Integration of Text Mining with Systems Biology Provides New Insight into the Pathogenesis of Diabetic Neuropathy.

Diabetic neuropathy (DN) is the most common complication of diabetes affecting approximately 60% of all diabetic patients leading to significant mortality, morbidity, and poor quality of life. Though more than 50% of patients with DN develop substantial nerve damage prior to noticeable symptoms, no biomarkers for predicting the onset or progression of DN are currently available. Here we present a biomarker discovery platform integrating literature mining and a systems biology approach to identify potential DN biomarkers. A web-based target identification and functional analysis tool, SciMiner (http://jdrf.neurology.med.umich.edu/SciMiner), was developed that identifies targets using a context specific analysis of MEDLINE abstracts and full texts. A comprehensive list of 1,026 targets from diabetes and reactive oxygen species (ROS) related literature was compiled by SciMiner. The expression levels of nine genes, selected from the over-represented ROS-diabetes targets, were measured in the dorsal root ganglia (DRG) of diabetic and non-diabetic DBA/2J mice. Eight genes exhibited significant differential expression and the directions of expression change in six of those genes paralleled enhanced oxidative stress in the DRG, suggesting the involvement of ROS related targets in DN. A microarray analysis was also performed on sural nerve biopsies from two DN patient groups with fast or slow DN progression to identify gene expression profiles related to DN progression. In the fast progressing DN, defense response and inflammatory response related genes were up-regulated, while lipid metabolic process and peroxisome proliferator-activated receptor (PPAR) signaling pathway related genes were down-regulated. We also developed mRNA expression signatures that predict DN progression in humans with a high prediction accuracy. Ridge-regression based predictive models with 14 genes achieved a prediction accuracy of 92% (correct prediction of 11 out of 12 patients). Our results identifying the unique gene signatures of progressive DN and compiling ROS-diabetes targets can facilitate the development of new mechanism-based therapies and predictive biomarkers of DN.Ph.D.BioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77941/1/juhur_1.pd

Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs

Abstract Background Adverse drug reactions (ADRs), also called as drug adverse events (AEs), are reported in the FDA drug labels; however, it is a big challenge to properly retrieve and analyze the ADRs and their potential relationships from textual data. Previously, we identified and ontologically modeled over 240 drugs that can induce peripheral neuropathy through mining public drug-related databases and drug labels. However, the ADR mechanisms of these drugs are still unclear. In this study, we aimed to develop an ontology-based literature mining system to identify ADRs from drug labels and to elucidate potential mechanisms of the neuropathy-inducing drugs (NIDs). Results We developed and applied an ontology-based SciMiner literature mining strategy to mine ADRs from the drug labels provided in the Text Analysis Conference (TAC) 2017, which included drug labels for 53 neuropathy-inducing drugs (NIDs). We identified an average of 243 ADRs per NID and constructed an ADR-ADR network, which consists of 29 ADR nodes and 149 edges, including only those ADR-ADR pairs found in at least 50% of NIDs. Comparison to the ADR-ADR network of non-NIDs revealed that the ADRs such as pruritus, pyrexia, thrombocytopenia, nervousness, asthenia, acute lymphocytic leukaemia were highly enriched in the NID network. Our ChEBI-based ontology analysis identified three benzimidazole NIDs (i.e., lansoprazole, omeprazole, and pantoprazole), which were associated with 43 ADRs. Based on ontology-based drug class effect definition, the benzimidazole drug group has a drug class effect on all of these 43 ADRs. Many of these 43 ADRs also exist in the enriched NID ADR network. Our Ontology of Adverse Events (OAE) classification further found that these 43 benzimidazole-related ADRs were distributed in many systems, primarily in behavioral and neurological, digestive, skin, and immune systems. Conclusions Our study demonstrates that ontology-based literature mining and network analysis can efficiently identify and study specific group of drugs and their associated ADRs. Furthermore, our analysis of drug class effects identified 3 benzimidazole drugs sharing 43 ADRs, leading to new hypothesis generation and possible mechanism understanding of drug-induced peripheral neuropathy.https://deepblue.lib.umich.edu/bitstream/2027.42/144217/1/13326_2018_Article_185.pd

The Interaction Network Ontology-Supported Modeling and Mining of Complex Interactions Represented with Multiple Keywords in Biomedical Literature

Background: The Interaction Network Ontology (INO) logically represents biological interactions, pathways, and networks. INO has been demonstrated to be valuable in providing a set of structured ontological terms and associated keywords to support literature mining of gene-gene interactions from biomedical literature. However, previous work using INO focused on single keyword matching, while many interactions are represented with two or more interaction keywords used in combination. Methods: This paper reports our extension of INO to include combinatory patterns of two or more literature mining keywords co-existing in one sentence to represent specific INO interaction classes. Such keyword combinations and related INO interaction type information could be automatically obtained via SPARQL queries, formatted in Excel format, and used in an INO-supported SciMiner, an in-house literature mining program. We studied the gene interaction sentences from the commonly used benchmark Learning Logic in Language (LLL) dataset and one internally generated vaccine-related dataset to identify and analyze interaction types containing multiple keywords. Patterns obtained from the dependency parse trees of the sentences were used to identify the interaction keywords that are related to each other and collectively represent an interaction type. Results: The INO ontology currently has 575 terms including 202 terms under the interaction branch. The relations between the INO interaction types and associated keywords are represented using the INO annotation relations: ‘has literature mining keywords’ and ‘has keyword dependency pattern’. The keyword dependency patterns were generated via running the Stanford Parser to obtain dependency relation types. Out of the 107 interactions in the LLL dataset represented with two-keyword interaction types, 86 were identified by using the direct dependency relations. The LLL dataset contained 34 gene regulation interaction types, each of which associated with multiple keywords. A hierarchical display of these 34 interaction types and their ancestor terms in INO resulted in the identification of specific gene-gene interaction patterns from the LLL dataset. The phenomenon of having multi-keyword interaction types was also frequently observed in the vaccine dataset. Conclusions: By modeling and representing multiple textual keywords for interaction types, the extended INO enabled the identification of complex biological gene-gene interactions represented with multiple keywords

Understanding Nanog’s role in cell differentiation

Background: Pluripotency and differentiation are crucial cellular states for normal development and disease control. They are regulated by intrinsic and extrinsic factors. Master transcription factors, such as Nanog, Sox2, and Oct4, play a critical role in pluripotency, but their function in differentiation is not entirely clear. This study aims to investigate Nanog\u27s role in differentiation using mouse embryonic stem cells as a model. Methods: An integrative analysis was carried out using gene expression and chromatin immunoprecipitation sequencing data to determine the impact of Nanog binding on downstream gene expression. Target genes were identified as those whose expression was altered by Nanog binding, and a GSEA analysis was performed to identify shared biological processes. Additionally, Homer was utilized to conduct motif enrichment analysis at each time point. Results: Within 24 hours of retinoic acid treatment, Nanog is recruited to newly identified sites. These sites are primarily located in intergenic regions and the genes associated with them are mainly involved in development and differentiation, specifically mesodermal and mesenchymal development. Furthermore, these recently identified sites possess enriched motifs, such as LHX1 and FLI1, which play a significant role in mesodermal differentiation, in addition to the known pluripotent and developmental transcription factors. Conclusion: This study provides insight into the complex regulation of pluripotency and differentiation and highlights the potential role of Nanog in regulating mesodermal differentiation. It also suggests that Nanog may prefer mesodermal differentiation through indirect recruitment by factors involved in mesodermal transcription factors. Further research is needed to understand the exact mechanism of Nanog\u27s involvement in differentiation.https://commons.und.edu/grad-posters/1001/thumbnail.jp

Ontology-based Brucella vaccine literature indexing and systematic analysis of gene-vaccine association network

<p>Abstract</p> <p>Background</p> <p>Vaccine literature indexing is poorly performed in PubMed due to limited hierarchy of Medical Subject Headings (MeSH) annotation in the vaccine field. Vaccine Ontology (VO) is a community-based biomedical ontology that represents various vaccines and their relations. SciMiner is an in-house literature mining system that supports literature indexing and gene name tagging. We hypothesize that application of VO in SciMiner will aid vaccine literature indexing and mining of vaccine-gene interaction networks. As a test case, we have examined vaccines for <it>Brucella</it>, the causative agent of brucellosis in humans and animals.</p> <p>Results</p> <p>The VO-based SciMiner (VO-SciMiner) was developed to incorporate a total of 67 <it>Brucella </it>vaccine terms. A set of rules for term expansion of VO terms were learned from training data, consisting of 90 biomedical articles related to <it>Brucella </it>vaccine terms. VO-SciMiner demonstrated high recall (91%) and precision (99%) from testing a separate set of 100 manually selected biomedical articles. VO-SciMiner indexing exhibited superior performance in retrieving <it>Brucella </it>vaccine-related papers over that obtained with MeSH-based PubMed literature search. For example, a VO-SciMiner search of "live attenuated <it>Brucella </it>vaccine" returned 922 hits as of April 20, 2011, while a PubMed search of the same query resulted in only 74 hits. Using the abstracts of 14,947 <it>Brucella</it>-related papers, VO-SciMiner identified 140 <it>Brucella </it>genes associated with <it>Brucella </it>vaccines. These genes included known protective antigens, virulence factors, and genes closely related to <it>Brucella </it>vaccines. These VO-interacting <it>Brucella </it>genes were significantly over-represented in biological functional categories, including metabolite transport and metabolism, replication and repair, cell wall biogenesis, intracellular trafficking and secretion, posttranslational modification, and chaperones. Furthermore, a comprehensive interaction network of <it>Brucella </it>vaccines and genes were identified. The asserted and inferred VO hierarchies provide semantic support for inferring novel knowledge of association of vaccines and genes from the retrieved data. New hypotheses were generated based on this analysis approach.</p> <p>Conclusion</p> <p>VO-SciMiner can be used to improve the efficiency for PubMed searching in the vaccine domain.</p

Extension of the Interaction Network Ontology for literature mining of gene-gene interaction networks from sentences with multiple interaction keywords

Abstract. The Interaction Network Ontology (INO) has been demonstrated to be valuable in providing a structured ontological vocabulary for literature mining of gene-gene interactions from biomedical literature. Our analysis of the Learning Logic in Language (LLL) challenge and vaccine datasets showed that many interactions are signaled with 2 or more interaction keywords used in combination. In this paper, we extend the INO by adding combinatory patterns of two or more literature mining keywords to related INO interaction classes. An INO-based literature mining pipeline was further developed based on SPARQL queries and SciMiner, an in-house literature mining program. The majority of the gene interaction sentences from the LLL and vaccine datasets were found to use multiple keywords to represent interaction types. A comprehensive analysis of the LLL dataset identified 27 gene regulation interaction types each associated with multiple keywords. Special patterns were discovered from the hierarchical structure of these 27 INO types

Stem cell technology for neurodegenerative diseases

Over the past 20 years, stem cell technologies have become an increasingly attractive option to investigate and treat neurodegenerative diseases. In the current review, we discuss the process of extending basic stem cell research into translational therapies for patients suffering from neurodegenerative diseases. We begin with a discussion of the burden of these diseases on society, emphasizing the need for increased attention toward advancing stem cell therapies. We then explain the various types of stem cells utilized in neurodegenerative disease research, and outline important issues to consider in the transition of stem cell therapy from bench to bedside. Finally, we detail the current progress regarding the applications of stem cell therapies to specific neurodegenerative diseases, focusing on Parkinson disease, Huntington disease, Alzheimer disease, amyotrophic lateral sclerosis, and spinal muscular atrophy. With a greater understanding of the capacity of stem cell technologies, there is growing public hope that stem cell therapies will continue to progress into realistic and efficacious treatments for neurodegenerative diseases. Ann Neurol 2011;70: 353–361.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86937/1/22487_ftp.pd

A Network Pharmacology Approach for the Identification of Common Mechanisms of Drug-Induced Peripheral Neuropathy

Drug-induced peripheral neuropathy is a side effect of a variety of therapeutic agents that can affect therapeutic adherence and lead to regimen modifications, impacting patient quality of life. The molecular mechanisms involved in the development of this condition have yet to be completely described in the literature. We used a computational network pharmacology ap-proach to explore the Connectivity Map, a large collection of transcriptional profiles from drug perturbation experiments to identify common genes affected by peripheral neuropathy-inducing drugs. Consensus profiles for 98 of these drugs were used to construct a drug–gene perturbation network. We identified 27 genes significantly associated with neuropathy- inducing drugs. These genes may have a potential role in the action of neuropathy-inducing drugs. Our results suggest that molecular mechanisms, including alterations in mitochondrial function, microtubule and cytoskeleton function, ion chan-nels, transcriptional regulation including epigenetic mechanisms, signal transduction, and wound healing, may play a critical role in drug-induced peripheral neuropathy

PubChemSR: A search and retrieval tool for PubChem

<p>Abstract</p> <p>Background</p> <p>Recent years have seen an explosion in the amount of publicly available chemical and related biological information. A significant step has been the emergence of PubChem, which contains property information for millions of chemical structures, and acts as a repository of compounds and bioassay screening data for the NIH Roadmap. There is a strong need for tools designed for scientists that permit easy download and use of these data. We present one such tool, PubChemSR.</p> <p>Implementation</p> <p>PubChemSR (Search and Retrieve) is a freely available desktop application written for Windows using Microsoft <it>.NET </it>that is designed to assist scientists in search, retrieval and organization of chemical and biological data from the PubChem database. It employs SOAP web services made available by NCBI for extraction of information from PubChem.</p> <p>Results and Discussion</p> <p>The program supports a wide range of searching techniques, including queries based on assay or compound keywords and chemical substructures. Results can be examined individually or downloaded and exported in batch for use in other programs such as Microsoft Excel. We believe that PubChemSR makes it straightforward for researchers to utilize the chemical, biological and screening data available in PubChem. We present several examples of how it can be used.</p