Initial characterization of the human central proteome

<p>Abstract</p> <p>Background</p> <p>On the basis of large proteomics datasets measured from seven human cell lines we consider their intersection as an approximation of the human central proteome, which is the set of proteins ubiquitously expressed in all human cells. Composition and properties of the central proteome are investigated through bioinformatics analyses.</p> <p>Results</p> <p>We experimentally identify a central proteome comprising 1,124 proteins that are ubiquitously and abundantly expressed in human cells using state of the art mass spectrometry and protein identification bioinformatics. The main represented functions are proteostasis, primary metabolism and proliferation. We further characterize the central proteome considering gene structures, conservation, interaction networks, pathways, drug targets, and coordination of biological processes. Among other new findings, we show that the central proteome is encoded by exon-rich genes, indicating an increased regulatory flexibility through alternative splicing to adapt to multiple environments, and that the protein interaction network linking the central proteome is very efficient for synchronizing translation with other biological processes. Surprisingly, at least 10% of the central proteome has no or very limited functional annotation.</p> <p>Conclusions</p> <p>Our data and analysis provide a new and deeper description of the human central proteome compared to previous results thereby extending and complementing our knowledge of commonly expressed human proteins. All the data are made publicly available to help other researchers who, for instance, need to compare or link focused datasets to a common background.</p

Efficient and Stable Low Iridium Loaded Anodes for PEM Water Electrolysis Made Possible by Nanofiber Interlayers

Significant reduction of the precious metal catalyst loading is one of the key challenges for the commercialization of proton-exchange membrane water electrolyzers. In this work we combine IrOx nanofibers with a conventional nanoparticle-based IrOx anode catalyst layer. With this hybrid design we can reduce the iridium loading by more than 80% while maintaining performance. In spite of an ultralow overall catalyst loading of 0.2 mg(Ir)/cm(2), a cell with a hybrid layer shows similar performance compared to a state-of-the-art cell with a catalyst loading of 1.2 mg(Ir)/cm(2) and clearly outperforms identically loaded reference cells with pure IrOx nanoparticle and pure nanofiber anodes. The improved performance is attributed to a combination of good electric contact and high porosity of the IrOx nanofibers with high surface area of the IrOx nanoparticles. Besides the improved performance, the hybrid layer also shows better stability in a potential cycling and a 150 h constant current test compared to an identically loaded nanoparticle reference.BMBF, 05KI9VFA, Ultrahochauflösende Untersuchung des Wassertransports in alkalischen Brennstoff- und Elektrolysezellen mittels Neutronenradiographie und –Tomographie (NeutroSense

Trait-based analysis of the human skin microbiome

The past decade of microbiome research has concentrated on cataloging the diversity of taxa in different environments. The next decade is poised to focus on microbial traits and function. Most existing methods for doing this perform pathway analysis using reference databases. This has both benefits and drawbacks. Function can go undetected if reference databases are coarse-grained or incomplete. Likewise, detection of a pathway does not guarantee expression of the associated function. Finally, function cannot be connected to specific microbial constituents, making it difficult to ascertain the types of organisms exhibiting particular traits—something that is important for understanding microbial success in specific environments. A complementary approach to pathway analysis is to use the wealth of microbial trait information collected over years of lab-based, culture experiments.https://doi.org/10.1186/s40168-019-0698-

Systems-pharmacology dissection of a drug synergy in imatinib-resistant CML

Occurrence of the BCR-ABL[superscript T315I] gatekeeper mutation is among the most pressing challenges in the therapy of chronic myeloid leukemia (CML). Several BCR-ABL inhibitors have multiple targets and pleiotropic effects that could be exploited for their synergistic potential. Testing combinations of such kinase inhibitors identified a strong synergy between danusertib and bosutinib that exclusively affected CML cells harboring BCR-ABL[superscript T315I]. To elucidate the underlying mechanisms, we applied a systems-level approach comprising phosphoproteomics, transcriptomics and chemical proteomics. Data integration revealed that both compounds targeted Mapk pathways downstream of BCR-ABL, resulting in impaired activity of c-Myc. Using pharmacological validation, we assessed that the relative contributions of danusertib and bosutinib could be mimicked individually by Mapk inhibitors and collectively by downregulation of c-Myc through Brd4 inhibition. Thus, integration of genome- and proteome-wide technologies enabled the elucidation of the mechanism by which a new drug synergy targets the dependency of BCR-ABL[superscript T315I] CML cells on c-Myc through nonobvious off targets

Exploring the functional composition of the human microbiome using a hand-curated microbial trait database

Even when microbial communities vary wildly in their taxonomic composition, their functional composition is often surprisingly stable. This suggests that a functional perspective could provide much deeper insight into the principles governing microbiome assembly. Much work to date analyzing the functional composition of microbial communities, however, relies heavily on inference from genomic features. Unfortunately, output from these methods can be hard to interpret and often suffers from relatively high error rates. We built and analyzed a domain-specific microbial trait database from known microbe-trait pairs recorded in the literature to better understand the functional composition of the human microbiome. Using a combination of phylogentically conscious machine learning tools and a network science approach, we were able to link particular traits to areas of the human body, discover traits that determine the range of body areas a microbe can inhabit, and uncover drivers of metabolic breadth. Domain-specific trait databases are an effective compromise between noisy methods to infer complex traits from genomic data and exhaustive, expensive attempts at database curation from the literature that do not focus on any one subset of taxa. They provide an accurate account of microbial traits and, by limiting the number of taxa considered, are feasible to build within a reasonable time-frame. We present a database specific for the human microbiome, in the hopes that this will prove useful for research into the functional composition of human-associated microbial communities.https://doi.org/10.1186/s12859-021-04216-

Modeling the ongoing dynamics of short and long-range temporal correlations in broadband EEG during movement

Electroencephalogram (EEG) undergoes complex temporal and spectral changes during voluntary movement intention. Characterization of such changes has focused mostly on narrowband spectral processes such as Event-Related Desynchronization (ERD) in the sensorimotor rhythms because EEG is mostly considered as emerging from oscillations of the neuronal populations. However, the changes in the temporal dynamics, especially in the broadband arrhythmic EEG have not been investigated for movement intention detection. The Long-Range Temporal Correlations (LRTC) are ubiquitously present in several neuronal processes, typically requiring longer timescales to detect. In this paper, we study the ongoing changes in the dynamics of long- as well as short-range temporal dependencies in the single trial broadband EEG during movement intention. We obtained LRTC in 2 s windows of broadband EEG and modeled it using the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model which allowed simultaneous modeling of short- and long-range temporal correlations. There were significant (p < 0.05) changes in both broadband long- and short-range temporal correlations during movement intention and execution. We discovered that the broadband LRTC and narrowband ERD are complementary processes providing distinct information about movement because eliminating LRTC from the signal did not affect the ERD and conversely, eliminating ERD from the signal did not affect LRTC. Exploring the possibility of applications in Brain Computer Interfaces (BCI), we used hybrid features with combinations of LRTC, ARFIMA, and ERD to detect movement intention. A significantly higher (p < 0.05) classification accuracy of 88.3 ± 4.2% was obtained using the combination of ARFIMA and ERD features together, which also predicted the earliest movement at 1 s before its onset. The ongoing changes in the long- and short-range temporal correlations in broadband EEG contribute to effectively capturing the motor command generation and can be used to detect movement successfully. These temporal dependencies provide different and additional information about the movement

A Computational Approach to Analyze the Mechanism of Action of the Kinase Inhibitor Bafetinib

Prediction of drug action in human cells is a major challenge in biomedical research. Additionally, there is strong interest in finding new applications for approved drugs and identifying potential side effects. We present a computational strategy to predict mechanisms, risks and potential new domains of drug treatment on the basis of target profiles acquired through chemical proteomics. Functional protein-protein interaction networks that share one biological function are constructed and their crosstalk with the drug is scored regarding function disruption. We apply this procedure to the target profile of the second-generation BCR-ABL inhibitor bafetinib which is in development for the treatment of imatinib-resistant chronic myeloid leukemia. Beside the well known effect on apoptosis, we propose potential treatment of lung cancer and IGF1R expressing blast crisis

Computational approaches for quantifying proteins and posttranslational modifications from labeled mass spectrometry data

Proteomische Technologien sind fundamental wichtige Werkzeuge der biologischen und medizinischen Forschung. Mit modernen Massenspektrometern können Forscher innerhalb von wenigen Stunden tausende Proteine in biologischen Proben detektieren und quantifizieren. Für die Protein-Quantifizierung werden isotop-kodierte Labels verwendet, mit denen die Proteine der jeweiligen Proben markiert werden können. Besonders populär sind die isobaren Methoden iTRAQ und TMT, mit welchen bis zu 10 Proben in einem Experiment verglichen werden können. Die Daten von diesen Experimenten haben eine komplexe Struktur, sind hoch-dimensional und verrauscht. Obwohl die passende statistische Modellierung und effiziente Software essentiell für den Erfolg der Datenanalyse sind, gibt auf dem Bereich der quantitativen Proteomik wenig umfassende und offene bioinformatische Analyseframeworks. In dieser Arbeit wird deshalb ein bioinformatisches Softwarepaket und statistisches Rahmenwerk entwickelt, welche die Analyse von quantitativen proteomischen Daten ermöglichen und erleichtern. Der erste Teil dieser Arbeit beschreibt statistische Modelle welche eine bessere Inferenz für quantitative proteomische Experimente ermöglichen; durch die Modellierung der technischen Variabilität mit einer intensitätsabhängigen Varianzfunktion und der biologischen Variabilität mittels einer endlastigen Verteilung. Die Leistungsfähigkeit dieser Methode wurde an speziell erzeugten Test-Datensätzen getested, welche gleichbleibende Hintergrundproteine und eingemischten Proteine in bekannten Konzentrationen beinhaltet. Mittels Resampling konnte demonstriert werden, das die Methode sowohl die Rate der falsch-positiv selektierten Proteine kontrolliert, als auch eine gute Performanz im selektieren echt positiver Proteine hat. An weiteren biologischen Datensätzen wurde weiters gezeigt, dass die Methode mit unterschiedlichen Massenspektrometern und Setups funktioniert. Die Modelle wurden in einem neuartigen R-Softwarepaket namens isobar implementiert, welches Teil des Bioconductor-Projekt ist. Zusammen mit dem statistischen Rahmenwerk implementiert isobar Methoden für einen kompletten Workflow von massenspektrometrischen Peaklisten zur Proteinquantifizierung und Analyseergebnissen im PDF und XLS Format. Protein-Gruppierung wird innerhalb des Paket implementiert. Eine Analyse kann automatisiert und in vorhandede Analyse-Pipelines integriert werden. isobar ist nach den Bioconductor Design-Prinzipien konzipiert und in dem objektorientierten S4 Klassensystem implementiert. Die oben genannte Methoden und Software wurden für die Quantifizierung von Protein-Unterschiede entwickelt. Neben der unterschiedlichen Expression von Proteinen, sind post-translationale Modifikationen (PTM) zentrale Modulatoren der Proteinfunktion. PTMs sind von großer Bedeutung in vielen Forschungsfragen, und können ebenfalls mit Massenspektrometrie identifiziert und quantifiziert werden. Im zweiten Teil der Arbeit werden deswegen die statistischen Modelle und das R-Paket für die quantitative PTM Analyse erweitert. Dies inkludiert die Integration von Modulen zur Lokalisierung der Modifikation in der Peptidsequenz, die Anpassung des PTM-Ratios mit Protein-Ratios, und das Erstellen von erweiterten Analyseberichten mit spezifischen Details zu identifizierten PTMs. Die Methoden und die Software wurden in mehreren Publikationen angewendent und erweitert. Das isobar-Paket wird weiters über einhundert mal pro Monat über Bioconductor installiert. Abschließend kann gesagt werden, dass diese Arbeit mit neuer bioinformatischer Software und Methoden zur Weiterentwicklung der Proteinforschung mit iTRAQ und TMT beiträgt.Proteomic technologies are a fundamentally important tools of biological and medical research. Modern mass spectrometric equipment enables researchers to identify thousands of proteins in biological samples in a matter of hours. For the quantitative comparison of protein content, isotope-coded mass labels are employed which mark the proteins of the respective samples. Especially popular are the isobaric methods iTRAQ and TMT which make the simultaneous quantitative comparison of up to 10 samples possible. The data of these experiments are complex, high-dimensional, and noisy. Even though suitable statistical modeling and efficient software tools are pivotal for the success of the data analysis, few comprehensive and open bioinformatical analysis frameworks exist for quantitative proteomics. In this thesis, thus a software package and statistical framework are developed, which enable and facilitate the analysis of isobarically labeled mass spectrometric data. The first part of the thesis describes statistical models for isobarically tagged data, which enable better inference by capturing technical variability in a intensity-dependent noise function, and biological variability with a heavy tailed distribution. The performance characteristics of this method were tested on especially prepared test datasets with spiked proteins at known ratios and unchanging background proteins. By resampling of the data, it could be demonstrated that the method both controls the rate of false positives and provides a good sensitivity in selecting true positives. Using additional biological datasets it further could be shown that the method works well with data from different types of mass spectrometers and setups. The methods were implemented in a novel R package called isobar, which is part of the Bioconductor project. Along with the statistical framework, isobar implements methods for a complete quantitative workflow from mass spectrometric peaklists to protein quantification and analysis reports in PDF and XLS formats. Protein grouping is implemented within the package. The analysis can also be automated and the package thus integrated into existing pipelines. isobar was designed according to Bioconductor design principles and is implemented in the S4 class system. The aforementioned methods and software were developed for the quantification of protein differences. Besides the protein expression change, differential post-translational modifications (PTMs) are prime modulator of protein function. PTMs are of great importance in meany research questions and can be identified and quantified with mass spectrometry. In the second part of the thesis, we thus extend the statistical models and R package for the quantitative PTM analysis. This includes the integration of modules for the localization of the PTM in the peptide sequence, the correction of the modified peptide ratio with the protein ratio, and the creation of extended analysis reports with specific details for identified PTMs. The methods and the software were applied and extended in several further publications. Furthermore, the isobar package is downloaded over 100 times per month from Bioconductor. In conclusion, this thesis contributes to the advancement of quantitative protein research with the development of novel bioinformatical software and methods.submitted by Florian Paul BreitwieserAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersZsfassung in dt. SpracheWien, Med. Univ., Diss., 2014OeBB(VLID)171434