Literature Mining and Ontology based Analysis of Host-Brucella Gene-Gene Interaction Network

Abstract

Brucella is an intracellular bacterium that causes chronic brucellosis in humans and various mammals. The identification of host-Brucella interaction is crucial to understand host immunity against Brucella infection and Brucella pathogenesis against host immune responses. Most of the information about the inter-species interactions between host and Brucella genes is only available in the text of the scientific publications. Many text-mining systems for extracting gene and protein interactions have been proposed. However, only a few of them have been designed by considering the peculiarities of host-pathogen interactions. In this paper, we used a text mining approach for extracting host-Brucella gene-gene interactions from the abstracts of articles in PubMed. The gene-gene interactions here represent the interactions between genes and/or gene products (e.g., proteins). The SciMiner tool, originally designed for detecting mammalian gene/protein names in text, was extended to identify host and Brucella gene/protein names in the abstracts. Next, sentence-level and abstract-level co-occurrence based approaches, as well as sentence-level machine learning based methods, originally designed for extracting intra-species gene interactions, were utilized to extract the interactions among the identified host and Brucella genes. The extracted interactions were manually evaluated. A total of 46 host-Brucella gene interactions were identified and represented as an interaction network. Twenty four of these interactions were identified from sentence-level processing. Twenty two additional interactions were identified when abstract-level processing was performed. The Interaction Network Ontology (INO) was used to represent the identified interaction types at a hierarchical ontology structure. Ontological modeling of specific gene-gene interactions demonstrates that host-pathogen gene-gene interactions occur at experimental conditions which can be ontologically represented. Our results show that the introduced literature mining and ontology-based modeling approach are effective in retrieving and analyzing host-pathogen gene-gene interaction networks.

Keywords: Brucella; Interaction Network Ontology (INO); SciMiner; host and pathogen gene name recognition; host–pathogen interaction extraction; support vector machines (SVM); text mining.

FIGURE 1

Sample host–pathogen interaction describing sentence (Arenas-Gamboa et al., 2008). The pathogen gene is shown in red and the host genes are shown in green.

FIGURE 2

Project design pipeline and workflow.

FIGURE 3

The dependency parse tree of a sample sentence. The tree is generated for the sentence “Furthermore, gap associated with murine IL-12 gene in a DNA vaccine formulation partially protected mice against experimental infection.” from the abstract of (Rosinha et al., 2002). Host and pathogen genes identified by SciMiner are shown in green and red, respectively. The Stanford parser was used to generate the parse tree. advmod, adverb modifier; amod, adjectival modifier; det, determiner; dobj, direct object; nn, noun compound modifier; nsubj, nominal subject; prep_against, preposition against; prep_in, preposition in; prep_with, preposition with; vmod, reduced non-finite verbal modifier.

FIGURE 4

Literature-mined host-Brucella gene–gene interaction results. (A) Venn diagram showing the number of unique host-Brucella interaction gene pairs retrieved and manually verified from sentence-level and abstract-level processing. (B) The literature-mined and manually verified host-Brucella gene–gene interaction network. Host genes are shown in green and Brucella genes are shown in red. Red edges correspond to interactions retrieved from sentence-level processing. Black edges correspond to interactions retrieved from abstract level processing. The more sentences/abstracts describe an interaction between gene pairs the thicker the edge connecting them.

FIGURE 5

The ontology hierarchy of literature mined INO interaction types. In total, six different INO interaction types were identified from this literature mining study. The number of interactions of a specific type is shown in red next to the interaction type. The ‘induction of production’ type is the most common type identified.

FIGURE 6

Ontology modeling of literature-mined host-Brucella interaction types. (A) Ontology modeling of the gene interaction from the sentence “In addition, after in vitro stimulation with rBLS, spleen cells from BLS-IFA-, BLS-Al-, or BLS-MPA-immunized mice proliferated and produced interleukin-2 (IL-2), gamma interferon (IFN-gamma), IL-10, and IL-4, suggesting the induction of a mixed Th1-Th2 response” (Velikovsky et al., 2003). (B) Ontology modeling of the Casp2-wboA gene interaction using the abstract content from the paper (Chen and He, 2009).