The relative resistance of children to sepsis mortality: from pathways to drug candidates
2018; Springer Nature; Volume: 14; Issue: 5 Linguagem: Inglês
10.15252/msb.20177998
ISSN1744-4292
AutoresRose Bernadette Joachim, Gabriel Altschuler, John N. Hutchinson, Hector R. Wong, Yoshihide Hayashizaki, Lester Kobzik,
Tópico(s)Neonatal and fetal brain pathology
ResumoArticle17 May 2018Open Access Source DataTransparent process The relative resistance of children to sepsis mortality: from pathways to drug candidates Rose B Joachim Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA Search for more papers by this author Gabriel M Altschuler Department of Neuroscience, Sheffield Institute for Translational Neurosciences, University of Sheffield, Sheffield, UK Search for more papers by this author John N Hutchinson orcid.org/0000-0002-7804-7576 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA Search for more papers by this author Hector R Wong Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA Search for more papers by this author Winston A Hide Corresponding Author [email protected] orcid.org/0000-0002-8621-3271 Department of Neuroscience, Sheffield Institute for Translational Neurosciences, University of Sheffield, Sheffield, UK Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA Search for more papers by this author Lester Kobzik Corresponding Author [email protected] orcid.org/0000-0003-4328-937X Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA Department of Pathology, Brigham & Women's Hospital, Boston, MA, USA Search for more papers by this author Rose B Joachim Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA Search for more papers by this author Gabriel M Altschuler Department of Neuroscience, Sheffield Institute for Translational Neurosciences, University of Sheffield, Sheffield, UK Search for more papers by this author John N Hutchinson orcid.org/0000-0002-7804-7576 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA Search for more papers by this author Hector R Wong Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA Search for more papers by this author Winston A Hide Corresponding Author [email protected] orcid.org/0000-0002-8621-3271 Department of Neuroscience, Sheffield Institute for Translational Neurosciences, University of Sheffield, Sheffield, UK Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA Search for more papers by this author Lester Kobzik Corresponding Author [email protected] orcid.org/0000-0003-4328-937X Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA Department of Pathology, Brigham & Women's Hospital, Boston, MA, USA Search for more papers by this author Author Information Rose B Joachim1,‡, Gabriel M Altschuler2,‡, John N Hutchinson3, Hector R Wong4, Winston A Hide *,2,3,‡ and Lester Kobzik *,1,5,‡ 1Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA 2Department of Neuroscience, Sheffield Institute for Translational Neurosciences, University of Sheffield, Sheffield, UK 3Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA 4Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA 5Department of Pathology, Brigham & Women's Hospital, Boston, MA, USA ‡These authors contributed equally to this work ‡These authors contributed equally to this work *Corresponding author. Tel: +44 11 42222233; E-mail: [email protected] *Corresponding author. Tel: +1 617 4322247; E-mail: [email protected] Mol Syst Biol (2018)14:e7998https://doi.org/10.15252/msb.20177998 See also: S Timmermans & C Libert (May 2018) PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Attempts to develop drugs that address sepsis based on leads developed in animal models have failed. We sought to identify leads based on human data by exploiting a natural experiment: the relative resistance of children to mortality from severe infections and sepsis. Using public datasets, we identified key differences in pathway activity (Pathprint) in blood transcriptome profiles of septic adults and children. To find drugs that could promote beneficial (child) pathways or inhibit harmful (adult) ones, we built an in silico pathway drug network (PDN) using expression correlation between drug, disease, and pathway gene signatures across 58,475 microarrays. Specific pathway clusters from children or adults were assessed for correlation with drug-based signatures. Validation by literature curation and by direct testing in an endotoxemia model of murine sepsis of the most correlated drug candidates demonstrated that the Pathprint-PDN methodology is more effective at generating positive drug leads than gene-level methods (e.g., CMap). Pathway-centric Pathprint-PDN is a powerful new way to identify drug candidates for intervention against sepsis and provides direct insight into pathways that may determine survival. Synopsis This study shows that relative resistance of children to mortality from sepsis is accompanied by key differences in pathway activity in blood transcriptome profiles. Drug candidates are identified by an in silico pathway-drug network approach and validated in a mouse model of sepsis. Data-mining of public transcriptome datasets from patients with sepsis identified differences in pathways in the leukocytes of children (high survival) vs. adults (high mortality). An in silico pathway-drug network (PDN) was built using expression correlation between drug, disease and pathway gene signatures followed by assessing the correlation of specific pathway clusters with drug-based signatures. Validation of the most correlated drug candidates by literature curation and in an endotoxemia model of murine sepsis demonstrated that the Pathprint-PDN methodology is more effective at generating positive drug leads than gene-level methods. Introduction Sepsis is a major cause of global morbidity and mortality for which there remains no targeted therapy (Opal, 2014; Seymour & Rosengart, 2015; Weiss et al, 2015). Central to sepsis pathophysiology is a dysregulated host inflammatory response (Aziz et al, 2013; Wiersinga et al, 2014; Singer et al, 2016), suggesting that host-directed immunomodulators could be of therapeutic benefit (Delano & Ward, 2016). There is little agreement or certainty about which particular cells or molecules are critical to defining sepsis outcomes (Marshall, 2014). As a result, transcriptome analyses and systems biology approaches have been eagerly embraced as better ways to identify drug targets for sepsis (Maslove & Wong, 2014; Sweeney et al, 2015; Wong et al, 2015; Davenport et al, 2016). Systematic computational analysis represents an exciting class of approaches for prediction and discovery of novel targets and therapeutic indications (Dubus et al, 2009; Dudley et al, 2011; Hurle et al, 2013) reflecting their ability to provide virtual access to large numbers of compounds and data relating to the target disease (Kim, 2015). However, the hope that "omics-based approaches" might guide the selection of promising therapeutics to target sepsis has not yet been realized. This is despite the fact that tools like the Connectivity Map (CMap) and Library of Network-Based Cellular Signatures (LINCS; Lamb et al, 2006; Prathipati & Mizuguchi, 2015), which use gene expression signatures to identify drug candidates, have been available for over a decade. Obstacles to progress in developing interventions for sepsis include discordant results across human studies focused on gene-level changes (Sweeney & Khatri, 2016), as well as the strongly debated limitations of animal models of sepsis for these types of analyses (Seok et al, 2013; Osuchowski et al, 2014). Here, we address these problems by using available data on human transcriptomes together with a powerful new approach that combines pathway-level analysis of human transcriptome samples with subsequent in vivo verification of findings in an animal model. We postulate that this "human-data-first" approach can improve results compared to prior efforts that began with findings in animal models. Our pathway-level analysis exploits a natural phenomenon in humans to directly compare two groups with widely disparate rates of survival from sepsis—children and adults. Using novel pathway-centered bioinformatic tools to optimize data analysis across multiple platforms, we were able to identify key differences in the responses of both age groups to sepsis as well as identify potential therapeutics. The comparison of data from septic children and adults arose from a striking finding, which at first glance seems unrelated to the problem of sepsis. Despite similar rates of infection during the 1918 influenza pandemic, children aged 5–14 experienced a remarkably lower rate of mortality compared to adults, dubbed the "honeymoon period" (Ahmed et al, 2007). Puberty (~ age 14 in the early 1900s) marked the age range in which mortality increased, suggesting that sex hormones could influence changes in fatality rates. Importantly, the "honeymoon period" is not limited to 1918 influenza-related resistance to mortality. Historical mortality rates are much lower in children after various high-fatality challenges, spanning from bubonic plague to measles. Contemporary data for trauma, the recent Ebola outbreaks, and other severe infections (Table 1) confirm the resistance. In particular, these data include lower case fatality rates for children with sepsis, both when linked to specific pathogens (e.g., candidemia, Group A streptococcal sepsis, staphylococcal sepsis), and when analyzed as a broad diagnostic category (Table 1). We postulated that the better outcomes in children reflect age-based differences in immune and inflammatory responses, possibly magnified by effects of more frequent co-morbidities in adults. Table 1. Epidemiological examples of childhood resistance to infectious and non-infectious injury Disorder Child vs. adult difference Child age range Adult age range Metricaa CFR, Case fatality rate; DP100K, Deaths per 100,000; DHR, Deaths to hospitalization ratio. References Historic data 1918 Pandemic flu 176.2 vs. 786.5 5–14 20–34 DP100K Linder and Grove (1947) Tuberculosis 30.3 vs. 206.9 5–14 20–34 DP100K Linder and Grove (1947) Measles 0.05 vs. 0.5 5–15 > 20 CFR Burnet (1952) Yellow fever 144 vs. 759 6–15 21–60 DP100K Canela Soler et al (2009) Typhoid fever 5 vs. 25 5–15 > 20 CFR Burnet (1952) Plague 7 vs. 28 6–10 > 16 DR Russell (1948) Modern data Ebola 57 vs. 81 60 vs. 72.5 5–15 5–15 20–60 > 16 CFR Rosello et al (2015) Team et al (2015) H1N1 2009 0.01 vs. 0.08 1.7 vs. 5.0 5–14 0–17 25–64 18–64 DHR DP100K Van Kerkhove et al (2011) Shrestha et al (2011) Group A strep sepsis 2.6 vs. 18 < 13 19–96 CFR Megged et al (2006) Staphylococcal sepsis 2 vs. 25 < 16 > 16 CFR Denniston and Riordan (2006) Laupland et al (2008) Sepsis 0.9 vs. 14.5 5–14 25–54 DP100K Melamed and Sorvillo (2009) Sepsis (with co-morbidities) 16.0 vs. 27.6 5–14 20–59 CFR Angus et al (2001) Sepsis (without co-morbidities) 6.3 vs. 12.8 5–14 20–59 CFR Angus et al (2001) Severe malaria 6.1 vs. 26.7 ≤ 10 21–50 CFR Dondorp et al (2008) Trauma (MOF) 17 vs. 35 < 16 > 16 CFR Calkins et al (2002) Acute chest syndrome (sickle cell) 1.1 vs. 4.3 < 20 > 20 CFR Vichinsky et al (1997) Candidemia 10.1 vs. 30.2 15.8 vs. 30.6 < 16 < 18 ≥ 16 > 18 CFR CFR Blyth et al (2009) Zaoutis et al (2005) Invasive pneumococcus infection 3.8 vs. 21.5 < 13 14–106 CFR Rahav et al (1997) Chicken pox 1.3 vs. 21.3 0.4 vs. 1.6 5–14 5–14 ≥ 20 15–44 CFR CFR Meyer et al (2000) Joseph and Noah (1988) Pneumonia 2.5 vs. 9.4 5–14 20–64 CFR Tornheim et al (2007) This table shows the difference in mortality between children and adults for a variety of infectious diseases and types of injury. The age range identified as "child" or "adult" varied across the studies. When age was more narrowly stratified for children and adults, an average mortality rate was calculated based on the age ranges of 5–12 and 20–60, respectively. a CFR, Case fatality rate; DP100K, Deaths per 100,000; DHR, Deaths to hospitalization ratio. To better understand the basis for this childhood resistance, we began by identifying public datasets of transcriptome profiling performed on blood leukocyte samples in the high vs. low survival groups (children and adults, respectively). The analysis used Pathprint (Altschuler et al, 2013; Davis & Ragan, 2013; https://bioconductor.org/packages/pathprint), a tool that is robust to batch effects and allows for comparison of gene expression at the pathway activity level across multiple array platforms. After identifying differences in pathway activity, we applied a novel method that is built upon the correlation of the expression of > 16,000 disease signatures from the Comparative Toxicogenomics Database (CTD), the Pharmacogenomics Knowledgebase (PharmGKB), pathway signatures from Wikipathways, KEGG, Netpath and Reactome, and drug signatures from CTD, PharmGKB, and CMap, across > 50,000 individual microarrays—the pathway drug network (PDN). The network neighborhood of the sepsis pathway signatures was used to identify the drugs that were most positively or negatively linked to high-survival (child) or high-mortality (adult) signatures. We assessed the validity of the top drug leads by analyzing prior data collected in preclinical animal models of sepsis and also by direct testing for improved survival in a mouse model of fatal endotoxemic shock. Results Key pathways differentiate the adult and child responses to sepsis A total of 12 datasets reporting transcriptome profiling of whole blood samples from sepsis patients were identified for analysis from The Gene Expression Omnibus (GEO) and ArrayExpress databases (Barrett et al, 2013; Kolesnikov et al, 2015). The ultimate study population included 167 adults and 95 children, composed of 55 and 64% males, and mean ages of 59 and 8, respectively (Table 2). The Pathprint analysis tool was used to compare activity of pathways in adults and children with sepsis. Substantial differences in active or depressed pathways were identified, as illustrated in Fig 1. After applying thresholds based on the greatest age-associated differences, the four pathway clusters (A–D), detailed in Table 3, were used for further analysis. Tables EV1–EV3 provide additional details of Pathprint scoring and the results for all significantly different pathways. Table 2. Demographic information on datasets used for data-mining Study GSE no. Age group Age mean Age range Sex Total Time when sampled Array GPL no. References M F 28750 Adult 60 38–82 6 4 10 ≤ 24 h 570 Sutherland et al (2011) 13015 Adult 55 40–81 11 18 29 Time of diagnosis 6947 Pankla et al (2009) 10474 Adult 58 18–83 18 16 34 ≤ 48 h 571 Howrylak et al (2009) 40586 Adult 59 37–75 8 7 15 ≤ 48 h 6244 Lill et al (2013) 57065 Adult 63 29–84 19 9 28 ~ 30 min after onset shock 570 Cazalis et al (2014) 33341 Adult 58 24–91 31 20 51 Time of diagnosis 571 Ahn et al (2013) 4607 Child 8 9–11 12 6 18 ≤ 24 h 570 Cvijanovich et al (2008) 9692 Child 7 5–9 6 2 8 ≤ 24 h 570 Cvijanovich et al (2008) 26440 Child 8 5–11 18 10 28 ≤ 24 h 570 Wynn et al (2011) 26378 Child 8 5–10 18 10 28 ≤ 24 h 570 Wynn et al (2011) 13904 Child 7 5–10 5 6 11 ≤ 24 h 570 Wong et al (2009) 40586 Child 8 7–8 2 0 2 ≤ 48 h 6244 Lill et al (2013) Summary Adult 59 18–91 93 74 167 Child 8 5–11 61 34 95 The GEO database was queried to identify microarray transcriptome datasets from sepsis whole blood samples of adults and children. Samples from patients aged 18–91 comprised the adult group and patients aged 5–11 comprised the children's group. The table above specifies each study GSE no., age category, age mean, age range, the number of male or female patients, the timing of sample acquisition in the sepsis course, the array GPL no., and the reference used to access the original study. Figure 1. Sample heatmap generated from adult vs. child comparison using PathprintPathprint analysis was used to analyze adult and child transcriptomes at the pathway level. To minimize intra-group variation and maximize inter-group variation, two filtering criteria were set in the generation of these data: (i) to maximize homogeneity within an age group based on minimizing the standard deviation, a cutoff of SD < 0.475 in the Pathprint score was used; (ii) to maximize differences between group comparisons using t-tests, Pathprint scores between groups were only included if P < 10−10. The heatmap above was generated using the pheatmap package. Source data are available online for this figure. Source Data for Figure 1 [msb177998-sup-0010-SDataFig1.xlsx] Download figure Download PowerPoint Table 3. Pathprint clusters chosen for drug candidate analysis using PDN Children Pathprint score Adults Pathprint score Children-adults difference P-value Cluster A (up in adults, down in children) IL-2 down reg. targets (Netpath) −0.94 0.97 −1.91 1.87E-88 Shigellosis (KEGG) −0.93 0.96 −1.88 2.04E-88 Endocytosis (KEGG) −0.82 0.99 −1.82 1.95E-56 B cell receptor down reg. targets (Netpath) −0.95 0.84 −1.79 1.69E-109 Signaling by NGF (Reactome) −0.83 0.95 −1.78 9.11E-66 Pathogenic Escherichia coli infection (KEGG) −0.96 0.82 −1.78 1.51E-113 Pentose Phosphate Pathway (Wikipathways) −0.79 0.99 −1.78 1.80E-50 EGFR1 Signaling Pathway (Wikipathways) −0.78 0.99 −1.77 6.46E-57 p38 MAPK Signaling Pathway (Wikipathways) −0.80 0.95 −1.75 2.91E-62 {HCLS1,17} (Static Module) −0.96 0.63 −1.59 1.28E-64 Cluster B (down in adults, up in children) {CTNNB1,130} (Static Module) 0.93 −0.95 1.87 2.71E-91 Metabolism of xenobiotics by cytochrome P450 (KEGG) 0.86 −0.98 1.84 2.55E-70 Drug metabolism—cytochrome P450 (KEGG) 0.84 −0.96 1.81 7.02E-66 Steroid hormone biosynthesis (KEGG) 0.97 −0.81 1.78 2.79E-128 Steroid Biosynthesis (Wikipathways) 0.87 −0.89 1.77 3.90E-84 Cluster C (unchanged adults, down in children) {EP300,115} (Static Module) −0.99 −0.02 −0.97 4.30E-75 {HDAC1,108} (Static Module) −0.99 −0.02 −0.97 2.46E-91 Keap1-Nrf2 Pathway (Wikipathways) −0.89 −0.07 −0.82 5.94E-48 Kit receptor up reg. targets (Netpath) −0.92 −0.12 −0.80 1.92E-52 Sulfur relay system (KEGG) −0.85 −0.18 −0.67 1.00E-29 TGF beta receptor up reg. targets (Netpath) −0.94 −0.09 −0.85 2.87E-67 Viral myocarditis (KEGG) −0.84 −0.15 −0.69 2.03E-32 Cluster D (unchanged adults, up in children) {FLI1,10} (Static Module) 0.72 0.23 0.48 7.52E-15 Melanoma (KEGG) 0.77 0.12 0.65 8.98E-26 Serotonin transporter activity (Wikipathways) 0.72 0.22 0.49 1.73E-14 Statin pathway (Wikipathways) 0.96 −0.08 1.04 4.30E-64 Four different clusters of pathways, generated through Pathprint analysis, were identified based on relative activation or inhibition in adults and children. The clusters were defined as follows: cluster A): expression up in adults, expression down in children; cluster B) expression down in adults, up in children; cluster C) expression unregulated (not significantly changed) in adults, down in children; cluster D) expression unregulated in adults, up in children. From each cluster, pathways showing the greatest divergence between the two age groups were selected for further analysis by PDN. This selection was based on a percentage (N) of samples that satisfied the criteria (N = 80% for clusters A–C; N = 70% for cluster D). More detailed descriptions can be found in Appendix Table S1. PDN base network: construction and benchmarking The PDN methodology is a novel, pathway-centric drug discovery approach that tests whether an experimental gene signature is positively or negatively correlated to a gene signature associated with drug treatment. It relies on a base network constructed using the expression correlations between each of 16,150 drug, disease, and pathway gene signatures (collected from eight different databases), averaged across 58,475 publicly available human microarrays. By measuring the correlation between pathway, drug, and disease gene signatures over more than fifty thousand experiments, one can hypothesize whether the action that regulates, or is regulated by two signatures (e.g., a drug and a survival associated phenotype), may be linked and/or have similar actions (or opposing actions in the case of negative correlation). Since no comprehensive gold standard exists for evaluating the relationships between drug and disease signatures, to test the efficacy of the new PDN approach it was necessary to construct our own benchmark. Our benchmarking protocol involved the comparison of curated, known drug–disease relationships from the National Drug File Reference Terminology (NDFRT) and Structured Product Labels (SPL) databases (1,055 in total), with the drug–disease relationships produced by the PDN methodology. Beyond our goal of replicating the NDFRT and SPL drug–disease relationships using the PDN, we also compared our methodology with an alternative approach, Network Enrichment Analysis (NEA), a method based on gene-level curated protein–protein interactions (PPI; Alexeyenko et al, 2012). While both the PDN and PPI network approaches performed better than randomly assigning drug–disease relationships, the PDN decisively outperformed the PPI network at low false discovery rates (Fig EV1). Based on this benchmarking exercise, true-positive rates (TPRs) and false-positive rates (FPRs) were measured for the PDN and used to create a series of network cutoffs (the probability at which an edge is defined as true). From these analyses, a PDN cutoff parameter was chosen for the final base network that yielded as high as possible TPR (40%) while still keeping the FPR low (6%). Click here to expand this figure. Figure EV1. Benchmarking: PDN and PPI Sensitivity vs. SpecificityTo provide a benchmark for new PDN methodology, we compared drug–disease relationships produced using PDNs with curated, known drug–disease relationships from the NDFRT and SPL databases. The true-positive (TP) and false-positive (FP) rates (Sensitivity and 1-Specificity) of the PDN predictions were compared to those generated using an alternative approach based on gene-level curated protein–protein interactions (PPI). The arrow points to the network cutoff parameters used in the study: TP rate, FP rate, pEdge (probability that there is an edge between any pair of nodes), and qval (q-value or FDR). Download figure Download PowerPoint PDN methodology results in high rates of positively validated drugs Once the base network was constructed and subjected to benchmarking analysis, the next step was to challenge the network with a set of query pathways taken from our pre-defined Pathprint clusters A–D. Sub-networks of the PDN were constructed that contained these cluster pathways, together with their neighborhood of connected nodes. After several pruning steps (described in the Materials and Methods), the resulting network focuses on the gene signatures that relate most strongly to our cluster pathways. Through this method, four network modules incorporating each of the Pathprint clusters A–D were created, containing 45 drug leads in total (Table 4). Table 4. Curation of drug lists by literature search through PubMed Pathprint to drugs Cluster Prior data? DEGs to drugs Prior data? Random Prior data? Fenoprofen A +/− 0297417-0002B Urapidil Glibenclamide A + Indomethacin +/− Trifluoperazine Asiaticoside A + SB-202190 + Metaraminol + Topiramate A Acetohexamide + Nomegestrol Suramin A +/− STOCK1N-35215 Coralyne Hyoscyamine A + Emetine Citicoline Pancuronium A Tacrine Octopamine N-acetyl-l-leucine A Thioridazine Sulfapyridine Mefenamic acid A + Suloctidil Butoconazole Apigenin A + Biotin 0175029-0000 Camptothecin B + Cyclopenthiazide Tracazolate Lincomycin B + Mebhydrolin +/− Tomatidine Ganciclovir B Triprolidine +/− Tetroquinone Fursultiamine B Colchicine Repaglinide Tocainide B + Cinchonine Tiletamine GW-8510 B Methoxamine Amikacin + Tanespimycin B + Tanespimycin + Butirosin Carbenoxolone B + Fluorometholone +/− Meptazinol + Tacrolimus B + Nicardipine + Tolnaftate Conessine B Quinpirole Fasudil + Khellin C Cycloheximide − Enilconazole Eldeline C Colchicine acid Sulfanilamide Sulfathiazole C Meteneprost − Theophylline Geldanamycin C + Puromycin Spiramycin Cefoxitin C Digoxin Omeprazole Procaine C + Naftidrofuryl Rolitetracycline Procyclidine C Terfenadine Dexpropranolol + Monorden C + Gelsemine Piribedil Hexetidine C Sulindac +/− Sulfathiazole Piperacetazine C + Drofenine Iobenguane Desipramine A & C Thioguanine Dicycloverine Cyclosporine A & C + Methylergometrine PF-0053978-00 Nifenazone A & C +/− Methotrexate Dipivefrin Tanespimycin A & C + Ethacrynic acid Aztreonam + Ethacrynic acid D Dexamethasone +/− Tomatidine Noscapine D Tolazoline Bicuculline + Tanespimycin D + 3-aminobenzamide + Ethosuximide Mebhydrolin D Epitiostanol Meclozine Vincamine D Benzthiazide Alimemazine Altretamine D 0179445-0000 Monensin Enalapril D + Lidocaine + Prestwick-691 Coralyne D + Alexidine Oxaprozin +/− Napelline D Dihydroergocristine Amiodarone Clindamycin D + Nifurtimox Ampicillin A literature search using PubMed was performed to compare the number of therapeutic leads, generated by both pathway- and DEG-based drug prediction methods, which were shown to confer a survival benefit in in vivo mouse models of sepsis. Compounds were scored as follows: positive (prior studies showing survival benefit were identified: (+); both (prior studies showing both benefit and harm to survival were identified: (+/−); negative (prior studies showing only harm to survival were identified: (−); blank (no relevant studies were identified: no entry). This approach and other drug discovery methodologies generate enormous quantities of possible drug leads that necessitate efficient validation methods. Considering the large number of previous studies that have evaluated compounds for possible benefit in sepsis using animal models, we reasoned that one metric for evaluating the results from the PDN would be how often the identified drug leads corresponded to agents already shown to have positive (or negative) effects experimentally. Hence, we conducted extensive literature curation for each of the 45 compounds or closely related agents (e.g., ibuprofen for NSAIDs) and scored the presence of prior publications showing benefit or harm for survival in animal models of sepsis. The validation efficacy of the drug list derived from Pathprint-to-PDN analysis was compared to three other gene-level drug discovery approaches as well as to a control approach (drugs selected at random from the entire list of CMap compounds). The first, a gene-level approach, also used PDN, but analyzed differentially expressed genes (DEGs) generated from a standard Limma analysis of children vs. adult transcriptomes, rather than pathway clusters (Appendix Fig S1). We found a substantially higher rate of positives in the list produced by a pathway-level PDN approach: 54%, compared to 27% for the gene-level approach, and 16% for randomly selected drugs. We also obtained up- and down-regulated DEGs from the BarCode method (McCall et al, 2010; Table EV4), an approach that categorizes gene expression as on or off, and used these genes, as well as the standard DEG list to query the LINCS database (Wang et al, 2016), a greatly expanded version of CMap (Lamb et al, 2006). The lists of compounds expected to have a positive effect on sepsis mortality (i.e., up- and down-regulated in adults compared to children) were also curated to assess the frequency of prior positive results in the literature. The percentage of positive drug leads achieved by the Pathprint-to-PDN methodology was significantly higher than with each of the four other methods (P < 0.02 by Fisher's exact test). The percent positives for each of the five categories of drug leads are summarized in Fig 2, and details of the lists and references identified are provided in Appendix Tables S1–S5. Figure 2. Comparison of several methods of drug candidate identificationFive methods of transcriptome analysis/drug candidate identification were compared in their ability to successfully produce drug targets in at least one prior study showing a survival benefit from sepsis. (i) Pathprint-PDN: Comparison of pathways by Pathprint and drug candidate analysis by pathway drug network (PDN); (ii) DEGs-PDN: Comparison of differentially expressed genes (DEGs) by standard methods and drug candidate analysis by PDN; (iii) Random: Drugs chosen at random from the CMap database; (iv) DEGs-LINCS: Comparison of DEGs generated by standard methods and drug candidate analysis using LINCS database; and (v) BarCode-LINCS: Comparison of DEGs generated by
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