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Proteomic profiles of myocardial tissue in two different etiologies of heart failure were investigated using high performance liquid chromatography (HPLC)/Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Right atrial appendages from 10 patients with hemodynamically significant isolated aortic valve disease and from 10 patients with isolated symptomatic coronary heart disease were collected during elective cardiac surgery. As presented in an earlier study by our group (Baykut et al., 2006), both disease forms showed clearly different pattern distribution characteristics. Interesting enough, the classification patterns could be used for correctly sorting unknown test samples in their correct categories. However, in order to fully exploit and also validate these findings there is a definite need for unambiguous identification of the differences between different etiologies at molecular level. In this study, samples representative for the aortic valve disease and coronary heart disease were prepared, tryptically digested, and analyzed using an FT-ICR MS that allowed collision-induced dissociation (CID) of selected classifier masses. By using the fragment spectra, proteins were identified by database searches. For comparison and further validation, classifier masses were also fragmented and analyzed using HPLC-/Matrix-assisted laser desorption ionization (MALDI) time-of-flight/time-of-flight (TOF/TOF) mass spectrometry. Desmin and lumican precursor were examples of proteins found in aortic samples at higher abundances than in coronary samples. Similarly, adenylate kinase isoenzyme was found in coronary samples at a higher abundance. The described methodology could also be feasible in search for specific biomarkers in plasma or serum for diagnostic purposes.
We report first results on elliptic flow of identified particles at midrapidity in Au+Au collisions at sqrt[sNN] = 130 GeV using the STAR TPC at RHIC. The elliptic flow as a function of transverse momentum and centrality differs significantly for particles of different masses. This dependence can be accounted for in hydrodynamic models, indicating that the system created shows a behavior consistent with collective hydrodynamical flow. The fit to the data with a simple model gives information on the temperature and flow velocities at freeze-out.
The minimum-bias multiplicity distribution and the transverse momentum and pseudorapidity distributions for central collisions have been measured for negative hadrons ( h-) in Au+Au interactions at sqrt[sNN] = 130 GeV. The multiplicity density at midrapidity for the 5% most central interactions is dNh-/d eta | eta = 0 = 280±1(stat)±20(syst), an increase per participant of 38% relative to pp-bar collisions at the same energy. The mean transverse momentum is 0.508±0.012 GeV/c and is larger than in central Pb+Pb collisions at lower energies. The scaling of the h- yield per participant is a strong function of pperp. The pseudorapidity distribution is almost constant within | eta |<1.
We report the first measurement of inclusive antiproton production at midrapidity in Au+Au collisions at sqrt[sNN] = 130 GeV by the STAR experiment at RHIC. The antiproton transverse mass distributions in the measured transverse momentum range of 0.25<pperp<0.95 GeV/c are found to fall less steeply for more central collisions. The extrapolated antiproton rapidity density is found to scale approximately with the negative hadron multiplicity density.
Large-scale molecular profiling studies in recent years have shown that central nervous system (CNS) tumors display a much greater heterogeneity in terms of molecularly distinct entities, cellular origins and genetic drivers than anticipated from histological assessment. DNA methylation profiling has emerged as a useful tool for robust tumor classification, providing new insights into these heterogeneous molecular classes. This is particularly true for rare CNS tumors with a broad morphological spectrum, which are not possible to assign as separate entities based on histological similarity alone. Here, we describe a molecularly distinct subset of predominantly pediatric CNS neoplasms (n = 60) that harbor PATZ1 fusions. The original histological diagnoses of these tumors covered a wide spectrum of tumor types and malignancy grades. While the single most common diagnosis was glioblastoma (GBM), clinical data of the PATZ1-fused tumors showed a better prognosis than typical GBM, despite frequent relapses. RNA sequencing revealed recurrent MN1:PATZ1 or EWSR1:PATZ1 fusions related to (often extensive) copy number variations on chromosome 22, where PATZ1 and the two fusion partners are located. These fusions have individually been reported in a number of glial/glioneuronal tumors, as well as extracranial sarcomas. We show here that they are more common than previously acknowledged, and together define a biologically distinct CNS tumor type with high expression of neural development markers such as PAX2, GATA2 and IGF2. Drug screening performed on the MN1:PATZ1 fusion-bearing KS-1 brain tumor cell line revealed preliminary candidates for further study. In summary, PATZ1 fusions define a molecular class of histologically polyphenotypic neuroepithelial tumors, which show an intermediate prognosis under current treatment regimens.