ANTICANCER RESEARCH 30: 2073-2080 (2010)

Review An Overview of Nanotechnology-based Functional Proteomics for Cancer and Cell Cycle Progression

CLAUDIO NICOLINI1,2 and EUGENIA PECHKOVA1,2

1Nanoworld Institute-CIRSDNNOB, University of Genova, 16132 , ; 2Fondazione EL.B.A., 00100 , Italy

Abstract. Nanogenomics and nanoproteomics allow the study Methods and comparison of the huge number of genes and proteins involved in the cell cycle progression of human T lymphocytes The proteins and corresponding genes chosen for the NAPPA and in its transformation in lymphoma. Nanogenomics has, study and their characteristics were: P53_Human: 43,653 however, many pitfalls that only functional proteomics, called kDa with a gene of 1182 bp; CDKN1A_Human (also called nucleic acid programmable protein array (NAPPA), is capable P21); 18,119 kDa with a gene of 495 bp; CDK2_Human: of overcoming by probing with unique sensitivity native in situ 33,930 kDa with a gene of 897 bp; JUN_Human: 35,676 protein-protein interactions. This allows identification of the kDa with a gene of 996 bp. key proteins involved in the control of cancer and proliferation The determination of key leader genes associated with cell in the light of recent label-free NAPPA approaches based on cycle progression (5, 6) and with human organ transplants nanotechnologies. Bioinformatics in combination with label (7) was recently successfully carried out by utilizing raw free NAPPA, anodic porous alumina (APA) and DNA analyser microarray data and non/statistical bioinformatics based on (DNASER) microarrays appear capable of providing the long- the identification of ‘key genes’ not as those mostly changing range framework for the basic molecular understanding of their expression, but as those having the strongest cancer and cell cycle progression. interconnections. Expression genes (8) are identified in the datasets of both normal T lymphocytes and the lymphoma A new approach for cancer research is emerging from genes as obtained from microarrays of lymphoma and nanogenomics and nanoproteomics as the interplay of normal T-cells (5, 7, 9) for up-regulated and down-regulated bioinformatics, mass spectrometry and biomolecular microarrays entries by means of cluster analysis. Cluster analysis was to a previously unforeseeable level. ‘Nanogenomics’ represents a performed with FuzMe software. The IDs of up- and down- new approach for cell biology and for medical diagnosis and regulated entries were checked in the Panther database (10, therapy (1, 2). This review summarizes the recent major features 11) against standard National Center for Biotechnology of nanoproteomics with a few key examples of the technological Information (NCBI) nomenclature (12) and CELERA developments in the nucleic acid programmable protein array nomenclature (13), in order to screen for identified and (NAPPA) (3), namely (label-free) NAPPA (4), and the unidentified entries. It is noteworthy that the Panther biomedical applications to the understanding of lymphocyte database can provide preliminary information about the proliferation and transformation to lymphoma The mechanisms molecular and cellular function of each entry. When of cell transformation and cell cycle progression, however, are necessary, nomenclature was also checked using a BLASTP still obscure and need further exploration for the optimal control (Basic Local Alignment Search Tool Protein) (14) search of human cancer. against the ENSEMBL database, in order to improve recognition of each entry via its nucleic acid sequence.

Results Correspondence to: Professor Claudio Nicolini, President Fondazione ELBA and Director CIRSDNNOB, University of Only four genes JUN, P53, CDK2 and CDK4, were utilized in Genova, Corso Europa 30, 16132 Genoa, Italy. Tel: +39 010 35338217, Fax +39 01035338215, e-mail: [email protected] the label-free NAPPA experimental studies (4) and the techniques used to monitor label-free protein–protein Key Words: Functional proteomics, cancer, proliferation, NAPPA interactions including self-assembling protein NAPPA microarrays, review. microarrays, atomic force microscopy (AFM), nanogravimetry

0250-7005/2010 $2.00+.40 2073 ANTICANCER RESEARCH 30: 2073-2080 (2010) quartz-crystal microbalance (QCM), mass spectrometry (MS) and anodic porous alumina (APA). In separate fluorescence labeling NAPPA experiments utilizing our DNA analyser (DNASER) (15, 16), a comparison was carried out with the well established fluorescence analysis (3). These experiments proved capable of underlining and assessing the feasibility of the above techniques towards understanding the molecular events involved in the control of cell cycle and cell transformation. In parallel, bioinformatics fluorescence microarray-based studies on PHA (phytohaemagglutinin)-stimulated normal T- lymphocytes (5) and lymphomas (9) shed new light in this field of research.

Label-free Technologies for NAPPA

NAPPA slides (Figure 1) obtained using the four genes JUN, P53, CDK2 and CDK4, were analyzed by means of several innovative label-free approaches (4) based on AFM, nanogravimetry, MS and APA. Details are provided in (4) of the quartz crystal microbalance Q-factor (QCM-Q) printing technology on the Figure 1. A: NAPPA Technology via Biotinylation of DNA. (i) Printing metal pad of quartz crystals, and the AFM using pure mica the array. (ii) In situ expression and immobilization using rabbit or gold-coated mica substrates. reticulocyte lysate. (iii) Detection of target proteins expressed with a C-terminal GST tag. B: Slides for quartz, mica, APA and MALDI. AFM. It was shown that to couple NAPPA to AFM (Figure 2), the optimal choice was to use a plain mica surface and to fully undertake an independent atomic characterization on the protein–protein interactions and to optimize NAPPA printing proteins being expressed in each gene-containing spot. as a function of pH, temperature, reagent composition and Subsequently the three genes JUN(1), P53(2) and CDK2 concentration. Such techniques could also be used to optimize respectively were spotted on golden mica slide to utilize the the conditions for cell-free expression of a given protein. optimal gold chemistry, similarly yielding excellent results (17). Mica is positioned on the glass slide in such a way as to facilitate a marginal thickness increase of the sample and to MS. This method (20, 21) has made significant progress in allow its subsequent removal after NAPPA printing spots terms of coupling a label-free method to NAPPA, clearly and/or their expression. demonstrating production of NAPPA protein (Figure 4) and Obviously several optical techniques (i.e. scanning laser few MS spectra of total protein. This technique can identify or one-shot imaging) have been demonstrated to be more proteins, in particular proteins that bind to the target proteins flexible, reliable and much less expensive than AFM. on the array. Thus, some gold slides were printed with the 4 Building multi-tip measuring heads is both difficult and different genes (each one with 16×300 micron spots) – first extremely expensive (i.e. to obtain a matrix of tips). in a known configuration, then in a configuration unknown to Nanoworld Institute (NWI) but known to Harvard Institute Nanogravimetry. The progress in nanogravimetry is not only of Proteomics (HIP). Towards this end, the samples have focused on the sample holder, but also in the actual shown gold faces, namely microscopy slides coated with identification of a novel highly sensitive parameter (quality gold: it is necessary to spot only on the tagged face for MS factor D) in addition to the frequency and its (20). After this, MS analysis was conducted successfully by hardware/software implementation (Figure 3). searching peptides on a database. The method involved This device has already been built and is in the optimizing tryptic digestion in order to obtain identifiable peptides. and validation phase with glycerol and various protein solutions (18). Quartz positioned on the glass slide in such a APA. The mechanical implications of grip and ball crush tests way as to facilitate a marginal thickness increase of the are clear, but the fluid exchanges on the APA arrays (mixing sample and to allow its subsequent removal after NAPPA reagents and washing them away) can be made optimal for printing spots and/or their expression. This label-free NAPPA (Figure 5), even if the open pore design to force fluid technique could be utilized to investigate the kinetics of through the device and exchange it with new fluid appears a

2074 Nicolini and Pechkova: Label-free NAPPA (Review)

Figure 2. Human kinases array spot after (left, above) and before (right, above) protein expression by atomic force microscopy (17).

complicated task never so far attempted by anyone (namely nanomanipulation without breakage and differential corrosion). At the same time we have successfully attempted simple NAPPA printing on APA (24), after proving: (i) the ability to spot a colored fluid on the APA surface in discrete spots; (ii) the ability to rapidly exchange that fluid with a different fluid; (iii) the ability to repeat these manipulations as needed; (iv) the production of the APA slides in a format compatible also with a fluorescence reader and not only with electrochemistry (24); (v) the APA slide format (either alone or over aluminum) has acceptable structural integrity for ‘routine’ manipulation and especially during fluorescence readings, mainly relying for the standalone configuration also on alternative printing which exploit capillary forces. Once we have these basics worked out, it would be extremely worthwhile trying with NAPPA whatever the result, considering what already being achieved for the high potential of this technology to be used in a high through output setting.

Bioinformatic Fluorescent Microarrays-based Studies of Cell Transformation and Cell Cycle

Bioinformatics based on the fluorescent gene microarrays of normal PHA-stimulated T-lymphocytes allow several Figure 3. Frequency variation (A) and D factor (B) of NAPPA quartz interesting observations. after CDK2 kinase expression by nanogravimetry.

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Figure 4. MALDI TOF spectra NAPPA array after protein trypsin digestion, 5-20 kDa range, for CDK2 (upper) and A (bottom) samples (for detailed explanations see (20)).

NAPPA-targeted prediction of gene interactions relevant progression from G2 to M phase, mainly via the regulatory to progression between phases of cell cycle in human T- role of the proteins encoded by those genes. Interaction lymphocytes. To properly construct NAPPA or to determine scores (associations) for the 11 proteins participating in both query proteins for existing NAPPA, the list of genes involved G2 and M phase are shown in Figure 6. To evaluate in G2 and M phases of T-lymphocytes has to be determined interactions between the proteins comprising the ‘G2 phase’ first (25), and then which genes are in common. Between 35 set (35 proteins) and ‘M phase’ set (38 proteins), we G2 phase and 38 M phase genes, 11 were common, out of calculated interaction scores according to our LeaderGene which 4, namely CDC2, NFKB1, CCNB1 and PPP2CA, were technique (5, 7, 8), using only the protein-protein interaction experimentally confirmed to participate in cell cycle subset of STRING database (Figure 6).

2076 Nicolini and Pechkova: Label-free NAPPA (Review)

Figure 5. A: FIB system images of cross-sectional morphologies of the microarray spot, resulting at the end of photolithographic microstructuring technique and 2 step anodization process. FIB-FEI measurements were made with gallium ions - 37 pA - both for cutting and imaging (22). B: Single NAPPA fluorescence spot on APA (left) and on glass (right) (23).

Figure 6. Interaction scores (as defined in (6)) for the 11 proteins Figure 7. Interaction network for genes distinguishing lymphoma from normal T-cells. Sub-network connecting the four leader genes which are participating in both G2 and M phase, with 4 being correlated to G2/M neutral according to their expression is shown with a dotted line. transition. Scores were calculated within each of the G2 and M phase protein sets.

nature as inferred from microarray data (9). From interaction- NAPPA-targeted prediction of protein interactions relevant to based bioinformatics alone (7), it is not possible to see which differences between lymphoma and normal T-cells. Genes of these genes are pro-lymphoma or pro-normal, since involved in interactions differ between lymphoma and normal interaction is recorded between two genes/proteins, without T-cells (7-9) being either of pro-lymphoma or pro-normal specifying its direction of action. Note that even though

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Figure 8. Involvement of highly interacting kinases in lymphoma versus normal T cells in regulatory networks. Complexes involving the kinases in question are shown in blue. Symbols indicate processes: diamond for general non-covalent interaction such as activation or inhibition, circle for transcription, triangle for translocation. Arrows in and out indicate input and output substances for processes, green arrows indicate positive regulation, red blunt-ended line indicates negative regulation.

Figure 9. Nanoproteomics based on DNASER technology (A) capable of manually produced interaction maps (26) in principle allow obtaining fluorescent NAPPA image (B). such direction to be defined, it takes too long to complete this. Patterns of gene expression (9) allow the pro-lymphoma or pro-normal nature of the gene to be specified, unless the data are either inconclusive or neutral, i.e. displaying normal T-cells is shown. It is immediately visible that the insignificant difference in expression levels between map is dominated by kinases and cytokines. One of the lymphoma or normal datasets, or yielding opposite results for kinases, namely ATM, forms its own subnetwork together copies of the same gene (10); furthermore, frequently the with the tumor suppressor p53, while kinase PTK6 remains gene is not specified in the microarray because of the only orphan. Taken together, our data indicate that the nomenclature problems or incompleteness of the microarray. more strongly genes are connected into an interaction Generally, microarray studies often neglect the network, the less likely they will tend to show a difference in nomenclature of genes and the available information about expression pattern, which is especially true for kinases gene interactions. This underestimation makes microarray controlling the neoplastic transformation of lymphocytes. In data at times difficult to properly evaluate. Intriguingly, terms of kinase classification, the highly-interacting kinases kinases seem abundant (10) in the subset of the neutral genes among those distinguishing lymphoma from normal T-cells shown in Figure 7, while among those showing marked are protein tyrosine kinases (SYK, LCK, TXK, LYK), differences in expression in lymphoma versus normal T-cells serine/threonine protein kinases (PRKCZ, PRKCQ), and one only one connection is present, namely CD4-SYK. Among phosphoinositide 3-kinase (ATM), the conservative domain the four proteins belonging to this neutral but strongly composition of these kinases shows that neither of them connected network, three are kinases (Figure 7, dotted line). contains a receptor (presumably transmembrane) domain. In Figure 8, the interaction network for lymphoma versus Notably, the pro-normal SYK kinase lacks one of the domains

2078 Nicolini and Pechkova: Label-free NAPPA (Review) present in other kinases, which suggests separate NAPPA Acknowledgements analysis for different kinase domains, e.g. by using the domains as separate query proteins (work in progress). This project was supported by grants to Fondazione EL.B.A., Rome, The analysis of the highly interacting kinases in terms of Italy, by MIUR for “Funzionamento” and by a FIRB International their involvement in regulatory networks (Figure 8) shows Grant on Proteomics and Cell Cycle (RBIN04RXHS) from MIUR (Ministero dell’Istruzione, Università e Ricerca) to CIRSDNNOB that the highly interacting kinases discriminating lymphoma of the University of Genova, Genova, Italy. from normal T-cells are involved in regulatory networks, as indicated from NCI-Nature data from the Pathway Interaction References Database of the National Cancer Institute (27). It can be noted that our absolute “leader” gene, LCK, appears in this network 1 Nicolini C: Nanogenomics for medicine. Nanomedicine 1: 147- in two different regulatory complexes. It is tentatively shown 151, 2006. in the map of Figure 8 that diacylglycerol signaling can be 2 Nicolini C: Nanogenomics in medicine. WIREs Nanomed important in mediating protein interactions determining the Nanobiotechnol 2: 59-76, 2010. differences between lymphoma and normal T-cells. 3 LaBaer J and Ramachandran N: Protein microarrays as tools for functional proteomics. Curr Opin Chem Biol 9: 14-19, 2005. With respect to the overall issue of gene transcription, 4 Nicolini C and LaBaer J: Nanotechnology applications of translation, and post-translation, NAPPA technology has been nucleic acid programmable protein arrays. In: Functional emerging in the past several years (28) as a possible means (24) Proteomics and Nanotechnology-based Microarrays. Nicolini C of identifing the proteins actually controlling cell cycle and LaBaer J (eds.). London: Pan Stanford Series on progression of human T lymphocytes and of human lymphomas. Nanobiotechnology Vol. 2, Ch. 1, pp. 1-29, 2010. The NAPPA array containing all HIP cloned fluorescent 5 Nicolini C, Spera R, E, Fiordoro S and Giacomelli L: Gene labeled human kinase (3) is shown in Figure 9 (right) along expression in the cell cycle of human T lymphocytes: II. Experimental determination by DNASER technology. J Cell with DNASER (Figure 9 left) utilized for obtaining its Biochem 97: 1151-1159, 2006. fluorescent image. The DNASER is a novel bioinstrumentation 6 Giacomelli L and Nicolini C: Gene expression of human T for real-time acquisition and elaboration of images from lymphocytes cell cycle: experimental and bioinformatic analysis. fluorescent DNA and protein microarrays that permits to J Cell Biochem 99: 1326-1333, 2006. acquire images faster than the traditional systems. The 7 Sivozhelezov V, Giacomelli L, Tripathi S and Nicolini C: Gene fluorescent microarray images are here processed to recognize expression in the cell cycle of human T lymphocytes: I. the protein spots, to analyze their superficial distribution on the Predicted gene and protein networks. J Cell Biochem 97: 1137- 1150, 2006. glass slide and to evaluate their geometric and intensity 8 Sivozhelezov V, Braud C, Giacomelli L, Pechkova E, Giral M, properties (5). NAPPA in conjunction with a bioinformatics Soulillou JP, Brouard S and Nicolini C: Immunosuppressive analysis (Figures 6-8) will be useful to determine protein drug-free operational immune tollerance in human kidney interactions involved in the progression of the cell cycle. transplant recipient: II Non-statistical gene microarray analysis. J Cell Biochem 103: 1693-1706, 2008. Conclusion 9 Franzke A, Koenecke C, Geffers R, Piao W, Hunger JK, Ganser A and Buer J: Classical Hodgkin's lymphoma: Molecular evidence for specific alterations in circulating T lymphocytes. The combination of the four label-free technologies, DNASER Tumor Biol 2: 329-333, 2006. fluorescent technology in conjunction with bioinformatics (5, 10 Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, 7, 8) can be effectively used to quantitatively probe the Daverman R, Diemer K, Muruganujan A and Narechania A: expression of key proteins involved in the control of PANTHER: a library of protein families and subfamilies indexed mammalian cell cycle and cell transformation, thus allowing by function. Genome Res 13: 2129-2141, 2003. query proteins for NAPPA to be identified and specific 11 Mi H, Guo N, Kejariwal A and Thomas PD: PANTHER version NAPPA microarrays to be designed. The results obtained and 6: protein sequence and function evolution data with expanded representation of biological pathways. Nucleic Acids Res 35: the work in progress indicate that the application of NAPPA D247-D252, 2007. may provide deeper insights in cellular processes than could 12 Maglott D, Ostell J, Pruitt KD and Tatusova T: Entrez Gene: be originally expected. Namely, instead of studying details of gene-centered information at NCBI. Nucleic Acids Res protein interactions of specific protein subsets/cascades, 33(Database Issue): D54-D58, 2005. opportunities are now provided to study processes at the 13 Noth S and Benecke A: Avoiding inconsistencies over time cellular level, for which only genomic approaches were and tracking difficulties in Applied Biosystems AB1700™/ previously available. Label-free NAPPA technology, in Panther™ probe-to-gene annotations. BMC Informatics 6: 307, 2005. combination with genomics and bioinformatics as specified in 14 Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller terms of abundance-based and interaction-based techniques, W and Lipman DJ: Gapped BLAST and PSI-BLAST: a new appears capable of more effectively approaching the numerous generation of protein database search programs. Nucleic Acids problems still open in cancer research and cell biology. Res 25: 3389-3402, 1997.

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15 Nicolini C, Malvezzi M, Tomaselli A, Sposito D, Tropiano G 23 Ramachandran N, Hainsworth E, Bhullar B, Eisenstein S, Rosen and Borgogno E: DNASER I: layout and data analysis. IEEE B, Lau AY, Walter JC and LaBaer J: Self-assembling protein Trans Nanobiosci 1: 67-72, 2002. microarrays. Science 305: 86-90, 2004. 16 Troitsky V, Ghisellini P, Pechkova E and Nicolini C: DNASER 24 Stura E, Larosa C, Bezerra T, Hainsworth E, Ramachandran N, II. Novel surface patterning for biomolecular microarray. IEEE LaBaer J and Nicolini C: Label-free NAPPA: anodic porous Trans Nanobiosci 1: 73-77, 2002. alumina. In: Functional Proteomics and Nanotechnology-based 17 Pechkova E, Sartore M, Giacomelli L and Nicolini C: Atomic Microarrays. Nicolini C and LaBaer J (eds.). London: Pan force microscopy of protein films and crystals. Rev Sci Instrum Stanford Series on Nanobiotechnology Vol. 2, Ch. 5, pp. 95-108, 78: 093704-1-093704-7, 2007. 2010. 18 Sartore M, Eggenhoffner R, Bezerra T, Stura E, Hainsworth E, 25 Sivozhelezov V, Spera R, Giacomelli L, Hainsworth E, LaBaer J, LaBaer J and Nicolini C: Label-free detection of NAPPA via Bragazzi NL and Nicolini C: Bioinformatics and Fluorescence atomic force microscopy. In: Functional Proteomics and DNASER for NAPPA studies on cell transformation and cell Nanotechnology-based Microarrays. Nicolini C and LaBaer J cycle. In: Functional Proteomics and Nanotechnology-based (eds.). London: Pan Stanford Series on Nanobiotechnology Vol. Microarrays. Nicolini C and LaBaer J (eds.). London: Pan 2, Ch. 6, pp. 109-120, 2010. Stanford Series on Nanobiotechnology Vol. 2, Ch. 2, pp. 31-59, 19 Adami M, Eggenhoffner R, Sartore M, Hainsworth E, LaBaer J 2010. and Nicolini C: Label-free NAPPA via nanogravimetry. In: 26 Aladjem MI, Pasa S, Parodi S, Weinstein JN, Pommier Y and Functional Proteomics and Nanotechnology-based Microarrays. Kohn KW: Molecular interaction maps – a diagrammatic Nicolini C and LaBaer J (eds.). London: Pan Stanford Series on graphical language for bioregulatory networks. Sci STKE 2: pe8, Nanobiotechnology Vol. 2, Ch. 4, pp. 95-108, 2010. 2004. 20 Spera R, Badino F, Hainsworth E, Fuentes M, Srivastava S, 27 Schaefer CF., Anthony K, Krupa S, Buchoff J, Day M, Hannay LaBaer J and Nicolini C: Label-free detection of NAPPA via T and Buetow KH: PID: the pathway interaction database. mass spectrometry. In: Functional Proteomics and Nucleic Acids Research 37: D674-D679, 2009. Nanotechnology-based Microarrays. Nicolini C and LaBaer J 28 Nicolini C and LaBaer J (eds.): Functional Proteomics and (eds.). London: Pan Stanford Series on Nanobiotechnology Vol. Nanotechnology-based Microarrays. London: Pan Stanford 2, Ch. 3, pp. 61-78, 2010. Series on Nanobiotechnology, Volume 2, 2010. 21 Spera R and Nicolini C: Nappa Microarrays and Mass Spectrometry: New Trends and Challenges. Essentials in Nanoscience Booklet Series, January 15, Nanoscience Works.org, Taylor & Francis Group, LLC, 2008. 22 Grasso V, Lambertini V, Ghisellini P, Valerio F, Stura E, Perlo P Received October 14, 2009 and Nicolini C: Nanostructuring of a porous alumina matrix for Revised May 4, 2010 a biomolecular microarray. Nanotechnology 17: 795-798, 2006. Accepted May 10, 2010

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