The impact of sequence database choice on metaproteomic results in gut microbiota studies

Background: Elucidating the role of gut microbiota in physiological and pathological processes has recently emerged as a key research aim in life sciences. In this respect, metaproteomics, the study of the whole protein complement of a microbial community, can provide a unique contribution by revealing which functions are actually being expressed by specific microbial taxa. However, its wide application to gut microbiota research has been hindered by challenges in data analysis, especially related to the choice of the proper sequence databases for protein identification. Results: Here, we present a systematic investigation of variables concerning database construction and annotation and evaluate their impact on human and mouse gut metaproteomic results. We found that both publicly available and experimental metagenomic databases lead to the identification of unique peptide assortments, suggesting parallel database searches as a mean to gain more complete information. In particular, the contribution of experimental metagenomic databases was revealed to be mandatory when dealing with mouse samples. Moreover, the use of a "merged" database, containing all metagenomic sequences from the population under study, was found to be generally preferable over the use of sample-matched databases. We also observed that taxonomic and functional results are strongly database-dependent, in particular when analyzing the mouse gut microbiota. As a striking example, the Firmicutes/Bacteroidetes ratio varied up to tenfold depending on the database used. Finally, assembling reads into longer contigs provided significant advantages in terms of functional annotation yields. Conclusions: This study contributes to identify host- and database-specific biases which need to be taken into account in a metaproteomic experiment, providing meaningful insights on how to design gut microbiota studies and to perform metaproteomic data analysis. In particular, the use of multiple databases and annotation tools has to be encouraged, even though this requires appropriate bioinformatic resources

DART-ID increases single-cell proteome coverage.

Analysis by liquid chromatography and tandem mass spectrometry (LC-MS/MS) can identify and quantify thousands of proteins in microgram-level samples, such as those comprised of thousands of cells. This process, however, remains challenging for smaller samples, such as the proteomes of single mammalian cells, because reduced protein levels reduce the number of confidently sequenced peptides. To alleviate this reduction, we developed Data-driven Alignment of Retention Times for IDentification (DART-ID). DART-ID implements principled Bayesian frameworks for global retention time (RT) alignment and for incorporating RT estimates towards improved confidence estimates of peptide-spectrum-matches. When applied to bulk or to single-cell samples, DART-ID increased the number of data points by 30-50% at 1% FDR, and thus decreased missing data. Benchmarks indicate excellent quantification of peptides upgraded by DART-ID and support their utility for quantitative analysis, such as identifying cell types and cell-type specific proteins. The additional datapoints provided by DART-ID boost the statistical power and double the number of proteins identified as differentially abundant in monocytes and T-cells. DART-ID can be applied to diverse experimental designs and is freely available at http://dart-id.slavovlab.net

Identification of serum biomarkers in dogs naturally infected with <i>Babesia canis canis</i> using a proteomic approach

&lt;b&gt;Background&lt;/b&gt;&lt;p&gt;&lt;/p&gt; Canine babesiosis is a tick-borne disease that is caused by the haemoprotozoan parasites of the genus Babesia. There are limited data on serum proteomics in dogs, and none of the effect of babesiosis on the serum proteome. The aim of this study was to identify the potential serum biomarkers of babesiosis using proteomic techniques in order to increase our understanding about disease pathogenesis.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Results&lt;/b&gt;&lt;p&gt;&lt;/p&gt; Serum samples were collected from 25 dogs of various breeds and sex with naturally occurring babesiosis caused by B. canis canis. Blood was collected on the day of admission (day 0), and subsequently on the 1st and 6th day of treatment.&lt;p&gt;&lt;/p&gt; Two-dimensional electrophoresis (2DE) of pooled serum samples of dogs with naturally occurring babesiosis (day 0, day 1 and day 6) and healthy dogs were run in triplicate. 2DE image analysis showed 64 differentially expressed spots with p ≤ 0.05 and 49 spots with fold change ≥2. Six selected spots were excised manually and subjected to trypsin digest prior to identification by electrospray ionisation mass spectrometry on an Amazon ion trap tandem mass spectrometry (MS/MS). Mass spectrometry data was processed using Data Analysis software and the automated Matrix Science Mascot Daemon server. Protein identifications were assigned using the Mascot search engine to interrogate protein sequences in the NCBI Genbank database.&lt;p&gt;&lt;/p&gt; A number of differentially expressed serum proteins involved in inflammation mediated acute phase response, complement and coagulation cascades, apolipoproteins and vitamin D metabolism pathway were identified in dogs with babesiosis.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Conclusions&lt;/b&gt;&lt;p&gt;&lt;/p&gt; Our findings confirmed two dominant pathogenic mechanisms of babesiosis, haemolysis and acute phase response. These results may provide possible serum biomarker candidates for clinical monitoring of babesiosis and this study could serve as the basis for further proteomic investigations in canine babesiosis

Mining Images in Biomedical Publications: Detection and Analysis of Gel Diagrams

Authors of biomedical publications use gel images to report experimental results such as protein-protein interactions or protein expressions under different conditions. Gel images offer a concise way to communicate such findings, not all of which need to be explicitly discussed in the article text. This fact together with the abundance of gel images and their shared common patterns makes them prime candidates for automated image mining and parsing. We introduce an approach for the detection of gel images, and present a workflow to analyze them. We are able to detect gel segments and panels at high accuracy, and present preliminary results for the identification of gene names in these images. While we cannot provide a complete solution at this point, we present evidence that this kind of image mining is feasible.Comment: arXiv admin note: substantial text overlap with arXiv:1209.148

Carl F. Craver and Lindley Darden: \u3cem\u3eIn Search of Mechanisms: Discoveries Across the Life Sciences\u3c/em\u3e

Carl Craver and Lindley Darden are two of the foremost proponents of a recent approach to the philosophy of biology that is often called the New Mechanism. In this book they seek to make available to a broader readership insights gained from more than two decades of work on the nature of mechanisms and how they are described and discovered. The book is not primarily aimed at specialists working on the New Mechanism, but rather targets scientists, students and teachers who are looking for a broad, philosophically and historically informed image of discovery in the life sciences

VM-MAD: a cloud/cluster software for service-oriented academic environments

The availability of powerful computing hardware in IaaS clouds makes cloud computing attractive also for computational workloads that were up to now almost exclusively run on HPC clusters. In this paper we present the VM-MAD Orchestrator software: an open source framework for cloudbursting Linux-based HPC clusters into IaaS clouds but also computational grids. The Orchestrator is completely modular, allowing flexible configurations of cloudbursting policies. It can be used with any batch system or cloud infrastructure, dynamically extending the cluster when needed. A distinctive feature of our framework is that the policies can be tested and tuned in a simulation mode based on historical or synthetic cluster accounting data. In the paper we also describe how the VM-MAD Orchestrator was used in a production environment at the FGCZ to speed up the analysis of mass spectrometry-based protein data by cloudbursting to the Amazon EC2. The advantages of this hybrid system are shown with a large evaluation run using about hundred large EC2 nodes.Comment: 16 pages, 5 figures. Accepted at the International Supercomputing Conference ISC13, June 17--20 Leipzig, German

Improving average ranking precision in user searches for biomedical research datasets

Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorisation method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries. Our system provides competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP among the participants, being +22.3% higher than the median infAP of the participant's best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system's performance increasing our baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively. Our similarity measure algorithm seems to be robust, in particular compared to Divergence From Randomness framework, having smaller performance variations under different training conditions. Finally, the result categorization did not have significant impact on the system's performance. We believe that our solution could be used to enhance biomedical dataset management systems. In particular, the use of data driven query expansion methods could be an alternative to the complexity of biomedical terminologies

Analysis of the proteinaceous components of the organic matrix of calcitic sclerites from the soft coral Sinularia sp.

An organic matrix consisting of a protein-polysaccharide complex is generally accepted as an important medium for the calcification process. While the role this "calcified organic matrix" plays in the calcification process has long been appreciated, the complex mixture of proteins that is induced and assembled during the mineral phase of calcification remains uncharacterized in many organisms. Thus, we investigated organic matrices from the calcitic sclerites of a soft coral, Sinularia sp., and used a proteomic approach to identify the functional matrix proteins that might be involved in the biocalcification process. We purified eight organic matrix proteins and performed in-gel digestion using trypsin. The tryptic peptides were separated by nano-liquid chromatography (nano-LC) and analyzed by tandem mass spectrometry (MS/MS) using a matrix-assisted laser desorption/ionization (MALDI) - time-of-flight-time-of-flight (TOF-TOF) mass spectrometer. Periodic acid Schiff staining of an SDS-PAGE gel indicated that four proteins were glycosylated. We identified several proteins, including a form of actin, from which we identified a total of 183 potential peptides. Our findings suggest that many of those peptides may contribute to biocalcification in soft corals