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Preterm delivery is one of the main causes of perinatal morbidity and mortality and it accounts for 75 % of perinatal mortality and more than half of the long-term morbidity. We applied a proteomic approach based on mass spectrometry (MS) for biomarkers discovery of preterm premature rupture of membranes (pPROM) by investigating amniotic fluid (AF) invasively and non-invasively collected. Amniotic fluid was obtained from vagina of women with pPROM (group 1), PROM at term (group 2) and by genetic amniocentesis (group 3). Pre-fractionated AF proteome was analyzed through matrix assisted laser desorption ionization-time of flight (MALDI-TOF) MS. The characterization of proteins/peptides of interest was obtained by high performance liquid chromatography–electrospray tandem MS. Three peptides overexpressed in pPROM and able to discriminate the groups 1 and 2 were detected. One peptide was identified as the fragment Gly452LAVPDGPLGLPPKPro466 of the protein KIAA1522, expressed by fetal brain and liver. This peptide was overexpressed in a patient of the group 3, completely asymptomatic at the time of the amniocentesis, who later developed pPROM. Amniotic fluid invasively and non-invasively collected can be analyzed by MALDI-TOF MS to obtain proteomic profiles. Proteomic analysis identified a peptide with promising diagnostic capability for pPROM.

Renal cell carcinoma (RCC) is typically asymptomatic and surgery usually increases patient's life only for early stage tumors. However, some cystic and solid renal lesions cannot be confidently differentiated from clear-cell-RCC. Therefore possible markers for early detection and to distinguish malignant kidney tumors are needed. To this aim, we applied MALDI-TOF and LC-MS/MS analysis to RPC18 MB purified serum of ccRCC, non-ccRCC patients and controls. A cluster of five signals differentiate malignant tumors from benign renal masses and healthy subjects. Moreover, a combination of six ions showed the highest specificity and sensitivity to distinguish ccRCC from controls. Healthy subjects were also differentiated from non-ccRCC by three features. Peptide ratios obtained by MALDI-TOF were compared with those from label-free LC-ESI and no statistical difference was found (p>0.05). ESI-results were linked with MALDI profiles by both TOF/TOF sequencing and MALDI FT-ICR accurate mass measurements. About 200 unique endogenous peptides, originating from 32 proteins, were identified. Among them, SDPR and ZYX were found down-expressed, while SRGN and TMSL3 were up-expressed. In conclusion, our results suggest the possibility to discriminate malignant kidney tumors based on a cluster of serum peptides. Moreover, label-free approach may represent a valid method to verify results obtained by MALDI-TOF. This article is part of a Special Issue entitled: Integrated omics.

Renal cell carcinoma (RCC), the most common neoplasm affecting the adult kidney, is characterised by heterogeneity of histological subtypes, drug resistance, and absence of molecular markers. Two-dimensional difference gel electrophoresis (2-D DIGE) technology in combination with mass spectrometry (MS) was applied to detect differentially expressed proteins in 20 pairs of RCC tissues and matched adjacent normal kidney cortex (ANK), in order to search for RCC markers. After gel analysis by DeCyder 6.5 and EDA software, differentially expressed protein spots were excised from Deep Purple stained preparative 2DE gel. A total of 100 proteins were identified by MS out of 2500 spots, 23 and 77 of these were, respectively, over- and down-expressed in RCC. The Principal Component Analysis applied to gels and protein spots exactly separated the two sample classes in two groups: RCC and ANK. Moreover, some spots, including ANXA2, PPIA, FABP7 and LEG1, resulted highly differential. The DIGE data were also confirmed by immunoblotting analysis for these proteins. In conclusion, we suggest that applying 2-D DIGE to RCC may provide the basis for a better molecular characterization and for the discovery of candidate biomarkers.

"Signal" alignments play critical roles in many clinical setting. This is the case of mass spectrometry data, an important component of many types of proteomic analysis. A central problem occurs when one needs to integrate (mass spectrometry) data produced by different sources, e.g., different equipment and/or laboratories. In these cases some form of "data integration'" or "data fusion'" may be necessary in order to discard some source specific aspects and improve the ability to perform a classification task such as inferring the "disease classes'" of patients. The need for new high performance data alignments methods is therefore particularly important in these contexts. In this paper we propose an approach based both on an information theory perspective, generally used in a feature construction problem, and on the application of a mathematical programming task (i.e. the weighted bipartite matching problem). We present the results of a competitive analysis of our method against other approaches. The analysis was conducted on data from plasma/ethylenediaminetetraacetic acid (EDTA) of "control" and Alzheimer patients collected from three different hospitals. The results point to a significant performance advantage of our method with respect to the competing ones tested.

Purpose To investigate the use of ClinProt technique to identify cancer markers in plasma of patients suffering from squamous cell carcinoma of the penis (SCCP). Materials and Methods Plasma of 36 healthy subjects and 25 patients with penile carcinoma who underwent surgical treatment between June 2010 and June 2011 was collected and analyzed by the ClinProt/MALDI/ToF technique. Then the peptides were identified from the C8 MB eluted fraction of patients' and control subjects' plasma by LIFT MS/MS. Results A cluster of 2 peptides (A=m/z 1897.22 ± 9 Da and B=m/z 2021.99 ± 9 Da) was able to discriminate patients from control subjects. Cross validation analysis using the whole casuistic showed 62.5% and 86.76% sensitivity and specificity, respectively. The cluster also showed very high sensitivity (100%) and specificity (97%) for SCCP patients that died due to the disease. Furthermore, patients with lymph node involvement presented sensitivity and specificity of 80% and 97%, respectively. These two peptides were identified by the proteomic approach based on a MALDI-TOF/TOF as fragments of C3 (m/z 1896.17) and C4a/b (m/z 2021.26) complement proteins. Conclusions The results showed that as the disease progresses, the fragments C3 and C4 A/B are less expressed in comparison with healthy subjects. These results may be useful as prognostic tools.

Mass Spectrometry (MS)-based technologies represent a promising area of research in clinical analysis. They are primarily concerned with measuring the relative intensity (i.e., signals) of many protein/peptide molecules associated with their mass-to-charge ratios. These measurements provide a huge amount of information which requires adequate tools to be interpreted. Following the methodology for testing hypotheses, we investigate the proteomic signals of the most common type of Renal Cell Carcinoma, the Clear Cell variant (ccRCC) [1]. By using mutual information, we detect changes in dependence values between signals from control to case groups (ccRCC or non-ccRCC). To this end, we sample and represent each population group through graphs, thus providing the observed dependence structures (many real domains are best described by relational models [2]). This way, graphs establish abstract frames of reference in our analysis giving the opportunity to test hypotheses over their properties. In other words, changes are detected by testing graph property modifications from group to group. We report the mass-to-charge values which identify bounded regions where changes have been detected. The main interest in handling such regions is to perceive which signal ranges are associated with some specific factors of interest (e.g., studying differentially expressed peaks between cases and controls) and thus, to suggest potential biomarkers for future analysis [3]. This study has been applied to samples collected at the "Ospedale Maggiore Policlinico" Foundation (Milano, Italy) using a standardized protocol. All samples were analyzed using an UltraFlex II MALDI-TOF/TOF MS instrument and mass spectra were acquired in the m=z range of 1000-12000. The samples cohort consists of 85 control subjects and 102 Renal Cell Carcinoma patients. It was possible to classify pathological group in patients affected by clear cell (ccRCC) and other different histological subtypes (respectively 79 ccRCC and 23 non-ccRCC). Table I reports the selected rejection regions (i.e., tests reject the null) at the 5% significance level. Testing hypotheses suggested by the data may induce statistical bias. For this reason, we evaluate the results to independent samples. We investigate whether test decisions are statistically independent from the region's property (i.e., distinguishing (DR) or non-distinguishing (ND) regions) when new samples are given. In other words, we want to know whether the property of a region can be statistically associated to test decisions when new samples are available. After that a new sample is provided, we verify test decisions over both the detected distinguishing regions and these regions out of the m=z bounding values previously detected. Table II summarizes the (Fisher's exact test) results confirming a significant association (a = 0.05 level) between decisions and region's property for both the class of tests. This work was supported by grants from the Italian Ministry of University and Research (PRIN n. 69373, FIRB n. RBRN07BMCT 011, FAR 2006 -- 2011), EuroKUP COST Action (BM0702) and the NEDD project ("Regione Lombardia").

Renal cell carcinoma (RCC) is representing about 3% of all adult cancers. A promising strategy for cancer biomarker discovery is subcellular comparative proteomics, allowing enriching specific cell compartments and assessing differences in protein expression patterns. We investigated the proteomic profile of a peculiar RCC subcellular compartment, plasma membrane microdomains (MD), involved in cell signalling, transport, proliferation and in many human diseases, such as cancer. Subcellular fractions were prepared by differential centrifugation from surgical samples of RCC and adjacent normal kidney (ANK). MD were isolated from plasma-membrane-enriched fractions after Triton X-100 treatment and sucrose density gradient ultracentrifugation. MD derived from RCC and ANK tissues were analyzed after SDS-PAGE separation by LC-ESI-MS/MS. We identified 93 proteins from MD isolated from RCC tissue, and 98 proteins from ANK MD. About 70% of the identified proteins are membrane-associated and about half of these are known as microdomain-associated. GRAVY scores assignment shows that most identified proteins (about 70%) are in the hydrophobic range. We chose a panel of proteins to validate their differential expression by WB. In conclusion, our work shows that RCC microdomain proteome is reproducibly different from ANK, and suggests that mining into such differences may support new biomarker discovery.

Primary cell cultures from renal cell carcinoma (RCC) and normal renal cortex tissue of 60 patients have been established, with high efficiency (more than 70%) and reproducibility, and extensively characterized. These cultures composed of more than 90% of normal or tumor tubular cells have been instrumental for molecular characterization of Annexin A3 (AnxA3), never extensively studied before in RCC cells although AnxA3 has a prognostic relevance in some cancer and it has been suggested to be involved in the hypoxia-inducible factor-1 pathway. Western blot analysis of 20 matched cortex/RCC culture lysates showed two AnxA3 protein bands of 36 and 33 kDa, and two-dimensional Western blot evidenced several specific protein spots. In RCC cultures the 36-kDa isoform was significantly down-regulated and the 33-kDa isoform up-regulated. Furthermore, the inversion of the quantitative expression pattern of two AnxA3 isoforms in tumor cultures correlate with hypoxia-inducible factor-1 alpha expression. The total AnxA3 protein is down-regulated in RCC cultures as confirmed also in tissues by tissue microarray. Two AnxA3 transcripts that differ for alternative splicing of exon III have been also detected. Real-time PCR quantification in 19 matched cortex/RCC cultures confirms the down-regulation of longer isoform in RCC cells. The characteristic expression pattern of AnxA3 in normal and tumor renal cells, documented in our primary cultures, may open new insight in RCC management. (Ant J Pathol 2010, 176:1660-1670; DOI: 10.2353/ajpath.2010.090402)

Objectives To investigate the possibility of using the ClinProt technique to find serum cancer related diagnostic markers that are able to better discriminate healthy subjects from patients affected by renal cell carcinoma (ccRCC). Renal cell carcinoma is the most common malignancy of the kidney. Biomarkers for early detection, prognosis, follow-up, and differential diagnosis of ccRCC from benign renal lesions are needed in daily clinical practice when imaging is not helpful. Methods Serum of 29 healthy subjects and 33 ccRCC patients was analyzed by the ClinProt/MALDI-ToF technique. Results A cluster of 3 peptides (A = m/z 1083 ± 8 Da, B = m/z 1445 ± 8 Da and C = m/z 6879 ± 8 Da) was able to discriminate patients from control subjects. Cross-validation analysis using the whole casistic showed 88% and 96% of sensitivity and specificity, respectively. Moreover, the cluster showed 100% sensitivity for the identification of patients at pT2 (n = 5) and pT3 (n = 8) and 85% for pT1 patients (n = 20). The intensity of peaks A and C continuously decreased from pT1 to pT3, whereas peak B increased in pT1 and pT2. Conclusions These results may be useful to set up new diagnostic or prognostic tools.

Type 1 diabetes (insulin-dependent diabetes mellitus, IDDM) is an autoimmune disease affecting about 0.12% of the world's population. Diabetic nephropathy (DN) is a major long-term complication of both types of diabetes and retains a high human, social and economic cost. Thus, the identification of markers for the early detection of DN represents a relevant target of diabetic research. The present work is a pilot study focused on proteomic analysis of serum of controls (n = 9), IDDM patients (n = 10) and DN patients (n = 4) by the ClinProt profiling technology based on mass spectrometry. This approach allowed to identify a pattern of peptides able to differentiate the studied populations with sensitivity and specificity close to 100%. Variance of the results allowed to estimate the sample size needed to keep the expected False Discovery Rate low. Moreover, three peptides differentially expressed in the serum of patients as compared to controls were identified by LC-ESI MS/MS as the whole fibrinopeptide A peptide and two of its fragments, respectively. The two fragments were under-expressed in diabetic patients, while Fibrinopeptide A was over-expressed, suggesting that anomalous turnover of Fibrinopeptide A could be involved in the pathogenesis of DN. © 2009 Elsevier B.V. All rights reserved.

Clinical data alignment plays a critical role in identifying important features for significant experiments. A central problem is data fusion i.e., how to correctly integrate data provided by different labs. This integration is done in order to increase ability of inferring target classes of controls and patients. Our paper proposes an approach based both on a information theoretic perspective, generally used in a feature construction problem [3] and on the approximated solution for a mathematical programming task (i.e. the weighted bipartite matching problem [6]). Numerical evaluations with two competitive approaches show the improved performance of the proposed method. For this evaluation we used data sets from plasma / ethylenediaminetetraacetic acid (EDTA) of controls and Alzheimer patients collected in three different hospitals.

Aquaporin (AQP)1 belongs to a ubiquitous family of water channel proteins characterized by sequence similarity and the presence of two NPA (Asp-Pro-Ala) motifs existing in almost all organs and tissues. Currently, 13 human AQPs are known and they are divided into two subgroups according to their ability to transport only water molecules, such as AQP1, or also glycerol and other small solutes. The genomic, structural and functional aspects of AQP1 are briefly described. An in-depth discussion is devoted to proteomic approaches that are useful for identifying and characterizing AQP1, mainly through electrophoretic techniques combined with different extraction procedures followed by mass spectrometry analysis. Moreover, the relevance of AQP1 in human diseases is also explained. Its role in human tumors and, in particular, those of the kidney (e.g., clear cell renal carcinoma) is discussed.

Renal cell carcinoma (RCC) accounts for about 3% of all human malignancies and its incidence is increasing. There are no standard biomarkers currently used in the clinical management of patients with renal cell carcinoma. A promising strategy for new biomarker detection is comparative proteomics of urinary exosomes (UE), nanovesicles released by every epithelial cell facing the urinary space, enriched in renal proteins and excluding high-abundance plasmatic proteins, such as albumin. Aim of the work is to establish the protein profile of exosomes isolated from urines of RCC patient compared with control subjects. We enrolled 29 clear cell RCC patients and 23 control healthy subjects (CTRL), age and sex-matched, for urine collection and vesicle isolation by differential centrifugation. Such vesicles were morphologically and biochemically characterized and proved to share exosome properties. Proteomic analysis, performed on 9 urinary exosome (UE) pooled samples by gel based digestion followed by LC-MS/MS, led to the identification of 261 proteins from CTRL subject UE and 186 from RCC patient UE, and demonstrated that most of the identified proteins are membrane associated or cytoplasmic. Moreover, about a half of identified proteins are not shared between RCC and control UE. Starting from these observations, and from the literature, we selected a panel of 10 proteins, whose UE differential content was subjected to immunoblotting validation. Results show for the first time that RCC UE protein content is substantially and reproducibly different from control UE, and that these differences may provide clues for new RCC biomarker discovery.

Engineered nanoparticles offer the chance to improve drug transport and delivery through biological barriers, exploiting the possibility to leave the blood circulation and traverse the endothelial vascular bed, blood-brain barrier (BBB) included, to reach their target. It is known that nanoparticles gather molecules on their surface upon contact with biological fluids, forming the "protein corona", which can affect their fate and therapeutic/diagnostic performance, yet no information on the corona's evolution across the barrier has been gathered so far. Using a cellular model of the BBB and gold nanoparticles, we show that the composition of the corona undergoes dramatic quantitative and qualitative molecular modifications during passage from the "blood" to the "brain" side, while it is stable once beyond the BBB. Thus, we demonstrate that the nanoparticle corona dynamically and drastically evolves upon crossing the BBB and that its initial composition is not predictive of nanoparticle fate and performance once beyond the barrier at the target organ.