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By Jay Krishnan

By Jay Krishnan. Introduction. Information gathered from Proteomic techniques + neuroscientific research = Information on protein composition and function of mammalian neurons ( neuroproteomic data)

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By Jay Krishnan

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  1. By Jay Krishnan

  2. Introduction • Information gathered from Proteomic techniques + neuroscientific research = Information on protein composition and function of mammalian neurons (neuroproteomic data) • Mass spectrometric (MS) analyses/identifies proteins associated with various synaptic preparations • Synaptosomes • Synaptic Membranes • Postsynaptic Density (PSD) • Synaptic Vesicles • Presynapse (PRE) AIM: This study has a goal to combine proteomics with graph theory analysis to characterize protein composition of the PRE nerve terminal

  3. Proteomics Procedures • Proteomics • In-gel digestion • In-solution digestion • Mass spectrometry • Database search and protein identification

  4. Getting the Proteins • Background Literature based PPI network of 6,442 proteins were created • 17,879 interactions extracted from 12,462 publications • Obtained from BioGrid, HPRD, PPID, and a CA1 neuronal regulatory network • 306 Proteins were obtained from proteomic studies

  5. Database search and protein identification • MS data and NCBI (RefSeq) allows same data to be searched that was obtained from the literature using the Sonar program • The data was now cross checked to identify the false positive rate or alpha errors • (False Positive Rate) = RP/ (NP +RP) • (RP + NP) = the matches observed between the random and normal databases • Protein and peptide scores were changed in order to eliminate the false positives

  6. Literature-based PRE PPI network • Interactions (306) are abstracted into a mixed graph where proteins are nodes and interactions are links • UniProt accession numbers; EntrezGene IDs were used to for standard protein identification so that data from different sources can be effectively combined • SNAVI was used to analyze and visualize the network

  7. Interactions between the Merged Data • Calcium plays a central role in neurotransmitter release from the PRE nerve terminal In Siliconetwork PRE interactions created by extracting PPI data from biochemical and physiological literature

  8. Review of Basoc Statistics • Z Score = how many standard deviations are you away from the mean • z = (x – u)/ sigma • Within two SD lies 68.2% of the data • Within 4 SD lies 95.4% of the data • Within 6 SD lies 99.7% of the data Normal Curve

  9. Statistical Analysis * This binomial proportion test was used to determine how, “good,” the 306 proteins obtained from studies in proteomics compared to the Backaround genes obtained from BioGrid , HPRD , PPID, and a CA1 neuronal regulatory network * N1 = number of proteins in the merged list (306) N2 = number of proteins in background data (6,442) P1 = number of direct interactions in merged list P2 = number of interactions in background list – law of large numbers

  10. Statistical Analysis • P (difference in proportion) = (p1-p2) / (N1 + N2) • H0 = (p1/N1) – (p2/N2) = 0 • Ha = (p1/N1) – (p2/N2) > 0 • P value – the probability of obtaining a statistic as extreme as the null hypothesis • If P value is lower that .05 we can reject the null hypothesis and verify that the merged list has a greater percentage of direct interactions

  11. Comparison of Proteins based on z-score After statistical analysis proteins with a z-score > 3 were compared to proteins with a z-score < -1 these proteins were than categorized based on Biological Process, Cellular Component and Molecular Function

  12. Confirming Genuity of Data (Western Blot) PRE fractions were separated by SDSPAGE and probed with selected antibodies to confirm the presence of the predicted proteins For further confirmation immunofluorescence studies were performed using cultured primary cortical neurons Validation of the predicted presynaptic protein complex by co-immunoprecipitation

  13. Predict a PRE complex • Proteins from merged list were analyzed for the presence of overlapping interactions • 21 pairs were observed • Percent SN = SN / (SN + ON1 + ON2) • SN = shared neighbors • ON1: other neighbors of a chosen protein • ON2: other neighbors of another chosen protein

  14. Interactions between Background proteins and Proteins from Merged List Protein interactions (17 proteins) between background proteins and merged proteins when combined

  15. Identification of Proteins using LC-MS/MS followed by In-Gel and In-Solution Digestion Output that helped identify what are the proteins and what they interacted with Sonar helps identifies the proteins based on based on statistical analysis and stored algorithms

  16. Core List – Confirmed Interactions; Contains101 proteins Core PRE list is a compiled lists of proteins gathered from… proteomic studies of PRE fractions Literature based PRE network (converted to list of components), and 3) Two published proteomic studies of PRE fractions

  17. Generating the final corepresynaptic list • With Proteomics and literature-based networks lists of proteins were created. • Core list = PRE Proteins identified twice in independent experiments • Schematic illustrating the data compilation process creates a core presynaptic list of 117 PRE proteins. • Protein lists from proteomic studies, two other published studies, and a literature-based presynaptic network were combined to form a merged list containing 306 proteins. • 16 intermediates identified from the merged list that interact directly with proteins from the core list. • These proteins were added to the core list

  18. Conclusion • Biological Relevant predictions deduced from the literature can be tested experimentally • A complex of PPI has been created successfully and proper constraints have been made to reduce the FPR

  19. Conclusion • A described approach to characterize the composition of the PRE nerve terminal was found • Testing (as indicated from p value and z score) proved that the merged list was a good list of proteins with interactions

  20. Future Research • Scientists can use the knowledge of PPI present in this paper in order to expand their knowledge over a designed/chosen protein • The network created can be always expanded and added to in the future as long as the same experimental procedures are used

  21. References • 1)Ma’ayan, A., Jenkins, S. L., Neves, S., Hasseldine, A. et al., Formation of regulatory patterns during signal propagation in a Mammalian cellular network. Science 2005, 309, 1078–1083. • 2) Krycer, James R., Chi NI Pang, and Mark R. Wilkins. "High throughput protein-protein interaction data: clues for the architecture of protein complexes." Proteome Science (2008). Print. • 3) Ling, Lee. Normal Curve. Digital image. Web.

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