BackgroundIn 2010 Colony Collapse Disorder (CCD), again devastated honey bee colonies in the USA, indicating that the problem is neither diminishing nor has it been resolved. Many CCD investigations, using sensitive genome-based methods, have found small RNA bee viruses and the microsporidia, Nosema apis and N. ceranae in healthy and collapsing colonies alike with no single pathogen firmly linked to honey bee losses.Methodology/Principal FindingsWe used Mass spectrometry-based proteomics (MSP) to identify and quantify thousands of proteins from healthy and collapsing bee colonies. MSP revealed two unreported RNA viruses in North American honey bees, Varroa destructor-1 virus and Kakugo virus, and identified an invertebrate iridescent virus (IIV) (Iridoviridae) associated with CCD colonies. Prevalence of IIV significantly discriminated among strong, failing, and collapsed colonies. In addition, bees in failing colonies contained not only IIV, but also Nosema. Co-occurrence of these microbes consistently marked CCD in (1) bees from commercial apiaries sampled across the U.S. in 2006–2007, (2) bees sequentially sampled as the disorder progressed in an observation hive colony in 2008, and (3) bees from a recurrence of CCD in Florida in 2009. The pathogen pairing was not observed in samples from colonies with no history of CCD, namely bees from Australia and a large, non-migratory beekeeping business in Montana. Laboratory cage trials with a strain of IIV type 6 and Nosema ceranae confirmed that co-infection with these two pathogens was more lethal to bees than either pathogen alone.Conclusions/SignificanceThese findings implicate co-infection by IIV and Nosema with honey bee colony decline, giving credence to older research pointing to IIV, interacting with Nosema and mites, as probable cause of bee losses in the USA, Europe, and Asia. We next need to characterize the IIV and Nosema that we detected and develop management practices to reduce honey bee losses.
Timely classification and identification of bacteria is of vital importance in many areas of public health. We present a mass spectrometry (MS)-based proteomics approach for bacterial classification. In this method, a bacterial proteome database is derived from all potential protein coding open reading frames (ORFs) found in 170 fully sequenced bacterial genomes. Amino acid sequences of tryptic peptides obtained by LC-ESI MS/MS analysis of the digest of bacterial cell extracts are assigned to individual bacterial proteomes in the database. Phylogenetic profiles of these peptides are used to create a matrix of sequence-to-bacterium assignments. These matrixes, viewed as specific assignment bitmaps, are analyzed using statistical tools to reveal the relatedness between a test bacterial sample and the microorganism database. It is shown that, if a sufficient amount of sequence information is obtained from the MS/MS experiments, a bacterial sample can be classified to a strain level by using this proteomics method, leading to its positive identification.
Public reporting burden for fthi collection of Informaiaon is estimated to, average I hour per response, including the time for reviewing Instructions. searchilng erristing data sources, gatheing and maintsteng the data needed, and completing and review"n this collection of Information. Send cofmment regarding this burden estimate or any othier aspect of this collection of Infomuation, Indluding suggestions for reducing fti burden to Departmrent PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORTDIR ECBC, ATTN: AMSRD-ECBC-RT-II, APG, MD 21010-5424 NUMBER GEO-CENTERS, Inc., Gunpowder Branch, APG, MD 21010-5424 ECBC-TR-415 SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSORIMONITOR'S ACRONYM(S) SPONSORIMONITOR'S REPORT NUMBER(S) DISTRIBUTION I AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTES ABSTRACTThis work discusses the significance of the pyrolyzate peaks observed in the gas chromatography-ion mobility spectrometry (GC-IMS) dataspace of the pyrolysis-GC-IMS (Py-GC-IMS) briefcase system. This system has the ability to detect and classifyi deliberately released bioaerosols in outdoor field scenarios. The bioaerosols include Gram-positive spores and Gram-negative bacteria, the MS-2 Escherichia coliphage virus, and ovalbumnin (OV) protein species. The work suggests certain improvements that can be made to the IMS detection System. A pyrolysis-gas chromatography-ion mobility spectrometry (Py-GC-IMS) briefcase system can detect and classify deliberately released bioaerosols in outdoor field scenarios. The bioaerosols include Gram-positive spores and Gram-negative bacteria, the MS-2 Escherichia coliphage virus, and ovalbumin (OV) protein species. However, the origin and structural identities of the pyrolyzate peaks observed in the GC-IMS dataspace, their microbiological information content, and taxonomic importance with respect to biodetection have not been determined. SUBJECT TERMS PyrolysisThe present work interrogates the identities of the peaks by inserting a time-offlight (TOF) mass spectrometry (MS) system in parallel with the IMS detector through a Tee connection in the GC module. Biological substances, producing ion mobility peaks from the pyrolysis of microorganisms, have been identified by their GC retention times, by matching their electron ionization mass spectra with authentic standards, and by the National Institute of Standards and Technology (NIST) mass spectral database.Strong signals from 2-pyridinecarboxamide were identified in Bacillus samples, including Bacillus anthracis, and their origins were traced to the cell wall peptidoglycan macromolecule. 3-Hydroxymyristic acid is a component of lipopolysaccharides (LPS) in the cell walls of Gram-negative organisms. The Gram-negative E. coli organism showed significant amounts of 3-Hydroxymyristic acid derivatives and degradation products in Py-GC-MS analyses. Some of the fatty acid derivatives were observed in very low abundance in the ion mobi...
Due to the possibility of a biothreat attack on civilian or military installations, a need exists for technologies that can detect and accurately identify pathogens in a near-real-time approach. One technology potentially capable of meeting these needs is a high-throughput mass spectrometry (MS)-based proteomic approach. This approach utilizes the knowledge of amino acid sequences of peptides derived from the proteolysis of proteins as a basis for reliable bacterial identification. To evaluate this approach, the tryptic digest peptides generated from double-blind biological samples containing either a single bacterium or a mixture of bacteria were analyzed using liquid chromatography-tandem mass spectrometry. Bioinformatic tools that provide bacterial classification were used to evaluate the proteomic approach. Results showed that bacteria in all of the double-blind samples were accurately identified with no false-positive assignment. The MS proteomic approach showed strain-level discrimination for the various bacteria employed. The approach also characterized doubleblind bacterial samples to the respective genus, species, and strain levels when the experimental organism was not in the database due to its genome not having been sequenced. One experimental sample did not have its genome sequenced, and the peptide experimental record was added to the virtual bacterial proteome database. A replicate analysis identified the sample to the peptide experimental record stored in the database. The MS proteomic approach proved capable of identifying and classifying organisms within a microbial mixture.The detection and accurate identification of pathogens of biological origin are of great importance to the armed forces and civilian sectors. Achieving these tasks is vital in the response to manmade or natural biothreat attacks in a proper and efficient manner to minimize the outbreak of epidemic cases. Several approaches reported in the literature have addressed the detection and identification of microorganisms based on the characterization of metabolites (1, 17) and genomic contents of bacterial cells (16). In these studies, the genomic sequence similarities generated from PCR were used to group bacteria at the genus/species level (27). Prior knowledge of the sample, or the targeting of one or a group of biological substances, is required in PCR techniques for proper primer utilization. However, proteins constitute greater than 60% of the dry weight of microorganism cellular components (4,8,12,13,22) and could provide in-depth information for the bacterial differentiation of species and their strains. Moreover, advancements in mass spectrometry (MS) ionization, detection methods, and data processing make MS a suitable analytical technique for the differentiation of microorganisms (5-7).Using MS techniques for bacterial differentiation relies on the comparison of the proteomic information generated from the analysis of either intact protein profiles (top down) or the product ion mass spectra of digested peptide sequences ...
Small-angle neutron scattering (SANS) has been used to extend the structural characterization of the MS2 phage by examining its physical characteristics in solution. Specifically, the contrast variation technique was employed to determine the molecular weight of the individual components of the MS2 virion (protein shell and genomic RNA) and the spatial relationship of the genomic RNA to its protein shell. A consequence of this work was to evaluate a novel particle counting instrument, the integrated virus detection system (IVDS) that, in combination with SANS, has the potential to provide rapid quantitative physical characterization of unidentified viruses and phage.
Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.
Whole cell protein and outer membrane protein (OMP) extracts were compared for their ability to differentiate and delineate the correct database organism to an experimental sample and for the degree of dissimilarity to the nearest neighbor database organism strains. These extracts were isolated from pathogenic and nonpathogenic strains of Yersinia pestis and Escherichia coli using ultracentrifugation and a sarkosyl extraction method followed by protein digestion and analysis using liquid chromatography tandem mass spectrometry (MS). Whole cell protein extracts contain many different types of proteins resident in an organism at a given phase in its growth cycle. OMPs, however, are often associated with virulence in Gram-negative pathogens and could prove to be model biomarkers for strain differentiation among bacteria. The mass spectra of bacterial peptides were searched, using the SEQUEST algorithm, against a constructed proteome database of microorganisms in order to determine the identity and number of unique peptides for each bacterial sample. Data analysis was performed with the in-house BACid software. It calculated the probabilities that a peptide sequence assignment to a product ion mass spectrum was correct and used accepted spectrum-to-sequence matches to generate a sequence-to-bacterium (STB) binary matrix of assignments. Validated peptide sequences, either present or absent in various strains (STB matrices), were visualized as assignment bitmaps and analyzed by the BACid module that used phylogenetic relationships among bacterial species as part of a decision tree process. The bacterial classification and identification algorithm used assignments of organisms to taxonomic groups (phylogenetic classification) based on an organized scheme that begins at the phylum level and follows through the class, order, family, genus, and species to the strain level. For both Gram-negative organisms, the number of unique distinguishing proteins arrived at by the whole cell method was less than that of the OMP method. However, the degree of differentiation measured in linkage distance units on a dendrogram with the OMP extract showed similar or significantly better separation than the whole cell protein extract method between the sample and correct database match compared to the next nearest neighbor. The nonpathogenic Y. pestis A1122 strain used does not have its genome available, and thus, data analysis resulted in an equal similarity index to the nonpathogenic 91001 and pathogenic Antiqua and Nepal 516 strains for both extraction methods. Pathogenic and nonpathogenic strains of E. coli were correctly identified with both protein extraction methods, and the pathogenic Y. pestis CO92 strain was correctly identified with the OMP procedure. Overall, proteomic MS proved useful in the analysis of unique protein assignments for strain differentiation of E. coli and Y. pestis. The power of bacterial protein capture by the whole cell protein and OMP extraction methods was highlighted by the data analysis techniques and reve...
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