Diabetic metabolite-protein network.

<p>The Cytoscape tool was used to generate the diabetes associated metabolites and their connections to metabolic enzymes/transporters. Overall 1660 diabetes related metabolite-protein pairs were established and visualized. Green triangles represent metabolites associated with diabetes, and red circles represent proteins associated with metabolites based on HMDB database.</p

Drug Repositioning for Diabetes Based on 'Omics' Data Mining

<div><p>Drug repositioning has shorter developmental time, lower cost and less safety risk than traditional drug development process. The current study aims to repurpose marketed drugs and clinical candidates for new indications in diabetes treatment by mining clinical ‘omics’ data. We analyzed data from genome wide association studies (GWAS), proteomics and metabolomics studies and revealed a total of 992 proteins as potential anti-diabetic targets in human. Information on the drugs that target these 992 proteins was retrieved from the Therapeutic Target Database (TTD) and 108 of these proteins are drug targets with drug projects information. Research and preclinical drug targets were excluded and 35 of the 108 proteins were selected as druggable proteins. Among them, five proteins were known targets for treating diabetes. Based on the pathogenesis knowledge gathered from the OMIM and PubMed databases, 12 protein targets of 58 drugs were found to have a new indication for treating diabetes. CMap (connectivity map) was used to compare the gene expression patterns of cells treated by these 58 drugs and that of cells treated by known anti-diabetic drugs or diabetes risk causing compounds. As a result, 9 drugs were found to have the potential to treat diabetes. Among the 9 drugs, 4 drugs (diflunisal, nabumetone, niflumic acid and valdecoxib) targeting COX2 (prostaglandin G/H synthase 2) were repurposed for treating type 1 diabetes, and 2 drugs (phenoxybenzamine and idazoxan) targeting ADRA2A (Alpha-2A adrenergic receptor) had a new indication for treating type 2 diabetes. These findings indicated that ‘omics’ data mining based drug repositioning is a potentially powerful tool to discover novel anti-diabetic indications from marketed drugs and clinical candidates. Furthermore, the results of our study could be related to other disorders, such as Alzheimer’s disease.</p></div

Diagram of COX2 inhibitors and their indication for treating type 1 diabetes.

<p>COX2 is known to convert arachidonate to PG H2, the precursor of PGs. PGs have an inflammation effect and sensitize neurons to pain or induce antigen-presenting cell dysfunction that predisposes a person to autoimmunity and type 1 diabetes. Therefore, inhibitors of COX2 have the potential to block PGs-mediated autoimmunity and treat type 1 diabetes.</p

Flow-chart of drug repositioning by mining ‘omics’ data.

<p>We retrieved 17 GWAS studies, 18 proteomics studies and 19 metabolomics studies that assessed diabetes patients until August 2014. 115 genes, 56 proteins and 227 metabolites were significantly associated with diabetes. An HMDB search revealed 1660 metabolite-protein pairs corresponding to 840 proteins. Overall, 992 unique proteins associated with diabetes were gathered and mapped to the TTD database and 108 of them had drug projects information. After removing those under experimental and preclinical stages, we obtained 35 protein targets, including 5 known anti-diabetic targets (27 drugs projects) and 30 unknown anti-diabetic targets (167 drugs projects). Pathogenesis knowledge was retrieved from the OMIM and Pubmed databases, 12 targets corresponding to 58 drugs were indicated to have novel indication for diabetes treatment. CMap analysis indicated that 9 of the 58 drugs have the potential to treat diabetes.</p

Information of the 12 targets and 58 drugs repurposed for treating diabetes based on “omics” data mining.

<p>* Melatonin receptor type 1B was previously repurposed as a target for diabetes treatment.</p><p># Information was retrieved from OMIM.</p><p>Information of the 12 targets and 58 drugs repurposed for treating diabetes based on “omics” data mining.</p

Stable PrP-deficiency prevents EMT-dependent polysialylation of NCAM1.

<p>(a) A post-translational modification of NCAM1 is missing in cells expressing no or low levels of PrP. Western blot analysis of selected NMuMG cell extracts revealed increased total levels of NCAM1 in all cell clones upon 48 h TGFB1 exposure. Whereas cells expressing wt levels of PrP give rise to a continuous pattern of NCAM1 signals, PrP-deficient cells exhibit more distinct NCAM1 bands, whose masses correspond to the expected masses of the three predominant NCAM1 isoforms. Note that the PrP^C band pattern observed in NMuMG cells tends to be more complex than the corresponding pattern in, for example, the Neuro2a cell model, possibly reflecting a greater heterogeneity of its N-glycans in these cells. (b) Screening of a larger number of stable PrP shRNA NMuMG clones further corroborated a direct correlation between PrP expression levels and post-translationally modified NCAM1 isoforms. Stable PrP shRNA clone 1, which exhibited no reduction in post-translationally-modified NCAM1 signals, turned out to express near wild-type levels of PrP, thereby establishing this clone as a false negative shRNA control. (c) Stable PrP-deficiency impairs polysialylation of NCAM1 at N-glycan acceptor sites. To characterize the post-translational NCAM1 modification lacking in PrP-deficient cells, extracts from wt or stable PrP kd NMuMG cells, which had been treated with TGFB1 for 48 h, were subjected to enzymatic digestion with glycosylases known to remove terminating sialic acids (exo-N), cut polysialic acid chains (endo-N) or hydrolyze the linkage of N-glycan groups to asparagine side-chains within ‘NxS/T’ acceptor sites (PNGase F). Note that complete removal of N-glycans abolishes the discriminating NCAM1 modification. (d) Interpretative panel of western blot bands observed in subpanel c. Red lines indicate expected cleavage sites for treatment conditions shown.</p

PrP-deficiency affects expression of a subset of proteins undergoing pronounced expression levels changes during EMT.

<p>List of proteins exhibiting >20% level differences in comparison of global proteomes of TGFB1-treated stable PrP kd versus wt NMuMG cells (dataset II). Coverage: percentages of primary structure of covered by peptide-to-spectrum matches; # Peptides: number of peptides matched to a given protein entry (note that instances of the same peptide being identified with different modifications counted separately in this tally); Count: number of TMT signature ion distributions, which passed stringent filtering criteria and were used for relative quantitation. Please see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133741#pone.0133741.s003" target="_blank">S1 Table</a> for complete list of proteins identified, including control samples, confidence scores and statistical measures.</p

Quantitative mass spectrometry identifies perturbed ‘response to metal ions’ and EMT markers, including NCAM1, affected in PrP-deficient cells.

<p>(a) Design of quantitative global proteome comparisons giving rise to datasets I to III. (b) Workflow of global proteome analyses conducted by comparative mass spectrometry. Note that this workflow was executed 3 times to generated datasets I to III, with the ‘x’ being replaced by the respective condition specified at the top of this panel. To facilitate comparison of datasets, the three experiments differed in the biological samples which were labeled with even-numbered TMT reagents. All three datasets shared the use of wt NMuMG cell extracts following 48 h TGFB1 exposure as reference samples labeled with odd-numbered TMT reagents. (c) Example graph depicting post-acquisition filtering of datasets and benchmarks of mass spectrometry analysis (shown for dataset I). (d) Profound overlap amongst top 200 proteins whose levels are most changed during EMT or following stable PrP kd. (e) Exposure of NMuMG cells to TGFB1 causes changes to proteins whose KEGG annotations identify them as players in pathways that contribute to ‘focal adhesion’ formation and ‘actin cytoskeleton regulation’. (f) Direct comparison of global proteomes of wt and stable PrP kd NMuMG cells following TGFB1 exposure identifies highly significant perturbations in biological processes with ‘response to inorganic substance’ and ‘response to metal ions’ GO annotations.</p

Inhibition of CTNNB1-dependent transcription phenocopies loss of PSA in NMuMG cells.

<p>(a) Comparison of global proteomes of stable PrP kd clones versus wt NMuMG cells and stable versus transient PrP-deficient cell clones. Of the total of 1421 proteins quantified in all global proteome analyses, relative levels of 41 proteins were changed by more than 20% in the direct comparisons. Four and three proteins had prior GO annotations, which identified them as ‘DNA binding’ and/or ‘Transcriptional regulators’. Based on these annotations, only β-catenin emerged as a DNA-binding transcriptional regulator whose levels are also changed during EMT. Note also that the level changes between stable kd cells and wt or transient kd NMuMG cells turned out to be equidirectional for all proteins whose levels changed more than 20%. (b) Transient kd of CTNNB1 or inhibitor-based disruption of protein-protein interactions between CTNNB1 and TCF or CBP reduces polysialylation of NCAM1. (c) Stable PrP ko or kd in NMuMG cells altered nuclear levels of SNAI1 and p133-CREB, developmental transcription factors known to interact with CTNNB1. Lamin A served as a nuclear reporter protein in these experiments, indicating both enrichment levels of nuclear fractions and equal protein loading. (d) Quantitation of nuclear levels of SNAI1 and p133-CREB in stable PrP ko or kd NMuMG clones versus wild-type or transient PrP kd NMuMG cells. The asterisks indicate significant differences in levels of SNAI1 (p = 0.029) and p133-CREB (p = 0.029) in cells that support or are impaired in NCAM1 polysialylation during EMT. (e) Cartoon depicting signaling pathways which may underlie differences in NCAM1 polysialylation in stable PrP-deficient cells.</p

PrP^C expression is transcriptionally upregulated during EMT.

<p>(a) Double-immunofluorescence analyses of NMuMG cells before and after 48 h exposure to TGFB1, depicting the changes to cell shape and actin cytoskeleton that accompany EMT in this cell model. (b) Western blot analysis of E-cadherin and PrP^C protein levels in NMuMG cell extracts during 72 h of exposure to TGFB1. (c) Profound upregulation of <i>Prnp</i> gene transcription accounts for increased PrP^C protein levels during EMT based on a time-course RT-PCR analysis of PrP transcripts in NMuMG cells following addition of TGFB1 to the cell culture medium. (d) Comparison of E-cadherin and PrP protein levels in wt NMuMG cells and PrP-deficient derivative cell clones obtained by CRISRP-Cas9-based PrP knockout or stable shRNA-based kd. The ‘negative control’ represents a cell clone which had been subjected to identical CRISPR-Cas9-based <i>Prnp</i> knockout procedures but did not result in a PrP knockout. (e) Immunofluorescence analysis of E-cadherin and F-actin in wt or PrP-deficient cells before and after TGFB1 exposure. Disorganized E-cadherin distribution at cell-cell junctions and failure of PrP-deficient cells to exhibit directional alignment following TGFB1 exposure.</p