Structural recognition between a four-way DNA junction and a resolving enzyme

Resolving enzymes bind highly selectively to four-way DNA junctions, but the mechanism of this structural specificity is poorly understood. In this study, we have explored the role of interactions between the dimeric enzyme and the helical arms of the junction, using junctions with either shortened arms, or circular permutation of arms. We find that DNA-protein contacts in the arms containing the 5' ends of the continuous strands are very important, conferring a significant level of sequence discrimination upon both the choice of conformer and the order of strand cleavage. We have exploited these properties to obtain hydroxyl radical footprinting data on endonuclease I-junction complexes that are not complicated by the presence of alternative conformers, with results that are in good agreement with the arm permutation and shortening experiments. Substitution of phosphate groups at the center of the junction reveals the importance of electrostatic interactions at the point of strand exchange in the complex. Our data show that the form of the complex between endonuclease I and a DNA junction depends on the core of the junction and on interactions with the first six base-pairs of the arms containing the 5' ends of the continuous strands.     

Deletion of the TPM1 and MDM20 Genes Affect the Mechanical and Structural Properties of Yeast Cells

Many diseases including cancer are associated with a disorganised cytoskeleton. The process of characterising how cytoskeletal disorganisation affects the mechanical properties of cells offers the potential to develop new drugs and treatment regimes that may exploit mechanical weakness in cells and tissues. This work investigated the role of actin associated proteins, namely tropomyosin 1 (tpm1p) and mitochondrial distribution and morphology protein 20 (mdm20p), on the mechanical and morphological properties of yeast cells. For the first time it was shown that deletion of both the TPM1 and MDM20 genes resulted in a decrease in Young’s modulus when compared to the wild-type cells. The deletion strains appeared to have aberrant cell walls when compared to the wild-type strain and also appeared to have lost the characteristic elliptical morphology that is normally exhibited by yeast. Deletion of the TPM1 gene resulted in a significant increase in mean conjugate cell diameter when compared to the wild-type cells, however deletion of the MDM20 gene did not have any significant effect upon the mean conjugate diameter of the yeast cells.     

An improved technique for obtaining well-spread metaphases from plants with numerous large chromosomes

Preparations that contain well-spread metaphase chromosomes are critical for plant cytogenetic analyses including chromosome counts, banding procedures, in situ hybridization, karyotyping and construction of ideograms. Chromosome spreading is difficult for plants with large and numerous chromosomes. We report here a technique for obtaining cytoplasm-free, well-spread metaphases from two Amaryllidaceae species: Sprekelia formosissima (2n = 120) and Hymenocallis howardii (2n = 96). The technique has three main steps: 1) pretreatment to cause chromosome condensation, 2) dripping onto tilted slides coated with a thin layer of pure acetic acid and 3) application of steam and acetic acid to produce cytoplasmic hydrolysis, which spreads the chromosomes.     

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.     

A general and efficient approach for the construction of RNA oligonucleotides containing a 5'-phosphorothiolate linkage

Oligoribonucleotides containing a 5'-phosphorothiolate linkage have provided effective tools to study the mechanisms of RNA catalysis, allowing resolution of kinetic ambiguity associated with mechanistic dissection and providing a strategy to establish linkage between catalysis and specific functional groups. However, challenges associated with their synthesis have limited wider application of these modified nucleic acids. Here, we describe a general semisynthetic strategy to obtain these oligoribonucleotides reliably and relatively efficiently. The approach begins with the chemical synthesis of an RNA dinucleotide containing the 5'-phosphorothiolate linkage, with the adjacent 2'-hydroxyl group protected as the photolabile 2'-O-o-nitrobenzyl or 2'-O-α-methyl-o-nitrobenzyl derivative. Enzymatic ligation of the 2'-protected dinucleotide to transcribed or chemically synthesized 5' and 3' flanking RNAs yields the full-length oligoribonucleotide. The photolabile protecting group increases the chemical stability of these highly activated oligoribonucleotides during synthesis and long-term storage but is easily removed with UV irradiation under neutral conditions, allowing immediate use of the modified RNA in biochemical experiments.     

Derlin-2-deficient mice reveal an essential role for protein dislocation in chondrocytes

Protein quality control is a balance between chaperone-assisted folding and removal of misfolded proteins from the endoplasmic reticulum (ER). Cell-based assays have been used to identify key players of the dislocation machinery, including members of the Derlin family. We generated conditional knockout mice to examine the in vivo role of Derlin-2, a component that nucleates cellular dislocation machinery. In most Derlin-2-deficient tissues, we found constitutive upregulation of ER chaperones and IRE-1-mediated induction of the unfolded protein response. The IRE-1/XBP-1 pathway is required for development of highly secretory cells, particularly plasma cells and hepatocytes. However, B lymphocyte development and antibody secretion were normal in the absence of Derlin-2. Likewise, hepatocyte function was unaffected by liver-specific deletion of Derlin-2. Whole-body deletion of Derlin-2 results in perinatal death. The few mice that survived to adulthood all developed skeletal dysplasia, likely caused by defects in collagen matrix protein secretion by costal chondrocytes.     

Protein phosphatase 2A (PP2A)-specific ubiquitin ligase MID1 is a sequence-dependent regulator of translation efficiency controlling 3-phosphoinositide-dependent protein kinase-1 (PDPK-1)

We have shown previously that the ubiquitin ligase MID1, mutations of which cause the midline malformation Opitz BBB/G syndrome (OS), serves as scaffold for a microtubule-associated protein complex that regulates protein phosphatase 2A (PP2A) activity in a ubiquitin-dependent manner. Here, we show that the MID1 protein complex associates with mRNAs via a purine-rich sequence motif called MIDAS (MID1 association sequence) and thereby increases stability and translational efficiency of these mRNAs. Strikingly, inclusion of multiple copies of the MIDAS motif into mammalian mRNAs increases production of the encoded proteins up to 20-fold. Mutated MID1, as found in OS patients, loses its influence on MIDAS-containing mRNAs, suggesting that the malformations in OS patients could be caused by failures in the regulation of cytoskeleton-bound protein translation. This is supported by the observation that the majority of mRNAs that carry MIDAS motifs is involved in developmental processes and/or energy homeostasis. Further analysis of one of the proteins encoded by a MIDAS-containing mRNA, namely PDPK-1 (3-phosphoinositide dependent protein kinase-1), which is an important regulator of mammalian target of rapamycin/PP2A signaling, showed that PDPK-1 protein synthesis is significantly reduced in cells from an OS patient compared with an age-matched control and can be rescued by functional MID1. Together, our data uncover a novel messenger ribonucleoprotein complex that regulates microtubule-associated protein translation. They suggest a novel mechanism underlying OS and point at an enormous potential of the MIDAS motif to increase the efficiency of biotechnological protein production in mammalian cells.     

Structure of a rare non-standard sequence k-turn bound by L7Ae protein

Kt-23 from Thelohania solenopsae is a rare RNA kink turn (k-turn) where an adenine replaces the normal guanine at the 2n position. L7Ae is a member of a strongly conserved family of proteins that bind a range of k-turn structures in the ribosome, box C/D and H/ACA small nucleolar RNAs and U4 small nuclear RNA. We have solved the crystal structure of T. solenopsae Kt-23 RNA bound to Archeoglobus fulgidus L7Ae protein at a resolution of 2.95 Å. The protein binds in the major groove displayed on the outer face of the k-turn, in a manner similar to complexes with standard k-turn structures. The k-turn adopts a standard N3 class conformation, with a single hydrogen bond from A2b N6 to A2n N3. This contrasts with the structure of the same sequence located in the SAM-I riboswitch, where it adopts an N1 structure, showing the inherent plasticity of k-turn structure. This potentially can affect any tertiary interactions in which the RNA participates.     

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.