They are rare and endangered and are regarded as the ‘national treasure’ and ‘living fossil’ in China. For the time being, there are only about 2500 giant pandas in the world. Therefore, we still have to do much more efforts to protect the giant pandas. In captive wildlife, the cataract incidence of mammalian always increases with age. Currently, in China, the proportion of elderly giant pandas who suffering from cataract has reached 20%. The eye disorder thus has a strong influence on the physical health and life quality of the elderly giant pandas. To discover the genes associated with the pathogenesis of cataract in the elderly giant panda and achieve the goal of early assessment and diagnosis of cataract in giant pandas during aging, we performed whole genome methylation sequencing in 3 giant pandas with cataract and 3 healthy giant pandas using methylation-dependent restriction-site associated DNA sequencing (MethylRAD). In the present study, we obtained 3.62M reads, on average, for each sample, and identified 116 and 242 differentially methylated genes (DMGs) between the two groups under the context of CCGG and CCWGG on genome, respectively. Further KEGG and GO enrichment analyses determined a total of 110 DMGs that are involved in the biological functions associated with pathogenesis of cataract. Among them, 6 DMGs including EEA1, GARS, SLITRK4, GSTM3, CASP3, and EGLN3 have been linked with cataract in old age.The giant panda  Ailuropoda melanoleuca), a world’s most protected rare animals, is also attacked by cataract with age. The studies have shown that the average life span of wild giant pandas is about 15–20 years old, while those in captivity usually live longer and can reach to the age of about 25–30 years. Her. Her19].Then, we analyzed the distribution of the methylated CCGG/CCWGG sites on distinct genomic sequences, including Utr3prime, Utr5prime, Upstream, Exon, Intron, and intergenic regions . Among tIn addition, we analyzed the overall methylation pattern of different positions on the genic regions. As presented in P value ≤ 0.05 and absolute log2FC value > 1 , SNARE interactions in vesicular transport , and RNA degradation . The pathways related with environmental information processing contained NF-kappa B signaling pathway , cAMP signaling pathway, HIF-1 signaling pathway . On the level of cellular process, there were 5 pathways, 3 of which had some associations with cataract, including cell cycle , apoptosis , p53 signaling pathway . On the level of metabolism, there were 17 pathways, 7 of which were related with cataract, including drug metabolism-cytochrome P450 , glycerolipid metabolism , beta-Alanine metabolism , tyrosine metabolism , phenylalanine metabolism , metabolism of xenobiotics by cytochrome P450 , steroid biosynthesis . For organismal systems, there were 16 pathways, 1 of which was related with cataract . For the level of human disease, there were 19 pathways, 5 of which were associated with cataract. Those were Epithelial cell signaling in Helicobacter pylori infection , Platinum drug resistance , Viral myocarditis , Fluid shear stress and atherosclerosis , Chemical carcinogenesis   .LOC105240942 and LOC100465932, hypermethylated), membrane raft , midbody . For molecular function, the DMGs were enriched in 181 terms, among which 29 were associated with cataract. 16 of the 29 terms had DMGs with same directional changes in one same term, such as structural constituent of cytoskeleton , SNARE binding , beta-catenin binding , calmodulin binding . For biological process, the DMGs were enriched in 690 terms, 219 of which were linked with cataract. 182 of the 219 terms had DMGs showing same directional changes, such as cardiac muscle tissue development , BMP signaling pathway , cellular metabolic process , cholesterol metabolic process , and regulation of membrane potential   .EEA1, GARS, SLITRK4, GSTM3, CASP3, and EGLN3.In this study, we identified a total of 338 DMGs between the groups. Among the DMGs, 116 have been previously supposed to be potentially linked with the development of cataract . Base onGLB1, CDKN2A and CDKN2B) highly correlated with aging[TP53I3 and CASP3) on this pathway would like to downregulate the functions of p53-mediate signaling pathway and promote the development of cataract. In addition, other direct evidence comes from the certain genes that have been previously reported to be associated with cataract pathogenesis. For example, Glutathione S-Transferase Mu 3  was considered to prevent the age-related cataract by protecting the lens from oxidative stress and a decreased expression level of GSTM3 was observed in the lens tissue of patients with age-related cataract, which correlated with the hypermethylation of GSTM3 promoters[In the present study, we analyzed the methylation profile differences between the aged giant pandas suffering from cataract and healthy giant pandas and identified hundreds of DMGs in giant pandas with age-related cataract. Notably, we found no methylation differences on the genes Click here for additional data file.S2 Table(XLS)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLS)Click here for additional data file.


PIK3CA mutations in lymphatic endothelial cells of LM patients, the full spectrum of molecular pathways involved in LM pathogenesis is poorly understood. Here, we performed RNA sequencing on blood samples obtained from ten LM patients and nine healthy subjects and found 421 differentially expressed genes that stratify LM subjects from healthy controls. Using this LM gene signature, we identified novel pathway alterations in LM, such as oxidative phosphorylation, MEK/ERK, bone morphogenetic protein (BMP), and Wnt/β-catenin pathways, in addition to confirming the known alterations in cell cycle and the PI3K/AKT pathway. Furthermore, we performed computational drug repositioning analysis to predict existing therapies (e.g. sirolimus) and novel classes of drugs for LM. These findings deepen our understanding of LM pathogenesis and may facilitate non-invasive diagnosis, pathway analysis and therapeutic development.Lymphatic malformation (LM) is a developmental anomaly of the lymphatic system that may lead to disfigurement, organ dysfunction and recurrent infection. Though several treatment modalities exist, pharmacotherapy is often associated with side effects and recurrence is common following surgical interventions. Moreover, despite the recent discovery of  Vascular anomalies represent a heterogeneous group of disorders that can be classified broadly into vascular tumors and vascular malformations . VasculaPIK3CA in isolated LMs as well as overgrowth syndromes such as KTS and CLOVES syndrome, and collectively termed these diseases PIK3CA-related overgrowth spectrum (PROS) [PIK3CA mutations are present in roughly 95% of LMs, with five activating hotspot mutations accounting for a majority of the cases [Researchers have recently identified activating mutations in m (PROS) . Accordihe cases , 11. DesThough the majority of LMs follow a benign clinical course, severe LMs may cause complications such as disfigurement, organ dysfunction and recurrent infection, leading to life-threatening conditions. Current treatment modalities include excision, pharmacotherapy, and sclerotherapy, and treatment options depend on the size and type of lesion, anatomic location and risk of complications. Complete excision of the lesion is often difficult, and recurrence rates are as high as 40% for incomplete excision and 17% for complete excision . A numbeLMs are often associated with risk of edema and cellulitis following upper respiratory viral infection, suggesting a propensity for a systemic inflammatory response. Therefore, we sought to determine if there is a LM-specific gene signature detectable in peripheral blood cells by performing RNA-sequencing (RNA-Seq) on blood samples of LM patients and healthy subjects. Here we demonstrate an LM gene signature in the blood that can stratify individuals with LM from healthy subjects. Consistent with previous studies, the PI3K/AKT pathway and cell cycle regulation are among the pathways involved in LMs. However, we also identify novel pathways including the MEK/ERK, bone morphogenetic protein (BMP), Wnt/β-catenin, and ephrin signaling pathways involved in LM formation. Finally, we employ this signature to predict potential therapeutics for LM and confirm existing therapies, which further validates our results.Ten LM patients (age 2–22 years old) seen at the vascular anomaly clinic at Stanford Hospital were enrolled in this study. None of the participants had surgery or medical treatment at least 6 months prior to the time of enrollment. Following approval by the Stanford Human Subjects IRB, the participants or their legal guardians in this manuscript have given written informed consent (as outlined in PLOS consent form) to publish these case details. One of the patients (LM 7) provided written consent to be enrolled in the study but declined to have her photograph published.Whole blood was collected in Tempus RNA tubes and stored at -80°C until RNA extraction, which was performed for all samples simultaneously by the Stanford Functional Genomics Facility (SFGF). RNA quantification and quality assurance were performed on an Agilent 2100 Bioanalyzer, and library construction was completed using the Illumina preparation kit according to the manufacturer’s instructions. Libraries were PolyA-enriched and followed by depletion of globin transcripts and ribosomal RNA. Paired-end sequencing of 75 base pair fragments was performed on the Illumina Nextseq, with an average coverage of 53 million reads per sample. Reads were aligned to the human genome (hg19) using Tophat (v2.1.1)  with an https://software.broadinstitute.org/GENE-E/index.html, last access on 2/2/2019), and hierarchical clustering analysis was performed using one minus Pearson correlation [® version 01–12, QIAGEN Redwood City) to identify pathways associated with each cluster. Upregulated genes in LM were converted to their corresponding probe set IDs (Affymetrix Human Genome U133 Plus 2.0) and analyzed using DAVID Functional Annotation Tool  [A total of 421 genes (253 upregulated and 168 downregulated) were selected using a threshold of Benjamini-Hochberg adjusted p-value ≤ 0.05. FPKM (fragments per kilobase of transcript per million mapped reads) of these genes were generated from Cufflinks (v 2.2.1)  and inpu21/2018) , 23 to i21/2018) . UpstreaHistology was performed by HistoWiz Inc. (histowiz.com) using standard operating procedures and fully automated workflow. Samples were processed, embedded in paraffin, and sectioned at 4 μm. Immunohistochemistry was performed on a Bond Rx autostainer (Leica Biosystems) with enzyme treatment (1:1000) using standard protocols. Antibodies used were rabbit anti-p-4E-BP1 (Thr37/46) , rabbit anti-p-ERK1/2 (Thr202/Tyr204) , and rabbit anti-β-catenin  antibodies. Bond Polymer Refine anti-rabbit HRP Detection (Leica Biosystems) was used according to manufacturer’s protocol. Sections were then counterstained with hematoxylin, dehydrated and film coverslipped using a TissueTek-Prisma and Coverslipper (Sakura). Whole slide scanning (40x) was performed on an Aperio AT2 (Leica Biosystems). The images were quantified using Halo image analysis software (Indica Labs) using CytoNuclear module.2FoldChange < -0.33 or > 0.33) were entered into the Query tool on CLUE  [Genes that meet the criteria  , and 744http://www.ilincs.org/ilincs/, last access on 9/21/2018) and pooled for each drug. LM signature genes with p-values < 0.05 were selected from the pooled data, and the average Log(DiffExp) was calculated for each gene. For genes where there was no signature with p-value < 0.05, three signatures with the lowest p-values were chosen and their average Log(DiffExp) was used. Twenty genes that are opposed by either sirolimus (N = 16) or both vincristine and prednisolone (N = 4) were analyzed using IPA to identify upstream regulators.Chemical perturbagen signatures  were obtained from iLINCS  and identified 253 upregulated and 168 downregulated genes in LM see .RPS6KB2) and cyclin D3 (CCND3)  . In addi (CCND3) . Further (CCND3) . The dowTo confirm the role of the PI3K/AKT/mTOR, Wnt/β-catenin, and MEK/ERK pathways in LM, we performed immunohistochemistry analysis. We observed higher expression of phospho-4E-BP1 and phospho-ERK in lymphatic endothelial cells (LECs) of LM patients compared to healthy subjects ; S2 Fig.We next performed computational drug repositioning analysis to identify potential therapeutics that antagonize the molecular pathways altered in LM. CLUE is a publicly available, cloud-based database that contains gene expression signatures of multiple cell lines in response to genetic or pharmacological perturbations (collectively termed perturbagens) . This soWe noted that most of the drugs that have shown efficacy in LM have negative CLUE scores . SirolimWe then identified 20 genes in the LM gene signature that are opposed by LM therapeutics . In ordePIK3CA mutation) and clinical presentation, suggesting that molecular heterogeneity may not correlate with clinical heterogeneity [In this study, we present for the first time a LM-specific gene signature that is detectable in peripheral blood. This 421-gene signature not only stratifies LM subjects from healthy individuals but also reveals the genetic diversity that exists within LM. Of note, subjects 9 and 10, who present with severe LMs of the head and neck clustered together. Other than this, however, we noted little correlation between clustering and the clinical characteristics of the patients, nor was there a significant difference between microcytic and macrocytic subtypes , Table 1PIK3CA mutations in peripheral blood of patients with PROS [PIK3CA mutations in peripheral blood in isolated LM. Consistent with this, we did not detect PIK3CA mutations in any of our blood samples. This data may support a systemic impact of LM beyond the endothelial cells. Further studies are needed to evaluate the implications of systemic PI3K/AKT activation in LM.Pathway analysis of our LM gene signature confirms the role of the PI3K/AKT/mTOR pathway in LM pathogenesis. The PI3K/AKT/mTOR pathway modulates numerous cellular processes including cell growth, cell proliferation and angiogenesis, and activating mutations in this pathway have been detected in LM tissues , 11. AKTith PROS , 31, to PIK3CD and PIK3R1 encode the catalytic subunit and the regulatory subunit of PI3K, respectively. PIK3CD encodes p110δ, the catalytic subunit of PI3Kδ, which is an isoform of PI3Kα encoded by PIK3CA. PIK3R1 encodes three isoforms of the regulatory subunit of PI3Ks and has been found to play an important role in embryonic lymphangiogenesis [The PI3K/AKT and MEK/ERK axes represent the two major pathways through which vascular endothelial growth factor-C (VEGF-C)/VEGF receptor-3 (VEGFR-3) signaling induces lymphangiogenesis. Our data suggest that key regulators of both pathways are associated with the LM gene signature: PIK3CD, PIK3R1, and MEK. ogenesis . ActivatWnt5a-null-mice has defective sprouting of dermal lymphatics, demonstrating the importance of Wnt5a in regulating lymphangiogenesis. In our immunochemistry experiments, we observed nuclear localization of β-catenin, which lends further credence to the involvement of the Wnt/β-catenin pathway in LM. These pathways offer novel potential molecular targets for LM therapy.We have also identified novel pathway alterations in LM, including the BMP, Eph/ephrin and Wnt/β-catenin pathways. BMPs belong to the transforming growth factor β family and play central roles in vascular and skeletal development . In partIn addition to the canonical regulators in LM such as PIK3CD, PIK3R1, and MEK, two additional upstream regulators were found: MTA3 and SOX11. Metastasis tumor antigen 3 (MTA3) is a member of a cancer progression-related protein family and has been found to be associated with lymph node involvement and lymphovascular space invasion in uterine non-endometrial cancer . SOX11 iWe also performed computational drug repositioning analysis on our LM gene signature. As expected, sirolimus has the most negative CLUE score (i.e. the most dissimilar gene signature) among the known LM therapeutics and is in the top 2% of predicted FDA-approved drugs in CLUE. Prednisolone , sunitinIn addition to confirming the known LM therapeutics, our LM gene signature suggests the therapeutic potential of BMP and Wnt modulators: dorsomorphin, LDN-193189, and calyculin A. Though these drugs are not approved by the FDA for human use, these results support the involvement of the BMP and Wnt pathways in LM and demonstrate the potential of the LM gene signature in facilitating drug discovery for LM. Other classes of FDA-approved drugs that may have therapeutic potential based on CLUE score include JAK inhibitors, calcium channel blockers, and KATP activators . FurtherIn summary, we have discovered a LM-specific gene signature in peripheral blood and identified novel pathway alterations in LM, including oxidative phosphorylation, MEK/ERK, BMP, Wnt/β-catenin and ephrin signaling pathways. This LM gene signature not only enables differentiation of LM subjects from healthy individuals but also sheds light on the molecular heterogeneity that exists within LMs. We also performed computational drug repositioning analysis and confirmed the therapeutic potential of sirolimus and several other medications, which further validates the utility of this gene signature in exploring other targeted therapeutics. Our findings illuminate the molecular pathways involved in LM pathogenesis and demonstrate the potential of LM gene signatures to aid in non-invasive diagnosis, pathway analysis and therapeutic development. Efforts are underway to investigate the therapeutic potential of our predicted drugs in preclinical and clinical studies.S1 Fig® version 01–12, QIAGEN Redwood City).Upstream regulators: (A) SOX11, (B) Mek, (C) MTA3, (D) PIK3CD, and (E) PIK3R1. Red, upregulated; green, downregulated; orange arrow, activation; blue arrow, inhibition; yellow arrow, data inconsistent with predicted state of downstream molecule; grey arrow, effect not predicted. Figure adapted from Ingenuity Pathway Analysis (IPA(TIF)Click here for additional data file.S2 Fig(A) anti-phospho-4E-BP1 and (B) anti-phospho-ERK antibodies. Magnification: 10X.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableKnown LM therapeutics are shown in bold.(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Radioresistance is one of the main obstacle limiting the therapeutic efficacy and prognosis of patients, the molecular mechanisms of radioresistance is still unclear. The purpose of this study was to identify the key genes and miRNAs and to explore their potential molecular mechanisms in radioresistant nasopharyngeal carcinoma.In this study, we analysis the differentially expressed genes and microRNA based on the database of GSE48501 and GSE48502, and then employed bioinformatics to analyze the pathways and GO terms in which DEGs and DEMS target genes are involved. Moreover, Construction of protein-protein interaction network and identification of hub genes. Finally, analyzed the biological networks for validated target gene of hub miRNAs.A total of 373 differentially expressed genes (DEGs) and 14 differentially expressed microRNAs (DEMs) were screened out. The up-regulated gene JUN was overlap both in DEGs and publicly available studies, which was potentially targeted by three miRNAs, including hsa-miR-203, hsa-miR-24 and hsa-miR-31. Moreover, Pathway analysis showed that both up-regulated gene and DEMs target genes were enriched in TGF-beta signaling pathway, Hepatitis B, Pathways in cancer and p53 signaling pathway. Finally, we further constructed protein-protein interaction network (PPI) of DEGs and analyzed the biological networks for above mentioned common miRNAs, the result indicated that JUN was a core hub gene in PPI network, hsa-miR-24 and its target gene were significantly enriched in P53 signaling pathway.These results might provide new clues to improve the radiosensitivity of Nasopharyngeal Carcinoma.The online version of this article (10.1186/s12920-019-0507-6) contains supplementary material, which is available to authorized users. Radiotherapy is a mainly treatment for nasopharyngeal carcinoma (NPC). However, radioresistance is one of the major factors to affect the therapeutic efficacy and prognosis of patients –3. AccorMicroarrays are considered to be an important method for identifying potential biomarkers in many diseases at the molecular level with more effective and detailed insights . SeveralIn this study, we analysis the differentially expressed genes and microRNA between radioresistant NPC CNE2-R cells and radiosensitive CNE2 cells based on the database of GSE48501 and GSE48502, and then employed bioinformatics to analyze the pathways and GO terms in which DEGs and DEMS target genes are involved. Moreover, Construction of protein-protein interaction network and identification of hub genes. Finally, analyzed the biological networks for validated target gene of hub miRNAs. Our data may provide an important contribution to identified biological markers and clarify the mechanisms of NPC radioresistance.P < 0.05), including 4 up-regulated miRNAs and 10 down-regulated miRNAs  analysis of DEGs and DEMs target genes. Our result indicated that the significantly enriched GO terms of up-regulated and down-regulated microRNAs target genes were mainly involved in mitotic cell cycle; RNA binding; nucleoplasm; cytosol; biosynthetic process; gene expression; cellular nitrogen compound metabolic process; ion binding Table . As showWe performed pathways enrichment analysis of DEMs target genes and DEGs using DIANA miRPATH tool and Clue go software respectively. For the pathway analysis, the top of 30 significant pathways were selected in the DEMs target genes  regulate radioresistance through different biological process, such as DNA repair, apoptosis, cell cycle, and protective autophagy . HoweverIn order to understand the mechanisms underlying in radioresistant NPC, we performed global analysis of key genes and microRNAs in radioresistant Nasopharyngeal Carcinoma by bioinformatics analysis. Our result demonstrated that a total of 373 DEGs and 14 DEMs between radioresistant NPC CNE2-IR cells and radiosensitive CNE2 cells were identified. Our study revealed that JUN was significantly up-regulated in radioresistant NPC CNE2-IR cell and which was overlap between the DEGs and the DigSee. Meanwhile, in Protein-Protein interaction network analysis, JUN was a core hub gene in PPI network. Recent research has shown that JUN is an important components of the activator protein-1 (AP-1) transcription factor and is closely related to cell proliferation, apoptosis and malignant transformation . JUN coumicroRNAs are single stranded, endogenous, 19–25 nucleotide (nt), which are thought to be modulated tumor radiosensitivity . TherefoTo completely understand the function of miRNAs and mRNAs in radioresistant NPC, we performed pathway enrichment analysis of DEGs and DEMs, the result demonstrated that 4 pathways are considered as the key pathways for the radiosensitivity of NPC, including TGF-beta signaling pathway, Hepatitis B, Pathways in cancer and p53 signaling pathway, which were regulated by miRNA and mRNA together. Moreover, hsa-miR-24 and its target gene were found have significantly enriched in P53 signaling pathway. P53 can predict cancer response to IR and chemotherapy . ImproviIn conclusion, this study demonstrates that the upregulated gene JUN was overlap both in DEGs and publicly available studies, was a core hub gene in PPI network, which was potentially targeted by three miRNAs, including hsa-miR-203, hsa-miR-24 and hsa-miR-31. Pathway analysis showed that both up-regulated gene and DEMs were enriched in TGF-beta signaling pathway, Hepatitis B, Pathways in cancer and p53 signaling pathway. Finally, we analyzed the biological networks for validated target gene of common miRNAs, the result indicated that miR-24 is frequently down-regulated in radioresistant NPC cell lines and significantly enriched in P53 signaling pathway. Based on these reasons, our study indicated that the JUN, miR-24 and P53 signaling pathway may be associated with radioresistance in Nasopharyngeal Carcinoma, and which may provide new clues for improving radiosensitivity in Nasopharyngeal Carcinoma. However, these results are only speculated by the combination of databases and bioinformatics methods, and still needs to be confirmed. In the following study, we explored the association between JUN expression and radioresistance in vitro. To further determine the clinical predictive value of JUN in NPC, we performed immunohistochemistry assays to examine the protein expression pattern of JUN in NPC specimens and normal nasopharyngeal epithelium specimens. And then, we performed a log-rank test analysis the Overall survival (OS) of patients with NPC based on JUN expression. Finally, Study the correlation between JUN expression and distant metastasis in patients with NPC.http://www.ncbi.nlm.nih.gov/geo/) [The mRNA expression profile of GSE48501 and microRNA profile of GSE48502 were downloaded from the GEO database   (Additioov/geo/) . The samhttp://www.ncbi.nlm.nih.gov/geo/geo2r/) is a web tool that can analyze almost GEO series. DEGs and DEMs were screened with GEO2R [P < 0.05 and fold-change≥1.5 were set as the cut-off criterion in the DEMs. Furthermore, we applied to the online tool Morpheus (https://software.broadinstitute.org/morpheus/) to generate a heat map of DEGs [GEO2R (th GEO2R  (Additio of DEGs  (Additiohttp://210.107.182.61/geneSearch/) to identify the radioresistant related genes for publicly available studies using the following keywords: “radioresistance” [http://bioinformatics.psb.ugent.be/webtools/Venn/) [We applied to disease gene search engine with evidence sentence   (Additiowww.funrich.org/) and Clue Go software respectively [P-value < 0.05 was considered significant GO and pathway term. DIANA miRPATH tool (http://www.funrich.org/) was used to analyze gene ontology and pathway analysis of DEMs target genes [P-value< 0.05 was set as the cut-off criterion in the significant GO terms and kegg pathway.Gene ontology (GO) biological process terms and pathway enrichment analysis of differentially expressed genes was performed using FunRich software  is an online tool to construct protein-protein interaction network [The STRING database ( network  (Additio network  (Additiohttp://ophid.utoronto.ca/mirDIP/index.jsp) integrates twelve microRNA prediction datasets from six different microRNA prediction databases [mirDIP online software


Extrarenal 1α-hydroxylation has also been described to have an important role in autocrine and paracrine signaling. Vitamin D deficiency (VDD) has been in the spotlight as a major public healthcare issue with an estimated prevalence of more than a billion people worldwide. Among individuals with chronic kidney disease (CKD), VDD prevalence has been reported to be as high as 80%. Classically, VD plays a pivotal role in calcium and phosphorus homeostasis. Nevertheless, there is a growing body of evidence supporting the importance of VD in many vital non-skeletal biological processes such as endothelial function, renin-angiotensin-aldosterone system modulation, redox balance and innate and adaptive immunity. In individuals with CKD, VDD has been associated with albuminuria, faster progression of kidney disease and increased all-cause mortality. Recent guidelines support VD supplementation in CKD based on extrapolation from cohorts conducted in the general population. In this review, we discuss new insights on the multifactorial pathophysiology of VDD in CKD as well as how it may negatively modulate different organs and systems. We also critically review the latest evidence and controversies of VD monitoring and supplementation in CKD patients.Vitamin D (VD) is a pro-hormone essential for life in higher animals. It is present in few types of foods and is produced endogenously in the skin by a photochemical reaction. The final step of VD activation occurs in the kidneys involving a second hydroxylation reaction to generate the biologically active metabolite 1,25(OH) This coesponses .2-VD  converts 25(OH)-VD into 1,25(OH)2-VD  8]. Und. Und2-VDg levels . Moreoveg levels ,10,11. Ng levels . TherefoThere is no absolute consensus about the definition of VD sufficiency. According to many experts, serum 25(OH)-VD level should be equal or greater than 75 nmol/L (30 ng/mL) . VD insuOn the other hand, the upper normal limit of 25(OH)-VD has been a matter of discussion. Excessive sun exposure has never been reported as a cause of VD intoxication . The higAlthough the mechanisms are not fully elucidated, VD may impact on vascular calcification. Smooth muscle calcification is a well-known complication of the CKD-associated bone mineral disease . Animal In individuals with CKD, VDD is highly prevalent and has been associated with albuminuria, faster progression of kidney disease and increased all-cause mortality ,23,24. RIn the general population, VDD is a well-recognized public health problem worldwide with prevalence ranging from 20% and 100% ,29,30. ASeveral studies have demonstrated that individuals with CKD are at high risk of VDD ,33,34,35Although 25(OH)-VD levels start to decrease in individuals with CKD stage 2, inadequate levels can be found in all stages of CKD ,37,38,39Patients with CKD, especially on HD, are likely to have less sunlight exposure ,40. Del 3 compared to control animals [Nutritional factors may also contribute to suboptimal 25(OH)-VD status in CKD. Patients with CKD frequently have low food intake due to numerous reasons such as reduced appetite, uremic-related gastrointestinal symptoms and dietary restrictions, i.e., low protein  and low phosphate diets ,44,45. U animals . NeverthProteinuria has also been described as a contributing factor in the pathogenesis of VDD ,47. The Serum levels of 25(OH)-VD were found to decline progressively with time in patients on peritoneal dialysis (PD) . Some auDiabetes mellitus is one of the most common causes of CKD worldwide. VDD and diabetes share a major attribute: both are pandemic . Lower lThere is a growing body of evidence supporting the importance of VD in many vital non-skeletal biological processes, such as endothelial function, renin-angiotensin-aldosterone system regulation, redox balance and innate and adaptive immunity . These aA number of studies have described an association between low 25(OH)-VD levels and endothelial dysfunction ,66,67,68Vitamin D has also been implicated as an agent which can modulate the renin-angiotensin-aldosterone system (RAAS), and therefore which may influence blood pressure and cardiovascular disease. Evidence for this interaction comes from animal models, molecular studies and clinical data. 2-VD supplementation suppressed renin production in a separate group of wild type animals. Other studies have also demonstrated that paricalcitol supplementation decreases renin and renin receptor expression in animal models of CKD [2-VD ligand-bound form [In one animal study, vitamin D receptor null mice were generated and demonstrated upregulation of renin and angiotensin II, as well as significant hypertension, increased water intake and increased left ventricular mass compared to wild type animals . Furthers of CKD . The mecund form . The intWhilst these data suggest a role for VD in RAAS regulation, human data linking VDD with hypertension as an end-point of RAAS activation have been mixed. Seasonal and regional blood pressure trends suggest a relationship between UV exposure and hypertension, and cross-sectional studies have demonstrated that VD levels correlate with hypertension prevalence, supporting a VD-RAAS link . HoweverLow levels of 25(OH)-VD have been associated with increased markers of oxidative stress. In different experimental models, VD deficient animals showed increased thiobarbituric acid reactive substances (TBARS) and decreased glutathione (GSH) levels, a biomarker of oxidative stress and a major endogenous antioxidant, respectively ,75,76. F2-VD, but also to upregulate the expression of 1α-hydroxylase [2-VD by mitochondrial 1-α-hydroxylase and binds to cytoplasmic VDR, thereby acting as a transcription factor for antibacterial peptides such as cathelicidin and beta-defensin 4A. [Previous in vitro studies highlighted monocytes and macrophages as one of the first non-renal cells with the ability not only to synthesize 1,25(OH)roxylase ,10. Oncensin 4A. ,78,79. Mnsin 4A. .2-VD may also have an anti-inflammatory effect in human T cells [2-VD has been reported to reduce the expression of the nuclear factor κB (NFκB). In addition, 1,25(OH)2-VD may promote a shift in the T helper (Th) cell response from Th1 to Th2, subsequently reducing Th1-mediated tissue damage and increasing the production of Th2 immunomodulatory cytokines [2-VD may inhibit the differentiation of B cells into plasma cells, thus modulating the production of antibodies [It has been shown that 1,25(OH) T cells , as 1,25ytokines ,83. Moreytokines . There itibodies ,83.The inverse correlation between 25(OH)-VD levels and parathyroid hormone (PTH) has been demonstrated across virtually all stages of CKD ,85,86. TA systematic review with meta-analysis of observational and randomized studies showed a significant decline in PTH levels with 25(OH)-VD supplementation . SimilarLow 25(OH)-VD has been linked with increased bone turnover and decreased bone mineral density (BMD) in patients with CKD. In a cohort study including 1,026 non-dialysis patients across all CKD stages, Ureña-Torres et al. showed that 25(OH)-VD ≤ 15 ng/mL was associated with high serum bone-specific alkaline phosphatase (BALP) and C-terminal cross-linked collagen type I telopeptides (CTX), both circulating bone remodeling biomarkers . SimilarThere is evidence that 25(OH)-VD may hold a direct and independent role on bone formation and mineralization. Coen et al. retrospectively analyzed bone histomorphometry and histodynamic for different levels of 25(OH)-VD in a cohort of 104 patients on hemodialysis for more than 12 months . The invThere has been a growing interest in the potential role of VD modulating metabolic pathways implicated in muscle function . OsteomaOverall, despite the potential benefits of 25(OH)-VD on biochemical markers of mineral metabolism, there is insufficient RCT data available showing unequivocal benefits of supplementation on muscle strength, risk of falls and prevention of fractures in individuals with CKD.Several recent observational studies have highlighted the importance of 25(OH)-VD in areas outside of traditional bone and mineral metabolism. A cross-sectional analysis of the Third National Health and Nutrition Examination Survey (NHANES III) revealed a progressively higher prevalence of albuminuria with decreasing 25(OH)-VD levels in a representative sample of the US population . These r2-VD production in the renal proximal tubules -VD levels, when the PTH was above the expected range for the stage of CKD. Fifty patients were allocated to the intervention group and 51 patients received no intervention. Despite of the small dose of VD3, the authors found a 53% reduction in the urine albumin:creatinine ratio after six months of VD3 treatment [3 in patients with concomitant diabetes, CKD stage 2–4 and low 25(OH)-VD in a small observational study [2 or placebo for the primary outcome change in the proteinuria status.Molina et al. published a well-designed single-centre, controlled trial enrolling individuals with CKD stage 3–4 and persistent albuminuria. Patients were assigned to receive 666 IU of VDreatment . Similaral study . NeverthRecently, many observational studies have examined the association between lower 25(OH)-VD, CKD progression and mortality. Ravani et al. followed up 168 consecutive new referrals to a CKD clinic over a period of six years. CKD stages ranged from 2–5 pre-dialysis, and most patients had stage 3 and stage 4 CKD. The levels of 25(OH)-VD predicted progression to dialysis and death in crude analysis and in multiple regression models . SimilarAltogether, despite the observational studies highlighting the role of VDD as a potential risk factor for progression of CKD and mortality, we did not identify any RCT aiming to verify whether there is a beneficial effect of 25(OH)-VD supplementation on these outcomes.Both the Kidney Disease Outcomes Quality Initiative (KDOQI) and Kidney Disease Improving Global Outcomes (KDIGO) experts recommend checking and supplementing low serum 25(OH)-VD levels in CKD and dialysis patients . In the With respect to the recommended dietary allowance of VD in the general population, the Institute of Medicine from the US and Canada recommended that adults up to the age of 70 years require 600 IU/d of VD, whereas adults 71 years and older require 800 IU/d . These r3 for VD repletion, but acknowledges that patients with CKD may require a more aggressive therapeutic plan [3 daily, nevertheless, this guideline did not address VD supplementation in individuals with VDD or VDI [Current guidelines suggest that patients with CKD stage 1–5 and VDD or VDI should receive supplementation using the same strategies as recommended for the general population ,26,114. D or VDI . In Aust2 and VD3 undergo identical hydroxylation processes and in theory are equally used by the body to generate 1,25(OH)2-VD [2 and VD3 in 30 healthy subjects. Both VD analogues produced similar initial increments in serum 25(OH)-VD, but individuals treated with VD3 had a more sustained response with a three-fold difference in the area under the curve on the 28th day [3 might have a higher affinity to both VDR and 25-hydroxylase [2 for VDBP compared to VD3, resulting in higher clearance and subsequently a shorter circulating half-life [3 was more effective at raising serum 25(OH)-VD concentrations compared to VD2 [3 over VD2, whereas there was no statistical difference between VD3 and VD2 in the pulled data from studies that used daily supplementation [3 may be more effective than VD2, clinicians should ultimately use the presentation commercially available in the context of their clinical practice. For instance, VD2 is mostly used in the United States, whilst in other countries, such as Australia and Brazil, VD3 is the most common presentation.Another matter of debate is around which form of VD should be used. VD(OH)2-VD . In fact(OH)2-VD . Armas e28th day . Severalroxylase ,119. Othalf-life ,121,122.d to VD2 . Four stentation . Althoug3 are considered safe for the general population [In summary, the studies reviewed here highlight the potential role of VD beyond bone mineral disease in patients with CKD. Currently, the strongest available evidence supports 25(OH)-VD supplementation, aiming to control secondary hyperparathyroidism in CKD patients. Despite the striking observational data showing the association between lower levels of 25(OH)-VD and various deleterious outcomes , there is still a lack of RCTs supporting the potential beneficial effects of supplementation. Many questions remain unanswered regarding the dosing, timing of administration and type of VD analogues in patients with CKD. In addition, the current guidelines are subject to criticism for being mainly opinion-based and derived from observational data. However, given the low-cost and high safety profile, patients with CKD might benefit from 25(OH)-VD supplementation in the setting of VDD and VDI. Although doses of up to 4,000 IU of VDpulation , we reco


To date, several SE databases have been developed by us and others. However, these existing databases do not provide downstream or upstream regulatory analyses of SEs. Pathways, transcription factors (TFs), SEs, and SE-associated genes form complex regulatory networks. Therefore, we designed a novel web server, SEanalysis, which provides comprehensive SE-associated regulatory network analyses. SEanalysis characterizes SE-associated genes, TFs binding to target SEs, and their upstream pathways. The current version of SEanalysis contains more than 330 000 SEs from more than 540 types of cells/tissues, 5042 TF ChIP-seq data generated from these cells/tissues, DNA-binding sequence motifs for ∼700 human TFs and 2880 pathways from 10 databases. SEanalysis supports searching by either SEs, samples, TFs, pathways or genes. The complex regulatory networks formed by these factors can be interactively visualized. In addition, we developed a customizable genome browser containing >6000 customizable tracks for visualization. The server is freely available at  Super-enhancers (SEs), composed of clusters of enhancers, regulate cell-type-specific expression programs through a unique transcriptional activity to drive expression of genes that define cell identity . Because→→→ in Figure →→→ in Figure ←←→ in Figure Several SE databases have been developed, including dbSUPER , SEA 1212 and SEWe obtained more than 330 000 SE regions involving 542 cells/tissues from the SEdb database  that washttp://chip-atlas.org) and GTRD (http://genome.ucsc.edu/cgi-bin/hgLiftOver) tool of UCSC  models based on the identification of SE-associated master TFs in a number of cell types  method is used to correct for multiple testing. Users can adjust the number of genes required to be enriched and set thresholds of P-value and FDR of the enrichment score , e Figure . Pathwaye Figure  and D. Fe Figure .II. Upstream regulatory analysis | > 1, Figure P-value < 0.001, GeneNumber: Min: 10 and Max: 300, Tissue Type: Colon, SE-Gene Linking Strategies: Closest active and FIMO: 1e–9). The output table showed that Wnt pathway was not only significantly enriched  but it was also the sole pathway identified by both pathway sources (KEGG and NetPath), and furthermore, it ranked highly as fifth and seventh among all pathways identified   Figure , again cP-value < 0.001) ’ and ‘Validated nuclear estrogen receptor alpha network ’  , the out< 0.001) . Moreove< 0.001) . In the Taken together, these data validated all of the key webserver predictions including: (i) pathway enrichment; (ii) terminal TF ranking; (iii) identification of downstream SEs and (iv) annotation of SE-associated genes.SEanalysis supports five different searching modes: ‘Searching by SE’, ‘Searching by Sample’, ‘Searching by TF’, ‘Searching by Pathway’ and ‘Searching by Gene’. SEanalysis provides data browsing, which is an interactive table with a sorting function that allows users to quickly search for samples and customize filters including ‘Data Sources’, ‘Biosample Type’, ‘Tissue Type’ and ‘Biosample Name’. To further view the SE of a given sample, users can click ‘Sample ID’ button.http://echarts.baidu.com).As mentioned above, SEs, SE-associated genes, TFs binding to SEs and upstream pathways of TFs form complex networks. To facilitate the understanding of the network, SEanalysis supports interactive visualization of networks using the visualization plugin Echarts (http://jbrowse.org)   containiwse.org) .http://www.licpathway.net/SEanalysis and requires no registration or login. The main framework of SEanalysis was developed based on Java 1.8.0 (https://www.oracle.com/technetwork/java/) and MySQL 5.7.16 (https://www.mysql.com/). JQuery 3.3.1 (http://jquery.com) and Bootstrap 3.3.7 (https://getbootstrap.com/) (an open source front-end framework) were used to design the front-end web interface. Google Chrome, Mozilla Firefox, Opera and Safari are the preferred browsers for display.SEanalysis is freely available to the research community at To provide comprehensive analysis of SE-associated regulatory networks, we designed and developed a novel web server, SEanalysis, with the following functions: (i) Pathway downstream analysis, (ii) Upstream regulatory analysis, (iii) Genomic region annotation. Compared with other SE databases, this webserver focuses on constructing and analyzing the networks between pathways, TFs, SEs, and SE-associated genes. SEanalysis also allows users to readily download SEs for different cells/tissues, in both bed and csv format. The output results of analyses can also be downloaded. In addition, SEanalysis supports external analytical tools of genomic regions such as GREAT  and UCSCThe rapid development of high-throughput sequencing technology leads to the accelerated accumulation of a large number of epigenomic datasets. SEanalysis will be updated and maintained accordingly. Our effort to establish this web server was prompted by the great need of researchers to understand the biology of epigenomic network regulation. These researchers include cell and molecular biologists, geneticists and data scientists. Moreover, the field of epigenomics is rapidly progressing, and the integrative analysis of epigenomic regulatory networks is one of the most investigated areas. Therefore, SEanalysis will be a valuable resource for experimental and computational biologists in the field of epigenomics.gkz302_Supplemental_FilesClick here for additional data file.


We present a method for automatically discovering signaling pathways from time-resolved phosphoproteomic data. The Temporal Pathway Synthesizer (TPS) algorithm uses constraint-solving techniques first developed in the context of formal verification to explore paths in an interaction network. It systematically eliminates all candidate structures for a signaling pathway where a protein is activated or inactivated before its upstream regulators. The algorithm can model more than one hundred thousand dynamic phosphosites and can discover pathway members that are not differentially phosphorylated. By analyzing temporal data, TPS defines signaling cascades without needing to experimentally perturb individual proteins. It recovers known pathways and proposes pathway connections when applied to the human epidermal growth factor and yeast osmotic stress responses. Independent kinase mutant studies validate predicted substrates in the TPS osmotic stress pathway. Kӧksal et al. present a computational technique, the Temporal Pathway Synthesizer (TPS), that combines time series global phosphoproteomic data and protein-protein interaction networks to reconstruct the vast signaling pathways that control post-translational modifications.  High-throughput proteomic assays illuminate the amazing breadth and complexity of the signal transduction pathways that cells employ to respond to extracellular cues. These technologies can quantify protein abundance or post-translational modifications (PTMs). Mass spectrometry, in particular, offers a broad view of PTMs, including phosphorylation, ubiquitination, acetylation, and methylation , and is Many gaps persist in our understanding of phosphorylation signaling cascades. For example, our mass spectrometry experiments show that nearly all proteins that are significantly (de)phosphorylated when the epidermal growth factor receptor (EGFR) is stimulated are absent from EGFR pathway maps. The low overlap is consistent with previous temporal phosphoproteomic studies of mammalian signaling . DiscordNetwork inference algorithms can explain the phosphorylation events that lie outside of canonical pathways and complement curated pathway maps. Specialized algorithms model time series data, which inform the ordering of phosphorylation changes and support causal instead of correlative modeling . TemporaA complementary challenge to interpreting off-pathway phosphorylation is that the cellular stimulus response includes mechanisms that are not captured in phosphoproteomic datasets. There is an interplay between phosphorylation changes and other integral parts of signaling cascades. Phosphorylation can affect protein stability, subcellular localization, and recognition of interaction partners . PhosphoWe present the Temporal Pathway Synthesizer (TPS) , a methoTPS first transforms a PPI graph into a condition-specific network by using mass spectrometry data to filter out irrelevant interactions. Next, TPS finds the orientation and sign of edges in the condition-specific interaction graph based on the order of the phosphorylation events. Phosphorylation timing is modeled separately for each observed phosphorylation site on a protein. TPS systematically explores all signed, directed graphs that may explain how signaling messages propagate from the stimulated source protein. Finally, TPS summarizes the valid graphs into a single aggregate network that explicitly tracks confident and ambiguous predictions. Our temporal pathway visualizer tool interactively visualizes the summary network alongside the temporal phosphoproteomic data .We study the dynamic signaling responses to human EGF stimulation and yeast osmotic stress. TPS recovers networks that explain how stimulus-responsive proteins are activated or inhibited via chains of physical interactions stemming from the upstream receptors. The highest-confidence TPS predictions are well supported by prior knowledge and consistent with kinase perturbations. These insights into well characterized human and yeast pathways exemplify how TPS can produce condition-specific pathway maps.To quantify global EGFR-mediated cellular signaling changes in HEK293 EGFR Flp-In (EGFR Flp-In) cells  with phoWe assessed how much of the observed phosphorylation could be explained by existing pathway databases. To obtain a comprehensive view of EGFR-mediated signaling, we collected eight EGFR-related reference pathways . Among tCrosstalk does not explain the low coverage. Most phosphorylated proteins (63%) are not present in the EGFR pathways or any BioCarta, Reactome, or PID pathway , demonstWe applied TPS to model the dynamic signaling response to EGFR stimulation in EGFR Flp-In HEK293 cells. Our workflow consists of three major steps: (1) preprocessing the protein-protein interaction network and temporal phosphorylation data; (2) transforming temporal information, subnetwork structure, and prior knowledge into logical constraints; and (3) summarizing all valid signaling pathway models to discover interactions with unambiguous directions and/or signs .We first discretized the time series phosphoproteomic data, using Tukey’s honest significant difference (HSD) test  to deterall of the valid pathway models, a strong assertion about the confidence in these edge directions. Thirty-eight of the directed edges have a consistent sign as well. The PPI connections, phosphorylation timing, and prior knowledge of kinase-substrate interaction direction all play distinct, important roles in reducing the number of valid pathway models  of the 273 phosphorylated proteins and 5 (13%) of the 38 unphosphorylated connective proteins in the TPS network are recognized as EGFR pathway members . We findThe TPS network can be used to prioritize proteins and interactions for additional experimental testing. To illustrate this process, we focused on edges for which the direction or sign were predicted confidently and one of the two proteins is a member of an EGFR reference pathway . For eacDasatinib decreased phosphorylation of CRK (isoform Crk-II) pY221, consistent with the TPS pathway edge . InhibitWe compared TPS to two existing methods that combine PPI networks and time series data and a third that uses only the phosphorylation data . The dynamic Bayesian network (DBN)  infers pBecause most of the differentially phosphorylated proteins are not members of any reference pathway, these pathways cannot be used to assess the overall quality of the predictions. The TimeXNet pathway, the largest of the three predicted networks, generally captures the most reference pathway interactions when ignoring edge direction and sign . HoweverSaccharomyces cerevisiae are better understood than their human counterparts and are not subject to cell-type-specific effects. Thus, we applied TPS to model the yeast osmotic stress response to assess its ability to recapitulate this frequently studied pathway and reveal additional interactions. The hyperosmotic stress response is primarily controlled by the high osmolarity glycerol (HOG) pathway. Although they are still not fully characterized, stress-response signaling cascades in the yeast The TPS osmotic stress pathway contains 216 proteins and 287 interactions . Thirty-RCK2 mutant subjected to osmotic stress  for each node, taking an event-based view of the signaling process . We deteThe summary for the combination of all constraints produces precise predictions that cannot be obtained by intersecting the summaries for the individual types of constraints. For instance, TPS infers that the relationship between F and G must be an activation from F to G because the sole way G can reach F in a tree rooted at A is through E, but F’s activation precedes E’s. This inference cannot be made by combining the models in panels A, B, and C. The simple example also highlights the differences in how the TPS constraint-based approach improves upon related methods based on correlation or the time point of maximum phosphorylation change . See alsTPS takes the undirected network from PCSF and transforms it into a collection of signed, directed graphs that explain dynamic signaling events.To find pathway models that agree with the phosphorylation dynamics, TPS first performs a discretization step that determines time intervals in which each protein may be differentially phosphorylated. The discrete set of activation and inhibition state changes is then used to rule out networks that violate the observed temporal behavior.The transformation consists of finding time points for each profile where phosphorylation significantly differs from either the baseline (pre-stimulation) or the previous time point. In the baseline comparison, this time point is accepted only if it is not preceded by an earlier, larger change with respect to the baseline. If there is a hypothetical phosphorylation level at which the protein is activated and acts upon its downstream targets, a signaling event occurs only at the first time this threshold value is reached. This criterion does not apply when comparing to the phosphorylation level at the previous time point. TPS supports missing values in the time series data. The time points for which a phosphopeptide is missing data are assumed to be insignificant in the discretized data.In our EGF study, we use Tukey’s HSD test to find significant differential phosphorylation. If comparing a time point to the baseline or the previous measurement produces a p value below a user-defined threshold, the time point is marked as a possible activation or inhibition event depending on whether the phosphorylation level increased or decreased relative to the earlier time point to which it was compared.We assume at most one signaling event happens for every node across time points. Our logical solver can explore all possible activation and inhibition events for every node, but the data are often too ambiguous to allow multiple events per node given a single type of stimulation. In the absence of perturbation experiments that test the pathway behavior under different initial conditions, it is impossible to distinguish between different Boolean logic functions governing the behavior of each node and whether a node responds to one or multiple regulators. We therefore formalize pathway models as signed, directed trees, which provide a sufficient basis for explaining the dynamic system behavior under these assumptions.TPS transforms each input into a set of constraints that declaratively specify valid signed, directed tree models that agree with the data . These constraints are expressed as Boolean formulas with linear integer arithmetic, ranging over symbolic variables that represent choices on edge signs and orientations as well as how the temporal data are interpreted. The constraints can then be solved by a satisfiability modulo theories (SMT) solver to find a network model that satisfies all constraints along with dynamic timing annotations for each interaction in the network.Using constraints, we restrict the possible orientation and sign assignments to signed, directed tree networks rooted at the source node . Furthermore, constraints express how every tree model must agree with the time series data by establishing a correspondence between the order of nodes on tree paths and their temporal order of activity according to the time series data. Finally, we declaratively rule out models that contradict the prior knowledge of kinase-substrate interaction directions. These constraints define a very large space of candidate networks that agree with the data.TPS can reason with large state spaces by summarizing all valid pathways instead of explicitly enumerating them. A summary network is the graph union of all signed, directed tree networks that satisfy the stated constraints . Timing We compute the summary graph by performing a linear number of SMT solver queries in terms of the size of the input graph. Each queries whether at least one signed, directed model contains a specific signed, directed edge. Because individual queries are computationally cheap, we can summarize the entire solution space without enumerating all models, which is typically intractable. The summary graph over-approximates the solution space. It is not possible to recover the exact set of valid models from the summary, only a superset of the models . This trTPS uses the Z3 theorem prover  via the We stimulated EGFR Flp-In cells  with 23.We used 25 nM Dasatinib (no. S1021), 400 nM SCH772984 (no. S7101), and 800 nM MK-2206  for kinase inhibition and antibodies pY221-CRK , pY10-ATP1A1 (no. 3060), and pS142/143-Zyxin  for western blotting . We normalized loading with β-actin (no. 3700) and imaged blots with an Odyssey Infrared Imaging System (Li-COR Biosciences).We used the Omics Integrator PCSF implementation  with msgThe accession number for the raw mass spectrometry proteomics data reported in this paper is PRIDE: PXD006697.https://github.com/koksal/tps) and our visualization tool for TPS output  are available as MIT-licensed open source software. An archival copy of TPS version 2.2, including instructions for running the software, example data, and scripts for linking PCSF and TPS, is available at https://doi.org/10.5281/zenodo.1215177.The processed data are in 1234567


Open PHACTS is a pre-competitive project to answer scientific questions developed recently by the pharmaceutical industry. Having high quality biological interaction information in the Open PHACTS Discovery Platform is needed to answer multiple pathway related questions. To address this, updated WikiPathways data has been added to the platform. This data includes information about biological interactions, such as stimulation and inhibition. The platform's Application Programming Interface (API) was extended with appropriate calls to reference these interactions.  These new methods of the Open PHACTS API are available now. The Open PHACTS Discovery Platform (OPDP) was designed to help identify protein targets and information about their associations with each other4. The OPDP supports target identification and validation by including target-target interactions from WikiPathways7. Of these interaction networks, proteins sharing a downstream path allows investigation of alternative drug target combinations. Even the knowledge of which biological pathways participate in disease-related processes provides insight in the pathway topology between the targets. The importance and need of providing access to interaction information for real-world research questions was outlined in a recent Open PHACTS paper8.Targeting proteins to ideally restore normal biological processes is a common starting point in drug discovery8. It integrates data using linked data approaches3 from chemical and biological sources such as ChEBI, ChEMBL, UniProt, and WikiPathways6. However, the OPDP did not previously include calls to access specific up- and downstream interaction effects. This information is needed for questions related to drug repositioning and repurposing. Up- or downstream targets may be interesting alternatives with similar therapeutic effect to targets, for which it is particularly hard to develop a drug agent. Thus, finding a target that has already been drugged or is more drug tractable will be advantageous. Here we describe how to identify alternative targets in the same cellular pathway using OPDP against the WikiPathways data.The Open PHACTS project was born out of the desire to integrate pharmacological data from multiple precompetitive sources to efficiently address scientific questions that cannot be answered with single data sources5. The native format for WikiPathways is Graphical Pathway Markup Language (GPML) based on the eXtensible Markup Language (XML) standard. The RDF export is transformed from the original GPML. In the RDF representation we use two distinct controlled vocabularies, to distinguish between the graphical notation of a pathway and the biological meanings expressed in the pathway. This is done to allow integration with other pathway repositories which use other graphical notations or none. The WikiPathways RDF also includes details about directed and undirected interactions. Directed biochemical interactions capture the source and target which are depicted as an arrow in simple pathway drawings. WikiPathways adds biological meaning to interactions with Molecular Interaction Map (MIM) interaction types, like inhibitions, enzyme catalyzed reactions, and stimulations9, as well as Systems Biology Graphical Notation (SBGN) interactions10. Reactome pathways in WikiPathways use SBGN interactions12. However, because MIM and SBGN use different drawing styles, we normalize their inhibition types into a common inhibition type, defined by the WikiPathways ontology (https://vocabularies.wikipathways.org/wp).The WikiPathways Resource Description Framework data (WPRDF) is released as part of the monthly releasesThe WikiPathways basic drawing tools also contain generic arrows and T-bar annotations that give the user the ability to create basic diagrams without the semantic meaning of MIM or SBGN notations. The interactions connecting these nodes are captured, but the only explicit information is that it is a directed interaction from a source to a target. To handle more complicated enzyme reaction drawings, where there is not a single line that directly connects targets in a cascade of enzymatic reactions, a query was developed that recognizes these types of reactions. However, this is not implemented in the current Open PHACTS Application Programming Interface (API)./pathway/getInteractions, returns all interactions involved in a pathway. To use this feature, the user specifies a pathway URI and OPDP returns its interactions including information about direction and the connected entities. The direction information is relayed as a starting node having awp:source annotation, while the end of the interaction has thewp:target annotation. In its simplest form, this means that if gene product A is interacting with a gene product B, then we havewp:source for product A andwp:target for product B. However, the presented new methods also support interactions with multiple sources and targets for more complex interactions that are more accurately represented this way.Version 2.1 of the OPDP API contains three new calls for interactions and their pathways. The first call,/pathways/interactions/byEntity, returns the direction of the interactions involving this entity. An entity is specified by a URI and can be a metabolite, protein, gene product, or RNA. API options allow the user to select only upstream or only downstream interactions. If a direction is not specified in the call, all the adjacent interactions will be retrieved regardless of their direction. The results also specify the interaction type . Vocabularies.wikipathways.org also identifies catalysis and binding events as well as a more generic directedInteraction in the case where the type of the interaction is not identified. This ability to select the interaction direction is specifically what allows users to answer scientific questions around upstream and downstream effects, such as those defined by Open PHACTS. The third API call is/pathways/interactions/byEntity/count which is a helper function that returns the number of interactions for a target.The second added call,https://dev.openphacts.org/docs/2.1). As in previous versions, the API uses HTTP GET to call methods and needs a (free) application ID and key (seehttps://dev.openphacts.org/signup)3.The OPDP API calls are backed by SPARQL searches against the loaded WikiPathways RDF. The query parameters that are required or optional are given in the documentation of Open PHACTS 14. The AKT protein has a central role and usefully shows the API call’s ability to return connected elements with the/pathways/interactions/byEntity and the/pathway/getInteractions calls. The API calls can help aid drug discovery by taking a target, in this case AKT, and easily identify other connected proteins that could potentially be used as drug targets with a common downstream effect.We are demonstrating the platform with three example calls. All the API calls require use of an application ID and an application key. This key and ID can be acquired by creating a free Open PHACTS account. The first example is an application to the PI3K/AKT pathway for cell growth regulation which contain important targets for cancer treatment/pathways/interactions/byEntity/count is also included. It returns the number of all interactions in which an entity is participates. This helps the user get a sense of the prevalence of the queried entity with interactions in pathways found on WikiPathways. An example result for this query can be found in/pathway/getInteractions . These sets include many of the popular gene, protein, and metabolite identifier:for genes, proteins, and RNAs Ensembl, Entrez Gene, UniProt, and others; for metabolites the ID sources can include HMDB, ChEBI, PubChem, CAS registry numbers, and ChemSpider. 2.  Retrieving specific gene symbol or compound names aside from the identifier URI, resource, and identifier can be part of a workflow in workflow tools like KNIME or Pipeline Pilot.  We did create a simple workflow to address this. In this example, we use the /pathway/getInteractions API call to return the directed interactions for a pathway and the resulting IDs for the interaction and then returned the more human readable labels for the URIs.   3.  Additional example output figures were added to the supplementary materials section to reflect the addition of example query results for the remaining calls. 4.  We added an Example Workflows section describing two simple workflows. More complex applications and workflows have been recently published for KNIME and Pipeline Pilot, which we added as reference 19.  The authors go into some detail about the MIM/SBGN representations used by WikiPathways. To my understanding, WikiPathways makes use of the format GPML as a data storage format. Some description of the relationship of GPML and the WPRDF format would be nice for readers to understand the data processing involved. https://github.com/swagger-api/swagger-codegen) project? Have the authors had any experience with these auto-generated packages in the context of their API? Looking at the code repository, the API interface seems to be built using Swagger. Is there the plan to provide supported client packages to access the API from various programming languages? Are users encouraged to produce their own using the swagger-codegen  the only ones available in the API? If not, is there a listing of the interaction types with description of the interaction type. It would be more clear to just state the call function name rather than "the first and third calls" and "the api call" in the example queries section. Additional examples of the result output for all described function calls would be helpful for the described API calls. Additional examples that highlight the integrative nature of Open PHACTS would be nice. For example, to show how results of the currently described API calls can paired the other data available, such as the CHEBI data from the platform.The tool provides additional features to the Open PHACTS platform to simplify the access of interaction information from WikiPathways. Some comments: I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. WikiPathways is represented using GPML , which is a modified XML format.  The WikiPathways RDF is divided into two divisions, the GPML and the WPRDF. The GPML side is the portion that is responsible for the drawing and graphical elements.  The WPRDF side is there to describe the semantic elements of the RDF.  We have updated the Methods section and clarified this in the first “Implementation” paragraph.The API is REST-like and dedicated clients are not required. However, a few client libraries are already available for various languages and platforms: JavaScript (ops.js), Java (ops4j), ropenphacts, and KNIME nodes. Personal experience with auto-generated packages has been negative, but based on code generation in general, not the Open PHACTS API.  Supported libraries are referenced in the summary sections along with the workflow environments.We intended to clarify that this simple example referred to single-source, single-target interactions, but that the framework also supports interactions with more than one source and/or target. Such complex interactions occur frequently. The last sentence of the third paragraph under Methods: Implementation has been updated to make this more clear.The Open PHACTS API includes the Identifier Mapping Service component which allows use of many identifier schemes, as long as suitable link sets are available (at http://data.openphacts.org/1.5/ims/linksets/). These sets include many of the popular gene, protein, and metabolite identifier:for genes, proteins, and RNAs Ensembl, Entrez Gene, UniProt, and others; for metabolites the ID sources can include HMDB, ChEBI, PubChem, CAS registry numbers, and ChemSpider. The caption of Figure 1 has been extended and a paragraph added to Operation section to reflect this.Yes, you are correct: if the user does not specify the directions for the interactions, all the immediate interactions are retrieved regardless of their direction. This can be found in the last paragraph of the Methods section and updated here to clarify.Vocabularies.wikipathways.org also identifies catalysis and binding events as well as a more generic directedInteraction in the case where the type of the interaction is not identified. This can be found in the last paragraph of the Methods: Implementation.The text in the under Example Queries section has been updated to use the names of the API calls used.Additional figures that show example outputs for the remaining figures can be found in the supplemental info section.Workflow tools make it possible to take advantage of the integrative nature of the OPDP to make API calls in succession.  With these tools, it is possible to perform a directional query of a target and identify alternative targets that can then be queried against the chemistry calls to identify active compounds for these alternative targets.  An Example Workflows section has been added describing two simple workflows and the code for the workflows can be found in the supplementary information.


Control of microvascular network growth is critical to treatment of ischemic tissue diseases and enhancing regenerative capacity of tissue engineering implants. Conventional therapeutic strategies for inducing angiogenesis aim to deliver one or more proangiogenic cytokines or to over-express known pro-angiogenic genes, but seldom address potential compensatory or cooperative effects between signals and the overarching signaling pathways that determine successful outcomes. An emerging grand challenge is harnessing the expanding knowledge base of angiogenic signaling pathways toward development of successful new therapies. We previously performed drug optimization studies by various substitutions of a 2-isoindole-1,3-dione scaffold to discover novel bioactive small molecules capable of inducing growth of microvascular networks, the most potent of which we termed phthalimide neovascularization factor 1 . We then showed that PNF-1 regulates the transcription of signaling molecules that are associated with vascular initiation and maturation in a time-dependent manner through a novel pathway compendium analysis in which transcriptional regulatory networks of PNF-1-stimulated microvascular endothelial cells are overlaid with literature-derived angiogenic pathways. In this study, we generated three analogues  through systematic transformations to PNF1 to evaluate the effects of electronic, steric, chiral, and hydrogen bonding changes on angiogenic signaling. We then expanded our compendium analysis toward these new compounds. Variables obtained from the compendium analysis were then used to construct a PLSR model to predict endothelial cell proliferation. Our combined approach suggests mechanisms of action involving suppression of VEGF pathways through TGF-β andNR3C1 network activation. Previously, we discovered a novel small molecule (PNF1) that is capable of inducing growth of microvascular networks, a mechanism that is very important in many regenerative applications. In this study, we alter the structure of PNF1 slightly to get three different analogues and focus on gaining insight into how these drugs induce their pro-angiogenic effects. This is done through a few techniques that result in a map of all the transcripts that are up- or downregulated as a result of administering the drug, a knowledge that is necessary for successful therapeutic strategies. Angiogenesis and neovascularization is important in a number of regenerative medicine therapeutics, including soft tissue regeneration. Having a deep understanding of the transcriptional mechanism of small molecules with this angiogenic potential will aid in designing specific immunomodulatory biomaterials. In the future, we will study these drugs and their angiogenic properties in impactful and clinically translatable applications. Despite the recent advancements in drug-induced neovascularization, further development of drug therapies is needed for improvement of functional vessel systems . The creIn previous studies, we discovered a novel small molecule, phthalimide neovascularization factor 1 (PNF-1 or SC-3–149), which is a potent stimulator of microvascular network growth . PNF-1 pWe then showed that PNF-1 regulates the transcription of signaling molecules that are associated with vascular initiation and maturation in a time-dependent manner through a novel pathway compendium analysis. In this analysis, transcriptional regulatory networks of PNF-1-stimulated microvascular endothelial cells are overlaid with literature-derived angiogenic pathways 7, 9−11]. Parallel in vivo experimental quantification of systems-level remodeling of microvascular network growth induced by PNF-1 delivery revealed a similar sequential pattern of inflammation-mediated microvascular initiation, expansion, and subsequent maturation. Intravital imaging analysis of intact microvessel networks show that PNF-1 induces increases in total microvessel length density at early time points, followed by changes in the luminal diameter of local arterioles and other hallmarks of vascular maturation. [, 9−11. PIn this study, we validated our pathway compendium analysis to generate new transcriptional regulatory network m odels from hum an m icrovascular endothelial cells (HMVECs) over multiple time points after PNF-1 stimulation. Network analysis by Ingenuity Pathway Analysis  was used to examine possible differences between our original analysis results owing to the expansion of the Ingenuity Pathway Knowledge Base (IPKB). Moreover, we evaluated the effects of other structurally similar investigational compounds analogous to PNF-1 that show mitogenic effects in human microvascular endothelial cells. We modeled these effects, namely cell proliferation, using a partial least squares regression (PLSR) model with data obtained from the compendium analysis. Even with the large amount of highly colinear parameters, PLSR allows us to construct an accurate predictive model. By comparing the relative “fingerprints” of each angiogenic pathway of four structurally similar small molecules, we elucidate shared mechanisms that allow for functional molecular grouping or phenotyping.This approach can increase the understanding of the complex balance between these protagonistic and antagonistic cues resulting in microvascular network growth. Identifying the balance between pro- and anti-angiogenic factors, the so called “angiogenic switch” may serve to enhance efforts at directed drug discovery of pro-angiogenic therapeutics.Four pthalidomide-based small molecules drugs were used in this study. Previously, we performed drug optimization studies by various substitutions of a 2-isoindole-1,3-dione scaffold to discover novel bioactive small molecules capable of inducing growth of microvascular networks, one of which we termed PNF-1. For this study, we generated new investigational compounds through systemic transformations to PNF-1 to evaluate the effects of electronic, steric, chiral, and hydrogen bonding changes on the endothelial cell phenotype behavior elicited by the drugs . Though Human microvascular endothelial cells (HMVECs)  were cultured in endothelial growth medium 2-microvascular (EGM-2-MV)  supplemented with 5% fetal bovine serum. Cells were cultured on p100 tissue culture plates at 37 °C in a humidified chamber with 5% carbon dioxide.4 cells/cm2 on p100 tissue culture plates, grown to confluence and subjected to 30 μM of the treatment group’s respective drug, which affects the proliferation of HMVECs due to their angiogenic properties [www.millipore.com) immediately prior to treatment and at the 24-h endpoint. Twenty-four hours after stimulation, total RNA was isolated using an RNeasy isolation kit  according to the manufacture’s protocol.HMVECs were plated at 2.5 × 10operties . Treatmep values for differential expression were calculated based on the Wilcoxon signed-rank test, and significant differentially expressed genes were selected with p values below 0.003. Replicate gene IDs were then removed and their collective p values averaged. Microarray processing resulted in between 600 and 1400 differentially expressed genes per drug treatment group. Differentially expressed genes for the seven PNF-1 time points were compared in MATLAB using the pathway compendium analysis presented previously [RNA samples were prepared for hybridization using the GeneChip One-Cycle Labeling and Control Reagents kit (Affymetrix). Expression profiles were created using GeneChip Human Genome U133 Plus 2.0 Array (Affymetrix), which contains a total of 47,000 human gene transcripts and variants. Expression profiles for PNF-1 treated HMVECs at seven time points  were retrieved from the Gene Expression Omnibus . Data previously . This anIPA was used in conjunction with the IPKB for gene network analysis. IPA has been used in previous gene network studies on microvascular remodeling as well as cellular responses to small molecules , 14, 15.Common nodes between treatment groups were identified using differential expression data. Common node comparisons are the simplest method for expression comparisons and do not require any statistical testing beyond differential expression. A gene is considered a common node if it is shared as a focus gene in two or more drug profiles.p values using Fisher’s exact test, which calculates the significance of enrichment of the gene expression data for genes downstream of an upstream regulator. The upstream regulators method was determined to be a more comprehensive method of identifying mechanistic overlap because of its inclusion of literature-derived genetic relationships in its scoring algorithms.Predicted upstream regulators of downstream focus genes were identified for each treatment group using the “upstream regulators” function. IPA identifies the upstream transcription factors that can explain the differential gene expression shown in experimental data. Confidence in activation or inactivation of upstream regulators is expressed via Top genetic networks for each treatment group were constructed based on literature-based node connections. Networks were generated and scored based on their connectivity of focus genes. Networks were ranked based on their IPA given scores, which represent the probability that each isolated network of genes could be achieved by chance alone. Scores greater than three have a 99.9% confidence level of not being generated by random chance. The top three networks for each treatment group were selected for further analysis outside of IPA. These top three networks were then analyzed using the “canonical pathways” and “functional analysis” tools.To further analyze the genetic profiles of the drugs with a specific emphasis on angiogenesis, we applied the network compendium presented by Wieghaus et al. to all four datasets . Using IMassive expansion of gene relationships can increase the total pool of interactions for each compendium category, thereby reducing the percentage calculations across the board. A percentage normalization method was used to provide an easier way to view the up-to-downstream regulation ratio, which is difficult to compare via the total genes method.X) were used, corresponding to the up- and downstream activation of each of the nine pathways specified in our compendium analysis. The cell growth rate data collected for each drug were used as the observed response variable (Y). Gene expression data and cell proliferation data from five treatments  were used to create the model. The model was created using MATLAB’s plsregress function on a 5 by 18 matrix of gene expression data and a 5 by 1 array of cell growth values. The resulting model was then applied to the gene expression data of each of the six treatments to predict its cell growth output, including PNF-1, which was not used in the model creation. The model underwent sensitivity testing to identify variables with an aberrant effect on prediction. A total error score was calculated using the residual sum of squares. The resulting model was reapplied after the removal of predictor variables that reduced the percent error.For the construction of the PLSR model, 18 predictor variables , were predicted as activated.SC-3–263 exhibited several significant growth factor-related upstream regulators. Tumor necrosis factor-alpha (TNF-α) was one of the highest scoring molecules predicted to be activated, along with growth factors platelet-derived growth factor BB , epidermal growth factor (EGF), and leukotriene, a regulator of inflammatory response. SC-3–143 had similar trends in upstream regulator activation, with predicted activation of TNF-α, PDGF BB, and β-estradiol, predominantly a sex hormone. β-estradiol plays many reproductive rolls, but it has also been shown to improve arterial blood flow in coronary arteries. SC-3–141 exhibited a mix of anticancer gene activation. The genes for TP53, a tumor suppressor protein, CDKN1A, which creates p21 protein and is tightly restricted by TP53, and 1-alpha, 25-dihydroxy vitamin DMany of the results of the upstream regulator analysis are consistent with pthalidomide-based small molecules. Because these drugs were originally synthesized with the intention of inhibiting cancer, it was not surprising that cancer-inhibiting upstream regulators such as TNF-α and TP53 were predicted to be activated. TNF-α has also been linked to new blood vessel formation suggesting a possible pro-angiogenic synergy with PDGF BB in SC-3–143 and SC-3–263.Individual nodes in each network were ranked for their total number of connections or molecular interactions within the network . ConcurrCells treated with PNF-1 upregulated the cDNA of TGB-1, which promotes cellular proliferation and differentiation as previously reported, and P13k, a family of enzymes involved in responses to growth factors, proliferation, and differentiation . In funcIn the network analysis of SC-3–141-treated cells, the most central network was built around vascular endothelial growth factor (VEGF), most of which was downregulated (including VEGF). Conversely, NR3C1 (glucocorticoid receptor), a pleiotropic gene complex was found to be significantly upregulated. In functional analysis, the four most significant canonical pathways were glucocorticoid receptor signaling, molecular mechanisms of cancer, pancreatic adenocarcinoma signaling, and the role of endothelial cells in rheumatoid arthritis. These pathways each had nine to 11 activated molecules in the combined network. Predicted functions for this network were cell cycle, cellular assembly and organization, cancer, and proliferation.In the network analysis of SC-3–143-treated specimens, immunoglobulin G (IgG), an antibody isotype, bone morphogenic protein 4 (BMP-4), a part of the TGF-β superfamily, and P13k were predicted to be upregulated in their respective network. Subsequently, NF-kB, which controls DNA transcription in response to stress, free radicals, antigens, or cytokines, and ERK 1/2 both displayed a high number of interactions, but were not differentially expressed. In functional analysis, the four most significant canonical pathways were systemic lupus erythematosus, molecular mechanisms of cancer, glucocorticoid receptor signaling, and TGF-β signaling. As with PNF-1, these pathways were not largely activated, with only five to ten molecules activated in the combined network. Predicted functions for this network were RNA post-translational modification and vitamin D-resistant rickets.p values for confidence in network function all fell below 10−10.Finally, in the network analysis of SC-3–263 treated specimens, cellular proto-oncogene (c-Fos), a transcription factor upregulated in response to many growth factors, and transforming growth factor-beta 1 (TGF-β1), which promotes cellular proliferation and differentiation, show a large number of connections and were concurrently upregulated. The SOS gene also showed a large number of molecular interactions but was not differentially expressed. In functional analysis, four canonical pathways stood out: the role of endothelial cells in rheumatoid arthritis, dendritic cell maturation, glucocorticoid receptor signaling, and TGF-β signaling. These pathways contained between ten and 20 activated molecules in the combined network. Top predicted functions for this network were morphology of cells, quantity of cells, and cellular differentiation. Alongside PNF-1 networks, SC-3–143 and SC-3–263 have TGF-β as a common theme in the functional analysis of canonical pathways and show a significant TGF-β-centered mechanistic dependence. These relationships suggest that these pthalidom ide-based molecules may act through similar TGF-β-dependent receptors or pathways. Significant VEGF pathway downregulation present in SC-3–141 is also consistent with pthalidomide-based molecules; however, this is likely not responsible for any proangiogenic effects . SC-3–14Since TGF-β centric networks appeared as the most common top network among the four drug conditions, we constructed a merged TGF-β network containing the combination of all genes present in TGF-β centric networks Figs.  and 5. TThe network compendium applied to all four datasets at the 24-h time point shows that the TNF-α pathway had the highest total expression across all groups, and the NR3C1 pathway was second highest for all groups except SC-3–141, which demonstrated elevated VEGF activity. All pathways except CCL2 had higher levels of downstream activation than upstream activation . AdditioThe compendium was then applied at all seven time points to PNF-1 . This alInitially, all 18 variables from the compendium analysis from 5 drugs  were used to predict cell proliferation after 24 h of stimulation for all 6 drugs  . We analStructural adaptation and stability of microvascular networks is critical to many therapeutic applications, particularly those in which immediate nutritive and metabolic demands are generated , 16, 17.PNF-1 and our three-novel pthalidomide analogues are distinctive drugs under development for therapeutic angiogenesis and regenerative medicine. Uncovering the mechanisms of action of these potentially therapeutic drugs is critical for commercialization and safe integration into clinical settings. PNF-1 has previously been investigated using in vivo angiogenic assays and network analysis tools –11. ThesTGF-β pathways were identified to be a critical component in all four pthalidomide analogue mechanisms. TGF-β is known to induce angiogenesis through PDGF BB and bFGF , 21. ThiThe re-application of the pathway compendium to the temporal profile of PNF-1 reaffirms the importance of TNF-α signaling in the physiological responses. Previous research implicates TN F-α induction of TGF-β through the ERK-specific MAPK pathway and AP-1 activation –25. The In conclusion, the pairing of compendium pathway analysis with the IPKB analysis is a comprehensive technique that aims to reduce bias in approaching suggestions for drug mechanisms. Looking for similarities between techniques and confirming interactions via literature served to link the two techniques and provide a more comprehensive analysis. This could serve as a useful framework to harness the vast knowledge base of angiogenic signaling pathways to develop successful new therapies. Both techniques proved useful in identifying the key gene targets of the pthalidomide analogues: TGF-β, TNF-α, and NR3C1. Though the likelihood of identifying a unifying common pathway for all four analogues was not likely, enough similarities exist for a number of generalizations to be made. The homogeneity of TGF-β and NR3C1 activation between groups mixed with the downstream activation of PDGF and bFGF activation suggests a complex mechanism that acts independent of the common VEGF pathway .