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.     

Chromium (VI) Can Activate and Impair Antioxidant Defense System

The changes in glutathione-dependent cycle enzymes and catalase activities under Cr(VI)-induced oxidative stress were investigated in two distinct cell lines: L-41−human epithelial-like cells and HLF−fetal human diploid lung fibroblasts, which differ in tissue origin, proliferation, and antioxidant enzymes activities. The chromium concentrations from 1 to 5 μM cause nontoxic effects and activate antioxidant enzymes to overcome oxidative stress. In spite of some differences in the endogenous antioxidant activities, both cell lines reveal the same range of toxic concentrations (20–30 μM). The irreversible inhibition of glutathione-dependent antioxidant enzymes develops under toxic concentrations and serves as a marker of toxicity. The endogenous antioxidant activity influences time-dependent expression of Cr(VI) toxicity and the dynamics of antioxidant enzymes activity under nontoxic conditions. The cell antioxidant defense system is an important marker of the cell adaptive capacity under nontoxic and toxic conditions.     

Some Kinetic Singularities of Mg2+-Dependent Mn-ATPase in Rat Brain Synaptic Membranes

Mn(2+) stimulated change of Mg-ATPase activity has been found in the synaptic fraction of rat brain that was named Mn-ATPase. Investigation of the molecular mechanism has shown that Mn-ATPase is a multi-sited enzyme system whose minimum functional unit is a dimer. Its substrate is the MgATP complex. The number of sites for Mn(2+) as for essential activators and that of full-effect inhibitors are equal, n?=?m?=?1. Studying regulation of the Mn-ATPase system by Mg(2+) has shown that Mg(2+) represents a double-sided effect modifier, namely, it activates the enzyme system at low concentration but inhibits at high concentration. Supposedly, binding-release of MgATP and Mg(2+) from the enzyme would be performed by a randomized mechanism. When analyzing experiments by using the kinetic method of complex curves, a "minimal model" for Mn-ATPase has been created.     

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.     

Satsurblia: New Insights of Human Response and Survival across the Last Glacial Maximum in the Southern Caucasus

The region of western Georgia (Imereti) has been a major geographic corridor for human migrations during the Middle and Upper Palaeolithic (MP/UP). Knowledge of the MP and UP in this region, however, stems mostly from a small number of recent excavations at the sites of Ortvale Klde, Dzudzuana, Bondi, and Kotias Klde. These provide an absolute chronology for the Late MP and MP–UP transition, but only a partial perspective on the nature and timing of UP occupations, and limited data on how human groups in this region responded to the harsh climatic oscillations between 37,000–11,500 years before present. Here we report new UP archaeological sequences from fieldwork in Satsurblia cavein the same region. A series of living surfaces with combustion features, faunal remains, stone and bone tools, and ornaments provide new information about human occupations in this region (a) prior to the Last Glacial Maximum (LGM) at 25.5–24.4 ka cal. BP and (b) after the LGM at 17.9–16.2 ka cal. BP. The latter provides new evidence in the southern Caucasus for human occupation immediately after the LGM. The results of the campaigns in Satsurblia and Dzudzuana suggest that at present the most plausible scenario is one of a hiatus in the occupation of this region during the LGM (between 24.4–17.9 ka cal. BP). Analysis of the living surfaces at Satsurblia offers information about human activities such as the production and utilisation of lithics and bone tools, butchering, cooking and consumption of meat and wild cereals, the utilisation of fibers, and the use of certain woods. Microfaunal and palynological analyses point to fluctuations in the climate with consequent shifts in vegetation and the faunal spectrum not only before and after the LGM, but also during the two millennia following the end of the LGM.     

Elevated Level of Fibrinogen Increases Caveolae Formation; Role of Matrix Metalloproteinase-9

The role of the inflammatory agent fibrinogen (Fg) in increased pial venular permeability has been shown previously. It was suggested that an activation of matrix metalloproteinase-9 (MMP-9) is involved in Fg-induced enhanced transcytosis through endothelial cells (ECs). However, direct link between Fg, caveolae formation, and MMP-9 activity has never been shown. We hypothesized that at an elevated level, Fg enhances formation of functional caveolae through activation of MMP-9. Male wild-type (WT, C57BL/6J) or MMP-9 gene knockout (MMP9?/?) mice were infused with Fg (4 mg/ml, final blood concentration) or equal volume of phosphate buffered saline (PBS). After 2 h, mice were sacrificed and brains were collected for immunohistochemical analyses. Mouse brain ECs were treated with 4 mg/ml of Fg or PBS in the presence or absence of MMP-9 activity inhibitor, tissue inhibitor of metalloproteinases-4 (TIMP-4, 12 ng/ml). Formation of functional caveolae was assessed by confocal microscopy. Fg-induced increased formation of caveolae, which was defined by an increased co-localization of caveolin-1 (Cav-1) and plasmalemmal vesicle-associated protein-1 and was associated with an increased phosphorylation of Cav-1, was ameliorated in the presence of TIMP-4. These results suggest that at high levels, Fg enhances formation of functional caveolae that may involve Cav-1 signaling and MMP-9 activation.     

Epidemiology of Visceral Leishmaniasis in Georgia

This study investigated the transmission and prevalence of Leishmania parasite infection of humans in two foci of Visceral Leishmaniasis (VL) in Georgia, the well known focus in Tbilisi in the East, and in Kutaisi, a new focus in the West of the country. The seroprevalence of canine leishmaniasis was investigated in order to understand the zoonotic transmission. Blood samples of 1575 dogs (stray and pet) and 77 wild canids were tested for VL by Kalazar Detect rK39 rapid diagnostic tests. Three districts were investigated in Tbilisi and one in Kutaisi. The highest proportions of seropositive pet dogs were present in District #2 (28.1%, 82/292) and District #1 (26.9%, 24/89) in Tbilisi, compared to 17.3% (26/150) of pet dogs in Kutaisi. The percentage of seropositive stray dogs was also twice as high in Tbilisi (16.1%, n = 670) than in Kutaisi (8%, n = 50); only 2/58 wild animals screened were seropositive (2. 6%). A total of 873 Phlebotomine sand flies were collected, with 5 different species identified in Tbilisi and 3 species in Kutaisi; 2.3% of the females were positive for Leishmania parasites. The Leishmanin Skin Test (LST) was performed on 981 human subjects in VL foci in urban areas in Tbilisi and Kutaisi. A particularly high prevalence of LST positives was observed in Tbilisi District #1 (22.2%, 37.5% and 19.5% for ages 5–9, 15–24 and 25–59, respectively); lower prevalence was observed in Kutaisi (0%, 3.2% and 5.2%, respectively; P<0.05). This study shows that Tbilisi is an active focus for leishmaniasis and that the infection prevalence is very high in dogs and in humans. Although exposure is as yet not as high in Kutaisi, this is a new VL focus. The overall situation in the country is alarming and new control measures are urgently needed.     

A Comparison of the Adaptive Immune Response between Recovered Anthrax Patients and Individuals Receiving Three Different Anthrax Vaccines

Several different human vaccines are available to protect against anthrax. We compared the human adaptive immune responses generated by three different anthrax vaccines or by previous exposure to cutaneous anthrax. Adaptive immunity was measured by ELISPOT to count cells that produce interferon (IFN)-γ in response to restimulation ex vivo with the anthrax toxin components PA, LF and EF and by measuring circulating IgG specific to these antigens. Neutralising activity of antisera against anthrax toxin was also assayed. We found that the different exposures to anthrax antigens promoted varying immune responses. Cutaneous anthrax promoted strong IFN-γ responses to all three antigens and antibody responses to PA and LF. The American AVA and Russian LAAV vaccines induced antibody responses to PA only. The British AVP vaccine produced IFN-γ responses to EF and antibody responses to all three antigens. Anti-PA (in AVA and LAAV vaccinees) or anti-LF (in AVP vaccinees) antibody titres correlated with toxin neutralisation activities. Our study is the first to compare all three vaccines in humans and show the diversity of responses against anthrax antigens.     

Intra-epidemic genome variation in highly pathogenic African swine fever virus (ASFV) from the country of Georgia

Background

African swine fever virus (ASFV) causes an acute hemorrhagic infection in suids with a mortality rate of up to 100%. No vaccine is available and the potential for catastrophic disease in Europe remains elevated due to the ongoing ASF epidemic in Russia and Baltic countries. To date, intra-epidemic whole-genome variation for ASFV has not been reported. To provide a more comprehensive baseline for genetic variation early in the ASF outbreak, we sequenced two Georgian ASFV samples, G-2008/1 and G-2008/2, derived from domestic porcine blood collected in 2008.

Methods

Genomic DNA was extracted directly from low-volume ASFV PCR-positive porcine blood samples and subjected to next generation sequencing on the Illumina Miseq platform. De novo and mapped sequence assemblies were performed using CLCBio software. Genomic illustrations, sequence alignments and assembly figures were generated using Geneious v10.2.4. Sequence repeat architecture was analyzed using DNASTAR GeneQuest 14.1.0.

Results

The G-2008/1 and G-2008/2 genomes were distinguished from each other by coding changes in seven genes, including MGF 110-1?L, X69R, MGF 505-10R, EP364R, H233R, E199L, and MGF 360-21R in addition to eight homopolymer tract variations. The 2008/2 genome possessed a novel allele state at a previously undescribed intergenic repeat locus between genes C315R and C147L. The C315R/C147L locus represents the earliest observed variable repeat sequence polymorphism reported among isolates from this epidemic. No sequence variation was observed in conventional ASFV subtyping markers. The two genomes exhibited complete collinearity and identical gene content with the Georgia 2007/1 reference genome. Approximately 56 unique homopolymer A/T-tract variations were identified that were unique to the Georgia 2007/1 genome. In both 2008 genomes, within-sample sequence read heterogeneity was evident at six homopolymeric G/C-tracts confined to the known hypervariable ~?7?kb region in the left terminal region of the genome.

Conclusions

This is the first intra-epidemic comparative genomic analysis reported for ASFV and provides insight into the intra-epidemic microevolution of ASFV. The genomes reported here, in addition to the G-2007/1 genome, provide an early baseline for future genome-level comparisons and epidemiological tracing efforts.