Aberrant cell signaling can cause cancer and other diseases and is a focal point of drug research. A common approach is to infer signaling activity of pathways from gene expression. However, mapping gene expression to pathway components disregards the effect of post-translational modifications, and downstream signatures represent very specific experimental conditions. Here we present PROGENy, a method that overcomes both limitations by leveraging a large compendium of publicly available perturbation experiments to yield a common core of Pathway RespOnsive GENes. Unlike pathway mapping methods, PROGENy can (i) recover the effect of known driver mutations, (ii) provide or improve strong markers for drug indications, and (iii) distinguish between oncogenic and tumor suppressor pathways for patient survival. Collectively, these results show that PROGENy accurately infers pathway activity from gene expression in a wide range of conditions. Deregulation of signalling is responsible for many cancer phenotypes. Leveraging available perturbation data, here the authors assess large-scale pathway activity patterns based on consensus downstream readout genes, enabling accurate prediction of the effects of mutations and small molecules. A wealth of molecular data have become available that reflects a cell’s state in different diseases. The challenge that remains is how to derive predictive and reliable biomarkers for disease status, treatment opportunities, or patient outcome in a way that is both relevant and interpretable. Of particular interest are methods that infer and quantify deregulation of signaling pathways, as those are key for many processes underpinning different diseases.1. Here, efforts like the TCGA2 and ICGC3 have pioneered molecular characterization of primary tumors on a large scale. In addition, the GDSC5 and CCLE6 have focussed on preclinical biomarkers of drug sensitivity in cancer cell lines. These initiatives have provided profound insight in the molecular markup of the disease. However, putting the genomic alterations investigated in the functional context of the pathways they alter may provide additional information on mechanisms of pathogenesis and treatment opportunities7.A particular example of this is cancer, which is largely caused by cell signaling aberrations created by driver mutations and copy number alterations8 or Reactome9) using Gene Set Enrichment Analysis10, or other methods that are able to take pathway structure into account13. While these methods can be applied to almost any pathway, they are based on mapping transcript expression to the corresponding signaling proteins and hence do not take into account the effect of post-translational modifications 5 and patient survival in 7254 primary tumors spanning 34 tumor types using TCGA data. We found that PROGENy significantly outperforms existing methods for these tasks.We performed a systematic comparison of PROGENy and other commonly used pathway methods for 11 cancer-relevant pathways. We investigated how well each method can recover pathway perturbations and constitutive activity mediated by driver mutations in The Cancer Genome Atlas (TCGA)We curated  associated with the pathway that was experimentally perturbed. The associations of a pathway signature with other pathways are weaker (p > 10–5), except for EGFR with MAPK/PI3K and TNFa with NFkB/MAPK  than EPSA25 on our curated set of experiments, and in addition to SPEED26 also infers the sign of pathway activity . KRAS mutations and amplifications show an increase in inferred MAPK/EGFR activity. Other methods do not detect a strong activation of the MAPK/EGFR pathways given those alterations  that additionally activate TNFa/NFkB (FDR < 10–15).In terms of proliferative signaling, we find that PROGENy identifies TP53 mutations, PROGENy finds a significant reduction in p53/DNA damage response activity (FDR < 10–64) and activation of the PI3K and Hypoxia pathways (FDR < 10–15). This is in contrast to loss of TP53, where we only find a reduction in p53/DDR (FDR < 10–3), but no strong evidence of modification of any other pathway (FDR > 0.04). The dual nature of TP53 mutations and loss are in line with the recent discovery that TP53 mutations can act in an oncogenic manner in addition to disrupting its tumor suppressor activity, which has been shown for individual cancer types31. In addition, this analysis suggests a link between TP53 mutations and genes that are induced by activation of canonical oncogenic signaling such as PI3K or the hypoxic response. Other methods  are associated with an expected stronger induction of hypoxic genes32 compared to other cancer types (FDR < 10–200). It is the only method to recover hypoxia as the strongest link with VHL mutations, while the other methods primarily report expression changes in unrelated pathways  compared to an effect on the PI3K-responsive genes  or resistance (absence of p53 activity) to this drug that our method captures, but none of the pathway expression-based methods do.We found 178 significant associations for PROGENy .Considering the overall number of associations, the other pathway methods provided a lower number across the range of significance Fig. . PROGENyBRAF, NRAS, or KRAS mutations using MAPK pathway activity and the MEK inhibitor Trametinib , 9130 (Trametinib), or 104 fold (Dabrafenib) more sensitive than those where it is inactive. For Trametinib, cell lines with active MAPK, but no mutation in BRAF, KRAS, or NRAS are 15 times more sensitive than cell lines that harbor a mutation in any of them, but our analysis determined that MAPK is inactive . This positive effect of p53 on survival is supported by the fact that ACC samples do not harbor any previously reported gain-of-function TP53 variants31. Kidney Renal Clear Cell Carcinoma (KIRC) and Low-Grade Glioma (LGG) show decreased survival with TNFa and JAK-STAT pathways, respectively, where specific activating mutations are much less known than for EGFR/MAPK. For these three associations, the top and bottom quartiles of PROGENy pathway activity were able to stratify patients in groups with over 25% difference in one year survival . While this approach has generated many important insights into cancer biology, it does not directly make statements about the impact of those aberrations on cellular processes and signal transduction in particular. Pathway methods, mostly used on gene expression, have produced mixed results when it comes to delivering actionable evidence. This can in part be due to lack of robustness, as suggested by the heterogeneity in responses of individual signatures  and cell death (Trail) pathways for patient survival.We found that despite the heterogeneity of individual gene expression experiments, PROGENy closely corresponds to pathway perturbations. PROGENy can recover the impact of known driver mutations from basal-gene expression, but also identify cases where a pathway is active without their presence. In contrast, pathway mapping only recovers known associations, where this effect is mediated by expression changes in pathway members, such as Overall, our results suggest that PROGENy provides a better measure of pathway activity than other pathway methods, irrespective of whether the latter was derived from gene sets or directed paths. The latter can be used for many more pathways, as information on the pathway components is more often available than perturbation experiments. However, our results indicate that one should be cautious when interpreting the expression level of a pathway as its activity.We have shown that PROGENy is able to refine our understanding of the impact of mutations, as well as their utility for cell line drug response and patient survival. It provides a strong evidence that in order to infer pathway activity, e.g., for patient stratification, a downstream readout should be used instead of mapping transcript expression levels to signaling molecules.While PROGENy provides a good estimate of pathway activity in large and heterogeneous data sets, signatures derived from, for instance, a specific tissue may still more closely reflect activation status given the same context. We see a hint of this when applying the Gatza et al. signatures for the TCGA breast cancer cohort, but more studies will be required to fully elucidate the differences between a common response and additional transcriptional modules that may not always be activated. We believe that our curated set of experiments and computational pipeline will be useful to further investigate this aspect of specialized vs. consensus signatures and when either of them should be used.http://gdac.broadinstitute.org/), release 28 January 2016.To obtain the TCGA data, we used the Firehose tool from the BROAD institute  for each TCGA study separately, to be able to use linear modeling techniques with the count-based RNA-seq data. The data used corresponds to 34 cancer types and a total of 9737 tumor and 641 matched normal samples.For gene expression, we used all data labeled “Level 3 RNA-seq v2”. We extracted the raw counts from the text files for each gene, discarded those that did not have a valid HGNC symbol, and averaged expression levels where more than one row corresponded to a given gene. We then performed a variance stabilizing transformation  and the IC50 values for each drug–cell line combination. For computing pan-cancer associations, we used the subset with TCGA-like cancer type label, leaving 805 cell lines.We used version 17a of the Genomics of Drug Sensitivity in Cancer (GDSC) datahttps://portals.broadinstitute.org/ccle). For microarray data (2013–03–18), we performed RMA normalization, and mapped the probes to HGNC gene symbols. We used drug profiling data version 2012–02–20 and drug metadata version 2015–02–24.We downloaded the Cancer Cell Line Encyclopedia (CCLE) microarray gene expression and drug sensitivity data from the CCLE web page , Ly294002  or the following ligands from Peprotech: TNF-alpha (20 ng/ml), TGF-beta 1 (10 ng/ml), IFN-gamma (50 ng/ml). Cell lines have been tested for Mycoplasma infection using Tenor GeM Classic (Minerva Biolabs).HEK293∆RAF1:ER cells were acquired and cultured as previously described38. Reads were mapped with STAR aligner v2.5.0c39 on hg19 using GENCODE v19 for annotation and quantified with subread featureCounts40. The preprocessing pipeline was written in Snakemake41. Raw read counts were then normalized with DESeq2 and variance stabilization transformed36.After 4 h of treatment, total RNA was extracted with Qiagen RNeasyMini Kit. Sequencing libraries were prepared using Illumina TruSeq mRNA Library Prep Kit v2 and sequenced on Illumina HiSeq 2000. Read quality was assessed using FastQC and sequencing adapters were trimmed using cutadapt42. A total of 10 µg protein was analyzed. The following analytes were used: AKTS473, c-JunS63, ERK1/2T202,Y204/T185,Y187, IkBaS32,S36, JNKT183,Y185, MEK1S217,S221 and mTORS2448. The beads and detection antibodies were diluted 1:3. For data acquisition, the xPONENT software was used.Protein extracts of cells were prepared by incubation with cell lysis buffer . The Bio‐Plex Protein Array system  was used, as described earlierThe following antibodies were used for western blot measurements: rabbit anti human p-SMAD2 (Ser465/467) (138D4) #3108, rabbit anti human p-Stat3 (Tyr 705) #9131 and rabbit anti human ß-Tubulin #2146. All primary antibodies were diluted 1:1000 and obtained from Cell Signaling Technology. Electrophoresis was performed and lysates were transferred onto nitrocellulose membranes. Unbound protein sites were blocked with 1:2 Odyssey Blocking Buffer (from Li‐COR) and PBS. Thereafter, specific proteins were detected by incubation with primary antibodies diluted in the same blocking buffer containing 0.1% Tween‐20 overnight at 4 °C followed by near‐infrared dye labeled secondary antibodies. For detection of phosphorylated SMAD2 and Stat3, a total of 30 and 60 µg protein was used, respectively. Membrane Images were taken using Li‐COR Odyssey Fc. The bands were quantified by determining the background corrected total intensities using ImageStudio software (Li-COR). All Signals were normalized to ß-Tubulin.Two biological replicates were measured both after 30 min and 1 h and outcomes were analyzed together by calculating log2 ratios to their respective solvent control (BSA).Our method is dependent on a sufficiently large number of available perturbation experiments that activate or inhibit one of the pathways we were looking at. The following conditions needed to be met in order for us to consider an experiment: 1) the compound or factor used for perturbation was one of our curated list of pathway-perturbing agents  and took their estimate (the fitted z-scores) as coefficient. We set all other gene coefficients to 0. This way, we obtained a matrix with HGNC symbols in rows and pathways in columns, where each pathway had 100 non-zero gene coefficients . In practice, this is achieved by a simple matrix multiplication between the gene expression matrix  and the model matrix :Each column in the matrix of perturbation-response genes corresponds to a plane in gene expression space, in which each cell line or tumor sample is located. If you follow its normal vector from the origin, the distance it spans corresponds to the pathway score 25 with the following modifications: (1) we used the mean of the treated and untreated arrays for each experiment in order to avoid bias by experiment size; (2) we calculated significance of differential expression with limma44, not SAM; and (3) we selected the top 100 significant genes due to very different gene numbers at 5 or 10% FDR.The EPSA model was built as previously publishedhttps://speed.sys-bio.net and running the method on our perturbation experiments, GSEA using Kolmogorov–Smirnov statistic), we use the negative logarithm of the p value as pathway score. For methods that score individual samples , we use the difference of the mean between basal and perturbed arrays for each experiment. For both, we normalize the pathway scores per experiment because of the different strength of perturbations. We then quantify how well each pathway signature ranks experiments, where a pathway was perturbed before experiments where a pathway was not perturbed by the Receiver Operator AUC. We quantify if a given method has a consistently higher AUC than another across pathways using a binomial test . For methods that work on differential expression  and gene expression upon perturbation, which were not included in any of the model building. For each of those experiments, we quantified the Blot bands in the original publication 8 categories, as well as Gatza et al. signatures 11 in our analyses, focussing on the subset of pathways used by the other methods  and a model of the cell signaling network48 from the corresponding TCGA publication (https://tcga-data.nci.nih.gov/docs/publications/coadread_2012/). We normalized our gene expression data from both GDSC and TCGA using ranks to assign equally spaced values between 0 and 1 for each sample within a given tissue. We then ran PARADIGM inference using the same options as in the above publication for each sample separately. We used nodes in the network representing pathway activity to our set of pathways  we use the difference in GSVA without kernel density estimator due to low sample numbers. For PROGENy, we exclude the experiment we quantify from model building .pheatmap) activation patterns as heatmaps  and show the relative  and strong amplifications (2) as altered, no change (0) as basal and discarded intermediate values  from our analysis. We focussed our analysis of the mutations and copy number alterations on the subset of 464 driver genes that were also used in the GDSC. We used the sets of mutations and CNAs to compute the linear associations between samples for all different methods we looked at.For comparing the impact of mutations across different pathway methods, we used TCGA cohorts, where tissue-matched controls were available, leaving 6549 samples across 13 cancer types. For mutated genes, we considered all genes that had a change of coding sequence  as mutated and all others as not mutated. For copy number alterations (CNAs), we used the thresholded GISTIC50s and 11 inferred pathway scores conditioned on MSI status, doing a total of 2915 comparisons for which we correct the p values using the False Discovery Rate. For pan-cancer associations, we used the cancer type as a covariate in order to discard the effect that different tissues have on the observed drug response. While this will also remove genuine differences in pathway activation between different cancer types, we would not be able to distinguish between those and other confounders that impact the sensitivity of a certain cell line from a given tissue to a drug. Our pan-cancer association are thus the same of intra-tissue differences in drug response explained by inferred  pathway scores.We performed drug association using an ANOVA between 265 drug ICNRAS, KRAS, or BRAF) with a discretized pathway score (upper and lower quartile vs. the rest), as well as the combination between the upper quartile of tissue-specific pathway scores and presence of a MAPK mutation. For a BRAF inhibitor, we show additional stratification on top of BRAF mutations, and for Nutlin-3a on top of TP53 mutations.We selected four of our strongest associations to investigate whether they provide additional information of what is known by mutation data. For two MEK inhibitors, we show the difference between wild-type and mutant MAPK pathway  to calculate survival associations for pan-cancer and each tissue-specific cohort. For the pan-cancer cohort, we fitted the model for each pathway and method separately, regressing out the study of origin and age of the patient. For the tissue-specific cohorts, we regressed out the age of the patients. We adjusted the p values using the FDR method for each method and study separately. We selected a significance threshold of 5 and 10% for the pan-cancer and cancer-specific associations for which we show a matrix plot and a volcano plot of associations, respectively.Starting from the pathway scores derived with GO/Reactome GSEA, SPIA, Pathifier, PARADIGM, and our method on the TCGA data as described above, we used Cox Proportional Hazard model  under accession number GSE97979. Phosphoprotein measurements are available as Supplementary Data 2.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2


Clinical pathways are evidence based multidisciplinary team approaches to optimize patient care. Pleural diseases are common and accounted for 3.4 billion US $ in 2014 US inpatient aggregate charges (HCUPnet data). An institutional clinical pathway  was implemented in conjunction with a dedicated pleural service. Design, implementation, and outcomes of the pleural pathway  in comparison to a previous era  are described. “pathway” era. System-wide consensus regarding institutional guidelines for care of pleural disease was achieved. Preimplementation activities included training, acquisition of ultrasound equipment, and system-wide education. An audit database was set up with the intent of prospective audits. An administrative database was used for harvesting outcomes data and comparing them with the “prior to pathway” era. Tuality Healthcare is a 215-bed community healthcare system in Hillsboro, OR, USA. With the objective of standardizing pleural disease care, locally adapted British Thoracic Society guidelines and a centralized pleural service were implemented in then = 9) compared to the “prior to pathway” era (n = 17). Gains in average case charges  and average length of stay (3.65 versus 2.78 days/case) were seen in the “pathway” era. 54 unique consults were performed. A total of 55 ultrasound examinations and 60 pleural procedures were performed. All-cause inpatient pleural admissions were lower in the “pathway” era ( A “pleural pathway” and a centralized pleural service are associated with reduction in case charges, inpatient admissions, and length of stay for pleural conditions. Clinical pathways have been described as evidence based multidisciplinary team approaches to optimize patient care for a presenting clinical diagnosis , 2. SaluA descriptive observational study design was used for studying outcomes of a clinical pathway and a centralized pleural disease service in a community healthcare system.Large spontaneous pneumothoraces: emergency room (ER) insertion of large bore chest tubes (>14 F) was followed by inpatient admission and general/thoracic surgery consultation.Unilateral pleural effusions presenting with mediastinal shift were admitted to the hospital. Stable unilateral pleural effusions were aspirated in the scheduled outpatient radiology setting and followed by the referring physician.Non-ultrasound-guided management of pleural diseases was carried out in the pulmonary clinic.Prolonged air leaks  were managed with watchful waiting and if needed via surgical intervention by thoracic surgery.Fibrinolytics (tPA + DNase) were not used in management of empyema.Tuality Healthcare  is a 215-bed healthcare system in Hillsboro, OR, USA. It is comprised of two hospitals and additional outpatient treatment facilities. Outpatient and inpatient consultative services for pulmonary disease are provided by pulmonary consultants and thoracic surgeons with procedural support from radiology. Pleural disease management prior to the introduction of the pleural pathway was provided using the following modalities: In May 2014, an institutional need was felt for standardizing pleural disease care in response to observed institutional variation in pleural care and low performance on a publicly reportable healthcare quality measure (Patient Safety Indicator # 6 measuring rates of iatrogenic pneumothorax). In response to the need, locally adapted BTS 2010 pleural guidelines –11 were A system-wide consensus was generated via multidisciplinary efforts across diverse division meetings  over a span of 3 months. Approvals for institutional guidelines emphasizing patient safety  was set up with the intent of prospective audits and capturing data about patients seen through this pathway. Database management support was provided by a trained medical assistant. A one-year audit was performed by comparing outcomes data from the “pathway” era  with a “prior to pathway” era . Outcomes data such as number, type of diagnostic and therapeutic procedures, ultrasound examinations performed, and adverse events were restricted to pleural pathway patients and housed in the audit database. with complication or comorbidity [CC]), 188 , 199 (pneumothorax with MCC), 200 (pneumothorax with CC), and 201 (pneumothorax without CC and MCC). Examples of conditions that define major comorbidity (MCC)/comorbidity (CC) are provided in footnotes of Data for average case charge (US $), average length of stay (LOS) in days, and number of inpatient admissions in both eras were extracted utilizing the visual analytic tools of a subscription service focusing on care variation . These data are derived from coding and billing records of the institution and are restricted to inpatients. The following diagnosis-related group (DRG) codes  were useInformation regarding annual number of inpatient admissions and ER visits was obtained from the Department of Finance's comparative statistics detail report. An exemption for institutional review board's review and need for individual consent was granted by Western IRB (submission tracking number 1-900642-1).Hospital recorded 3,549 inpatient admissions in the “prior to pathway” era and 3,154 inpatient admissions in the “pathway” era. Emergency room (ER) recorded 31,232 visits in the “prior to pathway” era and 32,688 visits in the “pathway” era.n = 5), iatrogenic pneumothoraces (n = 2), chest drain dislodgement (n = 3). One case of subcutaneous emphysema was recorded in the audit. Additional information regarding complications is provided in Audit findings during the pathway era are described in Outcomes data for pleural conditions are presented in While pleural effusion with significant comorbid conditions saw a decrease in charges, there was no observed decrease in LOS and inpatient admissions. Pleural effusion with no associated comorbidities saw an increase in case charges with no change in LOS.Pneumothorax irrespective of associated comorbidity saw a decrease in the number of inpatient admissions, length of stay, and average case charges.This study is the first in North America describing a dedicated ambulatory/inpatient pleural clinical pathway and associated gains in the efficiency domain of healthcare quality . Recent In our analysis, charges for pleural effusion care increased and were offset by a reduction in pneumothorax charges. In 1996, a small randomized trial found similar savings with a narrow bore chest tube insertion and ambulatory management of spontaneous pneumothorax patients (cost savings of 5660 US$/patient and savings of 5 bed days per case) . While dResources are needed for a functional pleural pathway including training, acquisition of ultrasound machines, and associated probes housed in nonradiology settings. However, it is likely that such investment yields returns by enhancing timely and safe care . Delay iComplications observed in our series deserve further attention. 7.3% of inserted drains fell out in a recent British audit highlighting the need for improvement in securement strategies . 14% (3/There are several methodological limitations to our study. Administrative databases were used for cost effectiveness metrics and were restricted to inpatients. Inpatient charges were utilized as surrogate for costs. Information was lacking about outpatient charges including radiology procedures. It is also possible that not all patients presenting with pleural conditions were channelled via the pleural pathway. Audit data were not available on patients outside the pathway during the study period. A cost benefit analysis incorporating input costs was not performed. Lack of propensity matching and an interrupted-time series design impacts the validity of these findings. We acknowledge that our series has a relatively small sample size and is based on a retrospective and limited time frame of observation. Lack of control cohorts in the baseline and the intervention period is a significant limitation. However, no prior attempts exist in reporting improvements in processes/outcomes after implementation of coordinated pleural care. We believe our study should be viewed as an attempt in stimulating further research in this field.In conclusion, we report the evolution of a clinical pathway and a centralized pleural service for the care of pleural disease in a community healthcare system. Lower case charges and length of stay were observed for inpatient pleural conditions, largely mediated by strides in pneumothorax management. However, these findings need further study.


This study aimed to investigate the underlying molecular mechanisms of Parkinson's disease (PD) by bioinformatics.  Using the microarray dataset GSE72267 from the Gene Expression Omnibus database, which included 40 blood samples from PD patients and 19 matched controls, differentially expressed genes (DEGs) were identified after data preprocessing, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein-protein interaction (PPI) network, microRNA- (miRNA-) target regulatory network, and transcription factor- (TF-) target regulatory networks were constructed. ICAM1) was the hub node in the PPI network; hsa-miR-7-5p, hsa-miR-433-3p, and hsa-miR-133b participated in PD pathogenesis. Six TFs, including zinc finger and BTB domain-containing 7A, ovo-like transcriptional repressor 1, GATA-binding protein 3, transcription factor dp-1, SMAD family member 1, and quiescin sulfhydryl oxidase 1, were related to PD.  Of 819 DEGs obtained, 359 were upregulated and 460 were downregulated. Two GO terms, “rRNA processing” and “cytoplasm,” and two KEGG pathways, “metabolic pathways” and “TNF signaling pathway,” played roles in PD development. Intercellular adhesion molecule 1 (ICAM1, hsa-miR-7-5p, hsa-miR-433-3p, hsa-miR-133b, and the abovementioned six TFs might play important roles in PD development. “rRNA processing,” “cytoplasm,” “metabolic pathways,” and “TNF signaling pathway” were key pathways involved in PD.  Parkinson's disease (PD) is one of the most common age-related neurodegenerative diseases . The ageCBX5), and transcription factor 3 (TCF3) are related to PD [β) pathway by urate reportedly protects dopaminergic neurons in a rat model of PD [DJ-1 and PARKIN [κB), GATA2, and PHD finger protein 10 are TFs involved in PD [Much effort has been spent in investigating PD pathogenesis, and the misfolding, aggregation, and aberrance of proteins are considered to be some of the main causes , 4, 5. Sed to PD , 7. Seveel of PD . In addiel of PD . The ubiel of PD –15. Furtel of PD . Furtherd PARKIN , and miRd PARKIN –20. Numed PARKIN , 22, andd PARKIN . Moreoveed in PD –27. HoweCBX5, TCF3, dedicator of cytokinesis 10, and mannosidase alpha class 1C in the blood of patients with PD compared with those of healthy controls. Moreover, crucial pathways related to chromatin remodeling and methylation were revealed. In the current study, we downloaded this microarray dataset to comprehensively analyze DEGs in patients with PD compared with those in matched controls by bioinformatics approaches and to describe their functional annotations. Compared with the previous analysis conducted by Calligaris et al. [In a previous study, the microarray dataset GSE72267 generated by Calligaris et al. [https://www.ncbi.nlm.nih.gov/geo/) [Gene expression profile data GSE72267 was extracted from the Gene Expression Omnibus database (ov/geo/) . The GSEp value of <0.05.The downloaded data in CEL files were preprocessed using the Affy package (version 1.50.0)  in R lanhttp://www.geneontology.org/) analysis is commonly used for functional studies of large-scale genomic or transcriptomic data and classifies functions with respect to three aspects: molecular function (MF), cellular component (CC), and biological process (BP) [http://www.kegg.jp/) pathway database [p value of <0.05.Gene ontology (GO) (ess (BP) , 32. Thedatabase  is wideldatabase . In thishttp://www.string-db.org/) [http://apps.cytoscape.org/apps/cytonca) [Search Tool for the Retrieval of Interacting Genes/Proteins   is an ondb.org/) , and thecytonca)  (parametcytonca)  in the Chttp://www.mir2disease.org/) database [http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk2/) [The miR2disease   is a comhttp://apps.cytoscape.org/apps/iRegulon) [The genes in the PPI network described above were further analyzed to identify TF-target interaction pairs that were then used to construct a TF-target regulatory network. The iRegulon plugin   in the CThe boxplot of the preprocessed data indicated good normalization . In totaGO and KEGG pathway enrichment analyses for the up- and downregulated DEGs were performed (Supplementary ESR1), mechanistic target of rapamycin (MTOR), ATM serine/threonine kinase (ATM), CD40 molecule (CD40) and thymidine kinase 2, mitochondrial (TK2), and five downregulated DEGs such as mitogen-activated protein kinase 14 (MAPK14), phosphatase and tensin homolog (PTEN), intercellular adhesion molecule 1 (ICAM1), aurora kinase A (AURKA), and protein kinase, DNA-activated, catalytic polypeptide (PRKDC) , and NF-κB signaling pathways  . Three s (PRKDC)  included (PRKDC) , includi (PRKDC)  included (PRKDC)  included (PRKDC) , such aspathways . The detAccording to the data from the miR2disease database, six miRNAs were identified to be associated with PD and 698 miRNA-gene pairs were obtained by searching miRWalk2.0. A total of 40 miRNA-target interaction pairs were obtained by comparing miRNA-gene pairs with DEGs, and subsequently, the miRNA-target regulatory network was constructed. The network  containeZBTB7A), ovo-like transcriptional repressor 1 (OVOL1), and GATA-binding protein 3, and three downregulated ones, that is, transcription factor dp-1 (TFDP1), SMAD family member 1 (SMAD1), and quiescin sulfhydryl oxidase 1 (QSOX1). The TF-target regulatory network , and six TFs  might play important roles in PD development.PD is the second most common age-related neurodegenerative disease. However, the pathogenesis and genes involved in PD are not well known . In thisOur results revealed that the upregulated DEGs were enriched in the KEGG pathway “metabolic pathways” and the GO term “rRNA processing,” and the downregulated DEGs were enriched in the KEGG pathway “TNF signaling pathway” and the GO term “cytoplasm.” A previous study  demonstrICAM1 was among the top 10 DEGs in the PPI network. Moreover, ICAM1 gene was involved in six KEGG pathways for subnetwork c. ICAM1 is involved in the adhesion and transmigration of leukocytes across the endothelium, promoting brain inflammation and resulting in brain diseases [ICAM1 and leukocyte function-associated antigen 1 [ICAM1 is involved in persistent inflammation in PD [ICAM1 might play roles in viral myocarditis and CAMs and thus contributed to PD.diseases . T helpentigen 1 . In addion in PD . Our resα-SYN, a PD-related gene [α-SYN overexpression, increasing the risk for PD [The miRNA-target regulatory network analysis identified three miRNAs involved in PD, namely, hsa-miR-7-5p, hsa-miR-433-3p, and hsa-miR-133b. A study described miR-7-2 dysregulation (the stem loop of hsa-miR-7-5p) in Parkinson's patient's leukocytes  and reveted gene . The vark for PD . hsa-miRk for PD ; howeverk for PD . hsa-miRk for PD . Notablyk for PD . TherefoZBTB7A, OVOL1, and GATA3 were upregulated in patients with PD compared with those in healthy matched controls, whereas TFDP1, SMAD1, and QSOX1 were downregulated. ZBTB7A is a tumor suppressor, which is involved in several cancers such as prostate and nonsmall cell lung cancers [OVOL1, encoding a zinc finger protein, is expressed in embryonic epidermal progenitor cells and is an inducer of mesenchymal-to-epithelial transition in human cancers [GATA3, a member of the GATA family, is a regulator of T-cell development and plays roles in endothelial cells [TFDP1 is involved in the cell cycle and contributes to hepatocellular carcinomas [SMAD1 is involved in multiple pathways [QSOX1 plays roles in some cancers such as breast cancer and neuroblastoma [TFs are important regulators of target gene expressions , 62. In  cancers –65. OVOL cancers , 67. GATal cells , 69. TFDrcinomas , 71, SMApathways , 73, andblastoma –76. HoweICAM1 might also play a vital role. Besides six TFs, three miRNAs, including hsa-miR-7-5p, hsa-miR-433-3p, and hsa-miR-133b, may be involved in PD. However, because of the study limitations, further investigation remains to be performed in the future.In conclusion, our data demonstrated that the metabolic pathways, TNF signaling pathway, rRNA processing, and cytoplasm play important roles in PD pathogenesis;


Diabetic peripheral neuropathy (DPN) is a common complication of diabetes mellitus (DM). It is not diagnosed or managed properly in the majority of patients because its pathogenesis remains controversial. In this study, human whole genome microarrays identified 2898 and 4493 differentially expressed genes (DEGs) in DM and DPN patients, respectively. A further KEGG pathway analysis indicated that DPN and DM share four pathways, including apoptosis, B cell receptor signaling pathway, endocytosis, and Toll-like receptor signaling pathway. The DEGs identified through comparison of DPN and DM were significantly enriched in MAPK signaling pathway, NOD-like receptor signaling pathway, and neurotrophin signaling pathway, while the “neurotrophin-MAPK signaling pathway” was notably downregulated. Seven DEGs from the neurotrophin-MAPK signaling pathway were validated in additional 78 samples, and the results confirmed the initial microarray findings. These findings demonstrated that downregulation of the neurotrophin-MAPK signaling pathway may be the major mechanism of DPN pathogenesis, thus providing a potential approach for DPN treatment. Diabetes mellitus (DM) has become one of the largest global public health problems of this century. According to the World Health Organization global report on diabetes, over 8.5% of the global population, approximately 422 million adults, have diabetes . DiabeteOver the past decade, there have been many achievements in understanding the pathogenesis of DPN. However, controversy remains because of a multifactorial etiology involving both metabolic and vascular factors . ChronicSystems biology approaches, such as whole genome expression profiling, may provide new insights into the molecular mechanisms of DPN without preset bias. However, previous studies have identified global transcriptomic changes only in animal models . In the A workflow was designed to identify candidate genes whose expression levels may differentiate among healthy controls (CN), DM patients, and DPN patients . After oThe participants were recruited from hospitals affiliated with Ningbo University from April 2015 to June 2016. The healthy controls had no family history of diabetes or neurologic disorders, exhibited normal glucose tolerance, and were free of any major chronic diseases. Type 2 DM cases were defined as meeting at least one of the following criteria: fasting plasma glucose ≥7 mmol/L, 2 h plasma glucose after oral glucose tolerance test (OGTT) ≥ 11.1 mmol/L, and use of glucose-lowering drugs or physician-diagnosed diabetes. Diabetic peripheral neuropathy was diagnosed by a positive assessment through neurologic examinations and nerve conduction studies, as previously described [Total RNA was isolated from peripheral blood mononuclear cells (PBMCs) by using a TaKaRa MiniBEST Universal RNA Extraction Kit according to the manufacturer's instructions. The RNA quality was determined on the basis of an optical density (OD) 260/280 ratio ≥ 1.8 and OD260/230 ratio ≥ 2.0 using a NanoDrop ND-1000 spectrophotometer. RNA integrity was determined using an Agilent 2100 Bioanalyzer. The intensity of the 18S and 28S rRNA bands was examined on a 1% formaldehyde-agarose gel. RNA samples with an RNA integrity number (RIN) of ≥6.0 and 28S/18S > 1.5 were subjected to microarray analysis.The microarray analysis was performed by BGI Inc., including RNA amplification, probe labeling, hybridization, and data extraction. Briefly, aliquots (100 ng) of total RNA were amplified and transcribed into fluorescent Cy5-labeled antisense RNAs (aRNAs) by using a OneArray Amino Allyl aRNA Amplification Kit  according to the manufacturer's instructions. The Cy5-labeled aRNAs were fragmented and then hybridized to the Human Whole Genome OneArray Version 6.0 . Nonspecific binding targets were washed out three times. The arrays were scanned by an Agilent G2505C Microarray Scanner . The fluorescence intensities of each spot were analyzed with Feature Extraction software . Overall, 18 microarray chips were analyzed in this study.2 (ratio)| ≥ 1. Hierarchical clustering was performed to visualize distinguishable gene expression profiles among the samples. The gene annotation and biological interpretation of the identified DEGs were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8 [http://geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes pathways (http://genome.jp/kegg/), were deemed significant at a Benjamini-Hochberg-corrected P < 0.05. The R/Bioconductor Pathview package was used for pathway-based gene data integration and visualization [The microarray data were normalized using the R/Bioconductor Limma package . An empiID) v6.8 . Biologilization .−ΔΔCT method was used to quantify the relative expression of each gene, using GAPDH expression to normalize each sample. All experiments were run in triplicate and repeated three times. Differences in expression among groups were evaluated with a one-way analysis of variance (ANOVA) using SPSS 16.0 software. A significant difference was considered to be indicated by P < 0.05.Seven representative genes selected from DEGs involved in the neurotrophin-MAPK signaling pathway were validated by qRT-PCR in another 26 independent samples from each group . Total RNA was extracted as described above, and double-stranded cDNA was synthesized using a PrimeScript RT Reagent Kit  according to the manufacturer's instructions. Real-time PCR was performed using LightCycler 480 SYBR Green I Master mix . The 2The gene expression data from this study are available in the Gene Expression Omnibus . Compared with healthy individuals, patients with DM and DPN exhibited 2795 and 3606 upregulated genes, respectively, whereas 103 genes and 887 genes were expressed at a lower level in DM and DPN patients compared with the healthy controls . Among tAccording to the GO analysis, the DEGs of DM and DPN patients were enriched in many similar GO terms as well as distinct GO terms . Comparehttps://doi.org/10.1155/2017/8103904. When the DEGs identified between DPN and DM patients were further analyzed with regard to upregulation and downregulation, the upregulated genes were enriched in the infectious disease category, whereas the downregulated genes were enriched in the “neurotrophin signaling pathway” and “MAPK signaling pathway” , NTRK2 , SH2B2 , MAPK3 , MAPK12 , MAPKAPK2 (mitogen-activated protein kinase-activated protein kinase 2), and ATF4 . The results confirmed the initial microarray findings for all of the genes except MAPK3, whose expression was not changed in DM patients or in DPN patients , brain-derived neurotrophic factor (BDNF), and neurotrophin- (NT-) 3/4/5. Glial cell-derived neurotrophic factors (GDNF) form a second family and include GDNF, neurturin, artemin, and persephin , 36. Defγ (PLCγ), SH2B adaptor proteins, ankyrin repeat-rich membrane spanning (ARMS), Csk homology kinase (CHK), insulin receptor substrate 1 (IRS1), and c-ABL1 [SH2B2  decreased in DPN patients, probably due to the downregulation of TrkB. TrkB activates three major intracellular signaling cascades: the mitogen-activated protein kinase (MAPK) pathway, phosphatidylinositol 3-kinase (PI3K)-Akt pathway, and the PLCγ-Ca2+ pathway [The binding of BDNF to TrkB leads to the dimerization and autophosphorylation of tyrosine residues in the intracellular domain of the receptor , which id c-ABL1 . Among t pathway . HoweverIn neuronal development and function, neurotrophins activate the p38 MAPK/MAPKAPK2 pathway through Trk receptor-mediated signaling mechanisms . SubsequNOD2 and NLRP1 remained unchanged in DM patients but increased significantly in DPN patients (3.76-fold for NOD2 and 2.07-fold for NLRP1). Therefore, the NOD-like receptor signaling pathway may be an important mechanism in the pathogenesis of diabetic complications.In addition to the “neurotrophin signaling pathway” and “MAPK signaling pathway,” the present study also showed that the “NOD-like receptor signaling pathway” was affected in DPN patients. Nucleotide-binding oligomerization domain containing 2 (NOD2), a member of the NOD-like receptor family, has been found to be one of the critical components of a signal transduction pathway linking renal injury to inflammation and podocyte insulin resistance in diabetic nephropathy . A recenTLR4 expression increased in both DM and DPN patients, and there was even higher expression in the DPN patients (TLR4 and TLR7, were expressed at much higher levels in DPN patients, they may not be sensitive biomarkers for DPN, because the TLR signaling pathway is involved in diabetes and multiple diabetic complications.It is now widely accepted that increased inflammation is a key etiological factor in the development of many chronic diseases, including diabetes . Similarpatients , in accopatients . These rIn summary, these findings provide the first demonstration that downregulation of the neurotrophin-MAPK signaling pathway may be the major pathogenesis of DPN. The use of growth factors in treating DPN has been extensively explored , and NGFSupplementary Table S1: Gene set lists of three enriched KEGG pathways.


The regulation of gonadotropin synthesis and release by gonadotropinreleasing hormone (GnRH) plays an essential role in the neuroendocrine control of reproduction. However, the mechanisms underlying gonadotropin regulation by GnRH pulse frequency and amplitude are still ambiguous. This study aimed to explore the molecular mechanisms and biological pathways associated with gonadotropin synthesis by GnRH pulse frequencies and amplitudes.Using GSE63251 datasets downloaded from the Gene Expression Omnibus (GEO), differentially expressed genes (DEGs) were screened by comparing the RNA expression from the GnRH pulse group, the GnRH tonic group and the control group. Pathway enrichment analyses of DEGs was performed, followed by protein-protein interaction (PPI) network construction. Furthermore, sub-network modules were constructed by ClusterONE and GO function and pathways analysed by DAVID. In addition, the relationship between the metabolic pathways and the GnRH pathway was verified in vitro.In total, 531 common DEGs were identified in GnRH groups, including 290 up-regulated and 241 down-regulated genes. DEGs predominantly enriched in 16 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, including 11 up-regulated pathways  and 5 down-regulated pathways (type II diabetes mellitus). Moreover, FBJ osteosarcoma oncogene (FOS) and jun proto-oncogene (JUN) had higher connectivity degrees in the PPI network. Three modules in the PPI were identified with ClusterONE. The genes in module 1 were significantly enriched in five pathways, including signallingthe insulin resistance and GnRH signalling pathway. The genes in modules 2 and 3 were mainly enriched in metabolic pathways and steroid hormone biosynthesis, respectively. Finally, knockdown leptin receptor (LEPR) and insulin receptor (INSR) reversed the GnRH-modulated metabolic related-gene expression.The present study revealed the involvement of GnRH in the regulation of gonadotropin biosynthesis and metabolism in the maintenance of reproduction, achieved by bioinformatics analyses. This, indicates that the GnRH signalling pathway played a central linkings role in reproductive function and metabolic balance. In addition, the present study identified the difference response between GnRH pulse and GnRH tone, indicated that abnormal GnRH pulse and amplitude may cause disease, which may provide an improved understanding of the GnRH pathway and a new insight for disease diagnosis and treatment. The hypothalamic-pituitary-gonadal (HPG) axis governs almost all mammalian reproduction events, from foetal development, through puberty to sexual maturity . RegulatGnRH is released intermittently pulses, and changes in GnRH pulse frequencies and amplitudes have differential effects on FSH and LH synthesis and release –11. HighThe function of GnRH neurons is controlled and modulated by a wide range of neuronal systems within the brain, which transmit feedback signals of sex steroids and mediate the response to stress, metabolic status and season –15. PrevThe availability of online databases and computer algorithms makes it possible to investigate the complex mechanisms of pathways. Recently, gene expression profile data associated with the gonadotropin subunit genes have been studied by many researchers. However, the detailed mechanism, associated with the gonadotropin response to GnRH, has not been studied further in a bioinformatics framework. In the present study, the signalling mechanisms of gonadotropin responses to GnRH were explored via a bioinformatics approach using the microarray data GSE63251signalling. Differentially expressed genes (DEGs) were firstly identified between cells with and without GnRH, followed by pathway enrichment analysis and construction of protein–protein interaction networks (PPI). Furthermore, the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the most remarkable module were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) software. To our knowledge, this is the first report in which GnRH have been studied in the context of network biology. In addition, on the basis of networks, we proposed the hypothesis that GnRH was able to modulate the metabolic state, via the related membrane receptor, which was verified in the current study. The findings of this study may provide a systematic perspective to understand underlying mechanisms and a new insight of the GnRH role in gonadotropin subunit gene transcriptions and metabolic balance.http://www.ncbi.nlm.nih.gov/geo/) database at the National Center for Biotechnology Information (NCBI), which is currently the largest fully public gene expression resource [The microarray data of GSE63251, contributed by Lawson et al. , were doresource . The micP-value. Fold changes (FCs) in the expression of individual genes were calculated and DEGs with P < 0.05 and |log FC| >1 were considered to be significant. DEG1 and DEG2 were then combined and the pooled dataset was referred to as the overlapping DEGs.Background correction and quartile data normalization was performed on the original array data . The DEGP < 0.05 using DAVID 6.7.The Kyoto Encyclopedia of Genes and Genomes (KEGG)  knowledghttp://www.string-db.org/) [http://cytoscape.org/) [P < 1 × 10−4. Furthermore, the GO and KEGG pathway enrichment analyses of the DEGs in the significant modules were performed using DAVID to analyse the gene function at the molecular level.The Search Tool for the Retrieval of Interacting Genes   is a webpe.org/) . Given tpe.org/) , 26. TheLβT2 cells, a gift from Dr. Pamela Mellon  were maintained in high-glucose DMEM , supplementedwith 10%foetal bovine serum (FBS) (Gibico), 100 U of penicillin/mL, and 100 μg of streptomycin sulphate/mL  in 5% CO2 humidified air at 37 °C .http://www.sigmaaldrich.com/china-mainland.html) according to manufacturer’s instructions.Mouse GnRH-10 was synthesized by Shanghai Apeptide Co Ltd. The interferencethe vectors for LEPR and INSR were constructed. The sequences that express short hairpin RNA (shRNA) targeted to gene mRNA sequences were cloned in a p-SUPER vector (the Sigma-Aldrich) , LEPR-shRNA or INSR-shRNA vectors were transfected using Lipofectamine 2000. Approximately 8 h after transfection, the cells were serum starved for 8 h and later stimulated with GnRH for 24 h, and then total RNA was extracted using TRIzol reagent.LβT-2 cells were cultured at a in a concentration of 1 × 10RNA was extracted using TRIzol reagent (TaKaRa), and 1 μg of total RNA was reverse transcribed into first-strand cDNA with a mix of oligo-dT and random primers using the Quantitect Reverse Transcription Kit (TaKaRa). RT-qPCR was performed on an Applied Biosystems 7500 Real-Time PCR System using SYBR green. Standard curves, generated from at least three dilution series of an abundant sample, were used for the relative quantification of related genes and β-actin (the same sample was used to generate standard curves for these genes). The expression levels of genes were normalized by dividing by β-actin mRNA expression levels , and activating transcription factor 4 (ATF 4) and 670 down-regulated DEGs, such as insulin receptor substrate 1 (IRS 1). In addition, a total of 403 up-regulated (such as activating transcription factor 3) and 447 down-regulated  DEGs were identified between the GnRH pulses group and control group. The schematic Venn diagram of differentially expressed genes is shown in Fig. The neuropeptide hormone GnRH is a hypothalamic peptide essential for normal mammalian reproductive function. Gonadotropin synthesis and release is dependent upon pulsatile stimulation by the hypothalamic neuropeptide GnRH. However, the role of GnRH pulse frequency was not examined in depth. This study investigated the gene profiles that responded to GnRH. Using bioinformatics approaches, a total of 1177 DEGs between perfusion tonic 100 nM GnRH and control group with |logFC| > 1 and P < 0.05 as the cut-off value. The results are shown in Table To gain further understanding of the potential functional roles of the DEGs in GnRH treatment, this study performed a KEGG pathways analysis and set Considering that proteins rarely function alone, it is necessary to study the protein–protein interactions. Therefore, protein-protein interactions were constructed in the present study, which helped to understand functions of proteins at the molecule level and explore cell regulatory mechanisms. Based on the STRING database, the PPI network of overlapping DEGs contained 334 nodes and 989 edges network were gained with the combined score > 0.4 using Cytoscape Fig. . ProteinP < 1 × 10−4were identified using the ClusterONE plug-in of Cytoscape. Module 1 had 99 nodes and 460 edges, module 2 had 24 nodes and 29 edges and module 3 had 20 nodes and 21 edges. KEGG enrichment in the sub-network is presented in Table In order to explore the functions of the networks modules, we implemented cluster analysis. As shown in Fig. NPY (Fig. PTPN11 (Fig. LEPR was interfered, genes which are responsible for energy intake and reproduction, respectively. In addition, the role of GnRH on the insulin signalling pathway was also detected. Interfering with LEPR facilitated the GnRH-suppressed GYS1 expression (Fig. PKLR expression (Fig. To date, many studies have confirmed that metabolic status, season, and stress were able to modulate the function of GnRH neurons –15. HoweNPY Fig.  and PTPNN11 Fig.  expressiion Fig. , which pion Fig. , which pGnRH plays a central role in the control of normal reproductive function by regulating the synthesis and release of the gonadotropins LH and FSH –32. The In the present study, the results of the GO and KEGG pathway enrichment analyses revealed the primary biological processes in which the DEGs were involved, including focal adhesion, type II diabetes mellitus, glycogen biosynthesis and metabolism, progesterone-mediated oocyte maturation. Furthermore, other pathways associated with GnRH regulation were activated simultaneously followed by treatment with GnRH, such as MAPK signalling pathway, cytokine-cytokine receptor interaction, and most importantly, the GnRH signalling pathway . Based oLEPR significantly suppressed GnRH-induced NPY and PTPN11 expression, which are responsible for energy intake and reproduction, respectively. Similarly, Interfering with LEPR facilitated the GnRH-suppressed GYS1 expression, which promoted the glycogenesis. Conversely, interfering with LEPR significant repressed GnRH-induced PKLR expression, which promoted glycolysis. These results provide a better understanding of the regulation of GnRH on energy intake and expenditure at the molecular level, which constitute useful insights into the relationship between reproduction and metabolic status.Metabolic dysfunctions are often linked to reproductive abnormalities . ObesityPrevious studies have demonstrated that different frequencies of pulsatile GnRH result in altered secretion patterns of FSH and LH. Increasing frequencies results in preferential secretion of LH, whereas decreasing frequencies leads to greater FSH secretion. Because FSH and LH synthesis and secretion are both modulated by GnRH through the same G protein-coupled GnRH receptor, one possible mechanism is that distinct signalling pathways are activated, depending on GnRH pulse frequency, leading to the differential regulation of FSH and LH production , 42. HowHowever, there are some limitations in our study. First, the small amounts of data used in the current study were downloaded from the GEO database, not generated by us. Because GEO is a huge data repository, a meta-analysis of the relevant molecular mechanism of GnRH may be performed in future studies. Second, the results were web-based and were not all verified by biological experiments. Thus, further experimental studies based on our findings are still needed.In summary, with this study, for the first time, we characterized GnRH to prove an insight into their role in gonadotropin regulation and metabolic balance using PPI networks and GO and KEGG enrichment analysis, which might provide comprehensive bioinformatics analysis of the mechanisms of GnRH. These results indicated that the GnRH signalling pathway interplayed with other signalling pathways to co-regulate the reproductive function and other biological process. In addition, the difference between GnRH pulse and GnRH tone revealed that abnormal GnRH pulse and amplitude may cause disease, which may provide an improved understanding of the GnRH pathway and a new insight for disease diagnosis and treatment. However, further experimentation and additional studies are needed to validate these results.


Python molurus bivittatus) have identified thousands of genes that are significantly differentially regulated during this process. However, substantial gaps remain in our understanding of coherent mechanisms and specific growth pathways that underlie these rapid and extensive shifts in organ form and function. Here we addressed these gaps by comparing gene expression in the Burmese python heart, liver, kidney, and small intestine across pre- and post-feeding time points , and by conducting detailed analyses of molecular pathways and predictions of upstream regulatory molecules across these organ systems.Previous studies examining post-feeding organ regeneration in the Burmese python  contains supplementary material, which is available to authorized users. Python molurus bivittatus) is the most well-studied [The ability to massively downregulate metabolic and physiological functions during extended periods of fasting has evolved in multiple species of snakes. This downregulation of physiological form includes the atrophy of organs such as the heart, kidney, liver, and small intestine. Upon feeding, the size and function of these organs, along with oxidative metabolism, is massively upregulated to accommodate digestion –4. Of th-studied . Within -studied , 6. Majo-studied , 7–10. T-studied , 11, 12.-studied , 7–9, 13Previous studies have examined aspects of this post-feeding response using morphological and physiological assays , 14–16, Our previous study of the Burmese python feeding response addressed some gaps through the use of increased replicates and more frequent time point sampling for one organ, the small intestine . We idenHere, we leverage fully replicated organ-specific time courses detailing gene-level responses to infer canonical pathways and regulatory molecules driving post-feeding organ growth in the Burmese python. We examined gene expression across four major organ systems – the heart, liver, kidney, and small intestine. We combined increased replicated sampling with statistical inferences of pathway activation and regulatory molecule prediction to identify the mechanistic drivers of cross-tissue, post-feeding organ regeneration. Despite highly organ-specific gene expression responses associated with organ regenerative growth, we found evidence for high degrees of overlap in predicted pathways and regulatory molecules underlying these growth processes between organs. Pathways predicted to be involved in regulating this physiological response include LXR/RXR activation, PI3K/AKT, and mTOR signaling. Interestingly, we also found strong and consistent evidence for the involvement of NRF2-mediated oxidative stress response and other stress-response pathways in this extreme example of rapid organ growth. Our results suggest that post-feeding, regenerative organ growth in the Burmese python may stem from small numbers of key effector molecules mediating a core set of growth and stress-response pathways, which in turn activate diverse, tissue-specific signaling cascades.Burmese pythons were obtained from commercial breeders. All animal care and tissue sampling was conducted using protocols approved by the University of Alabama Institutional Animal Care and Use Committee 14-06-0075). Burmese pythons were sampled at three physiological states: fasted , 1 day post-feeding (1DPF) and 4DPF, with the meal consumed equaling at least 25% of their body mass. Previous studies have shown that organ masses and functional phenotypes climax between 1 and 3 DPF 4-06-0075 and thatTotal RNA was extracted from ~50 mg of snap-frozen tissue using Trizol Reagent (Invitrogen), followed by mechanical cell disruption using a TissueLyzer for 10 min at 20 strokes/min, and precipitation of RNA using isopropanol. Individual Illumina mRNAseq libraries were constructed using either the Illumina TruSeq RNAseq kit or the NEB Next RNAseq kit, both of which included poly-A selection, RNA fragmentation, cDNA synthesis, and indexed Illumina adapter ligation. Completed RNAseq libraries were quantified on a BioAnalyzer (Agilent), pooled in equal molar ratios in various multiplex arrangements, and sequenced on either an Illumina GAIIx or Illumina HiSeq2000 , using default parameters. IPA uses gene identifiers and the fold-change value for each differentially expressed gene to identify enrichment patterns for Canonical Pathway Analysis (CPA) and Upstream Regulatory Molecule Analysis (URMA), and to infer the activation direction (activated versus inhibited) between particular time points. These two analyses both use observed gene expression data to infer unobserved features , but differ fundamentally in how they use expression data to make inferences. CPA predicts the involvement and activation/inhibition of canonical pathways based on observed evidence from gene expression data, specifically for genes that participate as higher-level regulatory molecules within a given pathway; analysis of observed gene expression data incorporates information from the Ingenuity Knowledge Base (including genes known to be involved within a given pathway) to provide both a statistical value of enrichment and a prediction of the biological involvement for the pathway as a whole . In contrast, URMA uses observed changes in gene expression specifically for genes at lower levels within pathways  to predict activation or inhibition of regulatory molecules upstream of these genes . Due to p-value of less than 0.01. We examined only those pathways that were significant, based on a predicted activation z-score, in at least one of the four organs for at least one of the post-feeding time points. For IPA analyses, the z-score is used to determine the statistical significance of the number of activated and inhibited predictions, and the sign of the value indicates the overall activation state . We used a p-value cutoff of 0.01 for the CPA in IPA to reduce potentially spurious inferences. Upstream regulators and hypotheses for global signaling molecules were identified using URMA in IPA, with a Fisher’s exact test overlap p-value threshold of 0.05. Pathway network figures were modified manually from predicted network figures generated in IPA. For analysis of specific pathways , we also determined the number of genes involved in each pathway that were assigned python orthologs by our orthology analyses, and how many of these genes were expressed at some level in our dataset , and we input fold changes per gene averaged across biological replicates, along with our estimate of the orthologous human Ensembl ID for each gene. Pathways important to cross-tissue physiological responses were isolated using the IPA CPA , with a right-tailed Fisher’s exact test We used our expression data from all python samples .Regression analysis across time points, which tends to be conservative, identified hundreds of genes that were significantly differentially expressed across all three time points with 722 genes in the heart, 750 genes in the kidney, 711 genes in the liver, and 1,284 genes in the small intestine. Of the 2,922 total genes differentially expressed across all four organs, 21% are unique to the heart, 16% are unique to the kidney, 15% are unique to the liver, and 32% are unique to the small intestine , ERK5 signaling , and Integrin signaling . Lastly, a number of pathways appeared to be organ-specific, including p38 MAPK and ERK5 signaling in the heart and 14-3-3-mediated signaling in the small intestine  between the fasted and 1 DPF time points supported many of the same molecular mechanisms underlying organ growth identified via CPA, such as stress response, growth, and lipid signaling pathways. We explored URM predictions for all classes of URMs except biological drugs, chemicals, and microRNAs. We found that many predicted URMs were shared among organs, with 51 shared among all four organs. Predicted URMs also showed substantial organ-specific patterns, with a large number of URMs uniquely predicted for each organ. The heart showed the largest number of unique URM predictions (269), while only 123, 167, and 137 unique URMs were predicted in the kidney, liver, and small intestine, respectively Fig. .Fig. 4PrTo identify regulators with broadly relevant patterns across multiple organs, we focused on URMs predicted significantly in at least three organs and with moderate to high activation z-score z > |1.5|) in at least one organ. A subset of the URMs meeting these criteria is shown in Fig. .5| in atIt is notable that while the inferences of activation directions of lipid signaling pathways across organs were largely ambiguous and sometimes inconsistent in our CPA Fig. , the assIn addition to URMs involved in key predicted canonical pathways, upstream regulatory analysis predicted several other notable URMs with strong activation or informative trends across organs. Insulin and INSR were both predicted as strongly activated regulators in the kidney, liver, and small intestine, suggesting a possible role of insulin receptor signaling in facilitating this regenerative response, which is also consistent with activation of the mTOR pathway. Myc, a regulator within the ERK5 and p38 MAPK signaling pathways, was predicted as activated in all four organs, although strongest in the liver. Several regulators within the MAPK signaling pathway were also predicted in URMA, with ATF4 and ATF6 predicted as activated in the kidney, liver, and small intestine, and ERK predicted as activated in the kidney and inhibited in the heart and liver. These URMs suggest the involvement of the ERK and MAPK signaling pathways in this response, even though CPA predictions for these two pathways were not substantially strong , glutathione s-transferase mu 1 (GST), and peroxiredoxin 1 (PRDX1), providing confirmatory evidence of NRF2 activation. The response of this pathway in the heart is, however, less clear  aimed to capture the early stages of organs shifting expression towards organ atrophy and towards a reversion to the fasted state, and we expected to observe partial reversals in pathways predicted to be active between fasted and 1DPF, and perhaps additional new pathways involved in apoptosis and atrophy. However, we found few consistent or clear patterns of interpretable pathway involvement between the 1DPF and 4DPF time points , but insignificant in both the heart (predicted activation) and kidney (activation state undetermined). The liver lacked evidence of involvement of the mTOR signaling pathway from CPA Figs. –4. In UROur results identify mTOR as a central regulator and integrator of a number of diverse growth signals that drive post-feeding regenerative organ growth in Burmese pythons. Insulin signaling represents a key-regulating factor of the mTOR pathway , and we Evidence from previous studies also support the role of mTOR, PI3K/AKT, and AMPK signaling mechanisms in python post-feeding growth, at least in the heart. Western blots of python cardiac tissue post-feeding support the inference of early activation of mTOR and PI3K/AKT pathways by demonstrating that phosphorylated AKT and MTOR proteins increase significantly in abundance between 12 and 24 h post-feeding . These wMAPK and related pathways also appear to be prominently involved in organ growth responses post-feeding, which is sensible given their known interactions with multiple growth pathways, including PI3K/AKT signaling and mTOR –40. Our One of the strongest and most consistent signals in the canonical pathway and upstream regulatory molecule analyses was the involvement of the NRF2-mediated oxidative stress response pathway. Commonly associated with anti-aging and longevity –50, injup-values < 1.55e-10, z-scores > 3.0)  Fig. . The 24 The NRF2-mediated oxidative stress response pathway interacts with multiple pathways predicted in our canonical pathway analysis –57 Figs–4. The PIn addition to NRF2-mediated oxidative stress response, evidence for the involvement of other stress response signaling mechanisms in python post-feeding organ growth was also observed. EIF2 signaling, important in translational control and responsiveness to conditions of environmental stress , 62, is P > 0.01) in the small intestine (Fig. Previous studies have shown evidence that molecules responsible for triggering python post-feeding organ growth circulate in the blood of the Burmese python , 64. Riqine Fig. . LXR is We also examined PPAR signaling as a potential pathway for lipid signaling during this regenerative growth, given the central role of PPAR in mediating fatty acid signaling as well as its effects on gene expression . PPAR haPhysiological studies have shown that python post-feeding organ growth peaks between 1DPF and 3DPF , 5, 9 anOrgan regeneration in snakes represents an extreme and unique phenotype among vertebrates. However, other examples of regenerative growth do exist among vertebrates, such as limb regeneration in salamanders , fin regMultiple coordinated growth pathways appear to play an important role in facilitating regenerative organ growth in multiple tissues of the Burmese python, and the overlap of pathways across organs suggests common signaling molecules may drive this response – consistent with evidence that common factors circulating in the plasma of pythons are capable of eliciting growth , 64. Our