141 research outputs found
Self-assembling protein microarrays and surface plasmon resonance imaging to study high-throughput protein interactions
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A novel approach to sequence validating protein expression clones with automated decision making
<p>Abstract</p> <p>Background</p> <p>Whereas the molecular assembly of protein expression clones is readily automated and routinely accomplished in high throughput, sequence verification of these clones is still largely performed manually, an arduous and time consuming process. The ultimate goal of validation is to determine if a given plasmid clone matches its reference sequence sufficiently to be "acceptable" for use in protein expression experiments. Given the accelerating increase in availability of tens of thousands of unverified clones, there is a strong demand for rapid, efficient and accurate software that automates clone validation.</p> <p>Results</p> <p>We have developed an Automated Clone Evaluation (ACE) system – the first comprehensive, multi-platform, web-based plasmid sequence verification software package. ACE automates the clone verification process by defining each clone sequence as a list of multidimensional discrepancy objects, each describing a difference between the clone and its expected sequence including the resulting polypeptide consequences. To evaluate clones automatically, this list can be compared against user acceptance criteria that specify the allowable number of discrepancies of each type. This strategy allows users to re-evaluate the same set of clones against different acceptance criteria as needed for use in other experiments. ACE manages the entire sequence validation process including contig management, identifying and annotating discrepancies, determining if discrepancies correspond to polymorphisms and clone finishing. Designed to manage thousands of clones simultaneously, ACE maintains a relational database to store information about clones at various completion stages, project processing parameters and acceptance criteria. In a direct comparison, the automated analysis by ACE took less time and was more accurate than a manual analysis of a 93 gene clone set.</p> <p>Conclusion</p> <p>ACE was designed to facilitate high throughput clone sequence verification projects. The software has been used successfully to evaluate more than 55,000 clones at the Harvard Institute of Proteomics. The software dramatically reduced the amount of time and labor required to evaluate clone sequences and decreased the number of missed sequence discrepancies, which commonly occur during manual evaluation. In addition, ACE helped to reduce the number of sequencing reads needed to achieve adequate coverage for making decisions on clones.</p
Setting-up an antibody nucleic-acid programmable protein array (NAPPA) antibody microarray platform
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A Functional Genomic Yeast Screen to Identify Pathogenic Bacterial Proteins
Many bacterial pathogens promote infection and cause disease by directly injecting into host cells proteins that manipulate eukaryotic cellular processes. Identification of these translocated proteins is essential to understanding pathogenesis. Yet, their identification remains limited. This, in part, is due to their general sequence uniqueness, which confounds homology-based identification by comparative genomic methods. In addition, their absence often does not result in phenotypes in virulence assays limiting functional genetic screens. Translocated proteins have been observed to confer toxic phenotypes when expressed in the yeast Saccharomyces cerevisiae. This observation suggests that yeast growth inhibition can be used as an indicator of protein translocation in functional genomic screens. However, limited information is available regarding the behavior of non-translocated proteins in yeast. We developed a semi-automated quantitative assay to monitor the growth of hundreds of yeast strains in parallel. We observed that expression of half of the 19 Shigella translocated proteins tested but almost none of the 20 non-translocated Shigella proteins nor ∼1,000 Francisella tularensis proteins significantly inhibited yeast growth. Not only does this study establish that yeast growth inhibition is a sensitive and specific indicator of translocated proteins, but we also identified a new substrate of the Shigella type III secretion system (TTSS), IpaJ, previously missed by other experimental approaches. In those cases where the mechanisms of action of the translocated proteins are known, significant yeast growth inhibition correlated with the targeting of conserved cellular processes. By providing positive rather than negative indication of activity our assay complements existing approaches for identification of translocated proteins. In addition, because this assay only requires genomic DNA it is particularly valuable for studying pathogens that are difficult to genetically manipulate or dangerous to culture
Functional proteomics approaches for high throuput determination of CKIT and small molecules
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Translational biomarker: identification of biomarkers related to capecitabine response in solid tumors by nucleic acids programmable protein microarrays (NAPPA), antibody arrays, IFISH and SNPS approaches
Comunicaciones a congreso
Discovery of an autoantibody signature for the early diagnosis of knee osteoarthritis: data from the Osteoarthritis Initiative
[Abstract] Objective To find autoantibodies (AAbs) in serum that could be useful to predict incidence of radiographic knee osteoarthritis (KOA).
Design A Nucleic-acid Programmable Protein Arrays (NAPPA) platform was used to screen AAbs against 2125 human proteins in sera at baseline from participants free of radiographic KOA belonging to the incidence and non-exposed subcohorts of the Osteoarthritis Initiative (OAI) who developed or not, radiographic KOA during a follow-up period of 96 months. NAPPA-ELISA were performed to analyse reactivity against methionine adenosyltransferase two beta (MAT2β) and verify the results in 327 participants from the same subcohorts. The association of MAT2β-AAb levels with KOA incidence was assessed by combining several robust biostatistics analysis (logistic regression, Receiver Operating Characteristic and Kaplan-Meier curves). The proposed prognostic model was replicated in samples from the progression subcohort of the OAI.
Results In the screening phase, six AAbs were found significantly different at baseline in samples from incident compared with non-incident participants. In the verification phase, high levels of MAT2β-AAb were significantly associated with the future incidence of KOA and with an earlier development of the disease. The incorporation of this AAb in a clinical model for the prognosis of incident radiographic KOA significantly improved the identification/classification of patients who will develop the disorder. The usefulness of the model to predict radiographic KOA was confirmed on a different OAI subcohort.
Conclusions The measurement of AAbs against MAT2β in serum might be highly useful to improve the prediction of OA development, and also to estimate the time to incidence.Instituto de Salud Carlos III; PT17/0019/0014Instituto de Salud Carlos III; PI14/01707Instituto de Salud Carlos III; PI16/02124Instituto de Salud Carlos III; PI17/00404Instituto de Salud Carlos III; DTS17/00200Instituto de Salud Carlos III; CIBER-BBN CB06/01/0040Insituto de Salud Carlos III; CIBER-ONC CB16/12/00400Instituto de Salud Carlos III; RETIC-RIER-RD12/0009/0018Xunta de Galicia; IN606A-2016/012Instituto de Salud Carlos III; CPII17/0026Insituto de Salud Carlos III; CPII15/0001
Multi-platform Approach for Microbial Biomarker Identification Using Borrelia burgdorferi as a Model
The identification of microbial biomarkers is critical for the diagnosis of a disease early during infection. However, the identification of reliable biomarkers is often hampered by a low concentration of microbes or biomarkers within host fluids or tissues. We have outlined a multi-platform strategy to assess microbial biomarkers that can be consistently detected in host samples, using Borrelia burgdorferi, the causative agent of Lyme disease, as an example. Key aspects of the strategy include the selection of a macaque model of human disease, in vivo Microbial Antigen Discovery (InMAD), and proteomic methods that include microbial biomarker enrichment within samples to identify secreted proteins circulating during infection. Using the described strategy, we have identified 6 biomarkers from multiple samples. In addition, the temporal antibody response to select bacterial antigens was mapped. By integrating biomarkers identified from early infection with temporal patterns of expression, the described platform allows for the data driven selection of diagnostic targets
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A Full-Genomic Sequence-Verified Protein-Coding Gene Collection for Francisella tularensis
The rapid development of new technologies for the high throughput (HT) study of proteins has increased the demand for comprehensive plasmid clone resources that support protein expression. These clones must be full-length, sequence-verified and in a flexible format. The generation of these resources requires automated pipelines supported by software management systems. Although the availability of clone resources is growing, current collections are either not complete or not fully sequence-verified. We report an automated pipeline, supported by several software applications that enabled the construction of the first comprehensive sequence-verified plasmid clone resource for more than 96% of protein coding sequences of the genome of F. tularensis, a highly virulent human pathogen and the causative agent of tularemia. This clone resource was applied to a HT protein purification pipeline successfully producing recombinant proteins for 72% of the genes. These methods and resources represent significant technological steps towards exploiting the genomic information of F. tularensis in discovery applications
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