36 research outputs found

    PEP Search in MyCompoundID: Detection and Identification of Dipeptides and Tripeptides Using Dimethyl Labeling and Hydrophilic Interaction Liquid Chromatography Tandem Mass Spectrometry

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    Small peptides, such as dipeptides and tripeptides, are naturally present in many biological samples (e.g., human biofluids and cell extracts). They have attracted great attention in many research fields because of their important biological functions as well as potential roles as disease biomarkers. Tandem mass spectrometry (MS/MS) can be used to profile these small peptides. However, the type and number of fragment ions generated in MS/MS are often limited for unambiguous identification. Herein we report a novel database-search strategy based on the use of MS/MS spectra of both unlabeled and dimethyl labeled peptides to identify and confirm amino acid sequences of di/tripeptides that are separated using hydrophilic interaction (HILIC) liquid chromatography (LC). To facilitate the di/tripeptide identification, a database consisting of all the predicted MS/MS spectra from 400 dipeptides and 8000 tripeptides was created, and a search tool, PEP Search, was developed and housed at the MyCompoundID website (www.mycompoundid.org/PEP). To evaluate the identification specificity of this method, we used acid hydrolysis to degrade a standard protein, cytochrome c, to produce many di/tripeptides with known sequences for LC/MS/MS. The resultant MS/MS spectra were searched against the database to generate a list of matches which were compared to the known sequences. We correctly identified the di/tripeptides in the protein hydrolysate. We then applied this method to detect and identify di/tripeptides naturally present in human urine samples with high confidence. We envisage the use of this method as a complementary tool to various LC/MS techniques currently available for small molecule or metabolome profiling with an added benefit of covering all di/tripeptide chemical space

    The function of mRNAs with altered expression and their relationship to deregulated miRNAs after each manipulation.

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    <p>The proportion of differentially expressed mRNAs in each type of relationship to deregulated miRNAs is shown in a pie chart, in which the intensity of the red color indicates the degree of association, with a more intense color representing a direct association; blue is used to indicate an unspecified relationship. The five biological processes most significantly enriched by altered mRNAs resulting from each manipulation are listed.</p

    MyCompoundID MS/MS Search: Metabolite Identification Using a Library of Predicted Fragment-Ion-Spectra of 383,830 Possible Human Metabolites

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    We report an analytical tool to facilitate metabolite identification based on an MS/MS spectral match of an unknown to a library of predicted MS/MS spectra of possible human metabolites. To construct the spectral library, the known endogenous human metabolites in the Human Metabolome Database (HMDB) (8,021 metabolites) and their predicted metabolic products via one metabolic reaction in the Evidence-based Metabolome Library (EML) (375,809 predicted metabolites) were subjected to <i>in silico</i> fragmentation to produce the predicted MS/MS spectra. This spectral library is hosted at the public MCID Web site (www.MyCompoundID.org), and a spectral search program, MCID MS/MS, has been developed to allow a user to search one or a batch of experimental MS/MS spectra against the library spectra for possible match(s). Using MS/MS spectra generated from standard metabolites and a human urine sample, we demonstrate that this tool is very useful for putative metabolite identification. It allows a user to narrow down many possible structures initially found by using an accurate mass search of an unknown metabolite to only one or a few candidates, thereby saving time and effort in selecting or synthesizing metabolite standard(s) for eventual positive metabolite identification

    The comprehensive MPRN (cMPRN) and correlation between response frequency of miRNA-pathway regulations and their network centrality.

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    <p>(A) cMPRN was generated by integrating the six MPRNs corresponding to each experimental manipulation. Green circles and blue triangles represent functional miRNAs and target pathways, respectively. Node size and edge thickness are correlated with response frequency. (B) The degree distribution of cMPRN follows the power law. (C) The degree of nodes (including pathways and miRNAs) was positively correlated with their response frequency to different manipulations. (D) The betweenness of edges (miRNA-pathway regulation) was positively correlated with their response frequency.</p

    Identification of a Core miRNA-Pathway Regulatory Network in Glioma by Therapeutically Targeting miR-181d, miR-21, miR-23b, β-Catenin, CBP, and STAT3

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    <div><p>The application of microRNAs (miRNAs) in the therapeutics of glioma and other human diseases is an area of intense interest. However, it’s still a great challenge to interpret the functional consequences of using miRNAs in glioma therapy. Here, we examined paired deep sequencing expression profiles of miRNAs and mRNAs from human glioma cell lines after manipulating the levels of miRNAs miR-181d, -21, and -23b, as well as transcriptional regulators β-catenin, CBP, and STAT3. An integrated approach was used to identify functional miRNA-pathway regulatory networks (MPRNs) responding to each manipulation. MiRNAs were identified to regulate glioma related biological pathways collaboratively after manipulating the level of either post-transcriptional or transcriptional regulators, and functional synergy and crosstalk was observed between different MPRNs. MPRNs responsive to multiple interventions were found to occupy central positions in the comprehensive MPRN (cMPRN) generated by integrating all the six MPRNs. Finally, we identified a core module comprising 14 miRNAs and five pathways that could predict the survival of glioma patients and represent potential targets for glioma therapy. Our results provided novel insight into miRNA regulatory mechanisms implicated in therapeutic interventions and could offer more inspiration to miRNA-based glioma therapy.</p></div

    Global views of functional miRNA-pathway regulation networks (MPRNs).

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    <p>(A) Functional MRPNs resulting from each experimental manipulation. Green circles and blue triangles represent functional miRNAs and targeted pathways, respectively. The number and proportion of (B) miRNAs related to glioma and (C) pathways related to glioma in each MRPN, and (D) the overall proportion of miRNAs and pathways related to glioma in each MPRN are shown.</p

    Changes in the expression of significantly deregulated miRNAs and mRNAs after each manipulation.

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    <p>(A) Two-way hierarchical clustering of deregulated miRNAs resulting from all the six experimental manipulations, which are globally sorted into two groups, based on log2 fold changes. (B) One-way hierarchical clustering of deregulated mRNAs resulting from each manipulation based on log2 fold changes. Up- and downregulation of gene expression are represented by red and green colors, respectively, while black indicates no change relative to baseline levels. The number of (C) miRNAs and (D) mRNAs significantly deregulated by each manipulation, with the fraction of these that share targets with the manipulated molecule marked with red.</p

    Functional synergy and crosstalk between MPRNs activated by different targeted manipulations.

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    <p>(A) Functional synergy and crosstalk between MPRNs activated by three miRNA targeted manipulations. Since miR-181d was upregulated by all the three manipulations, its target pathways and fold changes are provided. Green circles and blue triangles represent functional miRNAs and targeted pathways, respectively. (B) Functional synergy and crosstalk between MPRNs activated by three TF targeted manipulations. Since eight miRNAs were shared by all the three manipulations, their fold changes under all three manipulations are shown. (C) Example of functional synergy between MPRNs associated with manipulations targeting miRNAs and TFs. Since protein processing in the endoplasmic reticulum pathway was shared by +miR-181d, −miR-23b, −β-catenin, and −STAT3, miRNAs regulating this pathway are shown. The distribution of mRNAs with altered expression in this pathway is shown in the diagram, where purple, cyan, and red rectangles represent mRNAs deregulated by manipulations targeting miRNA, TFs, or both, respectively. HSP90B1 (circled in green) was downregulated by all the four experimental conditions.</p

    Association between the core module of cMPRN and glioma patient survival.

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    <p>(A) Green circles and blue triangles represent functional miRNAs and target pathways, respectively, in the core module. (B) Distribution of genes differentially expressed in the same direction under the three conditions sharing pathways in cancer: −STAT3, −β-catenin, and –miR-23b. Red and green rectangles represented genes that are up- and downregulated by the three experimental manipulations, while the white ones represent other genes within pathways in cancer. Red and green lines represent signal transduction downstream of these up- and downregulated genes, respectively. (C) Kaplan-Meier survival plot of the two subgroups of glioblastoma patients sorted by k-means clustering based on expression levels of either miRNA or mRNA signatures or both in the core module.</p
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