Search

Host‐plant selection is a key factor driving the ecology and evolution of insects. While the majority of phytophagous insects is highly host specific, generalist behavior is quite widespread among bees and presumably involves physiological adaptations that remain largely unexplored. However, floral visitation patterns suggest that generalist bees do not forage randomly on all available resources. While resource availability and accessibility as well as nectar composition have been widely explored, pollen chemistry could also have an impact on the range of suitable host‐plants. This study focuses on particular pollen nutrients that cannot be synthesized de novo by insects but are key compounds of cell membranes and the precursor for molting process: the sterols. We compared the sterol composition of pollen from the main host‐plants of three generalist bees: Anthophora plumipes, Colletes cunicularius, and Osmia cornuta, as well as one specialist bee Andrena vaga. We also analyzed the sterols of their brood cell provisions, the tissues of larvae and nonemerged females to determine which sterols are used by the different species. Our results show that sterols are not used accordingly to foraging strategy: Both the specialist species A. vaga and the generalist species C. cunicularius might metabolize a rare C27 sterol, while the two generalist species A. plumipes and O. cornuta might rather use a very common C28 sterol. Our results suggest that shared sterolic compounds among plant species could facilitate the exploitation of multiple host‐plants by A. plumipes and O. cornuta whereas the generalist C. cunicularius might be more constrained due to its physiological requirements of a more uncommon dietary sterol. Our findings suggest that a bee displaying a generalist foraging behavior may sometimes hide a sterol‐specialized species. This evidence challenges the hypothesis that all generalist free‐living bee species are all able to develop on a wide range of different pollen types., We compared the sterol composition of pollen from the main host‐plants of three generalist bees: Anthophora plumipes, Colletes cunicularius, and Osmia cornuta, as well as one specialist bee Andrena vaga. We also analyzed the sterols of their brood cell provisions, the tissues of larvae, and nonemerged females to determine which sterols are used by the different species. Our findings suggest that a bee displaying a generalist foraging behavior might sometimes hide a sterol specialized species.

Targeted proteomic approaches such as multiple reaction monitoring (MRM) overcome problems associated with classical shotgun mass spectrometry experiments. Developing MRM quantitation assays can be time consuming, because relevant peptide representatives of the proteins must be found and their retention time and the product ions must be determined. Given the transitions, hundreds to thousands of them can be scheduled into one experiment run. However, it is difficult to select which of the transitions should be included into a measurement. We present a novel algorithm that allows the construction of MRM assays from the sequence of the targeted proteins alone. This enables the rapid development of targeted MRM experiments without large libraries of transitions or peptide spectra. The approach relies on combinatorial optimization in combination with machine learning techniques to predict proteotypicity, retention time, and fragmentation of peptides. The resulting potential transitions are scheduled optimally by solving an integer linear program. We demonstrate that fully automated construction of MRM experiments from protein sequences alone is possible and over 80% coverage of the targeted proteins can be achieved without further optimization of the assay.

Shotgun proteomics with Liquid Chromatography (LC) coupled to Tandem Mass Spectrometry (MS/MS) is a key technology for protein identification and quantitation. Protein identification is done indirectly: detected peptide signals are fragmented byMS/MS and their sequence is reconstructed. Afterwards, the identified peptides are used to infer the proteins present in a sample. The problem of choosing the peptide signals that shall be identified with MS/MS is called precursor ion selection. Most workflows use data- dependent acquisition for precursor ion selection despite known drawbacks like data redundancy, limited reproducibility or a bias towards high-abundance proteins. In this thesis, we formulate optimization problems for different aspects of precursor ion selection to overcome these weaknesses. In the first part of this work we develop inclusion lists aiming at optimal precursor ion selection given different input information. We trace precursor ion selection back to known combinatorial problems and develop linear program (LP) formulations. The first method creates an inclusion list given a set of detected features in an LC-MS map. We show that this setting is an instance of the Knapsack Problem. The corresponding LP can be solved efficiently and yields inclusion lists that schedule more precursors than standard methods when the number of precursors per fraction is limited. Furthermore, we develop a method for inclusion list creation based on a list of proteins of interest. We employ retention time and detectability prediction to infer LC-MS features. Based on peptide detectability, we introduce protein detectabilities that reflect the likelihood of detecting and identifying a protein. By maximizing the sum of protein detectabilities we create an inclusion list of limited size that covers a maximum number of proteins. In the second part of the thesis, we focus on iterative precursor ion selection (IPS) with LC-MALDI MS/MS. Here, after a fixed number of acquired MS/MS spectra their identification results are evaluated and are used for the next round of precursor ion selection. We develop a heuristic which creates a ranked precursor list. The second method, IPS LP, is a combination of the two inclusion list scenarios presented in the first part. Additionally, a protein-based exclusion is part of the objective function. For evaluation, we compared both IPS methods to a static inclusion list (SPS) created before the beginning of MS/MS acquisition. We simulated precursor ion selection on three data sets of different complexity and show that IPS LP can identify the same number of proteins with fewer selected precursors. This improvement is especially pronounced for low abundance proteins. Additionally, we show that IPS LP decreases the bias to high abundance proteins. All presented algorithms were implemented in OpenMS, a software library for mass spectrometry. Finally, we present an online tool for IPS that has direct access to the instrument and controls the measurement., Flüssigkeitschromatographie (LC) gekoppelt mit Tandemmassenspektrometrie (MS/MS) ist eine Schlüsseltechnologie für die Proteinidentifikation und Quantifizierung in proteomischen Proben. Dabei werden Proteine indirekt identifiziert: detektierte Peptidsignale werden durch MS/MS fragmentiert und anschließend wird die Peptidsequenz rekonstruiert. Über die identifizierten Peptide werden schließlich die Proteine in der Probe identifiziert. Das Problem der Auswahl der Peptidsignale, die über MS/MS sequenziert werden sollen, heißt Precursor-Ionen-Selektion (PS). Die meisten Selektionsverfahren benutzen rein intensitätsbasierte Ansätze – sogenannte Datenabhängige Akquisition (DDA) – trotz bekannter Schwächen wie Datenredundanz, begrenzter Reproduzierbarkeit oder einer Neigung zur Identifikation häufiger Proteine. In dieser Arbeit entwickeln wir für unterschiedliche Aspekte der PS Formulierungen als Optimierungsprobleme mit dem Ziel den bekannten Schwächen entgegenzusteuern. Im ersten Teil der Arbeit werden für unterschiedliche Anfangsinformationen optimale Inklusionslisten erstellt. Dabei führen wir PS auf bekannte kombinatorische Probleme zurück und entwickeln Formulierungen als Lineare Programme (LP) zur Lösung der Probleme. Die erste Methode basiert auf einer Liste von LC-MS-Features. Wir zeigen, dass sich diese Situation auf das Rucksackproblem zurückführen läßt. Das zugehörige LP erstellt effiziente Inklusionslisten, die mehr Precursor enthalten als Standardmethoden, wenn die Anzahl an Precursor-Ionen pro Fraktion begrenzt ist. Außerdem entwickeln wir eine Methode basierend auf einer Liste an zu identifizierenden Proteinsequenzen. Wir benutzen Schätzverfahren für RT und Detektierbarkeit um repräsentative LC-MS-Features für diese Proteine vorherzusagen. Basierend auf der Peptiddetektierbarkeit führen wir eine Proteindetektierbarkeit ein. Indem wir die Summe dieser maximieren, erstellen wir eine größenbeschränkte Inklusionsliste, die eine maximale Anzahl an Proteinen abdeckt. Im zweiten Teil der Arbeit beschäftigen wir uns mit iterativer PS (IPS) mit LC-MALDI MS/MS. Dabei werden nach einer bestimmten Anzahl an aufgenommenen MS/MS- Spektren deren Identifikationsergebnisse ausgewertet und diese zur weiteren PS benutzt. Wir entwickeln einerseits eine Heuristik, die eine priorisierte Inklusionsliste erstellt.Für die zweite Methode, IPS_LP, kombinieren wir die beiden LP-Formulierungen aus dem ersten Teil und erweitern sie um eine proteinbasierte Exklusion. Für die Auswertung vergleichen wir unsere IPS- Methoden mit einer statischen Inklusionsliste (SPS), die vor Beginn der MS/MS- Messung erstellt wurde. Wir simulieren die PS auf drei Datensätzen mit unterschiedlicher Komplexität und zeigen, dass IPS_LP die gleiche Proteinanzahl wie SPS identifiziert, dabei aber weniger MS/MS-Messungen benötigt. Diese Verbesserung wird insbesondere für Proteine mit geringer Abundanz deutlich. Außerdem können wir zeigen, dass die Neigung zur Identifikation häufiger Proteine gesenkt wird. Unsere Algorithmen wurden als Teil von OpenMS, einer Softwarebibliothek für Massenspektrometrie, implementiert. Im letzten Teil stellen wir außerdem ein Onlinetool vor, dass direkten Zugriff auf das Massenspektrometer hat und die Messungen steuert.

Background Liquid chromatography mass spectrometry (LC-MS) maps in shotgun proteomics are often too complex to select every detected peptide signal for fragmentation by tandem mass spectrometry (MS/MS). Standard methods for precursor ion selection, commonly based on data dependent acquisition, select highly abundant peptide signals in each spectrum. However, these approaches produce redundant information and are biased towards high-abundance proteins. Results We present two algorithms for inclusion list creation that formulate precursor ion selection as an optimization problem. Given an LC-MS map, the first approach maximizes the number of selected precursors given constraints such as a limited number of acquisitions per RT fraction. Second, we introduce a protein sequence-based inclusion list that can be used to monitor proteins of interest. Given only the protein sequences, we create an inclusion list that optimally covers the whole protein set. Additionally, we propose an iterative precursor ion selection that aims at reducing the redundancy obtained with data dependent LC-MS/MS. We overcome the risk of erroneous assignments by including methods for retention time and proteotypicity predictions. We show that our method identifies a set of proteins requiring fewer precursors than standard approaches. Thus, it is well suited for precursor ion selection in experiments with limited sample amount or analysis time. Conclusions We present three approaches to precursor ion selection with LC-MALDI MS/MS. Using a well-defined protein standard and a complex human cell lysate, we demonstrate that our methods outperform standard approaches. Our algorithms are implemented as part of OpenMS and are available under http://www.openms.de.

Centrosome morphology and number are frequently deregulated in cancer cells. Here, to identify factors that are functionally relevant for centrosome abnormalities in cancer cells, we established a protein-interaction network around 23 centrosomal and cell-cycle regulatory proteins, selecting the interacting proteins that are deregulated in cancer for further studies. One of these components, LGALS3BP, is a centriole- and basal body-associated protein with a dual role, triggering centrosome hypertrophy when overexpressed and causing accumulation of centriolar substructures when downregulated. The cancer cell line SK-BR-3 that overexpresses LGALS3BP exhibits hypertrophic centrosomes, whereas in seminoma tissues with low expression of LGALS3BP, supernumerary centriole-like structures are present. Centrosome hypertrophy is reversed by depleting LGALS3BP in cells endogenously overexpressing this protein, supporting a direct role in centrosome aberration. We propose that LGALS3BP suppresses assembly of centriolar substructures, and when depleted, causes accumulation of centriolar complexes comprising CPAP, acetylated tubulin and centrin.

Any representation of a group G on a vector space V extends uniquely to a representation of G on the free metabelian Lie algebra on V. In this paper we study such representations and make some group-theoretic applications.

Background Mass spectrometry is an essential analytical technique for high-throughput analysis in proteomics and metabolomics. The development of new separation techniques, precise mass analyzers and experimental protocols is a very active field of research. This leads to more complex experimental setups yielding ever increasing amounts of data. Consequently, analysis of the data is currently often the bottleneck for experimental studies. Although software tools for many data analysis tasks are available today, they are often hard to combine with each other or not flexible enough to allow for rapid prototyping of a new analysis workflow. Results We present OpenMS, a software framework for rapid application development in mass spectrometry. OpenMS has been designed to be portable, easy-to-use and robust while offering a rich functionality ranging from basic data structures to sophisticated algorithms for data analysis. This has already been demonstrated in several studies. Conclusion OpenMS is available under the Lesser GNU Public License (LGPL) from the project website at http://www.openms.de.

Denote by F a free group of finite rank d with normal subgroup R of finite index and let G = F R . By a result of Kuz'min the homology groups H n ( F R′ , Z ) decompose into the direct sum of a free abelian group of finite rank dn and a finite abelian group. In this paper the ranks dn are computed and certain relations are derived. Moreover, it is shown that the Poincare duality group F R′ is non-orientable if and only if d is even and the 2-Sylow subgroups of G are non-trivial and cyclic.

Let 1 → R → F → G → 1 be a free presentation of the group G . In the present paper we study the homology groups of G with coefficients in the higher relation modules R c = γ c R / γ c +1 R ( c ≥2), where γ c R denotes the c th term of the lower central series of R . It is shown that H n ( G , R c ) for n ≥1 as well as the torsion subgroup of H 0 ( G , R c ) are annihilated by multiplication with c ∗ , where c ∗ = 4 for c = 2 and c ∗ = c for c ≥ 3. For finite G a similar result holds for the Tate cohomology groups: c ∗ · H n (G, R c ) = 0 , n ϵ Z . Moreover, the rank of the free abelian direct summand of H 0 ( G , R c ) will be determined.

The data currently described was generated within the EU/FP7 HeCaToS project (Hepatic and Cardiac Toxicity Systems modeling). The project aimed to develop an in silico prediction system to contribute to drug safety assessment for humans. For this purpose, multi-omics data of repeated dose toxicity were obtained for 10 hepatotoxic and 10 cardiotoxic compounds. Most data were gained from in vitro experiments in which 3D microtissues (either hepatic or cardiac) were exposed to a therapeutic (physiologically relevant concentrations calculated through PBPK-modeling) or a toxic dosing profile (IC20 after 7 days). Exposures lasted for 14 days and samples were obtained at 7 time points (therapeutic doses: 2-8-24-72-168-240-336 h; toxic doses 0-2-8-24-72-168-240 h). Transcriptomics (RNA sequencing & microRNA sequencing), proteomics (LC-MS), epigenomics (MeDIP sequencing) and metabolomics (LC-MS & NMR) data were obtained from these samples. Furthermore, functional endpoints (ATP content, Caspase3/7 and O2 consumption) were measured in exposed microtissues. Additionally, multi-omics data from human biopsies from patients are available. This data is now being released to the scientific community through the BioStudies data repository (https://www.ebi.ac.uk/biostudies/)., Scientific Data, 9 (1), ISSN:2052-4463