In this paper, chromatographic analysis of active substance olopatadine hydrochloride, which is used in eye drops as antihistaminic agent, and its impurity E isomer by hydrophilic interaction liquid chromatography (HILIC) and application of design of experiments (DoE) methodology are presented. In addition, benzalkonium chloride is very often used as a preservative in eye drops. Therefore, the evaluation of its chromatographic behavior in HILIC was carried out as well. In order to estimate chromatographic behavior and set optimal chromatographic conditions, DoE methodology was applied. After the selection of important chromatographic factors, Box-Behnken design was utilized, and on the basis of the obtained models factor effects were examined. Then, multi-objective robust optimization is performed aiming to obtain chromatographic conditions that comply with several quality criteria simultaneously: adequate and robust separation of critical peak pair and maximum retention of the first eluting peak. The optimal conditions are identified by using grid point search methodology. The experimental verification confirmed the adequacy of the defined optimal conditions. Finally, under optimal chromatographic conditions, the method was validated and applicability of the proposed method was confirmed.
CRISPR-Cas9 is a versatile genome editing technology for studying the functions of genetic elements. To broadly enable the application of Cas9 in vivo, we established a Cre-dependent Cas9 knockin mouse. We demonstrated in vivo as well as ex vivo genome editing using adeno-associated virus (AAV)-, lentivirus-, or particle-mediated delivery of guide RNA in neurons, immune cells, and endothelial cells. Using these mice, we simultaneously modeled the dynamics of KRAS, p53, and LKB1, the top three significantly mutated genes in lung adenocarcinoma. Delivery of a single AAV vector in the lung generated loss-of-function mutations in p53 and Lkb1, as well as homology-directed repair-mediated Kras(G12D) mutations, leading to macroscopic tumors of adenocarcinoma pathology. Together, these results suggest that Cas9 mice empower a wide range of biological and disease modeling applications.
Surgery for acute aortic dissection is challenging, especially in cases of cerebral malperfusion. Should we perform only the aortic repair, or should we also reconstruct the arch vessels when they are severely affected by the disease process? Here we present a case of acute aortic dissection with multiple tears that involved the brachiocephalic artery and caused cerebral and right upper-extremity malperfusion. The patient successfully underwent complete replacement of the brachiocephalic artery and the aortic arch during deep hypothermic circulatory arrest, with antegrade cerebral protection. We have found this technique to be safe and reproducible for use in this group of patients.
Pseudouridine is the most abundant RNA modification, yet except for a few well-studied cases, little is known about the modified positions and their function(s). Here, we develop ?-seq for transcriptome-wide quantitative mapping of pseudouridine. We validate ?-seq with spike-ins and de novo identification of previously reported positions and discover hundreds of unique sites in human and yeast mRNAs and snoRNAs. Perturbing pseudouridine synthases (PUS) uncovers which pseudouridine synthase modifies each site and their target sequence features. mRNA pseudouridinylation depends on both site-specific and snoRNA-guided pseudouridine synthases. Upon heat shock in yeast, Pus7p-mediated pseudouridylation is induced at >200 sites, and PUS7 deletion decreases the levels of otherwise pseudouridylated mRNA, suggesting a role in enhancing transcript stability. rRNA pseudouridine stoichiometries are conserved but reduced in cells from dyskeratosis congenita patients, where the PUS DKC1 is mutated. Our work identifies an enhanced, transcriptome-wide scope for pseudouridine and methods to dissect its underlying mechanisms and function.
Hydrophilic interaction liquid chromatography (HILIC) has emerged in recent years as a valuable alternative to reversed-phase liquid chromatography in the analysis of polar compounds. Research in HILIC is divided into two directions: the assessment of the retention mechanism and retention behavior, and the development of HILIC methods. In this work, four polar neutral analytes (iohexol and its related compounds A, B, and C) were analyzed on two silica and two diol columns in HILIC mode with the aim to investigate thoroughly the retention mechanisms and retention behavior of polar neutral compounds on these four columns. The adsorption and partition contribution to the overall HILIC retention mechanism was investigated by fitting the retention data to linear (adsorption and partition) and nonlinear (mixed-retention and quadratic) theoretical models. On the other hand, the establishment of empirical second-order polynomial retention models on the basis of D-optimal design made possible the estimation of the simultaneous influence of several mobile-phase-related factors. Furthermore, these models were also used as the basis for the application of indirect modeling of the selectivity factor and a grid point search approach in order to achieve the optimal separation of analytes. After the optimization goals had been set, the grids were searched and the optimal conditions were identified. Finally, the optimized method was subjected to validation.
Human milk oligosaccharides (HMO) represent the bioactive components of human milk, influencing the infant's gastrointestinal microflora and immune system. Structurally, they represent a highly complex class of analyte, where the main core oligosaccharide structures are built from galactose and N-acetylglucosamine, linked by 1-3 or 1-4 glycosidic linkages and potentially modified with fucose and sialic acid residues. The core structures can be linear or branched. Additional structural complexity in samples can be induced by endogenous exoglycosidase activity or chemical procedures during the sample preparation. Here, we show that using matrix-assisted laser desorption/ionization (MALDI) quadrupole-time-of-flight (Q-TOF) collision-induced dissociation (CID) as a fast screening method, diagnostic structural information about single oligosaccharide components present in a complex mixture can be obtained. According to sequencing data on 14 out of 22 parent ions detected in a single high molecular weight oligosaccharide chromatographic fraction, 20 different oligosaccharide structure types, corresponding to over 30 isomeric oligosaccharide structures and over 100 possible HMO isomers when biosynthetic linkage variations were taken into account, were postulated. For MS/MS data analysis, we used the de novo sequencing approach using diagnostic ion analysis on reduced oligosaccharides by following known biosynthetic rules. Using this approach, de novo characterization has been achieved also for the structures, which could not have been predicted.
N6-methyladenosine (m6A) is a common modification of mRNA with potential roles in fine-tuning the RNA life cycle. Here, we identify a dense network of proteins interacting with METTL3, a component of the methyltransferase complex, and show that three of them (WTAP, METTL14, and KIAA1429) are required for methylation. Monitoring m6A levels upon WTAP depletion allowed the definition of accurate and near single-nucleotide resolution methylation maps and their classification into WTAP-dependent and -independent sites. WTAP-dependent sites are located at internal positions in transcripts, topologically static across a variety of systems we surveyed, and inversely correlated with mRNA stability, consistent with a role in establishing "basal" degradation rates. WTAP-independent sites form at the first transcribed base as part of the cap structure and are present at thousands of sites, forming a previously unappreciated layer of transcriptome complexity. Our data shed light on the proteomic and transcriptional underpinnings of this RNA modification.
N(6)-methyladenosine (m(6)A) is the most ubiquitous mRNA base modification, but little is known about its precise location, temporal dynamics, and regulation. Here, we generated genomic maps of m(6)A sites in meiotic yeast transcripts at nearly single-nucleotide resolution, identifying 1,308 putatively methylated sites within 1,183 transcripts. We validated eight out of eight methylation sites in different genes with direct genetic analysis, demonstrated that methylated sites are significantly conserved in a related species, and built a model that predicts methylated sites directly from sequence. Sites vary in their methylation profiles along a dense meiotic time course and are regulated both locally, via predictable methylatability of each site, and globally, through the core meiotic circuitry. The methyltransferase complex components localize to the yeast nucleolus, and this localization is essential for mRNA methylation. Our data illuminate a conserved, dynamically regulated methylation program in yeast meiosis and provide an important resource for studying the function of this epitranscriptomic modification.
This paper presents multiobjective optimization of complex mixtures separation in hydrophilic interaction liquid chromatography (HILIC). The selected model mixture consisted of five psychotropic drugs: clozapine, thioridazine, sulpiride, pheniramine and lamotrigine. Three factors related to the mobile phase composition (acetonitrile content, pH of the water phase and concentration of ammonium acetate) were optimized in order to achieve the following goals: maximal separation quality, minimal total analysis duration and robustness of an optimum. The consideration of robustness in early phases of the method development provides reliable methods with low risk for failure in validation phase. The simultaneous optimization of all goals was achieved by multiple threshold approach combined with grid point search. The identified optimal separation conditions (acetonitrile content 83%, pH of the water phase 3.5 and ammonium acetate content in water phase 14 mM) were experimentally verified.
This paper presents the chemometrically assisted optimization and validation of the RP-HPLC method intended for the quantitative analysis of itraconazole and its impurities in pharmaceutical dosage forms. To reach the desired chromatographic resolution with a limited number of experiments in a minimum amount of time, Box- -Behnken design was used to simultaneously optimize some important chromatographic parameters, such as the acetonitrile content in the mobile phase, pH of the aqueous phase and the column temperature. Separation between itraconazole and impurity F was identified as critical and selected as a response during the optimization. The set optimal mobile phase composition was acetonitrile/ water pH 2.5 adjusted with o-phosphoric acid (50:50, V/V). Separations were performed on a Zorbax Eclipse XDB-C18, 4.6 × 150 mm, 5 ?m particle size column with the flow rate 1 mL min-1, column temperature set at 30 °C and UV detection at 256 nm. The established method was then subjected to method validation and the required validation parameters were tested. For the robustness evaluation, fractional factorial 24-1 design was utilized and factors that might significantly affect the system performance were defined. As other validation parameters were also found to be suitable, the possibility to apply the proposed method for the determination of itraconazole, its impurities B and F in any laboratory under different circumstances has been proven.
Vitamin D deficiency is a well-established risk factor for bone disease, but emerging data suggest that altered vitamin D homeostasis may play a role in the development of type 2 diabetes mellitus (T2DM), dyslipidemia hypertension, and other cardiovascular diseases (CVD). The aim of this study was to investigate the prevalence of vitamin D deficiency in patients with T2DM with/without CVD, to correlate it with anthropometric and metabolic parameters and to determine the predictors of vitamin D deficiency.
In this paper detailed analysis of a mixture of four amides (tropicamide, nicotinamide, tiracetam, and piracetam) and six sulfonamides (sulfanilamide, sulfacetamide, sulfamethoxazole, sulfafurazole, furosemide, and bumetanide) on aminopropyl column in hydrophilic interaction chromatography (HILIC) was carried out. Since, there are no papers on the topic of the assessment of the contribution of ion-exchange retention mechanism involved in the separation of the acidic compounds on aminopropyl column in HILIC mode, the authors utilized the retention data of the acidic sulfonamides for this purpose. Next, broad range of the aqueous buffer concentrations in the mobile phase was examined providing the separation under either HILIC or RP conditions. Turning points between these two mechanisms were determined and then the fitting of the experimental data in the localized and non-localized adsorption models in both RP and HILIC regions was assessed. Since not many papers in the literature were dealing with the estimation of factor influence on the retention behavior of neutral and acidic compounds on aminopropyl column in HILIC, Box-Behnken design and Response Surface Methodology were applied. On the basis of the obtained data, ten quadratic models were proposed and their adequacy was confirmed using ANOVA test. Furthermore, retention data was graphically evaluated by the construction of 3D response surface plots. Finally, good predictive ability of the suggested models was proved with five additional verification experiments.
Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that provides sensitive and accurate protein detection and quantification in complex biological mixtures. Statistical and computational tools are essential for the design and analysis of SRM experiments, particularly in studies with large sample throughput. Currently, most such tools focus on the selection of optimized transitions and on processing signals from SRM assays. Little attention is devoted to protein significance analysis, which combines the quantitative measurements for a protein across isotopic labels, peptides, charge states, transitions, samples, and conditions, and detects proteins that change in abundance between conditions while controlling the false discovery rate. We propose a statistical modeling framework for protein significance analysis. It is based on linear mixed-effects models and is applicable to most experimental designs for both isotope label-based and label-free SRM workflows. We illustrate the utility of the framework in two studies: one with a group comparison experimental design and the other with a time course experimental design. We further verify the accuracy of the framework in two controlled data sets, one from the NCI-CPTAC reproducibility investigation and the other from an in-house spike-in study. The proposed framework is sensitive and specific, produces accurate results in broad experimental circumstances, and helps to optimally design future SRM experiments. The statistical framework is implemented in an open-source R-based software package SRMstats, and can be used by researchers with a limited statistics background as a stand-alone tool or in integration with the existing computational pipelines.
Efficient experimental strategies are needed to validate computationally predicted microRNA (miRNA) target genes. Here we present a large-scale targeted proteomics approach to validate predicted miRNA targets in Caenorhabditis elegans. Using selected reaction monitoring (SRM), we quantified 161 proteins of interest in extracts from wild-type and let-7 mutant worms. We demonstrate by independent experimental downstream analyses such as genetic interaction, as well as polysomal profiling and luciferase assays, that validation by targeted proteomics substantially enriched for biologically relevant let-7 interactors. For example, we found that the zinc finger protein ZTF-7 was a bona fide let-7 miRNA target. We also validated predicted miR-58 targets, demonstrating that this approach is adaptable to other miRNAs. We propose that targeted mass spectrometry can be applied generally to validate candidate lists generated by computational methods or in large-scale experiments, and that the described strategy should be readily adaptable to other organisms.
Protein kinases enable cellular information processing. Although numerous human phosphorylation sites and their dynamics have been characterized, the evolutionary history and physiological importance of many signaling events remain unknown. Using target phosphoproteomes determined with a similar experimental and computational pipeline, we investigated the conservation of human phosphorylation events in distantly related model organisms (fly, worm, and yeast). With a sequence-alignment approach, we identified 479 phosphorylation events in 344 human proteins that appear to be positionally conserved over approximately 600 million years of evolution and hence are likely to be involved in fundamental cellular processes. This sequence-alignment analysis suggested that many phosphorylation sites evolve rapidly and therefore do not display strong evolutionary conservation in terms of sequence position in distantly related organisms. Thus, we devised a network-alignment approach to reconstruct conserved kinase-substrate networks, which identified 778 phosphorylation events in 698 human proteins. Both methods identified proteins tightly regulated by phosphorylation as well as signal integration hubs, and both types of phosphoproteins were enriched in proteins encoded by disease-associated genes. We analyzed the cellular functions and structural relationships for these conserved signaling events, noting the incomplete nature of current phosphoproteomes. Assessing phosphorylation conservation at both site and network levels proved useful for exploring both fast-evolving and ancient signaling events. We reveal that multiple complex diseases seem to converge within the conserved networks, suggesting that disease development might rely on common molecular networks.
Comprehensive characterization of a proteome is a fundamental goal in proteomics. To achieve saturation coverage of a proteome or specific subproteome via tandem mass spectrometric identification of tryptic protein sample digests, proteomics data sets are growing dramatically in size and heterogeneity. The trend toward very large integrated data sets poses so far unsolved challenges to control the uncertainty of protein identifications going beyond well established confidence measures for peptide-spectrum matches. We present MAYU, a novel strategy that reliably estimates false discovery rates for protein identifications in large scale data sets. We validated and applied MAYU using various large proteomics data sets. The data show that the size of the data set has an important and previously underestimated impact on the reliability of protein identifications. We particularly found that protein false discovery rates are significantly elevated compared with those of peptide-spectrum matches. The function provided by MAYU is critical to control the quality of proteome data repositories and thereby to enhance any study relying on these data sources. The MAYU software is available as standalone software and also integrated into the Trans-Proteomic Pipeline.
The nematode Caenorhabditis elegans is a popular model system in genetics, not least because a majority of human disease genes are conserved in C. elegans. To generate a comprehensive inventory of its expressed proteome, we performed extensive shotgun proteomics and identified more than half of all predicted C. elegans proteins. This allowed us to confirm and extend genome annotations, characterize the role of operons in C. elegans, and semiquantitatively infer abundance levels for thousands of proteins. Furthermore, for the first time to our knowledge, we were able to compare two animal proteomes (C. elegans and Drosophila melanogaster). We found that the abundances of orthologous proteins in metazoans correlate remarkably well, better than protein abundance versus transcript abundance within each organism or transcript abundances across organisms; this suggests that changes in transcript abundance may have been partially offset during evolution by opposing changes in protein abundance.
In this paper, the retention prediction models for mixture of ?-lactam antibiotics analyzed by hydrophilic interaction chromatography (HILIC) are presented. The aim of the study was to investigate the retention behavior of some organic acids and amphoteric compounds including cephalosporins (cefotaxime, cefalexin, cefaclor, cefuroxime, and cefuroxime axetil) and penicillins (ampicillin and amoxicillin). Retention of substances with acidic functional group in HILIC is considered to be interesting since the majority of publications in literature are related to basic compounds. In the beginning of the study, classical silica columns were chosen for the retention analysis. Then, preliminary study was done and factors with the most significant influence on the retention factors were selected. These factors with the impact on the retention factors were investigated employing Box-Behnken design as a tool. On the basis of the obtained results the mathematical models were created and tested using ANOVA test and finally verified. This approach enables the presentation of chromatographic retention in many ways (three-dimensional (3-D) graphs and simple two-dimensional graphical presentations). All of these gave the possibility to predict the chromatographic retention under different conditions. Furthermore, regarding the structure of the analyzed compounds, the potential retention mechanisms in HILIC were suggested.
MicroRNAs (miRNAs) are small, noncoding RNAs that negatively regulate gene expression. As miRNAs are involved in a wide range of biological processes and diseases, much effort has been invested in identifying their mRNA targets. Here, we present a novel combinatorial approach, RIP-chip-SRM (RNA-binding protein immunopurification + microarray + targeted protein quantification via selected reaction monitoring), to identify de novo high-confidence miRNA targets in the nematode Caenorhabditis elegans. We used differential RIP-chip analysis of miRNA-induced silencing complexes from wild-type and miRNA mutant animals, followed by quantitative targeted proteomics via selected reaction monitoring to identify and validate mRNA targets of the C. elegans bantam homolog miR-58. Comparison of total mRNA and protein abundance changes in mir-58 mutant and wild-type animals indicated that the direct bantam/miR-58 targets identified here are mainly regulated at the level of protein abundance, not mRNA stability.
The community working on model organisms is growing steadily and the number of model organisms for which proteome data are being generated is continuously increasing. To standardize efforts and to make optimal use of proteomics data acquired from model organisms, a new Human Proteome Organisation (HUPO) initiative on model organism proteomes (iMOP) was approved at the HUPO Ninth Annual World Congress in Sydney, 2010. iMOP will seek to stimulate scientific exchange and disseminate HUPO best practices. The needs of model organism researchers for central databases will be better represented, catalyzing the integration of proteomics and organism-specific databases. Full details of iMOP activities, members, tools and resources can be found at our website http://www.imop.uzh.ch/ and new members are invited to join us.
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