Proteomics in an academic environment

1. Proteomics in an academic environment Molecular and Microbial Ecology Lab. Jong Soo Park

2. Introduction • Academic expenditure: Only a fraction of expenditure available to industry • Introduction of new technologies: Expensive • Academic proteomics: May not be competitive • Proteome infrastructure: Interfaculty Institute for Cell Biology at the University of Tuebingen -> mammalian transcriptional regulation and signal transduction

3. Appropriate for our interfaculty status  technological improvements and projects • Isolelectric focusing equipment, phosphorimager, MALDI-TOF, robot systems, and so on.  purchase these from secured funding • Strong contact with local clinicians and some businesses

4. 2. Why not do proteome research in an academic environment? • Proteome analysis: To change the face of medical science • Proteomic research: Not be conducted in academic environments  because of high cost  excluded in establishing state-of-the-art proteomics facilities • Academic salary levels: Predetermined and not negotiable difficulties in retaining well-trained staff

5. 3. Why do proteome research in an academic environment? • Academic and nonprofit institutes: Cannot afford proteomics 3.1 Can universities afford proteomics? • The costs of proteome analysis: Completely establish a suitable facility Not more expensive than other types of research (e.g. mouse molecular genetics) • Proteomics: More cost effective than other areas of life sciences research!

6. The cost of running • From March to December in 1998: Fig. 1 ( 1 Euro = 1320 Won) • 1300 gels of 13% polyacrylamide (20 x 20 cm) were run  piperazine diacrylamide (PDA; croos-linker), diamine silver staining • The majority of 40,000 Euro: Spent on equipment • From June to December: Constantly running costs (2500 Euro per month)  Five staff (500 Euro per month per staff )

7. The cost of analyzing 2-D data • The most expensive commodity: The time of staff employed labor-intensive • To run 2-6 replicates of sample: Due to gel-to-gel variability • Using 2-DE image analysis software: ImageMaster 2-D Elite (Amersham Pharmacia), MELANIE II (Bio-Rad)  Not satisfactory  Let the buyer beware!! • Satisfactory data set: Have to spend many hours per gel  Due to not consistent between samples

8. The time consuming of automatic spot detection: about 4 h of staff time per gel • 15 control patients + 5 with illness: To generate a total experiments of 100 gel  Required 400 h for computer analysis!!  Major detractant from the appeal of industrial proteomics • The merits of University: Improve existing software and/or develop new software  because of inherent diversity of expertise

9. The cost of protein identification • Required for protein identification: Expensive  The purchase of state-of-the-art instrumentation for protein identification  For example, MALDI-TOF (500 proteins per day identified by MALDI-MS) • Automatic system: 500,000 Euro  Approximately cost from gel spot excision to MALDI  At least two expert staff would be desirable

10. The identification of proteins whose sequence is not known: Postsource decay (PSD), triple quadrupole, ion-trap, or Q-ToF machines in Chapter 5.  PSD: The most important is the ability to perform sequence analysis from a peptide within a mixture (Chaurand et al. 1999) • Application of capillary HPLC Edman sequences: Merit sensitive (10 to 20 fmol) Demerit Loss of sample during HPLC separation • Required machines for protein identification: 50,000-400,000 Euro  More than justify the costs

11. The cost of information management • The generation of large and complex datasets : Sophisticated database archiving and analysis software • Powerful algorithms: Used for data interpretation  Allow modeling and simulation • Two-DE analysis software packages: Nor to our knowledge is any similar database management and analysis system under development • Universities cannot perform excellent proteome analysis without industrial assistance

12. 3.2. Can industry afford proteomics without academia? • Pharmaceutical companies: R&D budgets Generate a return of 15-20% per annum (p.a.) in the USA to keep pace with the Dow Jones index • Research funds of a pharmaceutical company: Sufficient to support development of the technology in association with proteomics • New technologies  Initiated and developed by academia  industrial settings  improve industrial efficiency

13. 3.3. Is something rotten in the state of proteomics? Some perceived limitations • Limitations in current proteomics technology: Protein solubility, Throughput, Sensitivity, Dynamic range of abundance, Resolution/protein separation, The protein identification issue, Comparison with ‘genomic’ approaches Protein solubility • Very hydrophobic and large proteins: Poorly represented in 2-DE gels  Solution: chaotropes (disrupting the H-bonding of water at the surface of the protein) and surfactants The problem is far from solved

14. Throughput • Multiple gels on the same sample: Due to variability of 2-DE gels • Two-DE analysis software: Mildly effective at spot identification and cross matching between gels • Major bottleneck: time-consuming Largely alleviated by the use of multicolor visualization of proteins, differential isotopic labeling of proteins

15. Sensitivity • Most proteomics studies: Not currently limited by sensitivity • Similar 2-DE patterns: - 2 mg of whole cell protein with Coomassie blue, - 100 mg protein with diamine silver - 50 ng protein with a phosphorimager using 35S- methionine - 2 mg of protein with 35S-methionine  No advantage

16. Dynamic range of abundance • Two-DE gel: Typically visualize between 2,000 and 10,000 proteins per gel  Human genome: encoding between 65,000 and 100,000 functional gene  The number of different spots may be expected around 50,000  Up to 10,000 species are present on a 2-DE gels (housekeeping and structural proteins) • Dynamic range of protein abundances within cells: > 107 • Ideal analysis system: Be capable of 50,000 separation windows  Currently only some 5-20% (2,000-10,000/50,000) of soluble protein species are detectable

17. Resolution/protein separation • If 50,000 spots were distributed on a 20 x 20 cm gel and spots occupied an avg. of 2 mm2: Present at five spots per mm2 • Certain regions of the gel: Poorly resolved low-abundance protein spots  Partially solution: Narrower range PH gradients in isoelectric focusing

18. The protein identification issue • The identification of protein: Required picomole to >100 femtomole amounts  5% of protein spots (assuming there are 50,000 spots per cell) Comparison with ‘genomic’ approaches • Nucleic acid-based analyses are superior to protein studies: Due to PCR technology • Microarray: Sensitivity fluorescent molecules < radioactive method  Percentage of gene visualized by microchip technology = unclear

19. Chip technology: quickly and economically assay the abundance of mRNA No information for protein abundance 3.4 Academic proteomics is good for industry • Considerable technological advances: Rapidly and cost-effectively screening • Public-funded research centers and research projects: Provide technological improvements

20. 4. University proteomics offers many advantages 4.1 University are flexible and rapid-response industrial resources. The price is right! • Large firms: Seek to subcontract development projects to universities  Can generate more results per dollar  Without suffering excessive financial losses 4.2 University offer access to clinical samples • Many universities: Associated with world-class clinical research departments Ensure the highest quality of results

21. Physical proximity and reliable communication: Sample material can be transferred to academic lab within minutes, Enabling exact requirements to be specified  provide investors for minimal cost • The effects of trial drugs on test patients: Proteome facilities  Rapid on-the-spot assessment 4.3 Universities don’t just study medical applications • Understanding into embryology and development: Directed application of advanced proteomic and genomic studies to model organisms (e.g. frog)

22. 4.4 University basic research will be publicly accessible • Biological data management system: Data is worth money  Transformed into marketable pharmaceutical products • Biological information: Unavailable to the public  Proteome analysis will be performed by the industrial sector 4.5 Universities train students • The cost of training: Considerable  recruit staff with suitable qualifications  Universities

23. 4. Academic ‘spin-off’ companies • German universities: inexpert in patenting their results reap the commercial benefits • Many academic groups: Produce ‘spin-off’ companies to commercialize the expertise  But such phenomenon should not occur with proteomics

24. 5. Conclusion • Proteome research in academic institutions: desirable and necessary • Proteome research: Providing a huge unrealized potential • Technology development: arising from academic institutions  Proteome projects should be supported in academic institutions