Fast proteomics with dia-PASEF and analytical flow-rate chromatography.

Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs, and facilitate new approaches in systems biology and biomedical research. Here we propose combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition, and data analysis with the DIA-NN software suite, to achieve high-quality proteomic experiments from limited sample amounts, at a throughput of up to 400 samples per day. For instance, when benchmarking our workflow using a 500-µL/min flow rate and 3-min chromatographic gradients, we report the quantification of 5211 proteins from 2 µg of a mammalian cell-line standard at high quantitative accuracy and precision. We further used this platform to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-min chromatographic gradient and alternating column regeneration on a dual pump system. The method delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates.

Alignment-Annotator web server: rendering and annotating sequence alignments.

UNLABELLED: Alignment-Annotator is a novel web service designed to generate interactive views of annotated nucleotide and amino acid sequence alignments (i) de novo and (ii) embedded in other software. All computations are performed at server side. Interactivity is implemented in HTML5, a language native to web browsers. The alignment is initially displayed using default settings and can be modified with the graphical user interfaces. For example, individual sequences can be reordered or deleted using drag and drop, amino acid color code schemes can be applied and annotations can be added. Annotations can be made manually or imported (BioDAS servers, the UniProt, the Catalytic Site Atlas and the PDB). Some edits take immediate effect while others require server interaction and may take a few seconds to execute. The final alignment document can be downloaded as a zip-archive containing the HTML files. Because of the use of HTML the resulting interactive alignment can be viewed on any platform including Windows, Mac OS X, Linux, Android and iOS in any standard web browser. Importantly, no plugins nor Java are required and therefore Alignment-Anotator represents the first interactive browser-based alignment visualization. AVAILABILITY: http://www.bioinformatics.org/strap/aa/ and http://strap.charite.de/aa/.

Sequence alignment visualization in HTML5 without Java.

MOTIVATION: Java has been extensively used for the visualization of biological data in the web. However, the Java runtime environment is an additional layer of software with an own set of technical problems and security risks. HTML in its new version 5 provides features that for some tasks may render Java unnecessary. RESULTS: Alignment-To-HTML is the first HTML-based interactive visualization for annotated multiple sequence alignments. The server side script interpreter can perform all tasks like (i) sequence retrieval, (ii) alignment computation, (iii) rendering, (iv) identification of a homologous structural models and (v) communication with BioDAS-servers. The rendered alignment can be included in web pages and is displayed in all browsers on all platforms including touch screen tablets. The functionality of the user interface is similar to legacy Java applets and includes color schemes, highlighting of conserved and variable alignment positions, row reordering by drag and drop, interlinked 3D visualization and sequence groups. Novel features are (i) support for multiple overlapping residue annotations, such as chemical modifications, single nucleotide polymorphisms and mutations, (ii) mechanisms to quickly hide residue annotations, (iii) export to MS-Word and (iv) sequence icons. CONCLUSION: Alignment-To-HTML, the first interactive alignment visualization that runs in web browsers without additional software, confirms that to some extend HTML5 is already sufficient to display complex biological data. The low speed at which programs are executed in browsers is still the main obstacle. Nevertheless, we envision an increased use of HTML and JavaScript for interactive biological software. AVAILABILITY AND IMPLEMENTATION: Under GPL at: http://www.bioinformatics.org/strap/toHTML/.

The Impact of Acute Nutritional Interventions on the Plasma Proteome.

CONTEXT: Humans respond profoundly to changes in diet, while nutrition and environment have a great impact on population health. It is therefore important to deeply characterize the human nutritional responses. OBJECTIVE: Endocrine parameters and the metabolome of human plasma are rapidly responding to acute nutritional interventions such as caloric restriction or a glucose challenge. It is less well understood whether the plasma proteome would be equally dynamic, and whether it could be a source of corresponding biomarkers. METHODS: We used high-throughput mass spectrometry to determine changes in the plasma proteome of i) 10 healthy, young, male individuals in response to 2 days of acute caloric restriction followed by refeeding; ii) 200 individuals of the Ely epidemiological study before and after a glucose tolerance test at 4 time points (0, 30, 60, 120 minutes); and iii) 200 random individuals from the Generation Scotland study. We compared the proteomic changes detected with metabolome data and endocrine parameters. RESULTS: Both caloric restriction and the glucose challenge substantially impacted the plasma proteome. Proteins responded across individuals or in an individual-specific manner. We identified nutrient-responsive plasma proteins that correlate with changes in the metabolome, as well as with endocrine parameters. In particular, our study highlights the role of apolipoprotein C1 (APOC1), a small, understudied apolipoprotein that was affected by caloric restriction and dominated the response to glucose consumption and differed in abundance between individuals with and without type 2 diabetes. CONCLUSION: Our study identifies APOC1 as a dominant nutritional responder in humans and highlights the interdependency of acute nutritional response proteins and the endocrine system.

Metannogen: annotation of biological reaction networks.

MOTIVATION: Semantic annotations of the biochemical entities constituting a biological reaction network are indispensable to create biologically meaningful networks. They further heighten efficient exchange, reuse and merging of existing models which concern present-day systems biology research more often. Two types of tools for the reconstruction of biological networks currently exist: (i) several sophisticated programs support graphical network editing and visualization. (ii) Data management systems permit reconstruction and curation of huge networks in a team of scientists including data integration, annotation and cross-referencing. We seeked ways to combine the advantages of both approaches. RESULTS: Metannogen, which was previously developed for network reconstruction, has been considerably improved. From now on, Metannogen provides sbml import and annotation of networks created elsewhere. This permits users of other network reconstruction platforms or modeling software to annotate their networks using Metannogen's advanced information management. We implemented word-autocompletion, multipattern highlighting, spell check, brace-expansion and publication management, and improved annotation, cross-referencing and team work requirements. Unspecific enzymes and transporters acting on a spectrum of different substrates are efficiently handled. The network can be exported in sbml format where the annotations are embedded in line with the miriam standard. For more comfort, Metannogen may be tightly coupled with the network editor such that Metannogen becomes an additional view for the focused reaction in the network editor. Finally, Metannogen provides local single user, shared password protected multiuser or public access to the annotation data. AVAILABILITY: Metannogen is available free of charge at: http://www.bioinformatics.org/strap/metannogen/ or http://3d-alignment.eu/metannogen/. CONTACT: christoph.gille@charite.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

FASIMU: flexible software for flux-balance computation series in large metabolic networks.

BACKGROUND: Flux-balance analysis based on linear optimization is widely used to compute metabolic fluxes in large metabolic networks and gains increasingly importance in network curation and structural analysis. Thus, a computational tool flexible enough to realize a wide variety of FBA algorithms and able to handle batch series of flux-balance optimizations is of great benefit. RESULTS: We present FASIMU, a command line oriented software for the computation of flux distributions using a variety of the most common FBA algorithms, including the first available implementation of (i) weighted flux minimization, (ii) fitness maximization for partially inhibited enzymes, and (iii) of the concentration-based thermodynamic feasibility constraint. It allows batch computation with varying objectives and constraints suited for network pruning, leak analysis, flux-variability analysis, and systematic probing of metabolic objectives for network curation. Input and output supports SBML. FASIMU can work with free (lp_solve and GLPK) or commercial solvers (CPLEX, LINDO). A new plugin (faBiNA) for BiNA allows to conveniently visualize calculated flux distributions. The platform-independent program is an open-source project, freely available under GNU public license at http://www.bioinformatics.org/fasimu including manual, tutorial, and plugins. CONCLUSIONS: We present a flux-balance optimization program whose main merits are the implementation of thermodynamics as a constraint, batch series of computations, free availability of sources, choice on various external solvers, and the flexibility on metabolic objectives and constraints.

HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology.

We present HepatoNet1, the first reconstruction of a comprehensive metabolic network of the human hepatocyte that is shown to accomplish a large canon of known metabolic liver functions. The network comprises 777 metabolites in six intracellular and two extracellular compartments and 2539 reactions, including 1466 transport reactions. It is based on the manual evaluation of >1500 original scientific research publications to warrant a high-quality evidence-based model. The final network is the result of an iterative process of data compilation and rigorous computational testing of network functionality by means of constraint-based modeling techniques. Taking the hepatic detoxification of ammonia as an example, we show how the availability of nutrients and oxygen may modulate the interplay of various metabolic pathways to allow an efficient response of the liver to perturbations of the homeostasis of blood compounds.

Superimpose: a 3D structural superposition server.

The Superimposé webserver performs structural similarity searches with a preference towards 3D structure-based methods. Similarities can be detected between small molecules (e.g. drugs), parts of large structures (e.g. binding sites of proteins) and entire proteins. For this purpose, a number of algorithms were implemented and various databases are provided. Superimposé assists the user regarding the selection of a suitable combination of algorithm and database. After the computation on our server infrastructure, a visual assessment of the results is provided. The structure-based in silico screening for similar drug-like compounds enables the detection of scaffold-hoppers with putatively similar effects. The possibility to find similar binding sites can be of special interest in the functional analysis of proteins. The search for structurally similar proteins allows the detection of similar folds with different backbone topology. The Superimposé server is available at: http://bioinformatics.charite.de/superimpose.

A computational analysis of protein interactions in metabolic networks reveals novel enzyme pairs potentially involved in metabolic channeling.

Protein-protein interactions are operative at almost every level of cell structure and function as, for example, formation of sub-cellular organelles, packaging of chromatin, muscle contraction, signal transduction, and regulation of gene expression. Public databases of reported protein-protein interactions comprise hundreds of thousands interactions, and this number is steadily growing. Elucidating the implications of protein-protein interactions for the regulation of the underlying cellular or extra-cellular reaction network remains a great challenge for computational biochemistry. In this work, we have undertaken a systematic and comprehensive computational analysis of reported enzyme-enzyme interactions in the metabolic networks of the model organisms Escherichia coli and Saccharomyces cerevisiae. We grouped all enzyme pairs according to the topological distance that the catalyzed reactions have in the metabolic network and performed a statistical analysis of reported enzyme-enzyme interactions within these groups. We found a higher frequency of reported enzyme-enzyme interactions within the group of enzymes catalyzing reactions that are adjacent in the network, i.e. sharing at least one metabolite. As some of these interacting enzymes have already been implicated in metabolic channeling our analysis may provide a useful screening for candidates of this phenomenon. To check for a possible regulatory role of interactions between enzymes catalyzing non-neighboring reactions, we determined potentially regulatory enzymes using connectivity in the network and absolute change of Gibbs free energy. Indeed a higher portion of reported interactions pertain to such potentially regulatory enzymes.

Computer aided optimization of carbon atom labeling for tracer experiments.

Isotopomer tracer experiments are indispensable for the determination of flux rates in already known pathways as well as for the identification of new pathways. The information gained from such experiments depends on the labeling of the feed tracer metabolite, i.e. the atom positions carrying a label. Here we present an algorithm and a software tool to find an optimal carbon labeling pattern that assures the label to disseminate predominantly into those parts of the network under study. Our implementation is based on carbon fate maps and distinguishes between homotopic and prochiral atoms. In addition, the software can be used to generate carbon transition probability matrices, which can be used for the study of biochemical reaction mechanisms. In this article we present the algorithms and show an application of the software for glycolysis and the TCA cycle.

Computational analysis of protein-protein interactions in metabolic networks of Escherichia coli and yeast.

Protein-protein interactions are operative at almost every level of cell function. In the recent years high-throughput methods have been increasingly used to uncover protein-protein interactions at genome scale resulting in interaction maps for entire organisms. However, biochemical implications of high-throughput interactions are not always obvious. The question arises whether all interactions detected by in vitro experiments also play a functional role in the living cell. In this work we systematically analyze high-throughput protein-protein interactions stored in public databases in the context of metabolic networks. Classifying reaction pairs according to their topological distance revealed a significantly higher frequency of enzyme-enzyme interactions for directly neighbored reactions (distance = 1). To determine possible functional implications for these interactions we examined randomized networks using original enzyme interactions as well as randomly generated interaction data. A functional relevance of enzyme-enzyme interactions could be demonstrated for those reactions that exhibit low connectivity. As this is a characteristic of enzyme pairs in metabolic channeling we systematically searched the literature and indeed recovered a certain fraction of enzyme pairs that has already been implicated in metabolic channeling. However, a substantial number of enzyme pairs uncovered by our large-scale analysis remains that up to now has neither been functionally nor structurally classified and therefore present novel candidates of the metabolic channeling concept.

Impact of humic acids on the colonic microbiome in healthy volunteers.

AIM: To test the effects of humic acids on innate microbial communities of the colon. METHODS: We followed the effects of oral supplementation with humic acids (Activomin®) on concentrations and composition of colonic microbiome in 14 healthy volunteers for 45 d. 3 × 800 mg Activomin® were taken orally for 10 d followed by 3 × 400 mg for 35 d. Colonic microbiota were investigated using multicolor fluorescence in situ hybridization (FISH) of Carnoy fixated and paraffin embedded stool cylinders. Two stool samples were collected a week prior to therapy and one stool sample on days 10, 31 and 45. Forty-one FISH probes representing different bacterial groups were used. RESULTS: The sum concentration of colonic microbiota increased from 20% at day 10 to 30% by day 31 and remained stable until day 45 (32%) of humic acid supplementation (P < 0.001). The increase in the concentrations in each person was due to growth of preexisting groups. The individual microbial profile of the patients remained unchanged. Similarly, the bacterial diversity remained stable. Concentrations of 24 of the 35 substantial groups increased from 20% to 96%. Two bacterial groups detected with Bac303 (Bacteroides) and Myc657 (mycolic acid-containing Actinomycetes) FISH probes decreased (P > 0.05). The others remained unaffected. Bacterial groups with initially marginal concentrations (< 0.1 × 109/mL) demonstrated no response to humic acids. The concentrations of pioneer groups of Bifidobacteriaceae, Enterobacteriaceae and Clostridium difficile increased but the observed differences were statistically not significant. CONCLUSION: Humic acids have a profound effect on healthy colonic microbiome and may be potentially interesting substances for the development of drugs that control the innate colonic microbiome.

Reduced Mass and Diversity of the Colonic Microbiome in Patients with Multiple Sclerosis and Their Improvement with Ketogenic Diet.

Background: Colonic microbiome is thought to be involved in auto-immune multiple sclerosis (MS). Interactions between diet and the colonic microbiome in MS are unknown. Methods: We compared the composition of the colonic microbiota quantitatively in 25 MS patients and 14 healthy controls.Fluorescence in situ hybridization (FISH) with 162 ribosomal RNA derived bacterial FISH probes was used. Ten of the MS patients received a ketogenic diet for 6 months. Changes in concentrations of 35 numerically substantial bacterial groups were monitored at baseline and at 2, 12, and 23/24 weeks. Results: No MS typical microbiome pattern was apparent.The total concentrations and diversity of substantial bacterial groups were reduced in MS patients (P < 0.001). Bacterial groups detected with EREC (mainly Roseburia), Bac303 (Bacteroides), and Fprau (Faecalibacterium prausnitzii) probes were diminished the most. The individual changes were multidirectional and inconsistent. The effects of a ketogenic diet were biphasic. In the short term, bacterial concentrations and diversity were further reduced. They started to recover at week 12 and exceeded significantly the baseline values after 23-24 weeks on the ketogenic diet. Conclusions: Colonic biofermentative function is markedly impaired in MS patients.The ketogenic diet normalized concentrations of the colonic microbiome after 6 months.

Structural interpretation of mutations and SNPs using STRAP-NT.

Visualization of residue positions in protein alignments and mapping onto suitable structural models is an important first step in the interpretation of mutations or polymorphisms in terms of protein function, interaction, and thermodynamic stability. Selecting and highlighting large numbers of residue positions in a protein structure can be time-consuming and tedious with currently available software. Previously, a series of tasks and analyses had to be performed one-by-one to map mutations onto 3D protein structures; STRAP-NT is an extension of STRAP that automates these tasks so that users can quickly and conveniently map mutations onto 3D protein structures. When the structure of the protein of interest is not yet available, a related protein can frequently be found in the structure databases. In this case the alignment of both proteins becomes the crucial part of the analysis. Therefore we embedded these program modules into the Java-based multiple sequence alignment program STRAP-NT. STRAP-NT can simultaneously map an arbitrary number of mutations denoted using either the nucleotide or amino acid sequence. When the designations of the mutations refer to genomic sites, STRAP-NT translates them into the corresponding amino acid positions, taking intron-exon boundaries into account. STRAP-NT tightly integrates a number of current protein structure viewers (currently PYMOL, RASMOL, JMOL, and VMD) with which mutations and polymorphisms can be directly displayed on the 3D protein structure model. STRAP-NT is available at the PDB site and at http://www.charite.de/bioinf/strap/ or http://strapjava.de.

HotSwap for bioinformatics: a STRAP tutorial.

BACKGROUND: Bioinformatics applications are now routinely used to analyze large amounts of data. Application development often requires many cycles of optimization, compiling, and testing. Repeatedly loading large datasets can significantly slow down the development process. We have incorporated HotSwap functionality into the protein workbench STRAP, allowing developers to create plugins using the Java HotSwap technique. RESULTS: Users can load multiple protein sequences or structures into the main STRAP user interface, and simultaneously develop plugins using an editor of their choice such as Emacs. Saving changes to the Java file causes STRAP to recompile the plugin and automatically update its user interface without requiring recompilation of STRAP or reloading of protein data. This article presents a tutorial on how to develop HotSwap plugins. STRAP is available at http://strapjava.de and http://www.charite.de/bioinf/strap. CONCLUSION: HotSwap is a useful and time-saving technique for bioinformatics developers. HotSwap can be used to efficiently develop bioinformatics applications that require loading large amounts of data into memory.

Plasmodia express two threonine-peptidase complexes during asexual development.

Threonine-peptidases of the T1-family are multi-subunit complexes with broad substrate specificity. In eukaryotes, at least 14 genes encode subunits of the prototypic T1 threonine-peptidase, the proteasome. The proteasome determines the turnover of most proteins and thereby plays a fundamental role in diverse processes such as protein quality control, signal transduction, and cell cycle regulation. While eukaryotes and archaea possess a proteasome, bacteria generally express a second member of the T1-family, the proteasomal predecessor ClpQ/hslV that has a similar structure but is encoded by only one gene. The plasmodial genome is an exception because it encodes proteasomal subunits as well as a ClpQ/hslV-orthologe (Plasmodium falciparum-hslV; PfhslV). Structure, expression, and function of both types of peptidase-complex in P. falciparum are presently unknown. Our aim was to analyze both the coding sequences and derived proteins of both peptidase-complexes because highly specific and potent inhibitors can be designed against this class of enzymes. The proteasome was found expressed throughout the cell cycle, whereas PfhslV was detectable in schizonts and merozoites only. Treatment of P. falciparum with the threonine-peptidase inhibitor epoxomicin blocked two of three catalytically active proteasome subunits. This led to the accumulation of ubiquitinated proteins and, finally, to parasite death. In conclusion, we provide the first functional analysis of plasmodial threonine-peptidase-complexes and identify a lead compound for the development of a novel class of antimalarial drugs.

A comprehensive view on proteasomal sequences: implications for the evolution of the proteasome.

Proteasomes are large multimeric self-compartmentizing proteases, which play a crucial role in the clearance of misfolded proteins, breakdown of regulatory proteins, processing of proteins by specific partial proteolysis, cell cycle control as well as preparation of peptides for immune presentation. Two main types can be distinguished by their different tertiary structure: the 20S proteasome and the proteasome-like heat shock protein encoded by heat shock locus V, hslV. Usually, each biological kingdom is characterized by its specific type of proteasome. The 20S proteasomes occur in eukarya and archaea whereas hslV protease is prevalent in bacteria. To verify this rule we applied a genome-wide sequence search to identify proteasomal sequences in data of finished and yet unfinished genome projects. We found several exceptions to this paradigm: (1) Protista: in addition to the 20S proteasome, Leishmania, Trypanosoma and Plasmodium contained hslV, which may have been acquired from an alpha-proteobacterial progenitor of mitochondria. (2) Bacteria: for Magnetospirillum magnetotacticum and Enterococcus faecium we found that each contained two distinct hslVs due to gene duplication or horizontal transfer. Including unassembled data into the analyses we confirmed that a number of bacterial genomes do not contain any proteasomal sequence due to gene loss. (3) High G+C Gram-positives: we confirmed that high G+C Gram-positives possess 20S proteasomes rather than hslV proteases. The core of the 20S proteasome consists of two distinct main types of homologous monomers, alpha and beta, which differentiated into seven subtypes by further gene duplications. By looking at the genome of the intracellular pathogen Encephalitozoon cuniculi we were able to show that differentiation of beta-type subunits into different subtypes occurred earlier than that of alpha-subunits. Additionally, our search strategy had an important methodological consequence: a comprehensive sequence search for a particular protein should also include the raw sequence data when possible because proteins might be missed in the completed assembled genome. The structure-based multiple proteasomal alignment of 433 sequences from 143 organisms can be downloaded from the URL dagger and will be updated regularly.

Combining bioinformatics resources for the structural modelling of eukaryotic metabolic networks.

The architecture of the cellular metabolic network is almost completely available from several databases. This has paved the way for computational analyses. Whereas kinetic modelling is still restrained to small metabolic sub-systems for which enzyme-kinetic details are known, so-called structural modelling techniques can be applied to complete metabolic networks even if the kinetics and regulation of the underlying enzymes is still unknown. Structural modelling requires detailed information on the presence of metabolic enzymes in a specific cell type of interest and the thermodynamics of the reactions, determining their direction under cellular conditions. If compartments are distinguished the sub-cellular compartmentation of reactions and enzymes and the membrane transporters exchanging metabolites between cellular compartments must be included. All this information cannot be taken from a single data base but has to be compiled from various Bioinformatics resources. Here we present an approach towards the organization of Bioinformatics data that enables the flux-balance analysis of comprehensive compartmentalized metabolic networks of eukaryotic cells with special focus on human hepatocytes.

Three-Dimensional Modelling inside a Differential Pressure Laminar Flow Bioreactor Filled with Porous Media.

A three-dimensional computational fluid dynamics- (CFD-) model based on a differential pressure laminar flow bioreactor prototype was developed to further examine performance under changing culture conditions. Cell growth inside scaffolds was simulated by decreasing intrinsic permeability values and led to pressure build-up in the upper culture chamber. Pressure release by an integrated bypass system allowed continuation of culture. The specific shape of the bioreactor culture vessel supported a homogenous flow profile and mass flux at the scaffold level at various scaffold permeabilities. Experimental data showed an increase in oxygen concentration measured inside a collagen scaffold seeded with human mesenchymal stem cells when cultured in the perfusion bioreactor after 24 h compared to static culture in a Petri dish (dynamic: 11% O2 versus static: 3% O2). Computational fluid simulation can support design of bioreactor systems for tissue engineering application.

Noninvasive Oxygen Monitoring in Three-Dimensional Tissue Cultures Under Static and Dynamic Culture Conditions.

We present a new method for noninvasive real-time oxygen measurement inside three-dimensional tissue-engineered cell constructs in static and dynamic culture settings in a laminar flow bioreactor. The OPAL system (optical oxygen measurement system) determines the oxygen-dependent phosphorescence lifetime of spherical microprobes and uses a two-frequency phase-modulation technique, which fades out the interference of background fluorescence from the cell carrier and culture medium. Higher cell densities in the centrum of the scaffolds correlated with lower values of oxygen concentration obtained with the OPAL system. When scaffolds were placed in the bioreactor, higher oxygen values were measured compared to statically cultured scaffolds in a Petri dish, which were significantly different at day 1-3 of culture. This technique allows the use of signal-weak microprobes in biological environments and monitors the culture process inside a bioreactor.