Proteome of human endometrium: Identification of differentially expressed proteins in proliferative and secretory phase endometrium

Purpose: To exploit the potential of proteomics to identify and study additional yet‐unidentified important proteins present in human endometrium. Experimental design: The proteome of human endometrium would be established using 2‐DE and MALDI and the data analyzed to identify differential protein expression in the proliferative and secretory phase of the menstrual cycle using PDQuest software and MALDI. Results: In the present work, 2‐DE of human endometrium protein led to the resolution of over 200 spots. Subsequent MALDI analysis of 215 spots allowed the identification of 194 proteins. A total of 57 out of the 215 spots were found to be differentially expressed, out of which 49 could be identified using MALDI. These differentially expressed proteins included structural proteins, molecular chaperones, signaling proteins, metabolic proteins, proteins related to immunity, RNA biogenesis, protein biosynthesis and others. The differential expressions of seven representative proteins in secretory and proliferative phase endometrium tissue were confirmed by immunoblot analysis. Conclusion and clinical relevance: This study establishes the 2‐D proteome of human endometrium represented by 194 identified protein spots. The present data provides an important clue towards determining the function of these proteins with respect to endometrium related diseases.     

Assessment of the EEG complexity during activations from sleep

The present study quantitatively analyzes the EEG characteristics during activations (Act) that occur during NREM sleep, and constitute elements of sleep microstructure (i.e. the Cyclic Alternating Pattern). The fractal dimension (FD) and the sample entropy (SampEn) measures were used to study the different sleep stages and the Act that build up the sleep structure. Polysomnographic recordings from 10 good sleepers were analyzed. The complexity indexes of the Act were compared with the non-activation (NAct) periods during non-REM sleep. In addition, complexity measures among the different Act subtypes (A1, A2 and A3) were analyzed. A3 presented a quite similar complexity independently of the sleep stage, while A1 and A2 showed higher complexity in light sleep than during deep sleep. The current results suggest that Act present a hierarchic complexity between subtypes A3 (higher), A2 (intermediate) and A1 (lower) in all sleep stages.     

Conformational analysis of alkali metal complexes of anionic species of aspartic acid, their interconversion and deprotonation: a DFT investigation

Gas-phase geometry-optimized structures of aspartate complexes of anionic species (Hasp(-)) with lithium, sodium and potassium metal cations and transition-state structures for their interconversions were obtained using the density functional theory computations at the B3LYP/6-311++G(d,p) level of theory. The metal ion affinities of Hasp(-) species and deprotonation energies of [Hasp-M] complexes, M=Li+, Na+ and K+, and their conformers were obtained. Relative energies of the [Hasp-M] complex conformers, reaction energies, thermodynamic properties, rate and equilibrium constants of their complexation are reported. Binding energies of the most stable complexes with Li+, Na+ and K+ are -168.53, -133.34 and -117.68kcal/mol, respectively. The most stable complex conformer as a tri-coordinated form for aspartate complex with Li(+) and bi-coordinated form for aspartate complexes with Na+ and K+ were found.     

A mass spectrometric strategy for profiling glycoproteinoses, Pompe disease, and sialic acid storage diseases

Glycoproteinoses, Pompe disease, and sialic acid storage diseases are characterized by a massive accumulation of unprocessed oligosaccharides and/or glycoconjugates in urine. The identification of these glycocompounds is essential for a proper diagnosis. In this study, we investigated the potential of MALDI‐TOF‐MS to identify glycocompounds present in urine from patients with different inborn errors of glycan metabolism. Urinary glycocompounds were permethylated, and analyzed using GC‐MS and MALDI‐TOF‐MS. In order to confirm tentative assignments, a second aliquot of urine was purified on a C18 Sep‐Pak cartridge and glycocompounds were desalted on a column of nonporous graphitized carbon. The glycocompounds were then sequentially on‐plate digested using an array of exoglycosidases. A range of disease‐specific oligosaccharides as well as glycopeptides was identified for all oligosacchariduria models. In addition, free sialic acid accumulated in urine from a patient suffering from French‐type sialuria, has been detected by a GC‐MS approach, which could be applied to other sialic acid storage diseases. This procedure is simple, and can be performed in few simple steps in less than 24 h. This current method can be applied for newborn screening for other inherited metabolic diseases as well as for assessing treatments in clinical trials.     

Human pathogenic streptococcal proteomics and vaccine development

Gram-positive streptococci are non-motile, chain-forming bacteria commonly found in the normal oral and bowel flora of warm-blooded animals. Over the past decade, a proteomic approach combining 2-DE and MS has been used to systematically map the cellular, surface-associated and secreted proteins of human pathogenic streptococcal species. The public availability of complete streptococcal genomic sequences and the amalgamation of proteomic, genomic and bioinformatic technologies have recently facilitated the identification of novel streptococcal vaccine candidate antigens and therapeutic agents. The objective of this review is to examine the constituents of the streptococcal cell wall and secreted proteome, the mechanisms of transport of surface and secreted proteins, and describe the current methodologies employed for the identification of novel surface-displayed proteins and potential vaccine antigens.     

Urinary proteomic analysis of chronic allograft nephropathy

The pathogenesis of progressive renal allograft injury, which is termed chronic allograft nephropathy (CAN), remains obscure and is currently defined by histology. Prospective protocol-biopsy trials have demonstrated that clinical and standard laboratory tests are insufficiently sensitive indicators of the development and progression of CAN. The study aim was to determine if CAN could be characterized by urinary proteomic data and identify the proteins associated with disease. The urinary proteome of 75 renal transplant recipients and 20 healthy volunteers was analyzed using surface enhanced laser desorption and ionization MS. Patients could be classified into subgroups with normal histology and Banff CAN grades 2-3 with a sensitivity of 86% and a specificity of 92% by applying the classification algorithm Adaboost to urinary proteomic data. Several urinary proteins associated with advanced CAN were identified including α1-microglobulin, β2-microglobulin, prealbumin, and endorepellin, the antiangiogenic C-terminal fragment of perlecan. Increased urinary endorepellin was confirmed by ELISA and increased tissue expression of the endorepellin/perlecan ratio by immunofluoresence analysis of renal biopsies. In conclusion, analysis of urinary proteomic data has further characterized the more severe CAN grades and identified urinary endorepellin, as a potential biomarker of advanced CAN.     

Biomarker discovery in glomerular diseases using urinary proteomics

Renal biopsy remains the gold standard test for definitive diagnosis of glomerular diseases. This invasive procedure; however, has a potential risk for serious complications and is contraindicated in some patients. It is therefore essential to search for noninvasive biomarkers for the diagnosis and prognosis of glomerular diseases. The urine is the most appropriate sample for biomarker discovery in glomerular diseases. Urinary proteomics has thus gained a wide acceptance and has been extensively applied to this area. This review focuses mainly on applications of proteomic technologies to urinary proteome profiling for biomarker discovery in various glomerular diseases, including diabetic nephropathy, IgA nephropathy, membranous nephropathy, focal segmental glomerulosclerosis, primary membranoproliferative glomerulonephritis, lupus nephritis, antiglomerular basement membrane disease, minimal change disease, and pediatric nephrotic syndrome. Recent findings from these studies are summarized and discussed. These data clearly underline the great promise of urinary proteomics in biomarker discovery for glomerular diseases.     

Topology-controlled volume rendering

Topology provides a foundation for the development of mathematically sound tools for processing and exploration of scalar fields. Existing topology-based methods can be used to identify interesting features in volumetric data sets, to find seed sets for accelerated isosurface extraction, or to treat individual connected components as distinct entities for isosurfacing or interval volume rendering. We describe a framework for direct volume rendering based on segmenting a volume into regions of equivalent contour topology and applying separate transfer functions to each region. Each region corresponds to a branch of a hierarchical contour tree decomposition, and a separate transfer function can be defined for it. The novel contributions of our work are: 1) a volume rendering framework and interface where a unique transfer function can be assigned to each subvolume corresponding to a branch of the contour tree, 2) a runtime method for adjusting data values to reflect contour tree simplifications, 3) an efficient way of mapping a spatial location into the contour tree to determine the applicable transfer function, and 4) an algorithm for hardware-accelerated direct volume rendering that visualizes the contour tree-based segmentation at interactive frame rates using graphics processing units (GPUs) that support loops and conditional branches in fragment programs     

Efficient computation of Morse-Smale complexes for three-dimensional scalar functions

The Morse-Smale complex is an efficient representation of the gradient behavior of a scalar function, and critical points paired by the complex identify topological features and their importance. We present an algorithm that constructs the Morse-Smale complex in a series of sweeps through the data, identifying various components of the complex in a consistent manner. All components of the complex, both geometric and topological, are computed, providing a complete decomposition of the domain. Efficiency is maintained by representing the geometry of the complex in terms of point sets.