Chronic obstructive pulmonary disease (COPD) comprises multiple phenotypes such as airflow obstruction, emphysema, and frequent episodes of acute worsening of respiratory symptoms, known as exacerbations. The goal of this pilot study was to test the usefulness of unbiased metabolomics and transcriptomics approaches to delineate biological pathways associated with COPD phenotypes and outcomes. Blood was collected from 149 current or former smokers with or without COPD and separated into peripheral blood mononuclear cells (PBMC) and plasma. PBMCs and plasma were analyzed using microarray and liquid chromatography mass spectrometry, respectively. Statistically significant transcripts and compounds were mapped to pathways using IMPaLA. Results showed that glycerophospholipid metabolism was associated with worse airflow obstruction and more COPD exacerbations. Sphingolipid metabolism was associated with worse lung function outcomes and exacerbation severity requiring hospitalizations. The strongest associations between a pathway and a certain COPD outcome were: fat digestion and absorption and T cell receptor signaling with lung function outcomes; antigen processing with exacerbation frequency; arginine and proline metabolism with exacerbation severity; and oxidative phosphorylation with emphysema. Overlaying transcriptomic and metabolomics datasets across pathways enabled outcome and phenotypic differences to be determined. Findings are relevant for identifying molecular targets for animal intervention studies and early intervention markers in human cohorts. In 2015, the economic burden of COPD in the United States through 2020 was estimated to be ~$90 billion2, while in the European Union a recent 2017 release estimated the economic burden to be ~ €48.4 billion3. COPD occurs predominately in smokers; however, only 10–50% of smokers develop COPD4. Current therapy is focused on treating symptoms such as chronic cough and excessive sputum production, and preventing exacerbations. There are no therapies that reduce the progression of disease or mortality. Diagnosis and treatment are complicated because COPD may manifest as multiple phenotypes including airway obstruction, emphysema , and frequent exacerbations. Identifying biomarkers and pathways that distinguish COPD phenotypes can potentially lead to more specific treatments.Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the United States5, (2) obtaining suitable numbers of study subjects across phenotypes, (3) lack of validated informatics tools and strategies for large clinical datasets7, and (4) limitations within the omics technological platforms. The latter includes challenges in integrating clinical metadata with multiple datasets from functional genomics, proteomics, and/or metabolomics sources; i.e. a true “systems approach”.In addition to clinical challenges, there remain significant gaps in understanding the pathways and mechanisms involved in the pathogenesis of COPD phenotypes. Moreover, there are inherent challenges in clinical omics-based research that make studying COPD difficult, including (1) complexity of patient stratification across multiple phenotypes8; however alpha-1 antitrypsin deficiency accounts for only 1% of COPD cases9. Most recently, proteomic approaches have identified new biomarkers such as plasma sRAGE for the presence and progression of emphysema11. Metabolomics approaches have identified potential markers of disease severity or therapeutic candidates such as purines12, sphingolipids13, glycerophospholipids14, and amino acids which differentiate patients with or without emphysema and/or cachexia15. While these omic-centric studies have added to the knowledge base, only limited information is obtained. A systems- or pathways-based approach could further pinpoint potential mechanisms by integrating datasets and thereby increasing statistical power. This approach has been used in plant and bacterial studies and has recently been applied to human disease. For example, integrating functional genomics and metabolomics identified markers associated with poor outcome in human neuroendocrine cancers16, determined predictors of asthma control17, and determined novel therapeutic targets in lung cancer18.Some progress has been made in the fields of functional genomics and metabolomics; however, no discovery-based approach has yet resulted in validated clinical biomarkers. For example, genetic studies previously identified alpha-1 antitrypsin in 1964 as a cause of COPD19 and metabolomics provides a readout of the physiological state of the human body20. Therefore, we used metabolomics and transcriptomics to identify markers associated with six COPD health outcomes, then mapped the significant results to pathways for each outcome. The overall goal is to identify dysregulated pathways that distinguish outcomes.While single ‘omic studies have helped to address the knowledge gap, to date no study has integrated transcriptomics and metabolomics from a COPD cohort to determine if specific pathways are associated with individual COPD outcomes. This could be a useful strategy since functional genomics measures the association between genes, transcripts and clinical phenotypeThe novelty and strength of our study lies in the uniqueness of the highly phenotyped and matched samples across several stages of COPD severity. Using transcriptomics and metabolomics datasets provides information at a molecular level that is not available when only a single profiling technology is used. Determining how the molecular basis of disease phenotypes vary will improve treatment options and clinical outcomes since unique therapies can be adapted to each patient based on their individual phenotype.Clinical characteristics are provided in Table 21 category. Studies in COPDGene have found that these subjects are not as healthy as control subjects21 and it is not clear where these patients fit in the spectrum of COPD. We therefore included PRISm subjects in the transcriptomics analyses since patients within that category presented with outcomes such as exacerbations or emphysema. None of the PRISm subjects overlapped with the metabolomics dataset and were therefore not included in the metabolomics analysis.Approximately 10–15% of the current and former smoker population falls into the Preserved Ratio, Intact Spirometry (PRISm)1% predicted  while no transcript differences were associated with exacerbation severity. FEV1/FVC had the most significant metabolites (269) while emphysema and BDR had none. There was minimal overlap in the significant transcripts , cancer , regulation of dietary iron absorption (CYBRD1), lipid binding (OSBPL8), protein transport (TMCO3), protein binding (TOPORS), and the mitochondria 22. The gene interactions among these significant associations were investigated, with FEV1/FVC having the most interactions  was increased with increasing exacerbation frequency, acetylcarnitine* was decreased with increasing exacerbation severity, and octanoylcarnitine** was decreased with COPD for FEV1/FVC. The metabolite interactions for the significant associations are shown in Supplementary Document For the metabolomics analysis, only significant compounds that were confidently identified are shown in Table 1 progression analysis. Three compounds overlapped between the two comparisons. These were PC(36:4)* , PE-Cer(d16:1/18:0) , and an unannotated compound at mass 771.5351 Da . PC(36:4)* and PE-Cer(d16:1/18:0) are negatively correlated with emphysema progression and positively correlated with FEV1 progression, while the unannotated compound was negatively correlated with both emphysema and FEV1 progression.Clinical data from quantitative computerized tomography (CT) scans and lung function measurements from each patient was used to quantify disease progression. This clinical data was incorporated with metabolomics to identify small molecule changes associated with disease severity. There were 90 significant compounds in the emphysema progression analysis compared to 142 significant compounds in FEV1/FVC, 22 pathways with FEV1% predicted, 7 with exacerbation frequency, and 15 with exacerbation severity . There were eight unique pathways related to exacerbation severity, with arginine and proline metabolism as the most significant. Sphingolipid metabolism was significant in exacerbation severity with ganglioside GD3 (d18:0/23:0), lactosylceramide (d18:1/14:0), and the glycosphingolipid Fucα1–4GlcNAcβ1-3Galβ1-3GlcNAcβ1-3Galβ1-4Glcβ-Cer(d18:1/22:0) positively associated (FDR < 0.0001) with disease severity. Sphingolipid metabolism was also significant in the lung function outcomes (FDR < 0.0001), with two glycosphingolipids positively associated with FEV1% predicted (lung size), and three glycosphingolipids and four ceramides positively associated with FEV1/FVC (airflow obstruction). Emphysema was related to two unique pathways (oxidative phosphorylation and mRNA surveillance pathway). There were no unique pathways associated with BDR. Individual pathway entities are available in Supplementary Document Three pathways were unique to FEVFVC Fig. , with faOxidative phosphorylation was uniquely associated with emphysema and therefore examined more closely. All gene transcripts were decreased Fig.  and are 23 was used to determine what was responsible for the differences. Exacerbation severity comprised eight degradation pathways and three energy pathways, while exacerbation frequency comprised three degradation pathways and no perturbed energy pathways  were decreased  and five gene transcripts  were increased . Lipids that decreased with disease severity were LysoPE(16:0)*, DG(36:2)*, PC(36:2)*, PE(P-16:0), PI(41:1) (FDR ≤ 0.05), and LysoPC(20:0)* . Conversely, PG(29:2), PS(39:7), and TG(42:0) increased (FDR ≤ 0.05) with disease severity.Three pathways were examined to determine association between gene transcripts and metabolites. Glycerophospholipid metabolism was based on FEVted Fig. . As dise1/FVC with an associated increase with disease of triglycerides, cholestrol esters, phospholipids, and bile acids, and a decrease in monoglycerides, diglycerides, and docosahexaenoic acid (DHA) with disease. The SCARB1 gene located in the brush border-membrane, and ABCA1 located in the basolateral membrane, were associated with an increase in expression in COPD subjects compared to healthy controls. The protein encoded by SCARB1 is a plasma membrane receptor for high density lipoprotein cholesterol (HDL)24. Lastly, glycerolipid metabolism, also uniquely significant in FEV1/FVC revealed an increased association with phosphatidic acid and the AGPAT9 gene that encodes it  and still have no obstruction and occasionally is the reverse (for mild COPD). Also, not surprising is the minimal overlap across all the tested outcomes since COPD is a heterogeneous disease. This approach was important in allowing us to distinguish phenotypes and outcomes.Overlaying the significant transcripts and compounds using Venn diagrams allowed us to view the differences amongst the various outcomes in a discernable manner. For example, while a larger overlap between FEVOverall, a metabolomic signature differentiated the COPD outcomes, which was supported in the transcriptomics analysis for corresponding pathways. Both omics datasets demonstrated that specific COPD phenotypes and outcomes are associated with different transcriptome and metabolome pathways. This supports the premise that distinct COPD phenotypes have distinct mechanisms. There were also differences in the types of omics associations. For example, emphysema appears to have a larger number of gene associations compared to exacerbation severity, which has more metabolic associations. Therefore, we chose to focus the discussion on the most significant and/or unique pathways for each outcome, as a means of narrowing down the large amount of data generated from this study.26. We then observed that sphingolipid metabolism was significant in exacerbation severity. In our previous studies, we observed that trihexosylceramides, a subclass of sphingolipids, were positively associated with severe exacerbations13.We first interpreted exacerbations as a group and observed that for exacerbation frequency, there are no significant differences in the energy pathways and very few associations in the degradation pathways. Conversely, for exacerbation severity, the energy pathways are more dysregulated and accompanied by an increase in the number of degradation pathways, particularly carbohydrate degradation, nucleoside degradation, fatty acid degradation, and amino acid degradation. This is supported by the energy association in degradation pathways; carbohydrate breakdown provides a source of cellular energy for other syntheses such as protein transport, nucleosides act as signaling molecules or provide chemical energy to cells, and fatty acids store energy in the cell27. Since COPD exacerbations are associated with viral29 and bacterial31 infections, the perturbation of CD8 but not CD4 in our study suggests that a crucial connection is not being made, and this may predispose patients to frequent exacerbations as their immune system is weakened. An alternative explanation is that CD4 T cells were activated and CD8 T cells are now destroying the viral or bacterial pathogen in the more frequent exacerbations. Interestingly, an outcome-pathway shift is observed where the perturbed genes within the MHC I pathway for exacerbation frequency end at the T cell receptor signaling pathway which is uniquely perturbed in FEV1% predicted.Interestingly we also observed for exacerbations that while antigen processing and presentation was unique to exacerbation frequency, only the MHC I pathway (leading to CD8 T cell activation) and not the MHC II pathway (leading to CD4 T cell activation) is affected. The activation of CD4 T cells is beneficial to sustaining memory of CD8 T cells following acute infection32. In a recent study, exposure of human lung fibroblasts to various doses of nicotine and e-cigarette condensate inhibited myofibroblast differentiation and inhibited oxidative phosphorylation complex III33. Therefore, in spite of adjusting for smoking status, our results are not unexpected as our cohort is comprised of current and former smokers.We then examined the emphysema phenotype. Oxidative phosphorylation was unique to and significant in the emphysema outcome. Cigarette smoking (CS) is a major cause of emphysematous COPD. CS is known to produce reactive oxygen species (ROS). Excessive production of ROS can lead to oxidative damage, leading to cell apoptosis26. Phagosomes fuse with lysosomes allowing the ingested material to be digested; some of the material is recycled to the plasma membrane26. The perturbed transcripts in these pathways showed increased expression and may serve as markers of airflow reversibility.Next, we focused on the BDR outcome. Unlike the other outcomes, BDR was associated with two cytoplasmic pathways, lysosome and phagosome. Lysosomes break down proteins, nucleic acids, carbohydrates and lipids1% predicted outcome. Glycerophospholipid metabolism, although significant in many of the other outcomes, was highly significant in this lung function outcome. These lipids play a role in respiratory infections34 and in asthma and COPD35 since they constitute lung surfactant38. Surfactant, a complex combination of proteins and ~90% lipids39, is secreted by the airway epithelial cells into the airspace to reduce surface tension and provides a barrier to pathogens. In the FEV1% predicted outcome, the lipids classes LysoPE and PG, decreased and increased respectively, and have been previously suggested as markers of lung remodeling40. Their associated genes PLA2G6 and LPCAT2 were also decreased and increased respectively and could serve as targets in future studies to repair disrupted surfactant composition.We then examined FEV1/FVC. This is congruent with our recent publications that discuss in depth the significance of sphingolipids to COPD pathogenesis, including signaling related to structural cell fate, innate immune responses, and lymphocyte trafficking42. Other pathways uniquely and significantly associated with the FEV1/FVC was involved in fat digestion and absorption, typicaly reflecting perturbations between the intestine and blood. SCARB1 may be driving the cholesterol (CL) and cholesterol ester (CE) transport and regulation while the phospholipids (PL) may be driving ABCA1. Since bile acids and triglycerides were increased with disease, and the omega-3 fatty acid DHA was decreased with disease, this pathway suggests a diet or nutrition component associated with disease. This is supported by a recent publication on a cohort of 34,739 women where long-term consumption of fruits was inversely assocated with COPD incidence43. Diet has been suggested to play a role as a risk factor for many chronic diseases including COPD45, and suggestions have been made on the role of nutritional supplement therapy in the treatment of COPD46.Sphingolipids were also highly associated with this outcome and FEV47. Although MAPK signaling was not significant in our study, NF-κB signaling was significant in FEV1% predicted. MAPK and PI3K-AKT signaling pathways may therefore be the driving force for downstream events. Researchers have already begun exploring these targets51.Finally, we attempted to deconstruct the results into a pathway map where relations could be observed among the outcomes and pathways. Based on the overview pathway map, MAPK and PI3K-AKT signaling pathways appeared to be the unifying links among many of the perturbed pathways. In a recent study exposing human bronchial epithelial cells to urban particulate matter, airway inflammation was triggered via activation of MAPK and NF-κB signaling pathways53. Also non-fasting samples may affect results as food intake affects the plasma metabolite profile54, We therefore used stringent statistics to filter out many of the diet-associated compounds. Lastly, patients in this study were taking medications, which is unavoidable with this type of cohort. However, we did our best to filter those medications from the final dataset prior to analysis. Future work would incorporate lavage fluid and increase sample numbers to gain more power for discriminating metabolite-transcript-phenotype differences. Targeted quantitative analysis of pathway-specific molecules could provide increased power and more quantitative results. Although these results need to be validated in a larger cohort, they point to therapeutic targets to be tested in animal experiments, and the most significant species from each outcome could serve as non-invasive markers of disease outcomes.A limitation of our study is the use of plasma and PBMCs as these may not directly translate to the lung environment. However, our previous studies, both published and unpublished, have observed a high degree of congruency for compounds in lung lavage fluid, lung tissue and plasma in both human and animal studies33; cigarette smoking is a major cause of emphysema and produces reactive oxygen species, leading to oxidative damage. Interventions such as rotenone, antimycin A, or oligomycin can be used to inhibit oxidative phosphorylation56, while other small molecule interventions can be used to activate the pathway. Autophagy was highly significant for FEV1/FVC and can be inhibited using, 3-methyladenine58. Another pathway that can be leveraged is antigen processing and presentation. This pathway, decreased in exacerbation frequency and is dysregulated in a rat model of COPD59. Therefore, activating these pathways using either small molecules or gene interventions in animal models could be beneficial in developing phenotype-specific COPD treatments to reduce or halt lung damage or viral load. These pathway interventions in mouse models have the potential to lead to phenotype-specific treatment targets. Other follow up studies could include targeted analysis in COPD or smoking cohorts such as COSYCONET60, Birmingham61, SPIROMICS62, or KORA64.Despite these limitations, we were able to determine unique pathways that can be leveraged for treatment targets. For example, oxidative phosphorylation was decreased with worsening emphysema. It is perturbed due to cigarette smoke exposureOur pilot study has shown that using a pathway enrichment approach to analyze transcriptomics and metabolomics data from matched samples, identified perturbed biological pathways associated with COPD outcomes and identified pathways that distinguish between outcomes. In addition, our results show that the various phenotypes and outcomes have distinct pathways associated with each one, in spite of COPD being a heterogenous disease. Investigators may choose to select the strongest pathways  for follow up analyses using either mouse intervention studies via knockouts, mouse treatment studies, or using targeted analysis in other COPD or smoking cohorts. The long term clinical applicability of this work would be in the use of blood-based markers where a simple blood panel could identify patient outcomes and indicate patient-targeted treatment. This work could help in the management of COPD.65 which is a National Institute of Health–sponsored multicenter study of the genetic epidemiology of COPD. COPDGene was approved by the institutional review board at each participating center; all subjects were enrolled from January 2008 to April 2011 and provided written informed consent. The current analysis was approved by the National Jewish Health Institutional Review Board.All methods were performed in accordance with the relevant guidelines and regulations. Human subjects were from the COPDGene cohort65. There were ten Preserved Ratio, Intact Spirometry (PRISm)21 subjects included in the transcriptomics analysis since these subjects presented with exacerbations or emphysema but are considered an unclassified. Patients who were pregnant or with co-morbidities such as a history of other lung disease (except asthma), active cancer under treatment, and other cardiac hospitalizations, were excluded from the study65. Non-fasting blood was collected from a random sampling of subjects (n = 149) from a single clinical center  that represented the entire cohort and used for omics analysis. Peripheral blood mononuclear cells (PBMC) from 136 subjects were used for functional genomics and fresh frozen plasma from 131 subjects were used for metabolomics, with an overlap of 118 subjects for both omics technologies. The cohort characteristics are shown in Table The COPDGene study enrolled 10,192 non-Hispanic White and Black individuals, aged 45–80 years old with at least a 10 pack-year history of smoking, who had not had an exacerbation of COPD for at least the previous 30 days. Additional information on the COPDGene study and the collection of clinical data has been previously described1/FVC), FEV1 percent (%) predicted, percent (%) emphysema on CT scan10, exacerbation frequency, exacerbation severity, and bronchodilator response (BDR). COPD was defined as post bronchodilator ratio of FEV1/FVC < 0.70. Current or former smokers without spirometry evidence of airflow obstruction (FEV1/FVC ≥ 0.70) were classified as controls66. Subjects with without airflow obstruction but FEV1 < 80% of predicted where classified as (PRISm)21. Emphysema was measured using quantitative high resolution CT as previously described67 and quantified as the percent of lung attenuation lung voxels (LAA) ≤−950 HU on the inspiratory images for the whole lung. Emphysema was further categorized as none (LAA ≤ 5%), mild (5% < LAA ≤ 10%), moderate (10% < LAA ≤ 20%), or severe (20% < LAA). Exacerbations (flare ups) of COPD are characterized by acutely worse cough, sputum, and dyspnea. Only moderate exacerbations (treated by corticosteroids or antibiotics) or severe exacerbations  were evaluated. Patients had no exacerbations for at least 30 days prior to sample collection. BDR determined the reversibility of airway obstruction by measuring FEV1 before and after the administration of bronchodilator medication. A significant response was defined as an increase in FEV1 of 12% and 200 mL increase68.The clinical data included forced expiratory volume in 1 second/forced vital capacity 69. Plasma was used for untargeted liquid-chromatography mass spectrometry (LC-MS)-based metabolomics.Blood was drawn into a BD Vacutainer Cell Preparation Tube for peripheral blood mononuclear cells (PBMC) and plasma as previously describedAll solvents were LC-MS grade. Water and isopropyl alcohol were purchased from Honeywell Burdick & Jackson ; chloroform, acetonitrile, methanol, acetic acid, low retention microcentrifuge tubes, serological pipettes were purchased from Fisher Scientific ; plastic pipette tips were purchased from USA Scientific ; methyl tert-butyl ether was purchased from J.T. Baker ; internal standards were purchased from Avanti Polar lipids Inc. and Sigma Aldrich ; pyrex glass culture tubes were purchased from Corning Incorporated .69. Briefly, blood was collected from subjects, processed within 30 minutes and peripheral blood mononuclear cells (PBMC) were isolated from the supernatant for RNA isolation as previously described69. Fresh frozen plasma was isolated using P100 tubes as previously described70 and stored at −80 °C until sample preparation for untargeted liquid-chromatography mass spectrometry (LC-MS)-based metabolomics. For LC-MS, 100 µL of each sample underwent protein precipitation using methanol, followed by liquid-liquid extraction using methyl-tert butyl ether as previously described72 to obtain an aqueous fraction and a lipid fraction. All samples were prepared randomly to avoid batch effects from phenotype, gender, or GOLD stage.Blood samples for functional genomics analysis were prepared as previously describedThe samples from the lipid fraction were analyzed randomly and each sample vial was run randomly in triplicate using an Agilent 1290 series pump with an Agilent Zorbax Rapid Resolution HD (RRHD) SB-C18, 1.8 micron (2.1 × 100 mm) analytical column and an Agilent Zorbax SB-C18, 1.8 micron (2.1 × 5 mm) guard column. The autosampler tray temperature was set at 4 °C, column temperature was set at 60 °C, and the sample injection volume was 4 µL. The flow rate was 0.7 mL/min with the following mobile phases: mobile phase A was water with 0.1% formic acid, and mobile phase B was 60:36:4 isopropyl alcohol:acetonitrile:water with 0.1% formic acid. Gradient elution was as follows: 0–0.5 minutes 30–70% B, 0.5–7.42 minutes 70–100% B, 7.42–9.9 minutes 100% B, 9.9–10.0 minutes 100–30% B, followed by column re-equilibration. The lipid fraction mass spectrometry conditions were as follows: Agilent 6210 Time-of-Flight mass spectrometer (TOF-MS) in positive ionization mode with dual electrospray (ESI) source, mass range 60–1600 m/z, scan rate 2.03, gas temperature 300 °C, gas flow 12.0 L/min, nebulizer 30 psi, skimmer 60 V, capillary voltage 4000 V, fragmentor 120 V, reference masses 121.050873 and 922.009798 (Agilent reference mix).The samples from the aqueous fraction were analyzed randomly with each sample vial run randomly in triplicate using an Agilent 1200 series pump using a Phenomenex Kinetex HILIC, 2.6 µm, 100 Å (2.1 × 50 mm) analytical column and an Agilent Zorbax Eclipse Plus-C8 5 µm (2.1 × 12.5 mm) narrow bore guard column. The autosampler tray temperature was set at 4 °C, column temperature was set at 20 °C, and the sample injection volume was 1 µL. The flow rate of 0.6 mL/min with the following mobile phases: mobile phase A was 50% ACN with pH 5.8 ammonium acetate, and mobile phase B was 90% ACN with pH 5.8 ammonium acetate. Gradient elution was as follows: 0.2 minutes 100% B, 0.2–2.1 minutes 100–90% B, 2.1–8.6 minutes 90–50% B, 8.6–8.7 minutes 50–0% B, 8.7–14.7 minutes 0% B, 14.7–14.8 minutes 0–100% B, followed by column re-equilibration. The aqueous fraction mass spectrometry conditions were as follows: Agilent 6520 Quadrupole Time-of-Flight mass spectrometer (Q-TOF-MS) in positive ionization mode with ESI source, mass range 50–1700 m/z, scan rate 2.21, gas temperature 300 °C, gas flow 10.0 L/min, nebulizer 30 psi, skimmer 60 V, capillary voltage 4000 V, fragmentor 120 V, reference masses 121.050873 and 922.009798 (Agilent reference mix).The HILIC and C18 chromatographic methods discussed above were replicated for LC-MS/MS analysis using 10, 20, and 40 eV collision energies on a 6520 Q-TOF-MS (Agilent) with a 500 ms/spectra acquisition time, 4 m/z isolation width, and 1 minute delta retention time.Samples were randomly selected from 2000 subjects from the COPDGene cohort comprising both healthy subjects and COPD patients to create aliquots of pooled QC plasma samples. At least 20 of the 131 subjects in this study were included in the pooled sample. Samples were prepared as described above and pooled for use as quality control (QC) samples to monitor instrument reproducibility across multiple days. These pooled QC samples were injected after every five samples, followed by a solvent blank. Total ion chromatograms of all samples were evaluated for retention time reproducibility and intensity overlap. Instrument QC samples were analyzed to ensure that peak areas of spiked internal standards in the plasma samples were reproducible with coefficient of variations ≤10%.4 adducts for positive ionization mode, charge states limited to 2, and absolute height >3000 counts). The ‘Find by Formula’ algorithm merged multiple features such as ions, adducts and dimers into a single compound. The final data set was then re-imported into MPP for metabolite annotation.A final list of compounds was obtained by collapsing features  into compounds as follows: Spectral peaks were extracted using the following parameters in MassHunter software B.07 (Agilent Technologies): Find by Molecular Feature algorithm, single charge, proton, sodium, potassium, ammonium adducts in positive ionization mode. Data were imported into Mass Profiler Professional software 14.5  for mass (15 ppm) and retention time alignment (0.2 minutes). Data from sample preparation blanks and instrument blanks were background subtracted to eliminate noise from contaminants. Because LC-MS data can result in missing values, data was further processed using the ‘Find by Formula’ algorithm parameters , Human Metabolome Database (HMDB), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Lipid Maps and applies isotope ratios, accurate mass, chemical formulas, and scores to provide preliminary identifications. A database score ≥70 out of a possible 100 was considered acceptable for annotation confidence. Chosen elements for molecular formula generation were: C, H, N, O, S, and P. An error window of ≤10 ppm was used with a neutral mass range up to 2500 Da and positive ions selected as H, Na, K, and NH73. For compounds where their fragments were not present in a reference spectral library, MetFrag75 was used for in silico fragmentation. A list of the confirmed and putative IDs is available in Supplementary Documents For improved confidence, only the statistically significant compounds were fragmented as described in the Tandem MS section above, and searched using either an in-house mass, retention time, and MSMS library built from purchased standards, or their MSMS fragments were matched to reference standards from the NIST14 and NIST17 MSMS spectral libraryWithin the manuscript and in Table 76. Only compounds identified in at least 2 of 3 sample vial injection triplicates were summarized to obtain one measurement per compound per subject, all others were filtered out. A maximum coefficient of variation of 0.5 was set as a threshold for utilizing the mean of the triplicates or the median of the triplicates. This threshold was selected to account for when the extraction software may have missed a peak, thus resulting in one replicate having low to no signal and therefore causing a larger CV across replicates. The data was then filtered for compounds that were found in at least 80 percent of subjects. Missing data was then imputed using Bayesian Principal Component Analysis (BPCA), which uses linear combinations of principal axis vectors to estimate the missing value and is not sensitive to the level of missing data77. The data was then normalized using the Cross-Contribution Compensating Multiple Standard Normalization (CRMN) method to remove unwanted batch variation78. Following filtering, imputation, and normalization the numbers of metabolites decreased from 1,813 to 662 and 6,183 to 2,337 compounds in the aqueous and lipid fractions respectively, for a total of 2,999 compounds. Many of the compounds that were removed from further analysis were attributed to instrument noise, software extraction artefacts, and single hits from individual subjects.Processing of metabolomics data was performed using the R package MSPrep69. Following filtering, transcripts were reduced from 54,675 to 12,531. Subsequent statistical analysis on the reduced transcript list is described below.Transcript expression was measured using Affymetrix Human Genome U133 plus 2.0 Gene Array . Quality control, batch correction, normalization, filtering, and log transformation was performed as previously described1% predicted, a linear regression was used. For FEV1/FVC and % emphysema beta regressions were used. Number of exacerbations was modeled as a negative binomial; since many subjects had no exacerbations, a zero-inflation portion was added. Severity of exacerbations used logistic regression. Since BDR was constrained between 0 (no response) and 1 (positive response), logistic regression was used for BDR. For all models, covariates were included. Covariates for FEV1% predicted were age, BMI, gender, smoking status, parents have COPD, and ATS Pack-years. Covariates for FEV1/FVC were age, gender, asthma, and smoking status. Covariates for % emphysema were age, gender, BMI, FEV1% predicted, and smoking status. Covariates for exacerbation frequency and exacerbation severity were FEV1% predicted, gastro esophageal reflux, St. George’s Respiratory Questionnaire (SGRQ) score, and gender. Covariates for BDR were age, gender, smoking status and asthma. Significant differences within the cohort, such as age and smoking, were controlled for in subsequent analyses.To assess the significance of transcript and metabolite levels in COPD phenotypes, a series of regression models were fit for each outcome and the form of the model depended on the distribution of the outcome. For FEV79.Then, to assess the significance of various metabolites and transcripts, the log-transformed metabolite or transcript levels were added to the models as the independent variable, and the significance of the effect of these metabolite or transcript levels were reported. For the transcripts, p ≤ 0.015 was considered significant with FDR ≤ 0.1 adjustment. For the metabolites, p ≤ 0.05 was considered significant with FDR ≤ 0.15 adjustment. All analyses were performed in R80 ID conversion tool. The significant compounds were converted to KEGG IDs using Chemical Translation Service81. The lists of statistically significant transcripts and metabolites within each COPD outcome were searched using IMPaLA82 to obtain pathways relevant to each outcome. In IMPaLA, the gene transcripts and associated values were uploaded using ‘gene symbol’, the metabolites and associated values were uploaded in the ‘KEGG’ identifier, and pathway over-representation analysis was performed. A meta-analysis using Fisher’s method provided a combined p-value and a corrected p-value for the pathways. The results were filtered as follows: KEGG pathways, p ≤ 0.05, FDR ≤ 0.2 and ≥3 pathway hits. A more lenient FDR ≤ 0.2 cut-off was used since the individual samples within each group for each outcome or phenotype were small. Figure The significant transcript probes were converted to official gene symbols using DAVID Bioinformatics Database version 6.883.Venn diagrams were created to show the overlap of statistically significant transcript probes, compounds, and pathways for the COPD outcomes using the publicly available jvenn program23 (https://humancyc.org/dashboard/dashboard-intro.shtml) using ‘Analysis’. The gene symbols for the significant transcript probes and their coefficients were uploaded in tab-delimited format for expression analysis. The KEGG IDs for the significant compounds and their coefficients were uploaded in tab-delimited format for metabolomics analysis. The interactions between the top gene transcripts and top metabolites were explored using ConsensusPathDB (http://consensuspathdb.org/)84 and selecting ‘over-representation analysis’. The selected identifiers were ‘gene symbol’ and ‘ChEBI’ to determine gene interactions and pathways interactions for the gene transcripts and metabolites, respectively. For the transcriptomics data, protein, genetic, biochemical, and gene regulatory interactions were considered; for protein interactions, only binary protein interactions with high and medium confidence were allowed, while complex interactions were ignored. Intermediate nodes were allowed for all interactions. For the metabolomics data, only KEGG pathways with a minimum overlap of 2 and a p-value cut-off of 0.05 was allowed. Finally, outcome and pathway relationships were visualized using KEGG85 to plot pathways for each outcome.Cellular function relationships for each outcome were visualized using the ‘Omics Dashboard’ of HumanCycSupplementary Documents


B-relaxation distance, provides a parameterized transition between hypergraph connectivity and graph connectivity. B-relaxation distance is sensitive to the presence of small molecules that participate in many functionally unrelated reactions in the network. We also define a score that quantifies one pathway’s downstream influence on another, which can be calculated as B-relaxation distance gradually relaxes the connectivity constraint in hypergraphs. Computing this score across all pairs of 34 Reactome pathways reveals pairs of pathways with statistically significant influence. We present two such case studies, and we describe the specific reactions that contribute to the large influence score. Finally, we investigate the ability for connectivity measures to capture functional relationships among proteins, and use the evidence channels in the STRING database as a benchmark dataset. STRING interactions whose proteins are B-connected in Reactome have statistically significantly higher scores than interactions connected in the bipartite graph representation. Our method lays the groundwork for other generalizations of graph-theoretic concepts to hypergraphs in order to facilitate signaling pathway analysis.Characterizing cellular responses to different extrinsic signals is an active area of research, and curated pathway databases describe these complex signaling reactions. Here, we revisit a fundamental question in signaling pathway analysis: are two molecules “connected” in a network? This question is the first step towards understanding the potential influence of molecules in a pathway, and the answer depends on the choice of modeling framework. We examined the connectivity of Reactome signaling pathways using four different pathway representations. We find that Reactome is very well connected as a graph, moderately well connected as a compound graph or bipartite graph, and poorly connected as a hypergraph (which captures many-to-many relationships in reaction networks). We present a novel relaxation of hypergraph connectivity that iteratively increases connectivity from a node while preserving the hypergraph topology. This measure,  Signaling pathways describe how cells respond to external signals through molecular interactions. As we gain a deeper understanding of these signaling reactions, it is important to understand how molecules may influence downstream responses and how pathways may affect each other. As the amount of information in signaling pathway databases continues to grow, we have the opportunity to analyze properties about pathway structure. We pose an intuitive question about signaling pathways: when are two molecules “connected” in a pathway? This answer varies dramatically based on the assumptions we make about how reactions link molecules. Here, examine four approaches for modeling the structural topology of signaling pathways, and present methods to quantify whether two molecules are “connected” in a pathway database. We find that existing approaches are either too permissive (molecules are connected to many others) or restrictive (molecules are connected to a handful of others), and we present a new measure that offers a continuum between these two extremes. We then expand our question to ask when an entire signaling pathway is “downstream” of another pathway, and show two case studies from the Reactome pathway database that uncovers pathway influence. Finally, we show that the strict notion of connectivity can capture functional relationships among proteins using an independent benchmark dataset. Our approach to quantify connectivity in pathways considers a biologically-motivated definition of connectivity, laying the foundation for more sophisticated analyses that leverage the detailed information in pathway databases. A major effort in molecular systems biology is to identify signaling pathways, the networks of reactions that link extracellular signals to downstream cellular responses. Computational representations of signaling pathways have increased in complexity, moving from gene sets to pairwise interactions in the past two decades . Graphs Many pathway databases –20 have These new representations have improved fidelity to the underlying biology of signaling reactions but also exhibit increased mathematical and algorithmic complexity. In this light, we examine a fundamental topological concept: when are two molecules “connected” in a signaling pathway? Defining and establishing connectivity is the first step to determining downstream or upstream elements of a molecule, which may indicate the influence of its activity or the effect of its perturbation. Connectivity is also central to computational methods for identifying potential off-target effects, determining pathway crosstalk, and computing portions of pathways that may be altered in disease.B-relaxation distance, a parameterized relaxation of connectivity that offers a tradeoff between the permissive and restrictive representations. We show that this new version of connectivity uncovers more subtle structures within the pathway topologies than previous measures, and is sensitive to the presence of small molecules that participate in many reactions. We then consider 34 Reactome signaling pathways and use B-relaxation distance to capture the downstream influence of one pathway on another. B-relaxation distance allows us to gradually relax the connectivity constraints in hypergraphs, calculating pathway influence at each step. The directed graph representation of Reactome is too highly connected to enable the discovery of such relationships, and while these relationships appear in the bipartite graph representation, they only emerge further away from the upstream “source” pathway. We describe two case studies of pathway influence that we recovered, and describe the specific reactions that contribute to the large influence score. Finally, we use the STRING database to benchmark proteins that are connected in Reactome as a signal of functional relationships, and show that stricter measures of connectivity are enriched for higher-scoring functional interactions across multiple STRING evidence channels.We first begin by considering existing connectivity measures on four distinct representations of the Reactome pathway database , 14. We We considered four established directed representations of signaling pathway topology and their associated measures of connectivity . DirecteDirected graphs represent molecules as nodes and interactions as pairwise edges. Interactions may be directed (such as regulation) or bidirected . We use a Breadth First Search (BFS) traversal to calculate the distance of a source node to all other nodes in the graph.Compound graphs represent interactions between pairs of nodes, which may be molecules or groups of molecules . We use a previously-established algorithm that traverses the BioPAX structure as a compound graph according to biologically meaningful rules Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The authors have addressed my comments fully in the revised manuscript and response to reviewers. A very nice paper!I have some minor typographical and cosmetic comments:- The type size in the compiled manuscript in all figures except for 4 is too small. If possible, it would be nice to standardize the font size across the figures.- I think that the cross-refs for Fig 3B and 3C might be flipped in the text.- Figure 4A: Is it possible to stretch A so that one can look at the same pathway across both A and B?- Figure 4B: What is the threshold for significance? Are the p-values corrected for multiple testing?- In the Abstract: “pairs of pathways statistically significant influence” → “pairs of pathways with statistically significant influence”- Page 3, line 43: k hasn’t actually been defined before this point- Page 4, line 68: “built upon” – “encoded within”?- Page 6, lines 145-162: I got a bit confused re-reading this section. The text describes three sets of nodes to remove: 2778 small molecules defined by Reactome, 3 highly connected gene products/complexed, and another 155 small molecules from PathwayCommons. Which of these are “blacklisted nodes” – the ubiquitins and NPC? Were both sets of small molecules combined into one? Do the stats on the numbers of affected nodes/hyperedges reflect the removal of all three sets of nodes?- Page 10, line 242: “under any definition” -- definition of what?- Page 11, line 307: “Reactome pathwa” → “Reactome pathway”Reviewer #2: I was a reviewer for the conference version of the paper to GLBIO. The authors have done a great job in their response to reviewers. The manuscript is in great shape, and I don't have any major comments.Minor comment: p.11 line 23: typo, pathwa --> pathwayReviewer #3: As a new reviewer for this revised submission, these comments are intended to assess only the responsiveness of the revisions to the major concerns from the original reviewers. The major changes in the revision include several new analyses that better demonstrate how B-relaxation distance can be used in practice and how this distance measure relates to existing connectivity measures. The new figures showing reachable source-target paths as a function of distance satisfy prior concerns about how the parameterized B-relaxation measure relates to parameterized versions of other connectivity measures.Several reviewers questioned how B-relaxation can be evaluated empirically with more comprehensive applications. The new evaluation of STRING edges by score and edge type is a strong response to these comments. The results depict how pairs of proteins that are B-connected or have a particular B-relaxation distance tend to have higher STRING edge scores, especially for the STRING combined scores. This emphasizes how B-relaxation provides a different way to evaluate node connectivity that is not trivially recapitulated by the existing connectivity measures or Reactome pathway membership. The reviewers noted several additional potential applications that the authors defer for future work. This is reasonable to keep outside the scope of this manuscript and shows the opportunities for future studies based on B-relaxation connectivity. In addition, the authors added new permutation-based statistical testing for their Reactome pathway influence analysis. This is an appropriate control because the authors are correct that simulating realistic pathway hypergraphs, a previously-suggested alternative way to test the influence measures, is non-trivial.The minor comments regarding the precision and recall analysis for the pathway influence study would have been a helpful addition to the manuscript. However, this is a fairly minor request and would not substantially affect the conclusions about the merits of B-relaxation.Overall, all major concerns have been directly or indirectly addressed. The GraphSpace network visualizations and software to reproduce the major analyses in the manuscript are added benefits that complement the strong manuscript. The specific comments below are all minor comments that could help improve the presentation.Minor comments:1) Reactome has a large number of pathways, but only 34 are used for the pathway influence analysis and 140 are used for the STRING edge score analysis. It is unclear why these subsets where selected and why different pathways were used for the influence and STRING analyses.2) The STRING analysis excluded the "database" channel, which used Reactome as a source of evidence. However, if the "combined score" channel includes the "database" channel as an information source, there is still some degree of circularity in this analysis.3) The figures for the STRING analysis show scores in the 0-1000 range. However, some of the text describes scores in the 0-1 range.https://doi.org/10.1016/j.cels.2019.03.001).4) Using 2.2e-308, the smallest float in Python, in the STRING significance analysis may not be the best way to present these results. Wolfgang Huber discussed this issue recently in "Reporting p Values" (Cell Systems 5) In Figure 8 and other figures that visualize hypergraph properties, it is not immediately obvious that the blue regions overlap the red regions. Even if the figures are not changed, it would be helpful to explicitly note in the caption that node d is in both the blue and the red sets.6) The Discussion notes that connected proteins that are not in the same pathway may be missed from gene-set-like enrichment approaches. For most applications of gene set enrichment, this seems like a desirable property.https://github.com/Murali-group/halp and https://github.com/annaritz/pathway-connectivity and is well-organized in general. However:7) The Python software is provided in - The GPL3 licenses are not stated directly in the manuscript.- It is not clear which developmental branch of the halp repository was used for this study, which impedes reproducibility. Ideally this version would be merged to master and released.- There are test cases, but the Travis CI testing fails.- The pathway-connectivity repository contains data from third-party sources (e.g. STRING) that may require more direct attribution in the readme.- An added bonus for both repositories would be to archive the software on third-party services like Zenodo, Figshare, or Software Heritage to ensure they are more permanently available to readers.8) Typos and phrasing:- Abstract: "pathways statistically significant" is missing a word- P3: "molecules that that participate"- P3: The end of the introduction notes relationships that "emerge for larger values of k" but the parameter k has not yet been introduced and is not intuitive before that introduction- P9: "nodes that in"- P11: "within the same Reactome pathwa"- P16: "PaxTools java" could capitalize Java**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: No: The statistics underlying the figures are not provided as supplementary tables, but the authors have provided their input data and code to generate the results on GitHub.Reviewer #2: YesReviewer #3: Yes**********what does this mean?). 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Chicken gout resulting from nephropathogenic infectious bronchitis virus (NIBV) has become a serious kidney disease problem in chicken worldwide with alterations of the metabolic phenotypes in multiple metabolic pathways. To investigate the mechanisms in chicken responding to NIBV infection, we examined the global transcriptomic and metabolomic profiles of the chicken’s kidney using RNA-seq and GC–TOF/MS, respectively. Furthermore, we analyzed the alterations in cecal microorganism composition in chickens using 16S rRNA-seq. Integrated analysis of these three phenotypic datasets further managed to create correlations between the altered kidney transcriptomes and metabolome, and between kidney metabolome and gut microbiome. We found that 2868 genes and 160 metabolites were deferentially expressed or accumulated in the kidney during NIBV infection processes. These genes and metabolites were linked to NIBV-infection related processes, including immune response, signal transduction, peroxisome, purine, and amino acid metabolism. In addition, the comprehensive correlations between the kidney metabolome and cecal microbial community showed contributions of gut microbiota in the progression of NIBV-infection. Taken together, our research comprehensively describes the host responses during NIBV infection and provides new clues for further dissection of specific gene functions, metabolite affections, and the role of gut microbiota during chicken gout. Gout is a urate crystal deposition disease that occurs when supersaturation of individual tissues with urate arises, leading to the construction of monosodium urate (MSU) crystals in and around the joints, abdomen, and organs ,2. It is11 cells g−1) and are significant sites for recycling of urea, water regulation, and carbohydrate fermentations [NIBV has become the most common IBV strain in the commercial poultry industry. It is a gamma Coronavirus in the Coronavirus family that exhibit strong renal tropism and has become one of the most familiar pathogens that induce visceral gout outbreaks ,10,11. Intations ,16. TherNowadays, omics technology is increasingly being used to understand intricate biological systems and reveal the molecular characteristics behind complex cellular phenotypes ,18. ApplThe antiviral and metabolic changes caused by viruses result in a highly complicated process that requires the coordination of diverse intertwined signalling and metabolic pathways. Therefore, a large-scale and comprehensive analysis of viral target tissues is needed to better understand the pathogenesis of the NIBV infection. We first replicated the visceral gout model by infecting chickens with NIBV and conducted an integrated transcriptomic, metabolomics, and microbiomics analyses. The characterization of these identified genes, metabolites, and microbiota clearly reflected the dynamic changes in the biological processes in chickens with NIBV infection. Overall, our research comprehensively describes the host responses during NIBV infection and provides new clues for further dissection of specific gene functions, metabolite affections, and the role of gut microbiota during chicken gout.The virulent IBV strain that was used was the SX9 strain which was isolated and stored by the College of Animal Science and Technology, Jiangxi Agriculture.n = 120) and a diseased room . Birds in each experimental room were randomly divided into three subgroups (30 birds for each subgroup), with ad libitum access to food and water. Each chicken of Dis groups was intranasally injected with 0.2 mL 105 median embryo lethal doses of strain SX9 at 28 days of age [Two hundred forty Hy-Line Brown variety birds  were stochastically split into two experimental rooms: A control room  was used as an extraction liquid, and L-2-chlorophenylalanine (1 mg/mL stock in dH2O) was added as an internal standard. The metabolites are then derivatized with the methoxy amination hydrochloride (20 mg/mL in pyridine) and the STFA regent . Finally, GC–TOF/MS analysis was performed using an Agilent 7890 gas chromatograph system coupled with a Pegasus HT time-of-flight mass spectrometer. The energy was –70 eV in electron impact mode. After 6.04 min of solvent delay, mass spectrometry data were acquired in full-scan mode with an m/z range of 50–500 at a rate of 20 spectra per second.Metabolite extraction, metabolite derivatization, metabolite detection, and data analysis followed those of Yang et al. . First, http://www.genome.jp/kegg/) and National Institute of Standards and Technology  were utilized to search for metabolic pathways. MetaboAnalyst  was used for pathway enrichment analysis.Chroma TOF4.3X software  and the LECO-Fiehn Rtx5 database were used for raw peak exacting, data baseline filtering and calibration, peak alignment, deconvolution analysis, peak identification, and integration of the peak area. SIMCA14 software package  was used for further data analysis, including principal component analysis (PCA) and orthogonal projections to latent structures-discriminate analysis (OPLS-DA). In addition, commercial databases including Kyoto Encyclopedia of Genes and Genomes  using TopHat2 [Clean data of high quality were obtained from the raw data through in-house scripts by removing those containing adapters or poly-N sequences. All clean reads were aligned to the reference genome . The PCR products were detected on 2% agarose gel electrophoresis and bands of the desired size (approximately 400–450 bp) were chosen for further experiments. Sequencing libraries were generated using TrueSeq DNA PCR-free sample preparation kit  and index codes were added. Next, sequencing was performed on an Illumina HiSeq 2500 platform .Total genome DNA from samples was extracted from the cecal contents using CTAB/SDS method. DNA concentration and quality were determined and diluted to 1 ng/μL using sterile water. The 16S rRNA genes of each sample V3–V4 region were amplified. The primer set corresponding to primers 341F-806R with the unique barcode was applied for amplification (forward 341F: CCTAYGGGRBGCASCAG and reverse 806R: GGACTACNNGGGTATCTAAT). All PCR reactions were carried out with PhusionBased on their unique barcode, the paired-end reads were granted to samples. Those files were demultiplexed and quality filtered with quantitative insights into microbial ecology (QIIME) software (version 1.7.0) . The chiHeatmap for metabolomics, transcriptomics, and microbiomics data was generated using the pheatmap package. Corrplot package in R was used to visualize the correlations coefficient (Spearman method). Availability of data and materials: RNA-seq raw data are accessible through NCBI’ database: BioProject: PRJNA510170; 16S rRNA gene sequencing raw data are accessible through NCBI’ database: BioProject: PRJNA510025.The institutional animal care and use committee of Jiangxi Agricultural University approved these animal experiments and all animal experiments adhered rigorously to the animal care guidelines of Jiangxi Agricultural University . All the birds were sacrificed using carbon dioxide euthanasia, and all attempts were carried out to minimize the suffering of the animals.n = 10, p < 0.001, Student’s t-test, The chicks inoculated with strain SX9 showed obvious clinical signs from 3 to 9 “day post-infection” (dpi). The NIBV-infected chickens were listless, huddled together, and displayed ruffled feathers, while no clinical symptoms similar to the above were observed in the Con group. The 10 dpi survival rate of all NIBV-infected chickens under analysis was 80%, and the uric acid level in the serum of the Dis group was much higher than that of the Con group . The lists of differential metabolites are shown in The differential metabolites between Dis and Con groups were the key to explaining the occurrence of gout in chickens under NIBV infection. A total of 160 metabolites displayed significantly different levels based on VIP > 1  and p < 0.05), including valine, leucine and isoleucine biosynthesis, arginine and proline metabolism, alanine, aspartate and glutamate metabolism, D-glutamine and D-glutamate metabolism, aminoacyl-tRNA biosynthesis, and beta-alanine metabolism. Among them, there are two pathways with an impact factor > 0, including valine, leucine and isoleucine biosynthesis , and arginine and proline metabolism . These pathways are related to amino acid metabolism and glycometabolism, which are involved in protein synthesis and energy production, respectively. Moreover, metabolomics highlighted N-formyl-L-methionine, a well-known agent for kidney injury for its effects on the increase of the vascular tone/resistance and reduction of renal perfusion [In addition, the pathway enrichment results are shown in erfusion , as the To study the gene expression alterations of chicken’s kidney under NIBV infection, cDNA libraries from two groups were subjected to Illumina sequencing. A total of 81.18 Gb clean data was obtained, and the Q30 base percentage of each sample was not less than 90.37%. From the mapping results, the mapping efficiency between the reads and the reference genome of each sample was between 75.59% and 79.29%. The detail of the sequencing and mapping results is provided in PCA analysis of RNA-seq replicates from the kidneys of Dis and Con group revealed a great deal of variability a. A totahttp://www.geneontology.org) project was applied to explore the potential biological functions of DEGs. In this study, the 20 most enriched GO terms classified by molecular function (MF), cellular components (CC), and biological processes (BP) terms were listed in padj < 0.05) in chickens with NIBV infection. To further study the biochemical metabolic pathway and signal transduction pathways related to NIBV infection, KEGG analysis and pathway enrichment analysis were conducted in the KEGG pathway database. There were 901 of the 2868 DEGs that were annotated to 177 pathways in KEGG analysis. The level-2 KEGG classes are shown in p-value < 0.05 as determined by a Fisher’s exact test) are marked with red number. In environmental information processing pathways which include cytokine–cytokine receptor interaction and cell adhesion molecules (CAMs) were dramatically regulated. In cellular processes pathway, peroxisome was dramatically affected. In metabolism pathways, metabolism of xenobiotics by cytochrome P450, drug metabolism-cytochrome P450, pyruvate metabolism, arginine and proline metabolism, glycolysis/gluconeogenesis, and fatty acid degradation were significantly regulated. In organismal systems pathways, intestinal immune network for IgA production and Toll-like receptor signaling pathway were significantly regulated. Furthermore, most of these pathways of the upregulated genes enriched were immune system-related pathways, and “cytokine-cytokine receptor interaction” was the most represented pathway. Among these pathways we found upregulated, “Toll-like receptor signalling pathway”, “NOD-like receptor signalling pathway” and “RIG-I-like receptor signalling pathway” are pattern recognition signal receptor pathways involved in innate immunity. Simultaneously, the transcriptome showed that the “peroxisome” and amino acid metabolite pathways were suppressed in chickens infected with NIBV. Based on the above results, we predicted a schematic diagram of important pathways for chickens in the NIBV infection processes , family Lactobacillaceae  and the genus Bacteroides , genus Lactobacillus , genus Candidatus Arthromitus , and genus Turicibacter  in the Dis group (Proteobacteria (LDA = 4.7644853231) phylum was most dominantly present in Dis group. At the genus level, the microbiota of the Con group was enriched with Bacteroides (LDA = 4.80848153017) from the Bacteroidetes phylum and Lactobacillus (LDA = 4.22406025945) from the Firmicutes phylum. At the species level, the microbiota of the Con group was enriched with Bacteroides vulgatus (LDA = 4.79658975077) from the Bacteroidetes phylum.The top 10 microbes at the phylum d, familyis group g,h. LEfSis group i,j. DiffTlr7  and proline , isoleucine , threonine , and valine . Conversely, strong negative correlations were found between glutathione synthetase (GSS) and glutamine . Interestingly, we observed that most genes related to innate immune responses had strong positive correlations with differentially abundant metabolites, such as amino acids and fatty acids.The above data describe that NIBV infection alters kidney gene expression and metabolic pathways a. We filProteobacteria. Furthermore, other metabolites, including canavanine, 2,4-diaminobutyric acid, 5,6-dihydrouracil, malonamide, thymine, phenylalanine, 1,3-diaminopropane, 4-acetamidobutyric acid, proline, threonine, isoleucine, valine, and oxalacetic acid, which increased in the kidney of NIBV-infected chickens, were positively correlated with the presence of the phylum Proteobacteria. These observations indicated that the significantly modulated gut microbiota was correlated with host metabolic disorders.The gut microbiota is deliberated a massive “organ” able to perform complex functions and thereby produce a myriad of differentially abundant metabolites. To investigate the functional correlation between the gut microbiome changes and host metabolome alterations, a correlation plot was visualized by calculating Spearman’s correlation coefficients d. The reTlr4 transcription levels in this experiment. In addition, the transcriptomic analysis showed that NIBV infection also activated the RIG-I-like receptor signalling pathway  signalling pathway are usually activated in response to infections to stimulate inflammatory responses ,36,37. I2•− [Peroxisomes act to eliminate a microbial infection by modulating the canonical innate immunity pathways through ROS signalling . Several2•− ,54. ConsThe kidney is responsible for the elimination of 70% of the daily UA production . ATP-binTlr7 and proline, isoleucine, threonine, and valine. According to reports, isoleucine could maintain the development of immune organs and cells and stimulate the secretion of immune molecule substances in humans and animals [The present study highlights the correlation between differential expression of genes and differential abundance of metabolites in significantly enriched pathways, and the results showed that those differentially abundant metabolites that map in amino acid metabolism pathways have strongly positive correlations with DEGs related to innate immune responses. It is well known that organisms fuel or instruct the immune response to pathogen threats by modulating metabolic pathways . Innate  animals ,64, vali animals , and it  animals . We obseBacteroides, Lactobacillus, and Bacteroides vulgatus were significantly reduced in the cecal microbiota of NIBV-infected chickens based on LEfSe analysis. Similar to many other Bacteroides species, especially Bacteroides vulgatus which was shown to promote intestinal homeostasis, the Bacteroides vulgatus-mediated induction of semimature dendritic cells is associated with inflammation silencing [Bacteroides vulgatus promotes intestinal flora imbalance and activation of the immune system. Furthermore, it is well accepted that Lactobacillus may lower serum uric acid levels by reducing intestinal absorption of purines in humans [Lactobacillus in the caecum of the model group infected with NIBV further promoted an increase in uric acid levels in serum. The results of our correlation analysis further confirm this point, that is, the negative relationship between the abundance of Bacteroides vulgatus, Lactobacillus and the uric acid concentration. However, the mechanism of NIBV infection leading to a decrease in their abundance still needs further study. Taken together, multitudinous studies to date endorse the concept that a bloom of Proteobacteria in the gut reflects gut dysbiosis or an unstable gut microbial community [Proteobacteria, the increase in the abundance of Proteobacteria in this experiment may be closely related to the immune and metabolic changes caused by NIBV infection.Previous research has confirmed that the dysfunction of the gut microbiome is tightly associated with kidney and liver diseases, including liver cirrhosis , liver cilencing ,71. Thusn humans . Therefoommunity . In viewThis report is the first time that multi-omics approach was employed to profile the metabolic changes and immune responses in kidney, as well as the effects on the intestinal microbiome during NIBV infection. Our results showed that NIBV significantly increased uric acid synthesis, inhibited the function of peroxisomes, and significantly elevated the pattern recognition receptor signalling pathways. In summary, our study comprehensively describes the host responses during NIBV infection and provides new clues for further dissection of specific gene functions, metabolite affections, and the role of gut microbiota during chicken gout.


Leishmania spp. have not translated into the development of effective new chemotherapeutic compounds. As a result, biomarkers as surrogate disease endpoints present several potential advantages to be used in the identification of targets capable of facilitating therapeutic interventions considered to ameliorate disease outcome. More recently, large-scale genomic and proteomic analyses have allowed the identification and characterization of the pathways involved in the infection process in both parasites and the host, and these analyses have been shown to be more effective than studying individual molecules to elucidate disease pathogenesis. RNA-seq and proteomics are large-scale approaches that characterize genes or proteins in a given cell line, tissue, or organism to provide a global and more integrated view of the myriad biological processes that occur within a cell than focusing on an individual gene or protein. Bioinformatics provides us with the means to computationally analyze and integrate the large volumes of data generated by high-throughput sequencing approaches. The integration of genomic expression and proteomic data offers a rich multi-dimensional analysis, despite the inherent technical and statistical challenges. We propose that these types of global analyses facilitate the identification, among a large number of genes and proteins, those that hold potential as biomarkers. The present review focuses on large-scale studies that have identified and evaluated relevant biomarkers in macrophages in response to Leishmania infection.Leishmaniasis is a vector-borne, neglected tropical disease with a worldwide distribution that can present in a variety of clinical forms, depending on the parasite species and host genetic background. The pathogenesis of this disease remains far from being elucidated because the involvement of a complex immune response orchestrated by host cells significantly affects the clinical outcome. Among these cells, macrophages are the main host cells, produce cytokines and chemokines, thereby triggering events that contribute to the mediation of the host immune response and, subsequently, to the establishment of infection or, alternatively, disease control. There has been relatively limited commercial interest in developing new pharmaceutical compounds to treat leishmaniasis. Moreover, advances in the understanding of the underlying biology of  Leishmania, the causative agent, is transmitted to vertebrate hosts, including humans, by a bite from the sand fly during blood-feeding. Its pathogenesis involves the stimulation of different types of host immune responses that result in distinct clinical outcomes , in which a set of transcriptionally disturbed genes belonging to a common group of canonical pathways or biological processes reflect alterations in the pathways themselves. Gene co-expression networks can also be used to infer which genes are related to an infectious process. These networks offer the distinct advantage of enabling the discovery of previously unknown relationships by building on the notion of “guilty by association”  and other microarray-specific downstream analyses. Many studies have compared the performance of these algorithms under various scenarios, including the variation in the number of replicates, sequencing depth, and the use of other tools concomitantly, such as in mapping steps . Statistical strategies for detecting DEGs based on RNA-seq-derived count data rely mainly on the use of Poisson or negative binomial distributions. The first has the advantage of being simpler, with a single parameter, λ, entirely defined by the mean, and having a variance equal to the mean. However, this property limits its application when biological replicates are available, when this assumption regarding variance does not hold because biological replicates typically present high variability  and fold-changes (FC), which can be largely arbitrary and lead to the loss of true DEGs (if too conservative) or their false inclusion (if too relaxed). The finding that the different DEG tools present slightly different true positive and false positive rates performance further complicates the matter  were generated. To circumvent some of these issues and obtain a more expanded view of omics datasets, other analytical approaches can be used, and we detail some alternatives in the two sections that follow.A list of DEGs, however, is only a first step toward defining the biological processes that appear altered in a given experiment, and in most settings, the sole study of these genes will be too reductionist in nature and mostly ineffective as it must be conducted in a gene-by-gene fashion. Additionally, an intrinsic problem when detecting DEGs is the need to establish thresholds for h et al. . Others,h et al.  have rel2H, 13C, 15N, and 18O) followed by quantification using MS. Technological improvements in the field of MS and chromatography have leveraged the development of high-throughput proteomic analyses that permit a higher proteome coverage and are collectively termed label-free quantitative proteomics (LFQP), a highly accurate method that presents less susceptibility to technical errors. LFQP relies on measurements of individual samples by MS, and quantifies proteins based on either peak intensity or spectral counts of each peptide. Each of these broad techniques have their specificities regarding sample preparation, purification, separation, and ionization method, making the recommendation of specific computational tools for their analysis particularly more challenging than for RNA-seq data. For instance, the choice of labeling method will inform the corresponding choice of appropriate analytical packages, and a software that works well for 15N label-based quantification may not be suitable for analyzing 18O data  may lead to loss of important biological aspects of the data. Rather, we argue that those working with high-throughput omics datasets will benefit from more ample analyses, such as those discussed in the following sections.Proteomics allows the identification and quantification of many  of proteins present in a given sample. Recent advances in the experimental approaches available for accessing the proteome have allowed an improved resolution, with less input material, when compared to more classical techniques such as 2D gel electrophoresis (2D-GE) that can be followed by liquid chromatography coupled to mass spectrometry (LC-MS). Proteome quantification using MS can be generally classified as label-based or label-free approaches. The first relies on the differential labeling of samples using stable isotopes  of associated biological features than would be expected by chance. These biological features are usually cellular processes based on a common vocabulary (or ontology), including the Gene Ontology (GO) , there is over-representation (2 (chi-squared), hypergeometric, and binomial, and evaluate whether there is an overrepresentation of biomolecules in the corresponding annotations from the data sources (such as genes in a pathway) that could be deemed statistically significant, usually after correcting for multiple hypothesis tests. One of the most used tools in this class is DAVID, which registers over 15,900 citations , an appropriate metric could be, e.g., FC-values ordered in a decreasing manner, where the extremes represent biomolecules that are upregulated or downregulated (at the bottom) in a comparison of interest. Alternative metrics could be used for other data types, such as p-values and abundance. The Gene Set Enrichment Analysis tool  list-based and (2) rank-based methods. The first relies on a set of biomolecules of interest that can be derived from the list of DEGs (if working with RNA-seq data), proteins (if working with proteomics), or compounds, if the obtained data are from metabolomics experiments. To calculate the significance of the enrichment, these tools usually rely on statistical methods based on distributions, such as χvoom/limma and edgeR's pipeline for the identification of DEGs is indicative that somewhat “standard” tools are in use  and scales  that form these systems are intrinsically connected, such that single pathways or cellular processes seldom occur in isolation in a cell. Instead, the different cellular programs perform in a coordinated manner to achieve their biological functions. This behavior is amenable to modeling using a network-based framework. While there are various ways of applying network-based techniques, in this review, we focus on the construction of correlation networks and module detection approaches, but some methods to infer regulatory patterns are also described.While enrichment-based methods allow one to obtain a wider view of high-throughput omics datasets, and the elements (or nodes) that form these networks can be genes, proteins, or metabolites. In simple correlation networks, an interaction (or edge) between any two nodes is established when the value of their correlation, which can be obtained using Pearson's r or Spearman's ρ, passes a given threshold. A number of biological questions have been approached using this framework, and some applications in the context of leishmaniasis include the study of distinct states of infection with Leishmania infantum  or regulatory aspects (such as activation of a transcription factor leading to increased expression of the regulated gene). The choice of correlation metric for constructing these networks has been a subject of investigation  = xβ, where x represents elements in the correlation matrix, and a value of β ≥ 1  is chosen by the user such that the resulting correlation network adheres to a scale-free property while maintaining high connectivity , a strategy that may lead to a loss of information because correlations that fall even slightly below the threshold will be discarded. Defining such limits can also be overly arbitrary and dataset-specific. In contrast, correlation networks constructed using WGCNA mitigate these issues by applying a mathematical transformation to the correlation values, yielding a weighted network where the edge strengths are bounded by the transformed correlation values. The algorithm begins by first obtaining a correlation matrix from the input, usually expression data, but other omics datasets in a completely data-driven fashion. Although we have focused mostly on expression data to exemplify the use of this technique, correlation networks are agnostic to the data type and can be constructed using any biomolecule with interactions that are amenable to modeling using a systems framework, including proteins , both human macrophages and cells incubated with latex beads for 4 hpi were shown to be clustered together, indicating that macrophages in culture can undergo phagocytosis without disturbing their steady-state transcriptome. As previously described ], as opposed to later time points after infection , seem to be ideal for the identification of DEGs or specifically modulated pathways in mouse or human macrophages . These potential biomarkers are proteins that have previously been associated with an immunomodulatory response , which are membrane proteins implicated in the regulation of vesicle docking and fusion in exocytosis  related to the Bcl2 inhibitor of apoptosis, which is associated with inhibiting macrophages from resisting cell death , hemoxygenase1 (Hmox1), epithelium growth factor receptor (Egfr), vascular endothelial growth factor (Vegf), colony-stimulating factor 1 (Csf1), and colony-stimulating factor 3 (Csf3) , Csf1 and colony-stimulating factors 2 (Csf2), and superoxide dismutase 2 (Sod2). This finding suggests that L. major-infected macrophages probably evolved the ability to inhibit a deleterious innate inflammatory immune response , e.g., retinoic acid-inducible gene-(RIG)-I-like receptor, nucleotide-binding oligomerization domain-NOD-like receptor, and Toll-like receptor; for the immune system signaling pathways, the detected genes were either related to the cytokine-cytokine receptor interaction pathway, including Fc epsilon RI, Jak-STAT, T cell receptor, NF-kB, mitogen-activated protein kinase (MAPK), TNF, vascular endothelial growth factor (VEGF), ErbB, FoxO, hypoxia-inducible factor 1 (HIF-1), and phosphatidylinositol 3-kinase-Akt [PI3KAkt], or related to the TGF-β signaling pathway. In addition, among the downregulated genes in both murine and human models of infection, KEGG identified pathways related to energy metabolism (glycan and amino acid degradation), lysosome structure and processes and apoptosis. KEGG enrichment analysis identified the FoxO signaling pathway among the genes that were either up- or downregulated, which is implicated in the regulation of cell growth, gluconeogenesis, and adipogenesis. The findings presented in RNA-seq technology raise the possibility of translating these pathways to biomarkers as surrogate endpoints following extensive validation studies.Fernandes et al.  generateLeishmania. However, these studies did not provide information regarding translational and post-translational modifications and protein localization, which are essential to understanding gene functions. Thus, studying the proteins encoded by mRNAs is crucial for understanding the biological processes. Therefore, proteomic studies have gained significant relevance with the advancements in large-scale technologies and represent one of the most important tools for biomarker investigation. This approach has provided a wealth of protein expression data on the host response to infection by different pathogens , was the first attempt to employ a large-scale proteomic analysis to identify host cell proteins expressed in response to Leishmania infection and, among them, potential macrophage biomarkers that could be related to a susceptibility or resistance profiles , coronin 1B, HIF-1α, cytochrome C oxidase 6B (cox6B), osteoclast-stimulating factor-1 (OSTF1), protein phosphatase 2 (PP2), heterogeneous nuclear ribonucleoprotein F (HNRPF), PYD And CARD domain-containing protein (PYCARD), RAB1, Serpin, ribosomal protein S2 (RPS2), and myosin light chain  inhibitor, as a potential drug against leishmaniasis , an enzyme in the pentose phosphate pathway, compared to L. major-infected cells ], histones, and DNA repair and replication was also upregulated after L. donovani infection. Of note, several proteins identified in this study as differentially expressed between non-infected and L. donovani-infected macrophages had not been previously associated with the host cell response to Leishmania infection. Another exciting result of this work was the increased expression of the mitochondrial antiviral signaling protein (MAVS) after Leishmania infection. This protein is known to activate NF-κB and interferon (IFN) regulatory factors (IRF3 and IRF7), inducing the synthesis of type I interferons (IFN-α and IFN-β), which are essential during antiviral signaling. The silencing of endogenous MAVS expression by RNAi inhibits the activation of NF-κB, IRF3, and IRF7, leading to the blockade of interferon production and favoring viral infection , in response to arginine pool reduction in the host cell. To study phosphoproteins involved in the signaling pathway implicated in this response, the authors used a di-methylation tagging technique to investigate changes in the phosphorylation profile of Leishmania promastigotes after 5 and 15 min of arginine deprivation. Phosphoproteomic analysis revealed an increased phosphorylation of mitogen-activated protein kinase 2 (MPK2), indicating that this kinase could be involved in the arginine-deprivation response during Leishmania infection (Goldman-Pinkovich et al., Leishmania infection, the utilized approach could be of great importance to identify potential markers that could be used for the development of new drug treatments and to understand the disease outcome.The most recent study using a proteomic approach to better understand the host cell response to Leishmania parasites modulate the host cell proteome profile, reinforcing the idea that proteomic technology is a powerful technique that should be further explored by researchers to discover its full potential. Proteomics combined with bioinformatics represents a robust approach to investigate the global host response to infection and to identify new potential molecular markers that can control the fate of both host cell and pathogen during infection (Jean Beltran et al., Leishmania infection can be used as novel biomarkers and targets for the control of Leishmania infection.Taken together, these few studies show that Leishmania infection. The authors recommend compiling the findings from the studies referenced herein using macrophages, together with those obtained from blood, tissue and other cell types, and also relevant results from similar future studies, to form a complete set of potential biomarkers to aid in global analysis using transcriptomics, proteomics and metabolomics approaches. This data could be then used to identify and subsequently validate specific genes and proteins capable of enhancing the ability of researchers to identify host cell signatures at early time points in the context of leishmaniasis, in an effort to predict disease control or progression, and even the prognostic response to therapy.Combining the results from transcriptomic and proteomic investigations offers a more comprehensive body of information for the identification of possible biomarkers in PV was the main responsible for conception and design and also for the formulation of the final version of this article review. PR and JdM made substantial contributions to conception and design, and also participate in drafting the article or revising it critically for important intellectual content.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.