Cyclic mechanical strain regulates the development of engineered smooth muscle tissue.

We show that the appropriate combinations of mechanical stimuli and polymeric scaffolds can enhance the mechanical properties of engineered tissues. The mechanical properties of tissues engineered from cells and polymer scaffolds are significantly lower than the native tissues they replace. We hypothesized that application of mechanical stimuli to engineered tissues would alter their mechanical properties. Smooth muscle tissue was engineered on two different polymeric scaffolds and subjected to cyclic mechanical strain. Short-term application of strain increased proliferation of smooth muscle cells (SMCs) and expression of collagen and elastin, but only when SMCs were adherent to specific scaffolds. Long-term application of cyclic strain upregulated elastin and collagen gene expression and led to increased organization in tissues. This resulted in more than an order of magnitude increase in the mechanical properties of the tissues.     

Plant Cell Wall Is a Stumbling Stone for Molecular Biologists

Cell wall is a key structure of the plant organism engaged in numerous functions, and plants spend enormous resources on cell wall formation. Cell wall components are the most widespread organic substances on the Earth. However important is assembling plant cell wall polysaccharides, this process has been insufficiently studied by the methods of molecular genetics; in particular, too little is known of the genes that code for the relevant enzymes (glycosyltransferases, GT). The review addresses the current situation by expounding on GT classification, describing the characteristics of enzymes that synthesize cell wall polysaccharides, and summing up the existing knowledge of already identified and putative cellulose and callose synthases and GT localized in the Golgi apparatus. The methodology for searching and characterizing new genes that participate in cell wall formation is under discussion.__________Translated from Fiziologiya Rastenii, Vol. 52, No. 3, 2005, pp. 443–462.Original Russian Text Copyright © 2005 by Gorshkova, Nikolovski, Finaev.     

A Foundation for Reliable Spatial Proteomics Data Analysis

Quantitative mass-spectrometry-based spatial proteomics involves elaborate, expensive, and time-consuming experimental procedures, and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches for establishing high-quality proteome-wide datasets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis, as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering, and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques. We also propose sound analysis strategies for identifying dynamic changes in subcellular localization by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis.     

Label-Free Protein Quantification for Plant Golgi Protein Localization and Abundance

The proteomic composition of the Arabidopsis (Arabidopsis thaliana) Golgi apparatus is currently reasonably well documented; however, little is known about the relative abundances between different proteins within this compartment. Accurate quantitative information of Golgi resident proteins is of great importance: it facilitates a better understanding of the biochemical processes that take place within this organelle, especially those of different polysaccharide synthesis pathways. Golgi resident proteins are challenging to quantify because the abundance of this organelle is relatively low within the cell. In this study, an organelle fractionation approach targeting the Golgi apparatus was combined with a label-free quantitative mass spectrometry (data-independent acquisition method using ion mobility separation known as LC-IMS-MS^E [or HDMS^E]) to simultaneously localize proteins to the Golgi apparatus and assess their relative quantity. In total, 102 Golgi-localized proteins were quantified. These data show that organelle fractionation in conjunction with label-free quantitative mass spectrometry is a powerful and relatively simple tool to access protein organelle localization and their relative abundances. The findings presented open a unique view on the organization of the plant Golgi apparatus, leading toward unique hypotheses centered on the biochemical processes of this organelle.The plant Golgi apparatus plays an important role in protein and lipid glycosylation and sorting as well as biosynthesis of large amounts of extracellular polysaccharides. It contains a large and diverse set of glycosyltransferases and other enzymes that are required for the synthesis and modification of these polysaccharides (Parsons et al., 2012b; Oikawa et al., 2013). The protein composition of this organelle has been the focus of a number of studies; however, these studies largely report a catalog of Golgi-localized proteins, and to date, there are no comprehensive data on the relative abundance of the different protein constituents of the Golgi apparatus (Dunkley et al., 2004, 2006; Sadowski et al., 2008; Nikolovski et al., 2012; Groen et al., 2014). The quantification of the plant Golgi proteome has been considered challenging, because this organelle is proportionally of low abundance in the cell; therefore, its constituent proteins are rarely identified in conventional proteomics experiments. Investigation of such low-abundance proteins generally requires sample fractionation on the organelle, protein, or peptide level (Stasyk and Huber, 2004; Haynes and Roberts, 2007; Di Palma et al., 2012).Here, an organelle fractionation approach in conjunction with label-free quantitative proteomic analysis was used to assess the localization and relative abundance of proteins within the plant Golgi apparatus. Label-free quantification is an increasingly popular alternative to isotopic tagging quantitative methods; it does not require labeling reagents and can be applied to an unlimited number of samples (Neilson et al., 2011; Evans et al., 2012). This is particularly appealing within plant proteomics, because the most conventional labeling strategy, Stable Isotope Labeling by Amino Acids in Cell Culture, is not easily suited for quantitative plant proteomic studies. The average labeling efficiency achieved using exogenous amino acid supply to Arabidopsis (Arabidopsis thaliana) cell cultures was found to be only 70% to 80% (Gruhler et al., 2005). Quantitative strategies with ¹⁵N metabolic labeling have been described for plant proteome analysis; however, care should be taken to ensure complete ¹⁵N incorporation, because even small amounts of ¹⁴N in the labeled sample can have significant detrimental effects on the number of peptide identifications (Nelson et al., 2007; Guo and Li, 2011; Arsova et al., 2012).In all label-free methods, samples under comparison are analyzed during separate mass spectrometry (MS) experiments (Neilson et al., 2011). The information from identified peptides is then used for relative and/or absolute quantification. The simplest label-free method involves taking the number of spectra acquired and assigned to peptides from the same protein as a measure of abundance (Ishihama et al., 2005). In an alternative approach, ion current recorded for a peptide ion is used as a measure of its abundance. The assumption is made that ion intensity is proportional to peptide amount in the sample analyzed, which holds true for nanoflow and microflow liquid chromatography (LC) systems (Levin et al., 2011; Christianson et al., 2013). Comparing peptide ion current between samples is, thus, widely used for relative quantification (Silva et al., 2005). To allow such comparison, a peptide must be identified across all samples under investigation, which is often challenging in LC-MS experiments given the highly complex nature of proteomics samples that contain tens of thousands of different peptides (Michalski et al., 2011). Hence, most relative ion intensity-based label-free approaches usually involve a step of identification transfer (Pasa-Tolíc et al., 2004). This involves matching ions from different acquisitions (in one of which, the ion has not been identified and is assigned the sequence from its matching pair in the other acquisition).Additionally, label-free proteomics can be used for absolute quantification (i.e. to estimate abundance of different proteins relative to each other within a given sample). Several different approaches have been suggested on how to convert peptide intensities to protein amounts (for comparison, see Wilhelm et al., 2014). One of the first such methods was Top-3 described by Silva et al. (2006b), who made a notable and unexpected observation, stating that the average MS signal response for the three most abundant peptides per 1 mol of protein is constant within a coefficient of variation of less than 10% (Silva et al., 2006b).In all these approaches, the peptide ion current is typically computed as the area under the curve of the chromatographic elution profile that is reconstituted from separate MS1 survey scans in which intact precursors are recorded. Determining a chromatographic profile accurately requires that the MS1 scans are performed at optimal frequency (Lange et al., 2008) and for optimal duration to record the MS1 signal at a high signal-to-noise ratio. In typical data-dependent acquisitions, however, the mass spectrometer oscillates between MS1 survey scans recording the mass/charge (m/z) for precursor peptide ions and then, a series of MS2 scans fragmenting one peptide ion precursor at a time, producing fragmentation spectra necessary for identification (Sadygov et al., 2004). As a result, the duration and frequency of MS2 scans determine the identification rate in data-dependent acquisition experiments but compromise time spent in MS1 required for accurate area under the curve quantification. Several groups have suggested data-independent acquisition, in which individual peptide ions are not selected for fragmentation but rather, groups of peptides of similar m/z are fragmented together. The exact number of cofragmented precursors depends on the speed and sensitivity of instrument configuration (for review, see Law and Lim, 2013). The simplest approach involves alternating between low-energy and high-energy scans of equal duration; low-energy scans record precursor peptide ions, whereas in high-energy scans, all precursors entering the mass spectrometer are cofragmented, and their fragments are recorded simultaneously. The method was called MS^E for Waters qTOF Mass Spectrometers (Geromanos et al., 2009) or all-ion fragmentation for Thermo Orbitrap Mass Spectrometers (Geiger et al., 2010). The analysis required downstream of this type of data acquisition is challenging given that the information of fragment origin (i.e. from what precursor peptide ion fragment was generated) is lost completely and that the high number of coeluting peptides is expected to create highly overlapping fragment spectra on fragmentation. To address this problem, Hoaglund-Hyzer and Clemmer (2001) have suggested fractionating peptides by ion mobility separation before fragmentation and MS and assigning fragments to precursors based on similarity of both chromatographic and mobility profiles (Hoaglund-Hyzer and Clemmer, 2001). The method was termed parallel fragmentation, and since that time, it has been commercialized by Waters as IMS-MS^E or HDMS^E (Shliaha et al., 2013).To date, the application of label-free quantitative proteomics to plant biology has been very limited. Recently, Helm et al. (2014) applied the LC-IMS-MS^E with Top-3 quantification to quantify the Arabidopsis chloroplast stroma proteome, allowing quantitative modeling of chloroplast metabolism. Two other works used the LC-MS^E method to assess the quantitative changes of cytosolic ribosomal proteins in response to Suc feeding and the extracellular proteome in response to salicylic acid (Cheng et al., 2009; Hummel et al., 2012).A number of proteomics approaches have been described to assess protein localization on a large scale (for review, see Gatto et al., 2010). Purification approaches attempt to isolate organelles to high levels of purity and subsequently identify and quantify proteins using LC-MS; however, such attempts yield limiting success and high false discovery rates (Andersen et al., 2002; Parsons et al., 2012a). A known limitation of this technique is the inability to completely isolate an organelle of interest, which combined with high proteome dynamic range, can result in some more abundant contaminants being identified and quantified at higher amounts than the target organelle residents. Moreover, even if a target organelle could be isolated to a certain degree of purity, it would still be impossible to deconvolute organelle residents from transient proteins that traffic through the target organelle. This becomes especially challenging for the organelles of the secretory pathway. To address these challenges, several groups applied fractionation of all organelles by gradient centrifugation and subsequent protein quantification by LC-MS. This produces distributions across the gradient for all quantified proteins, which are then used to assign organelle localization based on the specific distributions of organelle marker proteins. This effectively solves the problem of organelle contamination and protein trafficking, because a protein is expected to have a distribution characteristic of its organelle of residence, even if it is identified in all fractions, including those enriched in other organelles. Current variations of this method differ mostly by the LC-MS strategy used for quantification; for example, spectral counting was applied for protein-correlating profiles (Andersen et al., 2003), isobaric mass tagging (Nikolovski et al., 2012) and isotope-coded affinity tagging (Dunkley et al., 2004) were applied for localization of organelle proteins by isotope tagging (LOPIT), and Stable Isotope Labeling by Amino Acids in Cell Culture was applied for nucleolus/nucleus/cytosolic fractionation (Boisvert and Lamond, 2010).Here, a label-free LC-IMS-MS^E method was used for the analysis of density ultracentrifugation fractions enriched for the Golgi apparatus. First, we use relative label-free quantification involving identification transfer using the previously published synapter algorithm (Bond et al., 2013) to assess distributions of Golgi-localized proteins across the density gradient. These distributions are significantly different from those of residents of other organelles, which results in unambiguous protein assignment to the Golgi apparatus by multivariate data analysis. Second, the Top-3 absolute quantification method as implemented in Protein Lynx Global Server (PLGS) was used to rank order the Golgi-localized proteins by abundance in the fraction most enriched for Golgi apparatus. In conclusion, we present the analysis of protein distribution and abundances of the Golgi apparatus-enriched portion of the ultracentrifugation density gradient, allowing for simultaneous protein quantification and localization and leading to the assessment of relative abundances of 102 Golgi-localized proteins.     

Novel TNF-α converting enzyme (TACE) inhibitors as potential treatment for inflammatory diseases

Our research on hydantoin based TNF-α converting enzyme (TACE) inhibitors has led to an acetylene containing series that demonstrates sub-nanomolar potency (K(i)) as well as excellent activity in human whole blood. These studies led to the discovery of highly potent TACE inhibitors with good DMPK profiles.     

Quantification of fibronectin adsorption to silicone-rubber cell culture substrates

As the role of mechanical force in cellular signaling gained recognition, investigators designed a number of devices to deliver controlled regimens of mechanical force to cultured cells. One type of device uses thin silicone-rubber membranes to support monolayer cell adhesion and to transmit mechanical force in the form of biaxial strain. We have observed that cell attachment and spreading are impaired on these membranes compared to polystyrene, even when both are passively coated with identical amounts of extracellular matrix. The purpose of these studies was to quantify the efficiency and stability of passive matrix adsorption onto commercially available elastic culture substrates. A theoretically saturating density (1 microg/cm2) of fibronectin was added to each well, and the initial efficiency of adsorption to the walls and elastic membranes was found to be 31 +/- 2% of the protein added. Strikingly, when the protein adsorbed specifically to the membranes was quantified after seven days, only 10-26 ng/cm2 fibronectin were present, revealing that most of the adsorption is to the sides of the wells. These results indicate that the adsorption of matrix proteins to silicone-rubber substrates is relatively inefficient and that investigators who use these systems must be aware of this fact and design their experiments accordingly.     

Mechanical stimulation and mitogen-activated protein kinase signaling independently regulate osteogenic differentiation and mineralization by calcifying vascular cells

Ectopic calcification of vascular tissue is associated with several cardiovascular pathologies and likely involves active regulation by vascular smooth muscle cells and osteoblast-like vascular cells. This process often occurs in sites with altered mechanical environments, suggesting a role for mechanical stimuli in calcification. In this study, we investigated the effect of mechanical stimulation on the proliferation, osteogenic differentiation, calcification, and mitogen-activated protein kinase (MAPK) signaling in calcifying vascular cells (CVCs), a subpopulation of aortic smooth muscle cells putatively involved in vascular calcification. Application of equibiaxial cyclic strain (7%, 0.25 Hz) to CVCs had no effect on cell proliferation, but accelerated alkaline phosphatase expression and significantly increased mineralization by 3.1-fold over unstrained cells. Fluid motion in the absence of strain also enhanced mineralization, but to a lesser degree. Because MAPK pathways mediate mechanically regulated osteoblast differentiation, we tested whether similar signaling was involved in mineralization by CVCs. In static cultures, pharmacological inhibition of the extracellular signal-regulated kinase (ERK1/2), p38 MAPK, and c-Jun N-terminal kinase pathways significantly attenuated mineral production by as much as -94%, compared with uninhibited CVCs. Strikingly, although mechanical stimulation activated each of the MAPK pathways, inhibition of these pathways had no effect on the mechanically induced enhancement of alkaline phosphatase activity or mineralization. These novel data indicate that mechanical signals regulate calcification by CVCs, and although MAPK signaling is critical to CVC osteogenic differentiation and mineralization, it is not involved directly in transduction of mechanical signals to regulate these processes under the conditions utilized in this study.     

Mechanism of hypoalbuminemia in rodents

Normal albumin loss from the plasma is thought to be minimized by a number of mechanisms, including charge repulsion with the capillary wall and an intracellular rescue pathway involving the major histocompatibility complex-related Fc receptor (FcRn)-mediated mechanism. This study investigates how these factors may influence the mechanism of hypoalbuminemia. Hypoalbuminemia in rats was induced by treatment with puromycin aminonucleoside (PA). To test the effects of PA on capillary wall permeability, plasma elimination rates were determined for tritium-labeled tracers of different-sized Ficolls, negatively charged Ficolls, and (14)C-labeled tracer of albumin in control and PA-treated Sprague-Dawley rats. Urinary excretion and tissue uptake were also measured. Hypoalbuminemia was also examined in two strains of FcRn-deficient mice: beta(2)-microglobulin (beta(2)M) knockout (KO) mice and FcRn alpha-chain KO mice. The excretion rates of albumin and albumin-derived fragments were measured. PA-induced hypoalbuminemia was associated with a 2.5-fold increase in the plasma elimination rate of albumin. This increase could be completely accounted for by the increase in urinary albumin excretion. Changes in the permeability of the capillary wall were not apparent, inasmuch as there was no comparable increase in the plasma elimination rate of 36- to 85-A Ficoll or negatively charged 50- to 80-A Ficoll. In contrast, hypoalbuminemic states in beta(2)M and FcRn KO mice were associated with decreases in excretion of albumin and albumin-derived fragments. This demonstrates that the mechanism of hypoalbuminemia consists of at least two distinct forms: one specifically associated with the renal handling of albumin and the other mediated by systemic processes.     

Crystal structure of eosinophil cationic protein at 2.4 A resolution

Eosinophil cationic protein (ECP) is located in the matrix of the eosinophil's large specific granule and has marked toxicity for a variety of helminth parasites, hemoflagellates, bacteria, single-stranded RNA virus, and mammalian cells and tissues. It belongs to the bovine pancreatic ribonuclease A (RNase A) family and exhibits ribonucleolytic activity which is about 100-fold lower than that of a related eosinophil ribonuclease, the eosinophil-derived neurotoxin (EDN). The crystal structure of human ECP, determined at 2.4 A, is similar to that of RNase A and EDN. It reveals that residues Gln-14, His-15, Lys-38, Thr-42, and His-128 at the active site are conserved as in all other RNase A homologues. Nevertheless, evidence for considerable divergence of ECP is also implicit in the structure. Amino acid residues Arg-7, Trp-10, Asn-39, His-64, and His-82 appear to play a key part in the substrate specificity and low catalytic activity of ECP. The structure also shows how the cationic residues are distributed on the surface of the ECP molecule that may have implications for an understanding of the cytotoxicity of this enzyme.     

Putative Glycosyltransferases and Other Plant Golgi Apparatus Proteins Are Revealed by LOPIT Proteomics

The Golgi apparatus is the central organelle in the secretory pathway and plays key roles in glycosylation, protein sorting, and secretion in plants. Enzymes involved in the biosynthesis of complex polysaccharides, glycoproteins, and glycolipids are located in this organelle, but the majority of them remain uncharacterized. Here, we studied the Arabidopsis (Arabidopsis thaliana) membrane proteome with a focus on the Golgi apparatus using localization of organelle proteins by isotope tagging. By applying multivariate data analysis to a combined data set of two new and two previously published localization of organelle proteins by isotope tagging experiments, we identified the subcellular localization of 1,110 proteins with high confidence. These include 197 Golgi apparatus proteins, 79 of which have not been localized previously by a high-confidence method, as well as the localization of 304 endoplasmic reticulum and 208 plasma membrane proteins. Comparison of the hydrophobic domains of the localized proteins showed that the single-span transmembrane domains have unique properties in each organelle. Many of the novel Golgi-localized proteins belong to uncharacterized protein families. Structure-based homology analysis identified 12 putative Golgi glycosyltransferase (GT) families that have no functionally characterized members and, therefore, are not yet assigned to a Carbohydrate-Active Enzymes database GT family. The substantial numbers of these putative GTs lead us to estimate that the true number of plant Golgi GTs might be one-third above those currently annotated. Other newly identified proteins are likely to be involved in the transport and interconversion of nucleotide sugar substrates as well as polysaccharide and protein modification.The Golgi apparatus is the central organelle in the secretory pathway, and in higher plants it is involved in the biosynthesis and transport of cell wall matrix polysaccharides, glycoproteins, proteoglycans, and glycolipids as well as in protein trafficking to different subcellular compartments. The last decade has produced substantial findings on the function of the Golgi apparatus: insights into the protein trafficking at the endoplasmic reticulum (ER)/Golgi interface, Golgi structural maintenance, its involvement in endocytosis, and its behavior during cell division (for review, see Faso et al., 2009). However, despite its importance, only a small proportion of the Golgi proteome has been studied: relatively few Golgi proteins have been localized, and even fewer have been functionally characterized.The Golgi apparatus is thought to contain a large and diverse group of membrane-bound glycosyltransferases (GTs). The current view is that different GT activities are required for synthesis of the linkage between different donor and acceptor sugars. Having in mind the diversity of linkage types found in cell wall polysaccharides, the number of different GTs involved is likely to be very large. For instance, it has been estimated that for the biosynthesis of pectin alone, the action of 65 different enzymatic activities is needed (Caffall and Mohnen, 2009). By the end of the year 2011, 468 Arabidopsis (Arabidopsis thaliana) sequences had been annotated in the Carbohydrate-Active EnZymes (CAZy) GT database (Cantarel et al., 2009; http://www.cazy.org). We estimate that two-thirds of these CAZy-classified GTs may be targeted to the Golgi. The remaining one-third are cytosolic or plastidic enzymes involved in processes including, secondary metabolism or starch synthesis. The reported sequences are classified into 43 CAZy families based on amino acid sequence similarities within which at least one member has been biochemically characterized. Each family is likely to have a common structural fold, and three-dimensional (3-D) structures have been resolved for 20 of these 43 families. These are divided mostly into two structural classes, having either a GT-A fold or a GT-B fold (Unligil and Rini, 2000; Bourne and Henrissat, 2001). Moreover, most of the structurally uncharacterized GT families are predicted to adopt either the GT-A or GT-B fold based on 3-D structural homology modeling (Coutinho et al., 2003; Lairson et al., 2008). Despite this conserved 3-D structure, different GT families have very low or undetectable sequence similarities. Consequently, predicting novel GTs based solely on their amino acid sequence similarities is not always achievable, and structural homology searches have also proven useful (Hansen et al., 2009).The length and properties of the transmembrane domain (TMD) of endomembrane proteins appear to play a role in protein sorting and location within the secretory pathway and can be used to predict protein localization (Hanton et al., 2005; Sharpe et al., 2010). In order to perform such predictions, a high number of experimentally localized proteins is required, but only limited data sets have been available for plants to date.In order to identify the most abundant CAZy-classified GTs as well as novel putative GTs, in this work we rigorously extended our proteomic studies of the Golgi apparatus. We have previously developed a high-throughput mass spectrometry (MS)-based quantitative proteomics technique for localization of organelle proteins by isotope tagging (LOPIT; Dunkley et al., 2004, 2006). Here, we report new LOPIT data sets and apply a new method of combining them with published LOPIT data sets, localizing an unprecedented number of plant organelle proteins. We have analyzed the TMD properties of the proteins assigned to the ER, Golgi, and plasma membrane (PM) and determined the organelle-specific features. Structural prediction analysis of the Golgi-localized proteins with unknown functions assessed the protein sequences for the potential to fold similarly to known GT structures. We found that the Golgi contains a substantial number of candidate GT families that have no characterized functions. These results yield a broader understanding of the Golgi function and its biochemical properties.