Additional file 1: of The Interaction Network Ontology-supported modeling and mining of complex interactions represented with multiple keywords in biomedical literature

Interaction keywords and INO type annotations for the LLL data set. This additional file includes annotations of the LLL dataset with INO multiple keyword interaction types. (XLSX 29 kb

Additional file 2: of Ontology-based literature mining of E. coli vaccine-associated gene interaction networks

A Cytoscape session file containing the E. coli gene-vaccine interaction network and its sub-networks. (CYS 76 kb

Early Gestational Weight Gain Rate and Adverse Pregnancy Outcomes in Korean Women

<div><p>During pregnancy, many women gain excessive weight, which is related to adverse maternal and neonatal outcomes. In this study, we evaluated whether rate of gestational weight gain (RGWG) in early, mid, and late pregnancy is strongly associated with adverse pregnancy outcomes. A retrospective chart review of 2,789 pregnant Korean women was performed. Weights were recorded at the first clinic visit, during the screening test for fetal anomaly, and during the 50g oral glucose challenge test and delivery, to represent early, mid, and late pregnancy, respectively. A multivariate logistic regression analysis was performed to examine the relationship between RGWG and adverse pregnancy outcomes. At early pregnancy, the RGWG was significantly associated with high risk of developing gestational diabetes mellitus (GDM), pregnancy-induced hypertension (PIH), large for gestational age (LGA) infants, macrosomia, and primary cesarean section (P-CS). The RGWG of mid pregnancy was not significantly associated with any adverse pregnancy outcomes. The RGWG at late pregnancy was significantly associated with a lower risk of developing GDM, preterm birth and P-CS, but with a higher risk of developing LGA infants and macrosomia. When the subjects were divided into three groups (Underweight, Normal, and Obese), based on pre-pregnancy body mass index (BMI), the relationship between early RGWG and adverse pregnancy outcomes was significantly different across the three BMI groups. At early pregnancy, RGWG was not significantly associated to adverse pregnancy outcomes for subjects in the Underweight group. In the Normal group, however, early RGWG was significantly associated with GDM, PIH, LGA infants, macrosomia, P-CS, and small for gestational weight (SGA) infants, whereas early RGWG was significantly associated with only a high risk of PIH in the Obese group. The results of our study suggest that early RGWG is significantly associated with various adverse pregnancy outcomes and that proper preemptive management of early weight gain, particularly in pregnant women with a normal or obese pre-pregnancy BMI, is necessary to reduce the risk of developing adverse pregnancy outcomes.</p></div

Systems Pharmacological Analysis of Drugs Inducing Stevens–Johnson Syndrome and Toxic Epidermal Necrolysis

Stevens–Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are serious cutaneous adverse reactions. We mined the approved labels in Drugs@FDA, identified the SJS/TEN list of 259 small molecular drugs and biologics, and conducted systems pharmacological network analyses. Pharmacological network analysis revealed that drugs with treatment-related SJS and/or TEN are pharmacologically diverse and that the largest subnetwork is associated with antiepileptic drugs and their pharmacological targets. Our pharmacological network analysis identified CTNNB1 [catenin (cadherin-associated protein), beta 1, 88 kDa] as a significant intermediator. This protein is involved in maintaining the functional integrity of the epithelium through regulating cell growth and adhesion between cells in various organs, including the skin. Leveraging a publicly accessible genome-wide transcriptional expression database, we found that human leukocyte antigen-related (HLA) genes were significantly perturbed by various SJS/TEN-inducing drugs. Notably, carbamazepine (CBZ) perturbed several HLA genes, among which <i>HLA</i>-<i>DQB1*0201</i> was reportedly shown to be associated with CBZ-induced SJS/TEN in caucasians. In short, systems analysis by leveraging a publicly accessible knowledge base and databases could produce meaningful results for further mechanistic investigation. Our study sheds light on the utility of systems pharmacology analysis for gaining insight into clinical drug toxicity

Risk analysis of developing adverse pregnancy outcomes–pre-pregnancy BMI.

<p>Odd ratios and 95% confidence intervals are shown in the table.</p><p>§: significant P < 0.05; GDM: gestational diabetes mellitus; PIH: pregnancy-induced hypertension; LGA: large for gestational age; SGA: small for gestational age; P-CS: primary caesarean section.</p><p>Risk analysis of developing adverse pregnancy outcomes–pre-pregnancy BMI.</p

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<p>The quintessential biological response to disease is inflammation. It is a driver and an important element in a wide range of pathological states. Pharmacological management of inflammation is therefore central in the clinical setting. Anti-inflammatory drugs modulate specific molecules involved in the inflammatory response; these drugs are traditionally classified as steroidal and non-steroidal drugs. However, the effects of these drugs are rarely limited to their canonical targets, affecting other molecules and altering biological functions with system-wide effects that can lead to the emergence of secondary therapeutic applications or adverse drug reactions (ADRs). In this study, relationships among anti-inflammatory drugs, functional pathways, and ADRs were explored through network models. We integrated structural drug information, experimental anti-inflammatory drug perturbation gene expression profiles obtained from the Connectivity Map and Library of Integrated Network-Based Cellular Signatures, functional pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases, as well as adverse reaction information from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The network models comprise nodes representing anti-inflammatory drugs, functional pathways, and adverse effects. We identified structural and gene perturbation similarities linking anti-inflammatory drugs. Functional pathways were connected to drugs by implementing Gene Set Enrichment Analysis (GSEA). Drugs and adverse effects were connected based on the proportional reporting ratio (PRR) of an adverse effect in response to a given drug. Through these network models, relationships among anti-inflammatory drugs, their functional effects at the pathway level, and their adverse effects were explored. These networks comprise 70 different anti-inflammatory drugs, 462 functional pathways, and 1,175 ADRs. Network-based properties, such as degree, clustering coefficient, and node strength, were used to identify new therapeutic applications within and beyond the anti-inflammatory context, as well as ADR risk for these drugs, helping to select better repurposing candidates. Based on these parameters, we identified naproxen, meloxicam, etodolac, tenoxicam, flufenamic acid, fenoprofen, and nabumetone as candidates for drug repurposing with lower ADR risk. This network-based analysis pipeline provides a novel way to explore the effects of drugs in a therapeutic space.</p

Image6.TIF

<p>The quintessential biological response to disease is inflammation. It is a driver and an important element in a wide range of pathological states. Pharmacological management of inflammation is therefore central in the clinical setting. Anti-inflammatory drugs modulate specific molecules involved in the inflammatory response; these drugs are traditionally classified as steroidal and non-steroidal drugs. However, the effects of these drugs are rarely limited to their canonical targets, affecting other molecules and altering biological functions with system-wide effects that can lead to the emergence of secondary therapeutic applications or adverse drug reactions (ADRs). In this study, relationships among anti-inflammatory drugs, functional pathways, and ADRs were explored through network models. We integrated structural drug information, experimental anti-inflammatory drug perturbation gene expression profiles obtained from the Connectivity Map and Library of Integrated Network-Based Cellular Signatures, functional pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases, as well as adverse reaction information from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The network models comprise nodes representing anti-inflammatory drugs, functional pathways, and adverse effects. We identified structural and gene perturbation similarities linking anti-inflammatory drugs. Functional pathways were connected to drugs by implementing Gene Set Enrichment Analysis (GSEA). Drugs and adverse effects were connected based on the proportional reporting ratio (PRR) of an adverse effect in response to a given drug. Through these network models, relationships among anti-inflammatory drugs, their functional effects at the pathway level, and their adverse effects were explored. These networks comprise 70 different anti-inflammatory drugs, 462 functional pathways, and 1,175 ADRs. Network-based properties, such as degree, clustering coefficient, and node strength, were used to identify new therapeutic applications within and beyond the anti-inflammatory context, as well as ADR risk for these drugs, helping to select better repurposing candidates. Based on these parameters, we identified naproxen, meloxicam, etodolac, tenoxicam, flufenamic acid, fenoprofen, and nabumetone as candidates for drug repurposing with lower ADR risk. This network-based analysis pipeline provides a novel way to explore the effects of drugs in a therapeutic space.</p