Julio Ng

Imaging mass spectrometry of intraspecies metabolic exchange revealed the cannibalistic factors of Bacillus subtilis

During bacterial cannibalism, a differentiated subpopulation harvests nutrients from their genetically identical siblings to allow continued growth in nutrient-limited conditions. Hypothesis-driven imaging mass spectrometry (IMS) was used to identify metabolites active in a Bacillus subtilis cannibalism system in which sporulating cells lyse nonsporulating siblings. Two candidate molecules with sequences matching the products of skfA and sdpC, genes for the proposed cannibalistic factors sporulation killing factor (SKF) and sporulation delaying protein (SDP), respectively, were identified and the structures of the final products elucidated. SKF is a cyclic 26-amino acid (aa) peptide that is posttranslationally modified with one disulfide and one cysteine thioether bridged to the α-position of a methionine, a posttranslational modification not previously described in biology. SDP is a 42-residue peptide with one disulfide bridge. In spot test assays on solid medium, overproduced SKF and SDP enact a cannibalistic killing effect with SDP having higher potency. However, only purified SDP affected B. subtilis cells in liquid media in fluorescence microscopy and growth assays. Specifically, SDP treatment delayed growth in a concentration-dependent manner, caused increases in cell permeability, and ultimately caused cell lysis accompanied by the production of membrane tubules and spheres. Similarly, SDP but not SKF was able to inhibit the growth of the pathogens Staphylococcus aureus and Staphylococcus epidermidis with comparable IC50 to vancomycin. This investigation, with the identification of SKF and SDP structures, highlights the strength of IMS in investigations of metabolic exchange of microbial colonies and also demonstrates IMS as a promising approach to discover novel biologically active molecules.

Synergistic allelochemicals from a freshwater cyanobacterium

The ability of cyanobacteria to produce complex secondary metabolites with potent biological activities has gathered considerable attention due to their potential therapeutic and agrochemical applications. However, the precise physiological or ecological roles played by a majority of these metabolites have remained elusive. Several studies have shown that cyanobacteria are able to interfere with other organisms in their communities through the release of compounds into the surrounding medium, a phenomenon usually referred to as allelopathy. Exudates from the freshwater cyanobacterium Oscillatoria sp. had previously been shown to inhibit the green microalga Chlorella vulgaris. In this study, we observed that maximal allelopathic activity is highest in early growth stages of the cyanobacterium, and this provided sufficient material for isolation and chemical characterization of active compounds that inhibited the growth of C. vulgaris. Using a bioassay-guided approach, we isolated and structurally characterized these metabolites as cyclic peptides containing several unusually modified amino acids that are found both in the cells and in the spent media of Oscillatoria sp. cultures. Strikingly, only the mixture of the two most abundant metabolites in the cells was active toward C. vulgaris. Synergism was also observed in a lung cancer cell cytotoxicity assay. The binary mixture inhibited other phytoplanktonic organisms, supporting a natural function of this synergistic mixture of metabolites as allelochemicals.

Interpretation of Tandem Mass Spectra Obtained from Cyclic Nonribosomal Peptides

Natural and non-natural cyclic peptides are a crucial component in drug discovery programs because of their considerable pharmaceutical properties. Cyclosporin, microcystins, and nodularins are all notable pharmacologically important cyclic peptides. Because these biologically active peptides are often biosynthesized nonribosomally, they often contain nonstandard amino acids, thus increasing the complexity of the resulting tandem mass spectrometry data. In addition, because of the cyclic nature, the fragmentation patterns of many of these peptides showed much higher complexity when compared to related counterparts. Therefore, at the present time it is still difficult to annotate cyclic peptides MS/MS spectra. In this current work, an annotation program was developed for the annotation and characterization of tandem mass spectra obtained from cyclic peptides. This program, which we call MS-CPA is available as a web tool (http://lol.ucsd.edu/ms-cpa_v1/Input.py). Using this program, we have successfully annotated the sequence of representative cyclic peptides, such as seglitide, tyrothricin, desmethoxymajusculamide C, dudawalamide A, and cyclomarins, in a rapid manner and also were able to provide the first-pass structure evidence of a newly discovered natural product based on predicted sequence. This compound is not available in sufficient quantities for structural elucidation by other means such as NMR. In addition to the development of this cyclic annotation program, it was observed that some cyclic peptides fragmented in unexpected ways resulting in the scrambling of sequences. In summary, MS-CPA not only provides a platform for rapid confirmation and annotation of tandem mass spectrometry data obtained with cyclic peptides but also enables quantitative analysis of the ion intensities. This program facilitates cyclic peptide analysis, sequencing, and also acts as a useful tool to investigate the uncommon fragmentation phenomena of cyclic peptides and aids the characterization of newly discovered cyclic peptides encountered in drug discovery programs.

Comment on "Protein Sequences from Mastodon and Tyrannosaurus rex Revealed by Mass Spectrometry"

Asara et al. (Reports, 13 April 2007, p. 280) reported sequencing of Tyrannosaurus rex proteins and used them to establish the evolutionary relationships between birds and dinosaurs. We argue that the reported T. rex peptides may represent statistical artifacts and call for complete data release to enable experimental and computational verification of their findings.

Blocked pattern matching problem and its applications in proteomics

Matching a mass spectrum against a text (a key computational task in proteomics) is slow since the existing text indexing algorithms (with search time independent of the text size) are not applicable in the domain of mass spectrometry. As a result, many important applications (e.g., searches for mutated peptides) are prohibitively timeconsuming and even the standard search for non-mutated peptides is becoming too slow with recent advances in high-throughput genomics and proteomics technologies. We introduce a new paradigm - the Blocked Pattern Matching (BPM) Problem - that models peptide identification. BPM corresponds to matching a pattern against a text (over the alphabet of integers) under the assumption that each symbol a in the pattern can match a block of consecutive symbols in the text with total sum a. BPM opens a new, still unexplored, direction in combinatorial pattern matching and leads to the Mutated BPM (modeling identification of mutated peptides) and Fused BPM (modeling identification of fused peptides in tumor genomes). We illustrate how BPM algorithms solve problems that are beyond the reach of existing proteomics tools.

Algorithm for identification of fusion proteins via mass spectrometry.

Identification of fusion proteins has contributed significantly to our understanding of cancer progression, yielding important predictive markers and therapeutic targets. While fusion proteins can be potentially identified by mass spectrometry, all previously found fusion proteins were identified using genomic (rather than mass spectrometry) technologies. This lack of MS/MS applications in studies of fusion proteins is caused by the lack of computational tools that are able to interpret mass spectra from peptides covering unknown fusion breakpoints (fusion peptides). Indeed, the number of potential fusion peptides is so large that the existing MS/MS database search tools become impractical even in the case of small genomes. We explore computational approaches to identifying fusion peptides, propose an algorithm for solving the fusion peptide identification problem, and analyze the performance of this algorithm on simulated data. We further illustrate how this approach can be modified for human exons prediction.

De novo sequencing of nonribosomal peptides

While nonribosomal peptides (NRPs) are of tremendous pharmacological importance, there is currently no technology capable of high-throughput sequencing of NRPs. Difficulties in sequencing NRPs slow down the progress in elucidating the non-ribosomal genetic code and negatively affect various screening programs aimed at the discovery of natural compounds of medical importance. We propose to employ multi-stage mass-spectrometry (MSn) for the data acquisition, followed by alignment-based heuristic algorithms for data analysis. Since mass spectrometry based analysis of NRPs is fast and inexpensive, this approach opens the possibility of high-throughput sequencing of many unknown NRPs accumulated in large screening programs.

Grafta: A 3D environment for biomolecular networks

The importance of a comprehensive environment for the depiction of biomolecular networks in the domain of system biology has been emphasised after the completion of genomic, proteomic and metabolomic initatives. Grafta is a software application developed for the three dimensional illustration of biomolecular interactions such as protein interaction networks. Grafta allows its user to move in a 3D environment through a complex assembly of biomolecules represented by 3D objects such as spheres. Their interactions are displayed by an array of 3D tubes. One novelty in Grafta is its anthropomorphic navigation of the viewpoint with respect to the displayed biomolecular network.