miRNA analysis reveals novel dysregulated pathways in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. Its complex pathogenesis and phenotypic heterogeneity hinder therapeutic development and early diagnosis. Altered RNA metabolism is a recurrent pathophysiologic theme, including distinct microRNA (miRNA) profiles in ALS tissues. We profiled miRNAs in accessible biosamples, including skin fibroblasts and whole blood and compared them in age- and sex-matched healthy controls versus ALS participants with and without repeat expansions to chromosome 9 open reading frame 72 (C9orf72; C9-ALS and nonC9-ALS), the most frequent ALS mutation. We identified unique and shared profiles of differential miRNA (DmiRNA) levels in each C9-ALS and nonC9-ALS tissues versus controls. Fibroblast DmiRNAs were validated by quantitative real-time PCR and their target mRNAs by 5-bromouridine and 5-bromouridine-chase sequencing. We also performed pathway analysis to infer biological meaning, revealing anticipated, tissue-specific pathways and pathways previously linked to ALS, as well as novel pathways that could inform future research directions. Overall, we report a comprehensive study of a miRNA profile dataset from C9-ALS and nonC9-ALS participants across two accessible biosamples, providing evidence of dysregulated miRNAs in ALS and possible targets of interest. Distinct miRNA patterns in accessible tissues may also be leveraged to distinguish ALS participants from healthy controls for earlier diagnosis. Future directions may look at potential correlations of miRNA profiles with clinical parameters.

IL-17RA promotes pathologic epithelial inflammation in a mouse model of upper respiratory influenza infection.

The upper respiratory tract (nasopharynx or NP) is the first site of influenza replication, allowing the virus to disseminate to the lower respiratory tract or promoting community transmission. The host response in the NP regulates an intricate balance between viral control and tissue pathology. The hyper-inflammatory responses promote epithelial injury, allowing for increased viral dissemination and susceptibility to secondary bacterial infections. However, the pathologic contributors to influenza upper respiratory tissue pathology are incompletely understood. In this study, we investigated the role of interleukin IL-17 recetor A (IL-17RA) as a modulator of influenza host response and inflammation in the upper respiratory tract. We used a combined experimental approach involving IL-17RA-/- mice and an air-liquid interface (ALI) epithelial culture model to investigate the role of IL-17 response in epithelial inflammation, barrier function, and tissue pathology. Our data show that IL-17RA-/- mice exhibited significantly reduced neutrophilia, epithelial injury, and viral load. The reduced NP inflammation and epithelial injury in IL-17RA-/- mice correlated with increased resistance against co-infection by Streptococcus pneumoniae (Spn). IL-17A treatment, while potentiating the apoptosis of IAV-infected epithelial cells, caused bystander cell death and disrupted the barrier function in ALI epithelial model, supporting the in vivo findings.

Gut microbiome correlates with plasma lipids in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a complex, fatal neurodegenerative disease. Disease pathophysiology is incompletely understood but evidence suggests gut dysbiosis occurs in ALS, linked to impaired gastrointestinal integrity, immune system dysregulation and altered metabolism. Gut microbiome and plasma metabolome have been separately investigated in ALS, but little is known about gut microbe-plasma metabolite correlations, which could identify robust disease biomarkers and potentially shed mechanistic insight. Here, gut microbiome changes were longitudinally profiled in ALS and correlated to plasma metabolome. Gut microbial structure at the phylum level differed in ALS versus control participants, with differential abundance of several distinct genera. Unsupervised clustering of microbe and metabolite levels identified modules, which differed significantly in ALS versus control participants. Network analysis found several prominent amplicon sequence variants strongly linked to a group of metabolites, primarily lipids. Similarly, identifying the features that contributed most to case versus control separation pinpointed several bacteria correlated to metabolites, predominantly lipids. Mendelian randomization indicated possible causality from specific lipids related to fatty acid and acylcarnitine metabolism. Overall, the results suggest ALS cases and controls differ in their gut microbiome, which correlates with plasma metabolites, particularly lipids, through specific genera. These findings have the potential to identify robust disease biomarkers and shed mechanistic insight into ALS.

Respiratory Syncytial Virus Infection Does Not Induce Epithelial-Mesenchymal Transition.

Respiratory syncytial virus (RSV) infection does not cause severe disease in most of us despite suffering from multiple RSV infections during our lives. However, infants, young children, older adults, and immunocompromised patients are unfortunately vulnerable to RSV-associated severe diseases. A recent study suggested that RSV infection causes cell expansion, resulting in bronchial wall thickening in vitro. Whether the virus-induced changes in the lung airway resemble epithelial-mesenchymal transition (EMT) is still unknown. Here, we report that RSV does not induce EMT in three different in vitro lung models: the epithelial A549 cell line, primary normal human bronchial epithelial cells, and pseudostratified airway epithelium. We found that RSV increases the cell surface area and perimeter in the infected airway epithelium, which is distinct from the effects of a potent EMT inducer, transforming growth factor ß1 (TGF-ß1), driving cell elongation-indicative of cell motility. A genome-wide transcriptome analysis revealed that both RSV and TGF-ß1 have distinct modulation patterns of the transcriptome, which suggests that RSV-induced changes are distinct from EMT. IMPORTANCE We have previously shown that RSV infects ciliated cells on the apical side of the lung airway. RSV-induced cytoskeletal inflammation contributes to an uneven increase in the height of the airway epithelium, resembling noncanonical bronchial wall thickening. RSV infection changes epithelial cell morphology by modulating actin-protein 2/3 complex-driven actin polymerization. Therefore, it is prudent to investigate whether RSV-induced cell morphological changes contribute to EMT. Our data indicate that RSV does not induce EMT in at least three different epithelial in vitro models: an epithelial cell line, primary epithelial cells, and pseudostratified bronchial airway epithelium.

COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning.

Rational vaccine design, especially vaccine antigen identification and optimization, is critical to successful and efficient vaccine development against various infectious diseases including coronavirus disease 2019 (COVID-19). In general, computational vaccine design includes three major stages: (i) identification and annotation of experimentally verified gold standard protective antigens through literature mining, (ii) rational vaccine design using reverse vaccinology (RV) and structural vaccinology (SV) and (iii) post-licensure vaccine success and adverse event surveillance and its usage for vaccine design. Protegen is a database of experimentally verified protective antigens, which can be used as gold standard data for rational vaccine design. RV predicts protective antigen targets primarily from genome sequence analysis. SV refines antigens through structural engineering. Recently, RV and SV approaches, with the support of various machine learning methods, have been applied to COVID-19 vaccine design. The analysis of post-licensure vaccine adverse event report data also provides valuable results in terms of vaccine safety and how vaccines should be used or paused. Ontology standardizes and incorporates heterogeneous data and knowledge in a human- and computer-interpretable manner, further supporting machine learning and vaccine design. Future directions on rational vaccine design are discussed.

Mitochondrial Oxidative Stress Regulates FOXP3+ T-Cell Activity and CD4-Mediated Inflammation in Older Adults with Frailty.

In healthy older adults, the immune system generally preserves its response and contributes to a long, healthy lifespan. However, rapid deterioration in immune regulation can lead to chronic inflammation, termed inflammaging, which accelerates pathological aging and diminishes the quality of life in older adults with frailty. A significant limitation in current aging research is the predominant focus on comparisons between young and older populations, often overlooking the differences between healthy older adults and those experiencing pathological aging. Our study elucidates the intricate immunological dynamics of the CD4/Treg axis in frail older adults compared to comparable age-matched healthy older adults. By utilizing publicly available RNA sequencing and single-cell RNA sequencing (scRNAseq) data from peripheral blood mononuclear cells (PBMCs), we identified a specific Treg cell subset and transcriptional landscape contributing to the dysregulation of CD4+ T-cell responses. We explored the molecular mechanisms underpinning Treg dysfunction, revealing that Tregs from frail older adults exhibit reduced mitochondrial protein levels, impairing mitochondrial oxidative phosphorylation. This impairment is driven by the TNF/NF-kappa B pathway, leading to cumulative inflammation. Further, we gained a deeper understanding of the CD4/Treg axis by predicting the effects of gene perturbations on cellular signaling networks. Collectively, these findings highlight the age-related relationship between mitochondrial dysfunction in the CD4/Treg axis and its role in accelerating aging and frailty in older adults. Targeting Treg dysfunction offers a critical basis for developing tailored therapeutic strategies aimed at improving the quality of life in older adults.

Non-invasive in vivo measurements of metabolic alterations in the type 2 diabetic brain by 1 H magnetic resonance spectroscopy.

Type 2 diabetes (T2D) is a complex chronic metabolic disorder characterized by hyperglycemia because of insulin resistance. Diabetes with chronic hyperglycemia may alter brain metabolism, including brain glucose and neurotransmitter levels; however, detailed, longitudinal studies of metabolic alterations in T2D are lacking. To shed insight, here, we characterized the consequences of poorly controlled hyperglycemia on neurochemical profiles that reflect metabolic alterations of the brain in both humans and animal models of T2D. Using in vivo 1 H magnetic resonance spectroscopy, we quantified 12 metabolites cross-sectionally in T2D patients and 20 metabolites longitudinally in T2D db/db mice versus db+ controls. We found significantly elevated brain glucose (91%, p < 0.001), taurine (22%, p = 0.02), glucose+taurine (56%, p < 0.001), myo-inositol (12%, p = 0.02), and choline-containing compounds (10%, p = 0.01) in T2D patients versus age- and sex-matched controls, findings consistent with measures in T2D db/db versus control db+ littermates. In mice, hippocampal and striatal neurochemical alterations in brain glucose, ascorbate, creatine, phosphocreatine, γ-aminobutyric acid, glutamate, glutamine, glutathione, glycerophosphoryl-choline, lactate, myo-inositol, and taurine persisted in db/db mice with chronic disease progression from 16 to 48 weeks of age, which were distinct from control db+ mice. Overall, our study demonstrates the utility of 1 H magnetic resonance spectroscopy as a non-invasive tool for characterizing and monitoring brain metabolic changes with T2D progression.

Single-cell RNA-seq uncovers novel metabolic functions of Schwann cells beyond myelination.

Schwann cells (SCs) support peripheral nerves under homeostatic conditions, independent of myelination, and contribute to damage in prediabetic peripheral neuropathy (PN). Here, we used single-cell RNA sequencing to characterize the transcriptional profiles and intercellular communication of SCs in the nerve microenvironment using the high-fat diet-fed mouse, which mimics human prediabetes and neuropathy. We identified four major SC clusters, myelinating, nonmyelinating, immature, and repair in healthy and neuropathic nerves, in addition to a distinct cluster of nerve macrophages. Myelinating SCs acquired a unique transcriptional profile, beyond myelination, in response to metabolic stress. Mapping SC intercellular communication identified a shift in communication, centered on immune response and trophic support pathways, which primarily impacted nonmyelinating SCs. Validation analyses revealed that neuropathic SCs become pro-inflammatory and insulin resistant under prediabetic conditions. Overall, our study offers a unique resource for interrogating SC function, communication, and signaling in nerve pathophysiology to help inform SC-specific therapies.

Cellular Heterogeneity and Molecular Reprogramming of the Host Response during Influenza Acute Lung Injury.

An exuberant host response contributes to influenza A virus (IAV) (or influenza)-mediated lung injury. However, despite significant information on the host response to IAV, the cellular framework and molecular interactions that dictate the development of acute injury in IAV-infected lungs remain incompletely understood. We performed an unbiased single-cell RNA sequencing (scRNAseq) analysis to examine the cellular heterogeneity and regulation of host responses in the IAV model of acute lung injury. At the cellular level, IAV infection promoted the overwhelming recruitment of monocytes that exhibited the cell differentiation trajectory to monocyte-derived macrophages. Together, monocytes and monocyte-derived myeloid cells constituted over 50% of the total immune cells in IAV-infected lungs. In contrast, IAV infection resulted in a significant loss of nonhematopoietic cells. Molecularly, our data show the multidimensional cell-cell communication dynamics of interferon and chemokine signaling between immune and nonimmune cells and the cell-specific molecular pathways regulating the host responses during IAV-induced lung injury. Our data provide a foundation for further exploring the mechanistic association of the IAV host response with acute lung injury. IMPORTANCE A dysregulated host response develops acute lung injury during IAV infection. However, the pathological immune mechanism(s) associated with acute lung injury during IAV infection is yet to be elucidated. In this study, we performed scRNAseq to examine the dynamics of host responses during the peak of IAV-mediated lung injury. At the cellular level, our data reveal significant myelopoiesis predominated by monocytes and macrophages and the simultaneous disruption of the nonhematopoietic cell framework, crucial for regulating inflammation and barrier integrity in IAV-infected lungs. Molecularly, we observed a complex cellular network involving cell-cell communications and a number of unique regulons dictating the outcome of interferon and chemokine responses during peak lung injury. Our data present a unique atlas of cellular changes and the regulation of global and cell-specific host responses during IAV infection. We expect that this information will open new avenues to identify targets for therapeutic intervention against IAV lung injury.

Metabolomics identifies shared lipid pathways in independent amyotrophic lateral sclerosis cohorts.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease lacking effective treatments. This is due, in part, to a complex and incompletely understood pathophysiology. To shed light, we conducted untargeted metabolomics on plasma from two independent cross-sectional ALS cohorts versus control participants to identify recurrent dysregulated metabolic pathways. Untargeted metabolomics was performed on plasma from two ALS cohorts (cohort 1, n = 125; cohort 2, n = 225) and healthy controls (cohort 1, n = 71; cohort 2, n = 104). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon, adjusted logistic regression and partial least squares-discriminant analysis, while group lasso explored sub-pathway level differences. Adjustment parameters included age, sex and body mass index. Metabolomics pathway enrichment analysis was performed on metabolites selected using the above methods. Additionally, we conducted a sex sensitivity analysis due to sex imbalance in the cohort 2 control arm. Finally, a data-driven approach, differential network enrichment analysis (DNEA), was performed on a combined dataset to further identify important ALS metabolic pathways. Cohort 2 ALS participants were slightly older than the controls (64.0 versus 62.0 years, P = 0.009). Cohort 2 controls were over-represented in females (68%, P < 0.001). The most concordant cohort 1 and 2 pathways centred heavily on lipid sub-pathways, including complex and signalling lipid species and metabolic intermediates. There were differences in sub-pathways that were enriched in ALS females versus males, including in lipid sub-pathways. Finally, DNEA of the merged metabolite dataset of both ALS and control cohorts identified nine significant subnetworks; three centred on lipids and two encompassed a range of sub-pathways. In our analysis, we saw consistent and important shared metabolic sub-pathways in both ALS cohorts, particularly in lipids, further supporting their importance as ALS pathomechanisms and therapeutics targets.

Gene expression profiles of diabetic kidney disease and neuropathy in eNOS knockout mice: Predictors of pathology and RAS blockade effects.

Diabetic kidney disease (DKD) and diabetic peripheral neuropathy (DPN) are two common diabetic complications. However, their pathogenesis remains elusive and current therapies are only modestly effective. We evaluated genome-wide expression to identify pathways involved in DKD and DPN progression in db/db eNOS-/- mice receiving renin-angiotensin-aldosterone system (RAS)-blocking drugs to mimic the current standard of care for DKD patients. Diabetes and eNOS deletion worsened DKD, which improved with RAS treatment. Diabetes also induced DPN, which was not affected by eNOS deletion or RAS blockade. Given the multiple factors affecting DKD and the graded differences in disease severity across mouse groups, an automatic data analysis method, SOM, or self-organizing map was used to elucidate glomerular transcriptional changes associated with DKD, whereas pairwise bioinformatic analysis was used for DPN. These analyses revealed that enhanced gene expression in several pro-inflammatory networks and reduced expression of development genes correlated with worsening DKD. Although RAS treatment ameliorated the nephropathy phenotype, it did not alter the more abnormal gene expression changes in kidney. Moreover, RAS exacerbated expression of genes related to inflammation and oxidant generation in peripheral nerves. The graded increase in inflammatory gene expression and decrease in development gene expression with DKD progression underline the potentially important role of these pathways in DKD pathogenesis. Since RAS blockers worsened this gene expression pattern in both DKD and DPN, it may partly explain the inadequate therapeutic efficacy of such blockers.

Obesity-induced neuroinflammation and cognitive impairment in young adult versus middle-aged mice.

BACKGROUND: Obesity rates are increasing worldwide. Obesity leads to many complications, including predisposing individuals to the development of cognitive impairment as they age. Immune dysregulation, including inflammaging (e.g., increased circulating cytokines) and immunosenescence (declining immune system function), commonly occur in obesity and aging and may impact cognitive impairment. As such, immune system changes across the lifespan may impact the effects of obesity on neuroinflammation and associated cognitive impairment. However, the role of age in obesity-induced neuroinflammation and cognitive impairment is unclear. To further define this putative relationship, the current study examined metabolic and inflammatory profiles, along with cognitive changes using a high-fat diet (HFD) mouse model of obesity. RESULTS: First, HFD promoted age-related changes in hippocampal gene expression. Given this early HFD-induced aging phenotype, we fed HFD to young adult and middle-aged mice to determine the effect of age on inflammatory responses, metabolic profile, and cognitive function. As anticipated, HFD caused a dysmetabolic phenotype in both age groups. However, older age exacerbated HFD cognitive and neuroinflammatory changes, with a bi-directional regulation of hippocampal inflammatory gene expression. CONCLUSIONS: Collectively, these data indicate that HFD promotes an early aging phenotype in the brain, which is suggestive of inflammaging and immunosenescence. Furthermore, age significantly compounded the impact of HFD on cognitive outcomes and on the regulation of neuroinflammatory programs in the brain.

Anxiety-like behavior and intestinal microbiota changes as strain-and sex-dependent sequelae of mild food allergy in mouse models of cow's milk allergy.

A number of studies have reported comorbidity of food allergies with various neuropsychiatric disorders, such as anxiety, depression, attention-deficit hyperactivity disorder, and autism. However, inconsistent results across clinical studies have left the association between food allergy and behavioral disorders inconclusive. We postulated that the heterogeneities in genetic background among allergic cohorts affect symptom presentation and severity of food allergy, introducing bias in patient selection criteria toward individuals with overt physical reactions. To understand the influence of genetic background on food allergy symptoms and behavioral changes beyond anaphylaxis, we generated mouse models with mild cow's milk allergy by sensitizing male and female C57BL/6J and BALB/cJ mice to a bovine whey protein, ß-lactoglobulin (BLG; Bos d 5). We compared strain- and sex-dependent differences in their immediate physical reactions to BLG challenge as well as anxiety-like behavior one day after the challenge. While reactions to the allergen challenge were either absent or mild for all groups, a greater number of BLG-sensitized BALB/cJ mice presented visible symptoms and hypothermia compared to C57BL/6J mice. Interestingly, male mice of both strains displayed anxiety-like behavior on an elevated zero maze without exhibiting cognitive impairment with the cross maze test. Further characterization of plasma cytokines/chemokines and fecal microbiota also differentiated strain- and sex-dependent effects of BLG sensitization on immune-mediator levels and bacterial populations, respectively. These results demonstrated that the genetic variables in mouse models of milk allergy influenced immediate physical reactions to the allergen, manifestation of anxiety-like behavior, levels of immune responses, and population shift in gut microbiota. Thus, stratification of allergic cohorts by their symptom presentations and severity may strengthen the link between food allergy and behavioral disorders and identify a population(s) with specific genetic background that have increased susceptibility to allergy-associated behavioral disorders.

Predictability of Macrosomic Birth based on Maternal Factors and Fetal Aneuploidy Screening Biochemical Markers in Hyperglycemic Mothers.

Background: Macrosomic birth weight has been implicated as a significant risk factor for developing various adult metabolic diseases such as diabetes mellitus and coronary heart diseases; it has also been associated with higher incidences of complicated births. This study aimed to examine the predictability of macrosomic births in hyperglycemic pregnant women using maternal clinical characteristics and serum biomarkers of aneuploidy screening performed in the first half of pregnancy. Methods: A retrospective observational study was performed on a cohort of 1,668 pregnant women who 1) had positive outcomes after undergoing 50-g oral glucose challenge test (OGCT) at two university-based hospitals and 2) underwent any one of the following maternal biomarker screening tests for fetal aneuploidy: triple test, quadruple test, and integrated test. Logistic regression-based models for predicting macrosomic births using maternal characteristics and serum biomarkers were developed and evaluated for prediction power. A nomogram, which is a graphical display of the best predictable model, was then generated. Results: The study cohort included 157 macrosomic birth cases defined as birth weight ≥3,820 g, which was equivalent to the top 10 percentile of the modeling cohort. Three primary models solely based on serum biomarkers achieved area under curves (AUCs) of 0.55-0.62. Expanded models, including maternal demographic and clinical factors, demonstrated an improved performance by 25% (AUCs, 0.69-0.73). Conclusion: Our prediction models will help to identify pregnancies with an elevated risk of macrosomic births in hyperglycemic mothers using maternal clinical factors and serum markers from routine antenatal screening tests. Prediction of macrosomic birth at mid-pregnancy may allow customized antenatal care to reduce the risk of macrosomic births.

Untargeted metabolomics yields insight into ALS disease mechanisms.

OBJECTIVE: To identify dysregulated metabolic pathways in amyotrophic lateral sclerosis (ALS) versus control participants through untargeted metabolomics. METHODS: Untargeted metabolomics was performed on plasma from ALS participants (n=125) around 6.8 months after diagnosis and healthy controls (n=71). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon rank-sum tests, adjusted logistic regression and partial least squares-discriminant analysis (PLS-DA), while group lasso explored sub-pathway-level differences. Adjustment parameters included sex, age and body mass index (BMI). Metabolomics pathway enrichment analysis was performed on metabolites selected by the above methods. Finally, machine learning classification algorithms applied to group lasso-selected metabolites were evaluated for classifying case status. RESULTS: There were no group differences in sex, age and BMI. Significant metabolites selected were 303 by Wilcoxon, 300 by logistic regression, 295 by PLS-DA and 259 by group lasso, corresponding to 11, 13, 12 and 22 enriched sub-pathways, respectively. 'Benzoate metabolism', 'ceramides', 'creatine metabolism', 'fatty acid metabolism (acyl carnitine, polyunsaturated)' and 'hexosylceramides' sub-pathways were enriched by all methods, and 'sphingomyelins' by all but Wilcoxon, indicating these pathways significantly associate with ALS. Finally, machine learning prediction of ALS cases using group lasso-selected metabolites achieved the best performance by regularised logistic regression with elastic net regularisation, with an area under the curve of 0.98 and specificity of 83%. CONCLUSION: In our analysis, ALS led to significant metabolic pathway alterations, which had correlations to known ALS pathomechanisms in the basic and clinical literature, and may represent important targets for future ALS therapeutics.

A 2018 workshop: vaccine and drug ontology studies (VDOS 2018).

This Editorial first introduces the background of the vaccine and drug relations and how biomedical terminologies and ontologies have been used to support their studies. The history of the seven workshops, initially named VDOSME, and then named VDOS, is also summarized and introduced. Then the 7th International Workshop on Vaccine and Drug Ontology Studies (VDOS 2018), held on August 10th, 2018, Corvallis, Oregon, USA, is introduced in detail. These VDOS workshops have greatly supported the development, applications, and discussion of vaccine- and drug-related terminology and drug studies.

Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels.

BACKGROUND: Use of medication can cause adverse drug reactions (ADRs), unwanted or unexpected events, which are a major safety concern. Drug labels, or prescribing information or package inserts, describe ADRs. Therefore, systematically identifying ADR information from drug labels is critical in multiple aspects; however, this task is challenging due to the nature of the natural language of drug labels. RESULTS: In this paper, we present a machine learning- and rule-based system for the identification of ADR entity mentions in the text of drug labels and their normalization through the Medical Dictionary for Regulatory Activities (MedDRA) dictionary. The machine learning approach is based on a recently proposed deep learning architecture, which integrates bi-directional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), and Conditional Random Fields (CRF) for entity recognition. The rule-based approach, used for normalizing the identified ADR mentions to MedDRA terms, is based on an extension of our in-house text-mining system, SciMiner. We evaluated our system on the Text Analysis Conference (TAC) Adverse Drug Reaction 2017 challenge test data set, consisting of 200 manually curated US FDA drug labels. Our ML-based system achieved 77.0% F1 score on the task of ADR mention recognition and 82.6% micro-averaged F1 score on the task of ADR normalization, while rule-based system achieved 67.4 and 77.6% F1 scores, respectively. CONCLUSION: Our study demonstrates that a system composed of a deep learning architecture for entity recognition and a rule-based model for entity normalization is a promising approach for ADR extraction from drug labels.

Network-Based Assessment of Adverse Drug Reaction Risk in Polypharmacy Using High-Throughput Screening Data.

The risk of adverse drug reactions increases in a polypharmacology setting. High-throughput drug screening with transcriptomics applied to human cells has shown that drugs have effects on several molecular pathways, and these affected pathways may be predictive proxy for adverse drug reactions. Depending on the way that different drugs may contribute to adverse drug reactions, different options may exist in the clinical setting. Here, we formulate a network framework to integrate the relationships between drugs, biological functions, and adverse drug reactions based on the high-throughput drug perturbation data from the Library of Integrated Network-Based Cellular Signatures (LINCS) project. We present network-based parameters that indicate whether a given reaction may be related to the effect of a single drug or to the combination of several drugs, as well as the relative risk of adverse drug reaction manifestation given a certain drug combination.

Genetic Architecture of Group A Streptococcal Necrotizing Soft Tissue Infections in the Mouse.

Host genetic variations play an important role in several pathogenic diseases, and we have previously provided strong evidences that these genetic variations contribute significantly to differences in susceptibility and clinical outcomes of invasive Group A Streptococcus (GAS) infections, including sepsis and necrotizing soft tissue infections (NSTIs). Our initial studies with conventional mouse strains revealed that host genetic variations and sex differences play an important role in orchestrating the severity, susceptibility and outcomes of NSTIs. To understand the complex genetic architecture of NSTIs, we utilized an unbiased, forward systems genetics approach in an advanced recombinant inbred (ARI) panel of mouse strains (BXD). Through this approach, we uncovered interactions between host genetics, and other non-genetic cofactors including sex, age and body weight in determining susceptibility to NSTIs. We mapped three NSTIs-associated phenotypic traits (i.e., survival, percent weight change, and lesion size) to underlying host genetic variations by using the WebQTL tool, and identified four NSTIs-associated quantitative genetic loci (QTL) for survival on mouse chromosome (Chr) 2, for weight change on Chr 7, and for lesion size on Chr 6 and 18 respectively. These QTL harbor several polymorphic genes. Identification of multiple QTL highlighted the complexity of the host-pathogen interactions involved in NSTI pathogenesis. We then analyzed and rank-ordered host candidate genes in these QTL by using the QTLminer tool and then developed a list of 375 candidate genes on the basis of annotation data and biological relevance to NSTIs. Further differential expression analyses revealed 125 genes to be significantly differentially regulated in susceptible strains compared to their uninfected controls. Several of these genes are involved in innate immunity, inflammatory response, cell growth, development and proliferation, and apoptosis. Additional network analyses using ingenuity pathway analysis (IPA) of these 125 genes revealed interleukin-1 beta network as key network involved in modulating the differential susceptibility to GAS NSTIs.