Mac-1 Regulates IL-13 Activity in Macrophages by Directly Interacting with IL-13Rα1.

Mac-1 exhibits a unique inhibitory activity toward IL-13-induced JAK/STAT activation and thereby regulates macrophage to foam cell transformation. However, the underlying molecular mechanism is unknown. In this study, we report the identification of IL-13Rα1, a component of the IL-13 receptor (IL-13R), as a novel ligand of integrin Mac-1, using a co-evolution-based algorithm. Biochemical analyses demonstrated that recombinant IL-13Rα1 binds Mac-1 in a purified system and supports Mac-1-mediated cell adhesion. Co-immunoprecipitation experiments revealed that endogenous Mac-1 forms a complex with IL-13Rα1 in solution, and confocal fluorescence microscopy demonstrated that these two receptors co-localize with each other on the surface of macrophages. Moreover, we found that genetic inactivation of Mac-1 promotes IL-13-induced JAK/STAT activation in macrophages, resulting in enhanced polarization along the alternative activation pathway. Importantly, we observed that Mac-1(-/-) macrophages exhibit increased expression of foam cell differentiation markers including 15-lipoxygenase and lectin-type oxidized LDL receptor-1 both in vitro and in vivo. Indeed, we found that Mac-1(-/-)LDLR(-/-) mice develop significantly more foam cells than control LDLR(-/-) mice, using an in vivo model of foam cell formation. Together, our data establish for the first time a molecular mechanism by which Mac-1 regulates the signaling activity of IL-13 in macrophages. This newly identified IL-13Rα1/Mac-1-dependent pathway may offer novel targets for therapeutic intervention in the future.

Toward an automatic method for extracting cancer- and other disease-related point mutations from the biomedical literature.

MOTIVATION: A major goal of biomedical research in personalized medicine is to find relationships between mutations and their corresponding disease phenotypes. However, most of the disease-related mutational data are currently buried in the biomedical literature in textual form and lack the necessary structure to allow easy retrieval and visualization. We introduce a high-throughput computational method for the identification of relevant disease mutations in PubMed abstracts applied to prostate (PCa) and breast cancer (BCa) mutations. RESULTS: We developed the extractor of mutations (EMU) tool to identify mutations and their associated genes. We benchmarked EMU against MutationFinder--a tool to extract point mutations from text. Our results show that both methods achieve comparable performance on two manually curated datasets. We also benchmarked EMU's performance for extracting the complete mutational information and phenotype. Remarkably, we show that one of the steps in our approach, a filter based on sequence analysis, increases the precision for that task from 0.34 to 0.59 (PCa) and from 0.39 to 0.61 (BCa). We also show that this high-throughput approach can be extended to other diseases. DISCUSSION: Our method improves the current status of disease-mutation databases by significantly increasing the number of annotated mutations. We found 51 and 128 mutations manually verified to be related to PCa and Bca, respectively, that are not currently annotated for these cancer types in the OMIM or Swiss-Prot databases. EMU's retrieval performance represents a 2-fold improvement in the number of annotated mutations for PCa and BCa. We further show that our method can benefit from full-text analysis once there is an increase in Open Access availability of full-text articles. AVAILABILITY: Freely available at: http://bioinf.umbc.edu/EMU/ftp.

A mutation-centric approach to identifying pharmacogenomic relations in text.

OBJECTIVES: To explore the notion of mutation-centric pharmacogenomic relation extraction and to evaluate our approach against reference pharmacogenomic relations. METHODS: From a corpus of MEDLINE abstracts relevant to genetic variation, we identify co-occurrences between drug mentions extracted using MetaMap and RxNorm, and genetic variants extracted by EMU. The recall of our approach is evaluated against reference relations curated manually in PharmGKB. We also reviewed a random sample of 180 relations in order to evaluate its precision. RESULTS: One crucial aspect of our strategy is the use of biological knowledge for identifying specific genetic variants in text, not simply gene mentions. On the 104 reference abstracts from PharmGKB, the recall of our mutation-centric approach is 33-46%. Applied to 282,000 abstracts from MEDLINE, our approach identifies pharmacogenomic relations in 4534 abstracts, with a precision of 65%. CONCLUSIONS: Compared to a relation-centric approach, our mutation-centric approach shows similar recall, but slightly lower precision. We show that both approaches have limited overlap in their results, but are complementary and can be used in combination. Rather than a solution for the automatic curation of pharmacogenomic knowledge, we see these high-throughput approaches as tools to assist biocurators in the identification of pharmacogenomic relations of interest from the published literature. This investigation also identified three challenging aspects of the extraction of pharmacogenomic relations, namely processing full-text articles, sequence validation of DNA variants and resolution of genetic variants to reference databases, such as dbSNP.

Hybrid curation of gene-mutation relations combining automated extraction and crowdsourcing.

BACKGROUND: This article describes capture of biological information using a hybrid approach that combines natural language processing to extract biological entities and crowdsourcing with annotators recruited via Amazon Mechanical Turk to judge correctness of candidate biological relations. These techniques were applied to extract gene- mutation relations from biomedical abstracts with the goal of supporting production scale capture of gene-mutation-disease findings as an open source resource for personalized medicine. RESULTS: The hybrid system could be configured to provide good performance for gene-mutation extraction (precision ∼82%; recall ∼70% against an expert-generated gold standard) at a cost of $0.76 per abstract. This demonstrates that crowd labor platforms such as Amazon Mechanical Turk can be used to recruit quality annotators, even in an application requiring subject matter expertise; aggregated Turker judgments for gene-mutation relations exceeded 90% accuracy. Over half of the precision errors were due to mismatches against the gold standard hidden from annotator view (e.g., incorrect EntrezGene identifier or incorrect mutation position extracted), or incomplete task instructions (e.g., the need to exclude nonhuman mutations). CONCLUSIONS: The hybrid curation model provides a readily scalable cost-effective approach to curation, particularly if coupled with expert human review to filter precision errors. We plan to generalize the framework and make it available as open source software. DATABASE URL: http://www.mitre.org/publications/technical-papers/hybrid-curation-of-gene-mutation-relations-combining-automated.

Towards precision medicine: advances in computational approaches for the analysis of human variants.

Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.

Translating Mendelian and complex inheritance of Alzheimer's disease genes for predicting unique personal genome variants.

OBJECTIVE: Although trait-associated genes identified as complex versus single-gene inheritance differ substantially in odds ratio, the authors nonetheless posit that their mechanistic concordance can reveal fundamental properties of the genetic architecture, allowing the automated interpretation of unique polymorphisms within a personal genome. MATERIALS AND METHODS: An analytical method, SPADE-gen, spanning three biological scales was developed to demonstrate the mechanistic concordance between Mendelian and complex inheritance of Alzheimer's disease (AD) genes: biological functions (BP), protein interaction modeling, and protein domain implicated in the disease-associated polymorphism. RESULTS: Among Gene Ontology (GO) biological processes (BP) enriched at a false detection rate <5% in 15 AD genes of Mendelian inheritance (Online Mendelian Inheritance in Man) and independently in those of complex inheritance (25 host genes of intragenic AD single-nucleotide polymorphisms confirmed in genome-wide association studies), 16 overlapped (empirical p=0.007) and 45 were similar (empirical p<0.009; information theory). SPAN network modeling extended the canonical pathway of AD (KEGG) with 26 new protein interactions (empirical p<0.0001). DISCUSSION: The study prioritized new AD-associated biological mechanisms and focused the analysis on previously unreported interactions associated with the biological processes of polymorphisms that affect specific protein domains within characterized AD genes and their direct interactors using (1) concordant GO-BP and (2) domain interactions within STRING protein-protein interactions corresponding to the genomic location of the AD polymorphism (eg, EPHA1, APOE, and CD2AP). CONCLUSION: These results are in line with unique-event polymorphism theory, indicating how disease-associated polymorphisms of Mendelian or complex inheritance relate genetically to those observed as 'unique personal variants'. They also provide insight for identifying novel targets, for repositioning drugs, and for personal therapeutics.