fast and furious. Nat Biotechnol 2004, 22:773-774. 3. Blake JA, Bult CJ: Beyond the data deluge: data integration and bio-ontologies. J Biomed Inform 2006, 39:314-320. 4. Open Biomedical Ontologies [http://obo.sourceforge.net] 5. Protein Ontology [http://pir.georgetown.edu/pro] 6. The OBO Foundry [http://obofoundry.org/] 7. Gene Ontology Consortium: The Gene Ontology (GO) project in 2006. Nucleic Acids Res 2006, 34:D322-326. 8. Eilbeck K, Lewis SE, Mungall CJ, Yandell M, Stein L, Durbin R, Ashburner M: The Sequence Ontology: a tool for the unification of genome annotations. Genome Biol 2005, 6:R44. 9. Disease Ontology [http://diseaseontology.sourceforge.net/] 10. International Classification of Diseases (ICD) [http:// www.who.int/classifications/icd/en/] 11. SNOMED Clinical Terms [http://www.snomed.org/] 12. Bodenreider O: The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res 2004, 32:D267-270. 13. Proteomics Standards Initiative Protein Modification [http:/ /psidev.sourceforge.net/mod/] 14. Garavelli JS: The RESID Database of Protein Modifications as a resource and annotation tool. Proteomics 2004, 4:1527-1533. 15. UniProt Consortium: The Universal Protein Resource (UniProt). Nucleic Acids Res 2007, 35:D193-197. 16. Hermjakob H, Montecchi-Palazzi L, Bader G, Wojcik J, Salwinski L, Ceol A, Moore S, Orchard S, Sarkans U, von Mering C, Roechert B, Poux S, Jung E, Mersch H, Kersey P, Lappe M, Li Y, Zeng R, Rana D, Nikolski M, Husi H, Brun C, Shanker K, Grant SG, Sander C, Bork P, Zhu W, Pandey A, Brazma A, Jacq B, Vidal M, Sherman D, Legrain P, Cesareni G, Xenarios I, Eisenberg D, Steipe B, Hogue C, Apweiler R: The HUPO PSI's molecular interaction format – a community standard for the representation of protein interaction data. Nat Biotechnol 2004, 22:177-183. 17. Kushida T, Takagi T, Fukuda KI: Event Ontology: A pathway-centric ontology for biological processes. Pac Symp Biocomput 2006, 11:152-163. 18. Yamamoto S, Asanuma T, Takagi T, Fukuda KI: The molecule role ontology: an ontology for annotation of signal transduction pathway molecules in the scientific literature. Comparative and Functional Genomics 2004, 5:528-536. 19. Joshi-Tope G, Gillespie M, Vastrik I, D'Eustachio P, Schmidt E, de Bono B, Jassal B, Gopinath GR, Wu GR, Matthews L, Lewis S, Birney E, Stein L: Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 2005, 33:D428-432. 20. Sidhu AS, Dillon TS, Chang E, et al.: Protein Ontology: Data Integration using Protein Ontology. In Database Modeling in Biology: Practices and Challenges Edited by: Ma Z, Chen JY. New York, Springer Inc; 2006:39-60. 21. Sahoo SS, Thomas C, Sheth A, York WS, Tartir S: Knowledge modeling and its application in life sciences: a tale of two ontologies. Proceedings of the 15th International Conference on World Wide Web 2006:317-326 [http://portal.acm.org/cita tion.cfm?id=1135826&jmp=abstract&coll=GUIDE&dl=GUI]. New York, ACM Press 22. Smith B, Ceuster W, Klagger B, Kohler J, Kumar A, Lomax J, Mungall C, Neuhaus F, Rector AL, Rosse C: Relations in Biomedical Ontologies. Genome Biol 2005, 6:R46. 23. Burglin TR, Cassata G: Loss and gain of domains during evolution of cut superclass homeobox genes. Int J Dev Biol 2002, 46:115-123. 24. Shimeld SM: Characterization of AmphiF-spondin reveals the modular evolution of chordate F-spondin genes. Mol Biol Evol 1998, 15:1218-1223. 25. Ceusters W, Elkin P, Smith B: Referent tracking: The problem of negative findings. Stud Health Technol Inform 2006, 124:741-746. 26. Mandal D, Moitra PK, Basu J: Mapping of a spectrin-binding domain of human erythrocyte membrane protein 4.2. Biochem J 2002, 364:841-847. 27. Polakowska RR, Eickbush T, Falciano V, Razvi F, Goldsmith LA: Organization and evolution of the human epidermal keratinocyte transglutaminase I gene. Proc Natl Acad Sci USA 1992, 89:4476-4480. 28. Korsgren C, Lawler J, Lambert S, Speicher D, Cohen CM: Complete amino acid sequence and homologies of human erythrocyte membrane protein band 4.2. Proc Natl Acad Sci USA 1990, 87:613-617. 29. Boeshans KM, Mueser TC, Ahvazi B: A three-dimensional model of the human transglutaminase 1: insights into the understanding of lamellar ichthyosis. J Mol Model 2007, 13:233-246. 30. Andreeva A, Howorth D, Brenner SE, Hubbard TJ, Chothia C, Murzin AG: SCOP database in 2004: refinements integrate structure and sequence family data. Nucleic Acids Res 2004, 32:D226-229. 31. Finn RD, Mistry J, Schuster-Bockler B, Griffiths-Jones S, Hollich V, Lassmann T, Moxon S, Marshall M, Khanna A, Durbin R, Eddy SR, Sonnhammer EL, Bateman A: Pfam: clans, web tools and services. Nucleic Acids Res 2006, 34:D247-251. 32. Wu CH, Nikolskaya A, Huang H, Yeh L-S, Natale DA, Vinayaka CR, Hu Z, Mazumder R, Kumar S, Kourtesis P, Ledley RS, Suzek BE, Arminski L, Chen Y, Zhang J, Cardenas JL, Chung S, Castro-Alvear J, Dinkov G, Barker WC: PIRSF family classification system at the Protein Information Resource. Nucleic Acids Res 2004, 32:D112-114. 33. Mi H, Lazareva-Ulitsky B, Loo R, Kejariwal A, Vandergriff J, Rabkin S, Guo N, Muruganujan A, Doremieux O, Campbell MJ, Kitano H, Thomas PD: The PANTHER database of protein families, subfamilies, functions and pathways. Nucleic Acids Res 2005, 33:D284-288. 34. Bork P, Sander C, Valencia A: Convergent evolution of similar enzymatic function on different protein folds: The hexokinase, ribokinase, and galactokinase families of sugar kinases. Protein Sci 1993, 2:31-40. 35. Blake JA, Eppig JT, Bult CJ, Kadin JA, Richardson JE: Mouse Genome Database Group. The Mouse Genome Database (MGD): updates and enhancements. Nucleic Acids Res 2006, 34:D562-567.Page 11 of 12 (page number not for citation purposes) BMC Bioinformatics 2007, 8(Suppl 9):S1 http://www.biomedcentral.com/1471-2105/8/S9/S1Publish with BioMed Central and every scientist can read your work free of charge "BioMed Central will be the most significant development for disseminating the results of biomedical research in our lifetime." Sir Paul Nurse, Cancer Research UK Your research papers will be: available free of charge to the entire biomedical community peer reviewed and published immediately upon acceptance cited in PubMed and archived on PubMed Central yours - you keep the copyright Submit your manuscript here: http://www.biomedcentral.com/info/publishing_adv.asp BioMedcentral 36. Wu CH, Huang H, Nikolskaya A, Hu Z, Barker WC: The iProClass integrated database for protein functional analysis. Comput Biol Chem 2004, 28:87-96. 37. Kretzschmar M, Liu F, Hata A, Doody J, Massague J: The TGF-beta family mediator Smad1 is phosphorylated directly and activated functionally by the BMP receptor kinase. Genes Dev 1997, 11:984-995. 38. Abdollah S, Macias-Silva M, Tsukazaki T, Hayashi H, Attisano L, Wrana JL: TbetaRI phosphorylation of Smad2 on Ser465 and Ser467 is required for Smad2–Smad4 complex formation and signaling. J Biol Chem 1997, 272:27678-27685. 39. Funaba M, Zimmerman CM, Mathews LS: Modulation of Smad2mediated signaling by extracellular signal-regulated kinase. J Biol Chem 2002, 277:41361-41368. 40. Yagi K, Goto D, Hamamoto T, Takenoshita S, Kato M, Miyazono K: Alternatively spliced variant of Smad2 lacking exon 3. Comparison with wild-type Smad2 and Smad3. J Biol Chem 1999, 274:703-709. 41. Matsuzaki K: Smad3 phosphoisoform-mediated signaling during sporadic human colorectal carcinogenesis. Histol Histopathol 2006, 21:645-662. 42. Mi H, Vandergriff J, Campbell M, Narechania A, Majoros W, Lewis S, Thomas PD, Ashburner M: Assessment of genome-wide protein function classification for Drosophila melanogaster. Genome Res 2003, 13:2118-2128. 43. Massague J, Seoane J, Wotton D: Smad transcription factors. Genes Dev 2005, 19:2783-2810. 44. Weinstein M, Yang X, Deng C: Functions of mammalian Smad genes as revealed by targeted gene disruption in mice. Cytokine Growth Factor Rev 2000, 11:49-58. 45. Dunn NR, Koonce CH, Anderson DC, Islam A, Bikoff EK, Robertson EJ: Mice exclusively expressing the short isoform of Smad2 develop normally and are viable and fertile. Genes Dev 2005, 19:152-163.Page 12 of 12 (page number not for citation purposes)


Review of Paul K. Moser, _The Evidence for God: Religious Knowledge Reexamined_

Philosophy

Notre Dame Philosophical Reviews 2011.01.01 Paul K. Moser The Evidence For God: Religious Knowledge Reexamined Paul K. Moser, The Evidence For God: Religious Knowledge Reexamined, Cambridge University Press, 2010, 280pp., $25.99 (pbk), ISBN 9780521736282. Reviewed by Thomas D. Senor, The University of Arkansas Taking up where 2008's The Elusive God left off, The Evidence for God is Paul Moser's second book in his attempt to reorient religious epistemology. As with the earlier volume, The Evidence for God is daring and provocative. Among the important topics it deals with are naturalism, fideism, natural theology, and the role that volition plays in our ascertaining evidence of God's existence. The book begins with a parable around which the entire monograph revolves. Imagine that you are hiking in a vast and remote wilderness area that is accessible only to hikers. To your great dismay, you discover that you are hopelessly lost: you have no method of determining either your exact location or a promising route back to civilization. The woods are filled with dangers (e.g., poisonous snakes, hungry carnivores, and potentially freezing temperatures) and you have no means of communication with the outside world. Worse still, you have only a meager supply of food and water. You've had one bit of good fortune: you've come across an old, dilapidated shack that contains a barely functional ham radio. The battery in the radio still has a bit of juice, although you doubt it will last long once the radio is turned on. In short, your situation is dire but not hopeless. What is your best bet for survival? According to Moser, what is needed is a trustworthy guide. Merely finding maps won't get you out of your predicament since you don't know how to place yourself on them -you don't know where you are. To increase the chances of success, the guide should be capable of interacting with you as you are making your way out of the wilderness since you will likely make a wrong turn somewhere and you'll need to be put straight. Given your predicament, Moser claims, you've got four primary options. Option 1: Despairing Seeing the hard road in front of you with at best a chance of rescue, you might just decide to give up. To do this is to be a practical atheist regarding a rescuer. Option 2: Passively Waiting 2 Another option is not to give up hope but to stay put and simply wait for rescue. You could just bide your time and hope to be discovered. Being reasonable, you don't believe you'll be saved but you don't disbelieveeither. As such, you become a practical agnostic about a rescuer. Option 3: Leaping The leaping option involves picking a path or direction, following it, and hoping for the best. One might focus on the goods involved in following a trail that other hikers have trod rather than on the result of rescue. In any event, the key here is action without evidence that the action will lead to the ultimate, desired end. Moser calls one who leaps a "practical fideist." Option 4: Discerning Evidence As opposed to the first three options, the fourth involves rationing the available food supply and taking a hard, rational look at your situation. Within the "discerning evidence" camp, two rather different approaches may be detected: 1. Purpose-neutral discerning of evidence: look for evidence of how to best find rescue that doesn't involve or presuppose the purposes of any potential rescuer. 2. Telic discerning of evidence: look for evidence that seems purposive. For example, whereas purpose-neutral evidence might be the shapes, lines, and textures of a map of the region, telic evidence would be markings on the map by an agent with an intention to guide the lost to safety. Moser's idea is this: humanity is lost in a figurative wilderness: here's how Moser puts it: we all face the prospect of ultimate physical death and social breakdown. From the perspective of our species overall, our food and water supplies are threateningly low, with little hope of being adequately replenished. On many fronts, our relationships with one another are unraveling, and have resulted in selfish factions and fights. The factions and fights often involve race, religion, nationality, or economic class but they sometimes cut across familiar lines. Selfishness transcends common categories, always, of course, for the sake of selfishness. We have become willing even to sacrifice the minimal well-being of others for our own selfish ends. As a result, economic injustices abound among us, wherever a sizeable group resides. Accordingly, genuine community has broken down on various fronts, and, in the absence of a rescuer, we shall all soon perish, whether rich or poor. (12-13) The possibility of a rescuer for humanity depends on the possibility of a being both capable and willing to save us. The primary matter of the book is to "use the wilderness parable to examine, without needless abstraction, the main approaches to knowledge of God's existence" (15). The approaches that Moser discusses are four: nontheistic naturalism, fideism, natural theology, and his preferred "personifying evidence of God" model. Having argued against the primary claims of the former perspectives and delineating his own position, Moser concludes the book with a chapter on potential defeaters, and in particular examines the epistemic impact of religious pluralism. In what follows, I'll sketch his discussion of each of these chapters and take issue with a couple points along the way. 3 Chapter 1 undertakes to examine whether appeals to the findings and nature of science undermine the rationality of belief in God. If naturalism is true, and if what it is for an object to be natural is for it to be (in principle) understandable via empirical science, then there is clearly no God, traditionally conceived. But why should we think that metaphysical naturalism is true? Whether or not there are good arguments for naturalism, empirical science itself would not seem to provide them. Furthermore, Moser argues that a thorough-going naturalism would demand that purposive explanation (i.e., explanation that appeals to the intentions or purposes of agents) be eliminated, reduced, or somehow shown to be accounted for by non-intentional, non-purposive explanations. Yet the prima facie plausibility (indeed, ubiquity) of intentional explanation makes it very hard to see how to do without it; and no good reductions are yet on the table. Moser concludes Chapter 1 with a dilemma for what he calls "Core Scientism," which is roughly the dual claim that every real entity knowable via (a completed) science, and every epistemically acceptable way of forming and revising beliefs, is grounded in the objects acknowledged by and the methods of (a completed) science. Either Core Scientism is itself not included in the sciences or its justification depends on a proper understanding of the nature of "empirical science." If the former, then the thesis is self-defeating for it asserts that only that which is knowable or justified via science is epistemically acceptable and yet it fails to meet this condition. If the latter, then it is being laid down simply as a desideratum of the proper understanding of "empirical science," in which case it is simply stipulative and innocuous. Moser concludes that the empirical sciences and the epistemology they employ are barriers neither to the existence of non-natural entities (e.g., God) nor to the possibility of reasonable belief about them. In Chapter 2, Moser turns his attention in a radically different direction. If the first chapter represents the pessimism of the lost hiker who thinks there is no hope of rescue and resigns himself to his fate, the second chapter focuses on the one whose hope manifests itself in blind action. The fideist is the believer who eschews evidence and who emphasizes the importance of faith as opposed to knowledge or even justified belief. SørenKierkegaard is the primary example that Moser offers but he also includes Rudolf Bultmann and Karl Barth in the fideistic camp. The fideist believes not just that it is in some important sense permissible for the believer to lack supporting evidence for the existence of God but that true faith requires an existential leap from a springboard other than a solid evidential base. The subjectivity of religious devotion requires a lack of objectivity; arguments and reason are the source of the objectivity that is rejected by faith. Why is there conflict between faith and reason? Moser proposes that, at least for Kierkegaard, it is the content of faith that produces the tension. Faith, or at least Christian faith, is incompatible with wellgrounded belief because what is believed is "inherently paradoxical, contradictory, or absurd." (101) Moser takes Kierkegaard at his word when he uses this kind of language and thinks that Kierkegaard takes the faith that he holds to be necessarily false. So the picture Moser paints of fideism is not simply the claim that religious belief without evidence is morally or epistemically or religiously appropriate, but rather the much stronger claim that reason can't have anything to do with Christian faith since the latter is contradictory (because the doctrine of the Incarnation is contradictory) and hence necessarily false. Moser contrasts the fideistic view of faith with what he labels "Christian faith." In the second half of this chapter, and in much of the last two-thirds of the book, the discussion leans heavily in the direction of biblical exegesis rather than analytic philosophy. This is never more true than with respect to Moser's 4 presentation of his preferred view of faith. According to Moser, the Christian view of faith is, essentially, "a willing, obedient entrustment to God that involves one's motivational heart and that therefore is inherently action oriented" (105). In calling people to faith, God seeks not only to move us cognitively and emotionally, but volitionally as well. When the believer entrusts herself to God, God works cooperatively with her to transform her from the default position of selfishness to being an expression of God's perfect love. Thus, while there is a doxastic component to faith, there is also a crucial volitional component. The fideism chapter includes a discussion of Alvin Plantinga's Reformed epistemology, which Moser includes under the more general category of "argument-indifferent theism." Moser finds a number of things not to like in Plantinga's epistemology of religious belief. He objects that on Plantinga's view, belief in the specific claims of Christianity is "caused" by the Holy Spirit and that this is inconsistent with the New Testament perspective that faith is a gift freely offered to all who have the ability to freely accept or reject it. However, the main difficulty that Moser has with Plantinga's view is simply that it is an instance of argument-indifferent theism, and thus it does not require that the believer possess "a trustworthy truth indicator for a belief" (140). In the end, Moser rejects fideism because he understands it to recommend an arbitrary and, in the case of the Kierkegaardian view, contradictory faith. In keeping with the guiding metaphor of the book, the best chance of getting out of the woods is not by blindly choosing a path (particularly if you can tell immediately that the path goes nowhere!) but instead by finding trustworthy evidence that the selected route will lead to safety. Chapter 3 takes on the epistemic significance of natural theology. Moser begins the chapter with a discussion leading to the claim that God's goal is to call people into a non-coercive relationship with God that will lead to the moral development and transformation of those who heed the call. The primary problems that Moser has with natural theology are two. First, the arguments fall short of arguing for a perfectly loving God. Cosmological arguments might lead to a first cause or ultimate explanation and teleological arguments might secure intelligence, but neither of these forms of reasoning can support the claim that the intelligent cause of the universe is a perfectly loving God. Moser thinks the ontological argument fails for reasons we don't have the space to discuss. But even if it didn't have the flaw that Moser cites, he still thinks it wouldn't be adequate since the concept involved is static in a way that the personally interactive occurrent evidence of the presence and the reality of the Jewish and Christian God is not. In particular, the evidence consisting of the content of a concept of God is not personally variable relative to the wills of humans toward God and God's will. As a result, the evidence offered in ontological arguments fails to fit with the personally interactive divine self-revelation that involves God's intermittent hiding and seeking relative to humans. (157-158) The trouble with the arguments, then, is that they provide the wrong kind of evidence. A God who wants to enter into dynamic, personal relationships with creatures will reveal himself in a way that invites creatures to enter more deeply into the relationship. A one-size-fits-all, impersonal model of evidence is not what we should expect given what we have reason to believe are God's purposes. In Chapter 4 we get an argument for the existence of God that doesn't pretend to be natural theology traditionally construed. After claiming that the personifying evidence would require God's altering our volitional structure (with our permission) so that we would not remain in the condition of sin that makes 5 our default will one of selfishness and hence unreceptive to moving in the direction of "unselfish love and forgiveness toward all persons" (204), Moser offers the following argument which he claims is for him -and presumably others who have heeded the call -a good argument since he has reasons for thinking that the premises are true. 1. Necessarily, if a human person is offered and receives the transformative gift, then this is the result of the authoritative power of a divine X of thoroughgoing forgiveness, fellowship in perfect love, worthiness of worship, and triumphant hope (namely, God). 2. I have been offered, and have willingly received, the transformative gift. 3. Therefore God exists. What is the "transformative gift"? Although the definition is rather robust, understanding the argument requires understanding it so it's worth the space we devote to it: The transformative gift =df one's being authoritatively convicted in conscience and forgiven by X of sin and thereby being authoritatively called into volitional fellowship with X in perfect love and into rightful worship toward X as worthy of worship and, on that basis, transformed by X from default tendencies to selfishness and despair to a new volitional center with a default position of unselfish love, including forgiveness, toward all people and of hope in the triumph of good over evil by X. (200) Given this understanding of the transformative gift, premise one of the argument is presumably secure: for one is not in a position to offer the gift unless one is capable of forgiving sin and is worship-worthy. But given just how propositionally rich the definition of the transformative gift is, the second premise will require significant justification (to say the least). For I'm justified in believing that premise only if I'm justified in believing the following conjunctive proposition: I have been authoritatively convicted in conscience & forgiven of sin & called into a volitional fellowship in perfect love & due to the previous conjuncts, transformed from selfishness and despair to a new volitional center of unselfish love and forgiveness, and hope in the triumph of good over evil & the one who has offered this to me is capable of forgiving sin and worthy of worship. The argument is clearly valid; in fact, the conclusion follows from the second premise alone. The question then is how, on Moser's view, is premise two justified? Given what Moser said in response to Plantinga (and in keeping with his general epistemological predilections), he'll have to hold that there are internally accessible signs of trustworthiness in order for the belief to be justified. The belief's being reliably grounded, say, will be insufficient. So what kinds of grounds does he have for holding that premise two is true? Moser writes: I could plausibly argue for the cognitive well-groundedness, or trustworthiness, of premise 2 on the basis of its central role in an undefeated best-available explanation of the whole range of my experience and my other evidence. This role includes this premise's figuring in a best-available answer to the following explanation-seeking question: why is my experience regarding the supposed provisions of the transformative gift (including my evident change from default 6 selfishness to a new volitional center with a default position of unselfish love toward all people) as it actually is now, rather than the opposite or at very least different? On the basis of my experiential evidence, the central role of premise 2 in answering such an explanationseeking question can figure in its being well-grounded for me and for anyone else who has similar evidence. (205-6) So premise two is to be justified by an inference to the best explanation of "the whole range" of the believer's experience and other evidence. But what precisely is the nature of the experience that is the ground of so significant an abductive inference? We get hints here and there but if we are looking for a robust, phenomenological characterization and philosophical exploration of the mode of evidence we receive and how it is that we are able to receive it, we'll be disappointed. According to Moser, we can have "direct, firsthand knowledge of God's reality and character" by "being acquainted with (at least) God's personal and perfectly loving will" (201). But what is it to be acquainted with perfect, unselfish love? Although Moser has a fair bit to say about the point of contact and the effects of such acquaintance (e.g., the conscience is a focal point for receiving a direct divine volitional challenge, that being acquainted with such love is to be acquainted with "God's inherent personal character and thus with the reality of God" (201), that such acquaintance can noncoercively lead to one's will being changed from selfishness to unselfish love of others, etc.), we never get anything that looks like a philosophical account of the nature of this kind of evidence. To be clear, I'm not implying that we should be given enlightening necessary and sufficient conditions for when human acquaintance with the divine takes place. Nor am I suggesting that we should be provided with epistemically useful rules for determining when such acquaintance is achieved. But if we are to think that this experience is evidence for the existence of God, we need to know better how to conceptualize its evidential role. A natural thought is that such acquaintance involves perceptual or at least quasi-perceptual experience. Yet except for his frequent use of "acquaintance," Moser gives no reason to think this -there is no discussion of perception or even of mystical religious experience which might be at least quasiperceptual. How we can have knowledge by acquaintance (as opposed to description) without having perceptual contact with that which is known is not addressed and is, to my mind, problematic. Here is another interpretation of the experiential evidence that figures prominently in Moser's religious epistemology: the experience is the recognition of the change in one's volitional center. One sees that one is now inclined toward love for others rather than selfishness. One's will has been altered for the better in ways that seem to be unnatural -at least in the sense that my natural default position has been moved away from selfishness and toward perfect love. This volitional change is in need of explanation and the best explanation is that it is the result of my having received the transformational gift. Although there is no doubt that this recognition has a role to play in Moser's defense of premise two of his argument, it can't be all the experiential ("personifying") evidence that the believer has. For if it were, there would be no inclination to call a mere recognition of a volitional shift an "acquaintance" with God. This surely implies, as Moser says elsewhere, "direct, firsthand" experience of God. And if it were the only role that experiential evidence plays, then premise two will not be justified. For it surely can't be reasonably argued that my noticing a surprising change for the better in my will by itself justifies the belief that I have been offered and received the transformational gift (recall that it entails the sizable and robust conjunction described above). 7 Despite a long and interesting discussion of the theological and biblical account of the nature of, and challenges to, volitional change, we never do get an epistemologically illuminating discussion of acquaintance and of the personifying, experiential evidence that one gets as one positively responds to the divine offer. The Evidence for God's concluding chapter tackles the primary potential defeaters for the justification of premise two: the problems of evil and of religious diversity. Although there is no room here to discuss the details of this chapter, I will say that Moser's discussion of diversity (which takes up most of the chapter) is bold, innovative, and nuanced. While defending a version of exclusivism, Moser argues that a God of perfect love could not make belief a requirement of salvation, and that one might yield to God's transforming call de reand fail to form any beliefs about having yielded to God or even about the existence of God. Moser's book is an interesting read that furthers his agenda in the epistemology of religious belief. If Moser has in mind making this work a trilogy, I would suggest that he use William Alston's book Perceiving God as a model: that is, I'd like to see him lay out more explicitly the epistemology of personifying evidence and tie it in with modes of evidential justification with which we are all familiar. Copyright © 2004 Notre Dame Philosophical Reviews ISSN: 1538 1617 University of Notre Dame


Where the standard approach in comparative neuroscience fails and where it works: General intelligence and brain asymmetries

Philosophy

ISSN: 1911-4745 Comparative Cognition & B e h av i o r R e v i e w s doi: 10.3819/CCBR.2018.130010 Volume 13, 2018 Where the Standard Approach in Comparative Neuroscience Fails and Where It Works: General Intelligence and Brain Asymmetries What do coarse-grained and taxon-neutral traits, such as brain size and intelligence, tell us about neurocognitive systems? What characteristics should a behavioral proxy have to allow us to properly compare cognitive abilities across different taxa? In their article "Beyond Brain Size: Uncovering the Neural Correlates of Behavioral and Cognitive Specialization," Logan and colleagues address these questions by reviewing the literature on the relationship between brain size and cognition. In the light of empirical and theoretical considerations, the authors suggest that, to understand how brain and cognition evolve, comparative biologists should focus on fine-grained, taxon-specific phenotypic traits within the relevant ecological and adaptive context of a given species. This would help to avoid reification, defined by the authors as mistaking an operationalized target of measurement with a real, causally meaningful object. Although we agree with this conclusion, there are two aspects analyzed by Logan and colleagues that we think may deserve further consideration. The first is the common tendency in comparative research to assess human-oriented phenotypic traits in nonhuman species; the second is the tendency, which is also widespread, to adopt coarse-grained traits, such as brain size, instead of fine-grained and more informative traits. Let us start by considering the first one. A general premise of Logan and colleagues' article is that Davide Serpico Department of Philosophy and Educational Sciences University of Turin Department of Antiquity, Philosophy, and History University of Genoa Elisa Frasnelli School of Life Sciences University of Lincoln Although brain size and the concept of intelligence have been extensively used in comparative neuroscience to study cognition and its evolution, such coarse-grained traits may not be informative enough about important aspects of neurocognitive systems. By taking into account the different evolutionary trajectories and the selection pressures on neurophysiology across species, Logan and colleagues suggest that the cognitive abilities of an organism should be investigated by considering the fine-grained and species-specific phenotypic traits that characterize it. In such a way, we would avoid adopting human-oriented, coarse-grained traits, typical of the standard approach in cognitive neuroscience. We argue that this standard approach can fail in some cases, but can, however, work in others, by discussing two major topics in contemporary neuroscience as examples: general intelligence and brain asymmetries. Keywords: general intelligence, brain asymmetries, comparative neuroscience 96 COMPARATIVE COGNITION & BEHAVIOR REVIEWS Serpico and Frasnelli comparative behavioral research is often characterized by a sort of "anthropocentric perspective." However, as the authors argue, human behavioral traits-and the related neural bases-are not necessarily shared by other species. This argument can be understood as a criticism against the so-called Great Chain of Being, a secular ideology that assumes a natural hierarchy of organisms with humankind at the top and then, successively, lower animals from primates to bacteria (see, e.g., Sternberg, 2017). Logan and colleagues clarify how misleading this interpretation of the tree of life is by highlighting the importance of the ecological and adaptive context of a species: The ability of the organisms to achieve their goals should be evaluated within the range of challenges they face within their natural environment. Therefore, phenotypic traits should be identified by accounting for what is actually "meaningful" for a given species rather than what is meaningful for humans. The second central point of the target article concerns the use of general, coarse-grained phenotypic traits and proxy measures to study cognition. Brain size represents, in the authors' view, a noninformative measure of neurocognitive systems. Indeed, such a broad measure cannot disentangle from one another important aspects such as the dimensions of specific brain areas, the neuron density, and the connectivity patterns between neurons, which may be more informative than brain size about the properties of a neurocognitive system. Hence, it is not surprising that correlational analyses of brain size tend to produce spurious associations at both the intraspecific and interspecific levels (reviewed by Logan et al.). We recognize that these criticisms could rule out misleading assumptions in comparative behavioral research. However, although the authors' arguments seem to be applicable to most research about high-level psychological constructs, we believe they may miss the mark in respect with structural features of nervous systems and the related behavioral manifestations. To explain our concerns about the approach proposed by Logan and colleagues, we use as examples two topics in neuroscience: general intelligence and brain asymmetries. These two examples elucidate where the coarsegrained and human-oriented comparative approach fails and where instead it can be appropriate and helpful. The authors' argument against anthropocentric comparative research sounds effective in the case of general intelligence and its putative underlying mechanism, namely, the g factor. General intelligence represents a psychological trait assessed by psychometric IQ tests that generally recruit linguistic, mathematical, logical, and spatial abilities. The g factor, instead, is often understood as a domain-general cognitive mechanism accounting for both individuals' performance in tests (i.e., the intelligent behavior) and individual intellectual differences within populations (see Burkart, Schubiger, & van Schaik, 2017; Serpico, 2017; Sternberg & Grigorenko, 2002). General intelligence is the subject of heated controversy in regards to two aspects tackled by Logan and colleagues with respect to brain size, that is, the granularity problem and the anthropocentric perspective widespread in comparative research. First, exactly like brain size, general intelligence represents a coarse-grained evaluation of a cognitive system, regardless of any detail about its structural and functional composition. However, many scholars have argued that intelligence, rather than reflecting a single neurocognitive mechanism, is composed of several distinct and autonomous-but not necessarily independent-cognitive mechanisms (see Hampshire, Highfield, Parkin, & Owen, 2012; Serpico, 2017; Sternberg & Grigorenko, 2002; Van der Maas et al., 2006). Logan and colleagues' argument about brain size is in line with the view that general intelligence, like other high-level psychological constructs, does not represent an informative measure of neurocognitive systems (see Craver, 2009, who discussed the problem of subtyping complex psychological traits into lower-level characteristics). Second, we think that general intelligence is exactly that sort of human-oriented trait that Logan and colleagues criticize as erroneously generalized to other species. Although the g factor likely plays a role, if any, in human cognition only (indeed, g accounts for the population variance in a battery of IQ tests), many authors have tried to assess its role in other species. For instance, in their review about the evolution of general intelligence, Burkart and colleagues (2017) took the g factor as a domain-general neurocognitive mechanism shared by humans, primates, birds, and rodents. The arguments provided by Logan and colleagues against Author Note: Davide Serpico, Department of Philosophy and Educational Sciences, University of Turin, Via Verdi, 8, Turin, Italy. Correspondence concerning this article should be addressed to Davide Serpico at email@example.com. Acknowledgments: We thank Anna Wilkinson for inviting us to contribute a commentary on this paper. 97General Intelligence and Brain Asymmetries VOLUME 13, 2018 the anthropocentric viewpoint in comparative studies seem to us well suited to describe, and possibly to rule out, this sort of reification of general intelligence. By contrast, the arguments provided by the authors seem to be less suited to be applied to structural properties of nervous systems. This is the case of brain and behavioral asymmetries, where a sort of humanoriented, coarse-grained approach seems to be promising and successful. Traditionally, lateralization (i.e., the different functional specialization of the right and left sides of the nervous system) was considered a uniquely human characteristic, related to handedness and linguistic functions (McManus, 1999). Over the past decades, thanks to research conducted in comparative psychology, neuroscience, and developmental biology, we have realized that lateralization is a widespread phenomenon among vertebrates (Rogers, Vallortigara, & Andrew, 2013). Moreover, there is emerging evidence of behavioral and brain asymmetries in invertebrates, suggesting that lateralization is a feature of simpler as well as more complex neurocognitive systems (reviewed by Frasnelli, 2013). In most vertebrates, for example, the right hemisphere is involved in responding to unexpected and novel stimuli (e.g., predators) and in interacting with conspecifics, and the left hemisphere is specialized in less complex and repetitive tasks (e.g., behavioral routines). Lateralization also characterizes learning and memory both in vertebrates (e.g., in birds; see Andrew, 1999; Cipolla-Neto, Horn, & McCabe, 1982; Clayton, 1993) and in invertebrates. For instance, honeybees present an asymmetrical use of the right and left antennae in learning and recalling olfactory memories: recall of short-term memory is implemented by the right side, whereas recall of long-term memory is possible through the left side (see Letzkus et al., 2006; Rogers & Vallortigara, 2008; for a review, see Frasnelli et al., 2014). This suggests that the shifts between the two sides of the brain from recently acquired information to more integrated and complete long-term records might constitute a considerable advantage for both arthropods and vertebrates. Thus, mechanisms controlling such shifts may have evolved, perhaps independently, in both phyla (see Frasnelli, Vallortigara, & Rogers, 2012). In sum, despite differences between the nervous systems of humans and other animals, there is good evidence that lateralization represents a structural feature implemented by similar mechanisms across different species and taxa (see Rogers et al., 2013). Thus, thanks to these similarities, animal models have allowed us to uncover the evolution of behavioral and neural asymmetries, and their developmental mechanisms as well. The take-home message is that the approach proposed by Logan and colleagues might be promising for behavioral traits reflecting higher-level cognitive aspects such as general intelligence but be potentially fragile for behavioral traits reflecting lower-level structural properties of the nervous system. As we have argued, the disagreement about general intelligence relies on its generality and on its dubious value in characterizing the neurocognitive system of nonhuman species. By contrast, what we know about lateralization points at the (partial) adequacy of a human-oriented, coarse-grained approach in comparative research. We do not mean to assume an anthropocentric viewpoint. Nevertheless, there are good reasons to think that some structural aspects of nervous systems-and their behavioral correlates-are highly conserved across different taxa, regardless of their granularity. Whether the conservation of this kind of traits is due to natural selection, to the modularity of the related genetic processes, or to some sort of developmental constraint in brain morphology is an empirical question that future research must address. References Andrew, R. J. (1999). The differential roles of right and left sides of the brain in memory formation. Behavioural Brain Research, 98, 289–295. doi:10.1016/S0166-4328(98)00095-3 Burkart, J. M., Schubiger, M. N., & van Schaik, C. P. (2017). The evolution of general intelligence. 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