Julian Uszkoreit

The mzQuantML data standard for mass spectrometry-based quantitative studies in proteomics

The range of heterogeneous approaches available for quantifying protein abundance via mass spectrometry (MS)1 leads to considerable challenges in modeling, archiving, exchanging, or submitting experimental data sets as supplemental material to journals. To date, there has been no widely accepted format for capturing the evidence trail of how quantitative analysis has been performed by software, for transferring data between software packages, or for submitting to public databases. In the context of the Proteomics Standards Initiative, we have developed the mzQuantML data standard. The standard can represent quantitative data about regions in two-dimensional retention time versus mass/charge space (called features), peptides, and proteins and protein groups (where there is ambiguity regarding peptide-to-protein inference), and it offers limited support for small molecule (metabolomic) data. The format has structures for representing replicate MS runs, grouping of replicates (for example, as study variables), and capturing the parameters used by software packages to arrive at these values. The format has the capability to reference other standards such as mzML and mzIdentML, and thus the evidence trail for the MS workflow as a whole can now be described. Several software implementations are available, and we encourage other bioinformatics groups to use mzQuantML as an input, internal, or output format for quantitative software and for structuring local repositories. All project resources are available in the public domain from the HUPO Proteomics Standards Initiative http://www.psidev.info/mzquantml.

A Combined Laser Microdissection and Mass Spectrometry Approach Reveals New Disease Relevant Proteins Accumulating in Aggregates of Filaminopathy Patients

Filaminopathy is a subtype of myofibrillar myopathy caused by mutations in FLNC, the gene encoding filamin C, and histologically characterized by pathologic accumulation of several proteins within skeletal muscle fibers. With the aim to get new insights in aggregate composition, we collected aggregates and control tissue from skeletal muscle biopsies of six myofibrillar myopathy patients harboring three different FLNC mutations by laser microdissection and analyzed the samples by a label-free mass spectrometry approach. A total of 390 proteins were identified, and 31 of those showed significantly higher spectral indices in aggregates compared with patient controls with a ratio >1.8. These proteins included filamin C, other known myofibrillar myopathy associated proteins, and a striking number of filamin C binding partners. Across the patients the patterns were extremely homogeneous. Xin actin-binding repeat containing protein 2, heat shock protein 27, nebulin-related-anchoring protein, and Rab35 could be verified as new filaminopathy biomarker candidates. In addition, further experiments identified heat shock protein 27 and Xin actin-binding repeat containing protein 2 as novel filamin C interaction partners and we could show that Xin actin-binding repeat containing protein 2 and the known interaction partner Xin actin-binding repeat containing protein 1 simultaneously associate with filamin C. Ten proteins showed significant lower spectral indices in aggregate samples compared with patient controls (ratio <0.56) including M-band proteins myomesin-1 and myomesin-2. Proteomic findings were consistent with previous and novel immunolocalization data. Our findings suggest that aggregates in filaminopathy have a largely organized structure of proteins also interacting under physiological conditions. Different filamin C mutations seem to lead to almost identical aggregate compositions. The finding that filamin C was detected as highly abundant protein in aggregates in filaminopathy indicates that our proteomic approach may be suitable to identify new candidate genes among the many MFM patients with so far unknown mutation.

Differential proteomic analysis of abnormal intramyoplasmic aggregates in desminopathy.

UNLABELLED Desminopathy is a subtype of myofibrillar myopathy caused by desmin mutations and characterized by protein aggregates accumulating in muscle fibers. The aim of this study was to assess the protein composition of these aggregates. Aggregates and intact myofiber sections were obtained from skeletal muscle biopsies of five desminopathy patients by laser microdissection and analyzed by a label-free spectral count-based proteomic approach. We identified 397 proteins with 22 showing significantly higher spectral indices in aggregates (ratio >1.8, p<0.05). Fifteen of these proteins not previously reported as specific aggregate components provide new insights regarding pathomechanisms of desminopathy. Results of proteomic analysis were supported by immunolocalization studies and parallel reaction monitoring. Three mutant desmin variants were detected directly on the protein level as components of the aggregates, suggesting their direct involvement in aggregate-formation and demonstrating for the first time that proteomic analysis can be used for direct identification of a disease-causing mutation in myofibrillar myopathy. Comparison of the proteomic results in desminopathy with our previous analysis of aggregate composition in filaminopathy, another myofibrillar myopathy subtype, allows to determine subtype-specific proteomic profile that facilitates identification of the specific disorder. BIOLOGICAL SIGNIFICANCE Our proteomic analysis provides essential new insights in the composition of pathological protein aggregates in skeletal muscle fibers of desminopathy patients. The results contribute to a better understanding of pathomechanisms in myofibrillar myopathies and provide the basis for hypothesis-driven studies. The detection of specific proteomic profiles in different myofibrillar myopathy subtypes indicates that proteomic analysis may become a useful tool in differential diagnosis of protein aggregate myopathies.

Sense and Nonsense of Pathway Analysis Software in Proteomics

New developments in proteomics enable scientists to examine hundreds to thousands of proteins in parallel. Quantitative proteomics allows the comparison of different proteomes of cells, tissues, or body fluids with each other. Analyzing and especially organizing these data sets is often a Herculean task. Pathway Analysis software tools aim to take over this task based on present knowledge. Companies promise that their algorithms help to understand the significance of scientists data, but the benefit remains questionable, and a fundamental systematic evaluation of the potential of such tools has not been performed until now. Here, we tested the commercial Ingenuity Pathway Analysis tool as well as the freely available software STRING using a well-defined study design in regard to the applicability and value of their results for proteome studies. It was our goal to cover a wide range of scientific issues by simulating different established pathways including mitochondrial apoptosis, tau phosphorylation, and Insulin-, App-, and Wnt-signaling. Next to a general assessment and comparison of the pathway analysis tools, we provide recommendations for users as well as for software developers to improve the added value of a pathway study implementation in proteomic pipelines.

PRIDE Inspector Toolsuite: Moving Toward a Universal Visualization Tool for Proteomics Data Standard Formats and Quality Assessment of ProteomeXchange Datasets

The original PRIDE Inspector tool was developed as an open source standalone tool to enable the visualization and validation of mass-spectrometry (MS)-based proteomics data before data submission or already publicly available in the Proteomics Identifications (PRIDE) database. The initial implementation of the tool focused on visualizing PRIDE data by supporting the PRIDE XML format and a direct access to private (password protected) and public experiments in PRIDE. The ProteomeXchange (PX) Consortium has been set up to enable a better integration of existing public proteomics repositories, maximizing its benefit to the scientific community through the implementation of standard submission and dissemination pipelines. Within the Consortium, PRIDE is focused on supporting submissions of tandem MS data. The increasing use and popularity of the new Proteomics Standards Initiative (PSI) data standards such as mzIdentML and mzTab, and the diversity of workflows supported by the PX resources, prompted us to design and implement a new suite of algorithms and libraries that would build upon the success of the original PRIDE Inspector and would enable users to visualize and validate PX “complete” submissions. The PRIDE Inspector Toolsuite supports the handling and visualization of different experimental output files, ranging from spectra (mzML, mzXML, and the most popular peak lists formats) and peptide and protein identification results (mzIdentML, PRIDE XML, mzTab) to quantification data (mzTab, PRIDE XML), using a modular and extensible set of open-source, cross-platform libraries. We believe that the PRIDE Inspector Toolsuite represents a milestone in the visualization and quality assessment of proteomics data. It is freely available at http://github.com/PRIDE-Toolsuite/.

BioContainers: an open-source and community-driven framework for software standardization

Abstract Motivation BioContainers (biocontainers.pro) is an open-source and community-driven framework which provides platform independent executable environments for bioinformatics software. BioContainers allows labs of all sizes to easily install bioinformatics software, maintain multiple versions of the same software and combine tools into powerful analysis pipelines. BioContainers is based on popular open-source projects Docker and rkt frameworks, that allow software to be installed and executed under an isolated and controlled environment. Also, it provides infrastructure and basic guidelines to create, manage and distribute bioinformatics containers with a special focus on omics technologies. These containers can be integrated into more comprehensive bioinformatics pipelines and different architectures (local desktop, cloud environments or HPC clusters). Availability and Implementation The software is freely available at github.com/BioContainers/.

PIA: An Intuitive Protein Inference Engine with a Web-Based User Interface.

Protein inference connects the peptide spectrum matches (PSMs) obtained from database search engines back to proteins, which are typically at the heart of most proteomics studies. Different search engines yield different PSMs and thus different protein lists. Analysis of results from one or multiple search engines is often hampered by different data exchange formats and lack of convenient and intuitive user interfaces. We present PIA, a flexible software suite for combining PSMs from different search engine runs and turning these into consistent results. PIA can be integrated into proteomics data analysis workflows in several ways. A user-friendly graphical user interface can be run either locally or (e.g., for larger core facilities) from a central server. For automated data processing, stand-alone tools are available. PIA implements several established protein inference algorithms and can combine results from different search engines seamlessly. On several benchmark data sets, we show that PIA can identify a larger number of proteins at the same protein FDR when compared to that using inference based on a single search engine. PIA supports the majority of established search engines and data in the mzIdentML standard format. It is implemented in Java and freely available at https://github.com/mpc-bioinformatics/pia.

ms-data-core-api: an open-source, metadata-oriented library for computational proteomics

Summary: The ms-data-core-api is a free, open-source library for developing computational proteomics tools and pipelines. The Application Programming Interface, written in Java, enables rapid tool creation by providing a robust, pluggable programming interface and common data model. The data model is based on controlled vocabularies/ontologies and captures the whole range of data types included in common proteomics experimental workflows, going from spectra to peptide/protein identifications to quantitative results. The library contains readers for three of the most used Proteomics Standards Initiative standard file formats: mzML, mzIdentML, and mzTab. In addition to mzML, it also supports other common mass spectra data formats: dta, ms2, mgf, pkl, apl (text-based), mzXML and mzData (XML-based). Also, it can be used to read PRIDE XML, the original format used by the PRIDE database, one of the world-leading proteomics resources. Finally, we present a set of algorithms and tools whose implementation illustrates the simplicity of developing applications using the library. Availability and implementation: The software is freely available at https://github.com/PRIDE-Utilities/ms-data-core-api. Supplementary information: Supplementary data are available at Bioinformatics online Contact: juan@ebi.ac.uk

Proteomics of rimmed vacuoles define new risk allele in inclusion body myositis

Sporadic inclusion body myositis (sIBM) pathogenesis is unknown; however, rimmed vacuoles (RVs) are a constant feature. We propose to identify proteins that accumulate within RVs.

FE65 regulates and interacts with the Bloom syndrome protein in dynamic nuclear spheres - potential relevance to Alzheimer's disease.

Summary The intracellular domain of the amyloid precursor protein (AICD) is generated following cleavage of the precursor by the &ggr;-secretase complex and is involved in membrane to nucleus signaling, for which the binding of AICD to the adapter protein FE65 is essential. Here we show that FE65 knockdown causes a downregulation of the protein Bloom syndrome protein (BLM) and the minichromosome maintenance (MCM) protein family and that elevated nuclear levels of FE65 result in stabilization of the BLM protein in nuclear mobile spheres. These spheres are able to grow and fuse, and potentially correspond to the nuclear domain 10. BLM plays a role in DNA replication and repair mechanisms and FE65 was also shown to play a role in DNA damage response in the cell. A set of proliferation assays in our work revealed that FE65 knockdown in HEK293T cells reduced cell replication. On the basis of these results, we hypothesize that nuclear FE65 levels (nuclear FE65/BLM containing spheres) may regulate cell cycle re-entry in neurons as a result of increased interaction of FE65 with BLM and/or an increase in MCM protein levels. Thus, FE65 interactions with BLM and MCM proteins may contribute to the neuronal cell cycle re-entry observed in brains affected by Alzheimer’s disease.