Pathway Analysis Tools Research Articles

Metabonomic Profile and Signaling Pathway Prediction of Depression-Associated Suicidal Behavior.

Suicide is the most severe consequence of depression which has become a leading cause of disability and a global disease burden. Recent evidence indicates a central role of small molecules in the pathogenesis of depression and associated suicidal behaviors. However, there lacks a systemic exploration of small molecules in the development of depression-associated suicide, and it remains unclear how they affect an individual’s behavior. In order to compare the metabonomic profiles between drug-naïve patients with depression-associated suicidal behaviors and healthy individuals, we conducted a systemic database search for studies of metabolic characteristics in depression-associated suicidal behavior. Manual data curation and statistical analysis and integration were performed in Excel. We further performed an enrichment analysis of signaling pathway prediction using the Reactome Pathway Analysis tool. We have identified 17 metabolites that expressed differently between drug-naïve patients with depression-associated suicidal behaviors and healthy controls. We have integrated these metabolites into biological signaling pathways and provided a visualized signaling network in depressed suicidal patients. We have revealed that “transport of small molecules”, “disease”, “metabolism” and “metabolism of proteins” were the most relevant signaling sections, among which “transport of inorganic cations/anions and amino acids/oligopeptides”, “SLC-mediated transmembrane transport”, and “metabolism of amino acids and derivatives” should be further studied to elucidate their potential pathogenic mechanism in the development of depression and associated suicidal behavior. In conclusion, our findings of these 17 metabolites and associated signaling pathways could provide an insight into the molecular pathogenesis of depression-associated suicidal behavior and potential targets for new drug inventions.

Rapid action of mechanism investigation of Yixin Ningshen tablet in treating depression by combinatorial use of systems biology and bioinformatics tools

Ethnopharmacological relevanceYixin Ningshen tablet is a CFDA-approved TCM formula for treating depression clinically. However, little is known about its active compounds and related potential target proteins, so far, no researches have been performed to investigate its mechanism of action for the treatment of depression. Aim of the studyHere we develop an original bioinformatics pipeline composed of text mining tools, database querying and systems biology combinatorial analysis, which is applied to rapidly explore the mechanism of action of Yixin Ningshen tablet in treating depression. Materials and methodsText mining and database query were applied to identify active compounds in Yixin Ningshen tablet for the treatment of depression. Then SwissTargetPrediction was used to predict their potential target proteins. PubMed was retrieved to summarize known depression related systems biology results. Ingenuity Pathway Analysis (IPA) tools and STRING were applied to construct a compound-target protein-gene protein-differential protein-differential metabolite network with the integration of compound-target interaction and systems biology results, as well as enrich the target proteins related pathways. ChEMBL and CDOCKER were used to validate the compound-target interactions. Results62 active compounds and their 286 potential target proteins were identified in Yixin Ningshen tablet for the treatment of depression. The construction of compound-target protein-gene protein-differential protein-differential metabolite network shrinked the number of potential target proteins from 286 to 133. Pathway enrichment analysis of target proteins indicated that Neuroactive ligand-receptor interaction, Calcium signaling pathway, Serotonergic synapse, cAMP signaling pathway and Gap junction were the common primary pathways regulated by both Yixin Ningshen Tablet and anti-depressant drugs, and MAPK, Relaxin, AGE-RAGE, Estrogen, HIF-1, Jak-STAT signaling pathway, Endocrine resistance, Arachidonic acid metabolism and Regulation of actin cytoskeleton were the specifically main pathways regulated by Yixin Ningshen tablet for the treatment of depression. Further validations based on references and molecular docking results demonstrated that Yixin Ningshen tablet could primarily target MAPT, CHRM1 and DRD1, thus regulating serotonergic neurons, cholinergic transmission, norepinephrine and dopamine reuptake for the treatment of depression. ConclusionsThis study displays the power of extensive mining of public data and bioinformatical repositories to provide answers for a specific pharmacological question. It furthermore demonstrates how the usage of such a combinatorial approach is advantageous for the biologist in terms of experimentation time and costs.

High Altitude Hypoxia Induces Cellular Immaturity of Pulmonary Arteries in the Fetal Lamb: Assessment of Protein Biomarkers

Pulmonary Arterial Hypertension (PAH) in newborns can be mediated by intrauterine hypoxia and which can cause significant aberrations on vascular structure and function. Previous evidence suggests that long term hypoxic stress can induce phenotypic modifications to various vascular beds, including those of the lung. We are now seeking to interrogate the influence of intrauterine hypoxia on pulmonary vascular phenotypes and to test the hypothesis that intrauterine hypoxia causes the vasculature to shift from a mature to an immature phenotype. This was evaluated by examining protein expression using proteomic analyses on pulmonary arteries isolated from near term fetal lambs that were exposed to intrauterine high altitude hypoxia (3801 m) for the latter 110+ days of pregnancy or those gestated near sea level. Protein networks were created via Ingenuity Pathway Analysis (IPA) and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Intrauterine hypoxia caused a number of significant changes including reducing the expression of smooth muscle myosin, fibronectin, and collagen along with upregulation of proteins involved in nucleotide synthesis, mitosis/cell proliferation, as well as cell adhesion and migration. The observed changes are consistent with a hypoxia‐induced phenotypic shift towards cells being more synthetic, proliferative, and migratory. Such findings provide insight into the mechanistic underpinnings related to the development of pulmonary hypertension due to gestational hypoxia. This work opens new avenues of investigation to determine common signaling pathways affected by intrauterine hypoxia with the aim of discovering the fundamental mechanisms driving hypoxia‐induced remodeling and senescence.Support or Funding InformationSupported in part by NIH grants HD083132 (LZ), HD098477 (SMW), RA was a Apprenticeship Bridge to College summer research fellow through the LLU Center for Health Disparities Research with funding from P20MD006988 (M. DeLeon) from the National Institute of Health Disparities and Minority Health of the National Institutes of Health.

P22 Micro-RNA enriched pathway impact analysis applied to synovial RNA-seq in early rheumatoid arthritis identifies response prediction pathways

Abstract Background We use a new pathway analysis tool MITHrIL (Mirna enriched paTHway Impact anaLysis) to analyse RNA-seq patterns in synovial biopsies from patients with rheumatoid arthritis from the Pathobiology of Early Arthritis Cohort (PEAC). MITHrIL augments pathways with missing regulatory elements, such as microRNAs, and their interactions with genes to enhance quantification of immunological pathway activation in bulk RNA-seq. MITHrIL pathways were compared with three major pathotypes (lympho-myeloid, diffuse myeloid and pauci-immune fibroid) in early treatment-naïve RA patients and responders vs non-responders to methotrexate-based DMARD regimens. Methods A differential gene expression analysis is performed on the PEAC observational cohort. The Log-Fold Changes retrieved from the pairwise comparisons between responders/non responders and across different pathotypes are used as input to the MITHrIL software. Using the KEGG biological pathways, MITHrIL proportionally spreads the known differential perturbation of a gene to the downstream nodes in the pathway. In so doing, a perturbation factor is assigned to each gene and, as a result, a list of differentially altered pathways is returned together with the corresponding statistical significance (p-values). Particular attention is given to immunological pathways. We show a list of differentially altered pathways for each comparison and provide specific insight to those more characterising pathways. Results The results show coherence with the previous findings providing greater granularity on how the gene level alterations can propagate through biological pathways. In particular, we found different gene expression levels in the pairwise comparisons across pathotypes highlighting different perturbations in the following pathways: chemokine signalling (adjusted P = 3.3E-13), Jak-STAT signalling (adjusted P = 6.5E-04), Leukocyte transendothelial migration (adjusted P = 3.7E-02), PI3K-Akt signalling (adjusted P = 4.6E-04), T cell receptor signalling (adjusted P = 3.7E-02), Antigen processing and presentation (adjusted P = 1.1E-08) and NF-kappa B signalling (adjusted P = 4.1E-02). We also found confirmation of changes in lymphoid and myeloid subtypes over time, while fibroid RA patients present no significant alteration in their expression levels after the methotrexate-based DMARD therapy. Finally, we found different levels of activation in MAPK and AMPK signalling pathways for patients that do not respond to the DMARD therapy in contrast to those who do. Conclusion A novel pathway analysis approach is used to show the most differentially active biological pathways between different RA pathotypes and responder-resistant patients to methotrexate-based DMARD therapy. The results identify responder-resistant gene expression pathway patterns in early RA which may help to stratify patients to biologic therapy at an earlier stage. Disclosures E. Sciacca None. S. Alaimo None. A. Pulvirenti None. V. Latora None. F. Humby None. A. Ferro None. M.J. Lewis None. C. Pitzalis None.

Pathway mapping of leukocyte transcriptome in influenza patients reveals distinct pathogenic mechanisms associated with progression to severe infection

BackgroundInfluenza infections produce a spectrum of disease severity, ranging from a mild respiratory illness to respiratory failure and death. The host-response pathways associated with the progression to severe influenza disease are not well understood.MethodsTo gain insight into the disease mechanisms associated with progression to severe infection, we analyzed the leukocyte transcriptome in severe and moderate influenza patients and healthy control subjects. Pathway analysis on differentially expressed genes was performed using a topology-based pathway analysis tool that takes into account the interaction between multiple cellular pathways. The pathway profiles between moderate and severe influenza were then compared to delineate the biological mechanisms underpinning the progression from moderate to severe influenza.Results107 patients (44 severe and 63 moderate influenza patients) and 52 healthy control subjects were included in the study. Severe influenza was associated with upregulation in several neutrophil-related pathways, including pathways involved in neutrophil differentiation, migration, degranulation and neutrophil extracellular trap (NET) formation. The degree of upregulation in neutrophil-related pathways were significantly higher in severely infected patients compared to moderately infected patients. Severe influenza was also associated with downregulation in immune response pathways, including pathways involved in antigen presentation such as CD4+ T-cell co-stimulation, CD8+ T cell and Natural Killer (NK) cells effector functions. Apoptosis pathways were also downregulated in severe influenza patients compare to moderate and healthy controls.ConclusionsThese findings showed that there are changes in gene expression profile that may highlight distinct pathogenic mechanisms associated with progression from moderate to severe influenza infection.

Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data

BackgroundMany functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.ResultsTo address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.ConclusionsOur analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.

Identification of significant protein markers by mass spectrometry after co-treatment of cells with different drugs: An in vitro survey platform.

Understanding drug-drug interactions and predicting the side effects induced by polypharmacy are difficult because there are few suitable platforms that can predict drug-drug interactions and possible side effects. Hence, developing a platform to identify significant protein markers of drug-drug interactions and their associated side effects is necessary to avoid adverse effects. Human liver cells were treated with ethosuximide in combination with cimetidine, ketotifen, metformin, metronidazole, or phenytoin. After sample preparation and extraction, mitochondrial proteins from liver cells were isolated and digested with trypsin. Then, peptide solutions were detected using a nano ultra-performance liquid chromatographic system combined with tandem mass spectrometry. The Ingenuity Pathway Analysis tool was used to simulate drug-drug interactions and identify protein markers associated with drug-induced adverse effects. Several protein markers were identified by the proposed method after liver cells were co-treated with ethosuximide and other drugs. Several of these protein markers have previously been reported in the literature, indicating that the proposed platform is workable. Using the proposed in vitro platform, significant protein markers of drug-drug interactions could be identified by mass spectrometry. This workflow can then help predict indicators of drug-drug interactions and associated adverse effects for increased safety in clinical prescriptions.

Pathway Analysis for Targeted and Untargeted Metabolomics.

Recent advances in analytical techniques, particularly LC-MS, generate increasingly large and complex metabolomics datasets. Pathway analysis tools help place the experimental observations into relevant biological or disease context. This chapter provides an overview of the general concepts and common tools for pathway analysis, including Mummichog for untargeted metabolomics. Examples of pathway mapping, MetScape, and Mummichog are explained. This serves as both a practical tutorial and a timely survey of pathway analysis for label-free metabolomics data.

Bioinformatics Review of the Role of HSV-1 in Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by the progressive loss of cognitive functions in affected individuals. Brain tissue pathology is associated with the formation of senile plaques which result from the over-production of amyloid β (Aβ), due to the cleavage of a membrane bound glycoprotein. It is unclear what causes AD and its associated pathologies, but age and genetic predisposition play an import role in the likelihood of disease development. Studies have shown that the reactivation of latent herpes simplex virus 1 (HSV-1) infection can lead to the neuropathy of acute herpes simplex encephalitis (HSE), which causes similar symptoms to AD. HSV-1 infection is a known risk factor for the development of AD, but no study has determined a definitive causal relationship. Using the Qiagen Ingenuity Pathway Analysis (IPA) tool, the inhibitory relationship between therapeutics for AD and HSV-1 were explored. Thirteen drugs developed to decrease Aβ buildup in AD and 32 drugs that act as HSV antivirals were retrieved from the data in the Qiagen Knowledge Base. These drugs were analyzed displayed as two separate networks. While many promising Aβ aggregation-targeting drugs have been discontinued due to lack of efficacy, HSV drugs could serve as potential therapeutics for those with AD. This review aims to describe new insights on how HSV-1 relates to the development of AD and highlight the mechanism of action of Aβ-related drugs and HSV drugs in the context of AD. With HSV-1 being a likely candidate for the causation of AD, there is a need to study the effects of HSV antiviral drugs on those who have AD.

Temporal Changes of Spinal Transcriptomic Profiles in Mice With Spinal Nerve Ligation.

Neuropathic pain (NP) is an intractable disease accompanying with allodynia, hyperalgesia and spontaneous pain. Accumulating evidence suggested that large volume of neurotransmitters, genes, and signaling pathways were implicated with the initiation and development of NP, while the underlying mechanism still remained poorly understood. Therefore, it was extremely important to further elucidate the potential regulatory networks for developing appropriate treatment options. Here, the RNA-Seq high-throughput sequencing was employed to determine the genes expression change in mice undergoing spinal nerve ligation (SNL). Meanwhile, the differentially expressed genes (DEGs) were analyzed by using integrated Differential Expression and Pathway analysis (iDEP) tools and String database. Then, quantitative real-time PCR (qRT-PCR) was employed to detect the expression of hub gens. The results showed that the DEGs mainly comprised 1712 upregulated and 1515 downregulated genes at 7 days, and consisted of 243 upregulated and 357 downregulated genes at 28 days after surgery, respectively. Additionally, 133 genes and two pathways including retrograde endocannabinoid signaling and cardiac muscle contraction collectively participated in biological reactions of 7th and 28th day after operation. Moreover, the results showed that the mRNA and protein expression of Ccl5, Cacna2d1, Cacna2d2, Cacnb2, Gabrb3, GluA1, and GluA2 were significantly upregulated in SNL-7/28d group than that of in Sham-7/28d group (SNL-7d vs. Sham-7d; SNL-28d vs. Sham-28d; P < 0.05). And the level of Glra2, Glra4, Glra3, Grik1, Grik2, NR1, NR2A, and NR2B was obviously increased in SNL-7d group compared to Sham-7d group (P < 0.05), but which was no statistical difference between SNL-28d group and Sham-28d group. Therefore, these results provided new perspectives and strategies for deeply illuminating the underlying mechanism, and identifying the key elements for treating NP.

Metabolic pathway analysis for in silico design of efficient autotrophic production of advanced biofuels

Herein, autotrophic metabolism of Cupriavidus necator H16 growing on CO2, H2 and O2 gas mixture was analyzed by metabolic pathway analysis tools, specifically elementary mode analysis (EMA) and flux balance analysis (FBA). As case studies, recombinant strains of C. necator H16 for the production of short-chain (isobutanol) and long-chain (hexadecanol) alcohols were constructed and examined by a combined tools of EMA and FBA to comprehensively identify the cell’s metabolic flux profiles and its phenotypic spaces for the autotrophic production of recombinant products. The effect of genetic perturbations via gene deletion and overexpression on phenotypic space of the organism was simulated to improve strain performance for efficient bioconversion of CO2 to products at high yield and high productivity. EMA identified multiple gene deletion together with controlling gas input composition to limit phenotypic space and push metabolic fluxes towards high product yield, while FBA identified target gene overexpression to debottleneck rate-limiting fluxes, hence pulling more fluxes to enhance production rate of the products. A combination of gene deletion and overexpression resulted in designed mutant strains with a predicted yield of 0.21–0.42 g/g for isobutanol and 0.20–0.34 g/g for hexadecanol from CO2. The in silico-designed mutants were also predicted to show high productivity of up to 38.4 mmol/cell-h for isobutanol and 9.1 mmol/cell-h for hexadecanol under autotrophic cultivation. The metabolic modeling and analysis presented in this study could potentially serve as a valuable guidance for future metabolic engineering of C. necator H16 for an efficient CO2-to-biofuels conversion.

Metabolic effects of repeated ketamine administration in the rat brain

Ketamine is a popular recreational drug used in club and dance music settings. Evidence suggests that chronic or repeated ketamine use could induce neurological and psychological harm, while the mechanisms underlying ketamine’s effects on the nervous system are still unclear. The aim of this study was to explore the metabolic changes that occur in the prefrontal cortex (PFC), hippocampus (Hip) and striatum of rats with repeated ketamine exposure and withdrawal intervention and to identify the potential metabolic pathways influenced by ketamine. An untargeted ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF-MS)-based metabolomics method coupled with multivariate and univariate statistical analysis was applied to analyze the metabolic profiles of the PFC, Hip, and striatum and to identify metabolite alterations. The pathway analysis tool in MetaboAnalyst was subsequently applied for pathway predictions. A total of 79, 54 and 58 changed metabolites were identified in the PFC, Hip and striatum, respectively, after repeated ketamine exposure. Pathway analysis indicated that purine metabolism and glycerophospholipid metabolism were the main pathways disturbed by ketamine in all three brain regions. After one week of withdrawal intervention, most changed metabolites in the Hip and striatum had been restored to control levels, while the metabolite alterations in the PFC were persistent. These results revealed that repeated ketamine exposure significantly changed purine metabolism and glycerophospholipid metabolism in the PFC, Hip and striatum, which might be involved in the neurotoxic effects of ketamine. Additionally, this study also identified that the PFC, rather than the Hip or striatum, was more likely to be the target region of the long-term effects of ketamine.

A-Lister: a tool for analysis of differentially expressed omics entities across multiple pairwise comparisons

BackgroundResearchers commonly analyze lists of differentially expressed entities (DEEs), such as differentially expressed genes (DEGs), differentially expressed proteins (DEPs), and differentially methylated positions/regions (DMPs/DMRs), across multiple pairwise comparisons. Large biological studies can involve multiple conditions, tissues, and timepoints that result in dozens of pairwise comparisons. Manually filtering and comparing lists of DEEs across multiple pairwise comparisons, typically done by writing custom code, is a cumbersome task that can be streamlined and standardized.ResultsA-Lister is a lightweight command line and graphical user interface tool written in Python. It can be executed in a differential expression mode or generic name list mode. In differential expression mode, A-Lister accepts as input delimited text files that are output by differential expression tools such as DESeq2, edgeR, Cuffdiff, and limma. To allow for the most flexibility in input ID types, to avoid database installation requirements, and to allow for secure offline use, A-Lister does not validate or impose restrictions on entity ID names. Users can specify thresholds to filter the input file(s) by column(s) such as p-value, q-value, and fold change. Additionally, users can filter the pairwise comparisons within the input files by fold change direction (sign). Queries composed of intersection, fuzzy intersection, difference, and union set operations can also be performed on any number of pairwise comparisons. Thus, the user can filter and compare any number of pairwise comparisons within a single A-Lister differential expression command.In generic name list mode, A-Lister accepts delimited text files containing lists of names as input. Queries composed of intersection, fuzzy intersection, difference, and union set operations can then be performed across these lists of names.ConclusionsA-Lister is a flexible tool that enables the user to rapidly narrow down large lists of DEEs to a small number of most significant entities. These entities can then be further analyzed using visualization, pathway analysis, and other bioinformatics tools.

Gene Expression and Cytokine Analyses Identify Markers of Progression from CLL-like Monoclonal B-Cell Lymphocytosis to Chronic Lymphocytic Leukemia

Gene Expression and Cytokine Analyses Identify Markers of Progression from CLL-like Monoclonal B-Cell Lymphocytosis to Chronic Lymphocytic Leukemia

Identification of key candidate genes and molecular pathways in white fat browning: an anti-obesity drug discovery based on computational biology

BackgroundObesity—with its increased risk of obesity-associated metabolic diseases—has become one of the greatest public health epidemics of the twenty-first century in affluent countries. To date, there are no ideal drugs for treating obesity. Studies have shown that activation of brown adipose tissue (BAT) can promote energy consumption and inhibit obesity, which makes browning of white adipose tissue (WAT) a potential therapeutic target for obesity. Our objective was to identify genes and molecular pathways associated with WAT and the activation of BAT to WAT browning, by using publicly available data and computational tools; this knowledge might help in targeting relevant signaling pathways for treating obesity and other related metabolic diseases.ResultsIn this study, we used text mining to find out genes related to brown fat and white fat browning. Combined with biological process and pathway analysis in GeneCodis and protein-protein interaction analysis by using STRING and Cytoscape, a list of high priority target genes was developed. The Human Protein Atlas was used to analyze protein expression. Candidate drugs were derived on the basis of the drug-gene interaction analysis of the final genes. Our study identified 18 genes representing 6 different pathways, targetable by a total of 33 drugs as possible drug treatments. The final list included 18 peroxisome proliferator-activated receptor gamma (PPAR-γ) agonists, 4 beta 3 adrenoceptor (β3-AR) agonists, 1 insulin sensitizer, 3 insulins, 6 lipase clearing factor stimulants and other drugs.ConclusionsDrug discovery using in silico text mining, pathway, and protein-protein interaction analysis tools may be a method of exploring drugs targeting the activation of brown fat or white fat browning, which provides a basis for the development of novel targeted therapies as potential treatments for obesity and related metabolic diseases.

Quantitative Proteomic and Network Analysis of Differentially Expressed Proteins in PBMC of Friedreich’s Ataxia (FRDA) Patients

Friedreich’s ataxia (FRDA) is an autosomal recessive neurodegenerative disorder caused by an expanded (GAA) trinucleotide repeat in the FXN gene. The extended repeats expansion results in reduced transcription and, thereby, decreased expression of the mitochondrial protein, frataxin. Given the ongoing drug trials, identification of reliable and easily accessible biomarkers for monitoring disease progression and therapeutic intervention is a foremost requirement. In this study, comparative proteomic profiling of PBMC proteins from FRDA patients and age- and gender-matched healthy controls was done using 2D-Differential in-Gel Electrophoresis (2D-DIGE). Protein–protein interaction (PPI) was analyzed using BioGRID and STRING pathway analysis tools. Using biological variance analysis (BVA) and LC/MS, we found eight differentially expressed proteins with fold change ≥1.5; p ≤ 0.05. Based on their cellular function, the identified proteins showed a strong pathological role in neuroinflammation, cardiomyopathy, compromised glucose metabolism, and iron transport, which are the major clinical manifestations of FRDA. Protein–protein network analysis of differentially expressed proteins with frataxin further supports their involvement in the pathophysiology of FRDA. Considering their crucial role in the cardiac and neurological complications, respectively, the two down-regulated proteins, actin α cardiac muscle 1 (ACTC1) and pyruvate dehydrogenase E1 component subunit β (PDHE1), are suggested as potential prognostic markers for FRDA.

In silico identification of putative roles of food-derived xeno-mirs on diet-associated cancer

Aim: Dietary miRNAs in foods were recently suggested to be absorbed into the human circulation and engage in human gene regulation. This started a debate on their possible impacts on human diseases which need further investigation. In this study, we aimed to identify the putative functions and possible implications of selected Xeno-miRs in human diseases by using bioinformatic tools. Methods: Seventy-five human absorbable Xeno-miR candidates were selected from literature and narrowed down the list to miRNAs that share sequence homologies with human miRNAs. Sixteen distinct Xeno-miRs of animal species (cow, pig, chicken) were identified to have homologs in human and they were subsequently analyzed with target prediction, functional and pathway analysis tools. Results and Conclusion: Thirteen human genes were common targets of the miRNA sets grouped by species and shown evidence of associations with various cancer categories, specifically in large intestine adenocarcinoma by Ingenuity Pathway Analysis. miRNA functional enrichment analyses also highlighted the putative involvements of the dietary miRNAs in cancer pathways. Our effort provides “in silico” evidence for implications of animal-derived dietary miRNAs in cancer-associated pathways, and this shed on light the necessities of future translational research design to investigate the roles of dietary Xeno-miRs in cancer pathophysiology and nutrition-based interventions towards cancer management.

PathfindR: An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks.

Pathway analysis is often the first choice for studying the mechanisms underlying a phenotype. However, conventional methods for pathway analysis do not take into account complex protein-protein interaction information, resulting in incomplete conclusions. Previously, numerous approaches that utilize protein-protein interaction information to enhance pathway analysis yielded superior results compared to conventional methods. Hereby, we present pathfindR, another approach exploiting protein-protein interaction information and the first R package for active-subnetwork-oriented pathway enrichment analyses for class comparison omics experiments. Using the list of genes obtained from an omics experiment comparing two groups of samples, pathfindR identifies active subnetworks in a protein-protein interaction network. It then performs pathway enrichment analyses on these identified subnetworks. To further reduce the complexity, it provides functionality for clustering the resulting pathways. Moreover, through a scoring function, the overall activity of each pathway in each sample can be estimated. We illustrate the capabilities of our pathway analysis method on three gene expression datasets and compare our results with those obtained from three popular pathway analysis tools. The results demonstrate that literature-supported disease-related pathways ranked higher in our approach compared to the others. Moreover, pathfindR identified additional pathways relevant to the conditions that were not identified by other tools, including pathways named after the conditions.

Notch Signaling Inhibition by LY411575 Attenuates Osteoblast Differentiation and Decreased Ectopic Bone Formation Capacity of Human Skeletal (Mesenchymal) Stem Cells.

Background Chemical biology approaches using small molecule inhibitors targeting specific signaling pathways are useful tools to dissect the molecular mechanisms governing stem cell differentiation and for their possible use in therapeutic interventions. Methods Stem cell signaling small molecule library functional screen was performed employing human bone marrow skeletal (mesenchymal) stem cells (hBMSCs). Alkaline phosphatase (ALP) activity and formation of mineralized matrix visualized by Alizarin red staining were employed as markers for osteoblastic differentiation. Global gene expression profiling was conducted using the Agilent microarray platform, and data normalization and bioinformatics were performed using GeneSpring software. Pathway analyses were conducted using the Ingenuity Pathway Analysis (IPA) tool. In vivo ectopic bone formation was performed using hBMSC mixed with hydroxyapatite–tricalcium phosphate granules that were implanted subcutaneously in 8-week-old female nude mice. Hematoxylin and eosin staining and Sirius red staining were performed to identify bone formation in vivo. Results Among the tested molecules, LY411575, a potent γ-secretase and Notch signaling inhibitor, exhibited significant inhibitory effects on osteoblastic differentiation of hBMSCs manifested by reduced ALP activity, mineralized matrix formation, and decreased osteoblast-specific gene expression as well as in vivo ectopic bone formation. Global gene expression profiling of LY411575-treated cells revealed changes in multiple signaling pathways, including focal adhesion, insulin, TGFβ, IL6, and Notch signaling, and decreased the expression of genes associated with functional categories of tissue development. Among the affected signaling networks were TGFβ1, SPP1, and ERK regulatory networks. Conclusions We identified γ-secretase inhibitor (LY411575) as a potent regulator of osteoblastic differentiation of hBMSC that may be useful as a therapeutic option for treating conditions associated with ectopic bone formation.

Metabolic profiling of human gliomas assessed with NMR

Little is known about the underlying metabolic alterations of gliomas. The objective of this study was to analyze metabolomic profiles of gliomas diagnosed according to revised WHO classification to demonstrate metabolic signatures beyond isocitrate dehydrogenase (IDH) 1/2 mutation. 1H NMR spectroscopy of tumor extracts was performed to analyze brain tumor metabolism. We detected 46 metabolites including 2-hydroxyglutarate from human brain tumors. Metabolic profiles obtained were analyzed using multivariate analysis and MetaboAnalyst 3.0, a pathway analysis tool. We found that lactate, glutamate, alanine, glutamine, 2-hydroxglutarate, serine, O-phosphocholine, glycine, glycerol, myo-inositol, aspartate, leucine, threonine, creatine, and valine had top-ranked VIP scores in metabolic pathway analyses of glioma. Major metabolism pathways perturbed in glioma included alanine/aspartate/glutamate metabolism, glycine/serine/threonine metabolism, pyruvate metabolism, taurine/hypotaurine metabolism, and d-glutamine/d-glutamate metabolism. Altered metabolites were defined between low-grade and high-grade gliomas. We identified metabolomics signatures of gliomas associated with 2-hydroxglutarate and glioma grade. Metabolic approach may lead to metabolomic cluster-precision strategy and development of metabolic anti-glioma therapy in the future.