Matching isotopic distributions from metabolically labeled samples

Motivation: In recent years stable isotopic labeling has become a standard approach for quantitative proteomic analyses. Among the many available isotopic labeling strategies, metabolic labeling is attractive for the excellent internal control it provides. However, analysis of data from metabolic labeling experiments can be complicated because the spacing between labeled and unlabeled forms of each peptide depends on its sequence, and is thus variable from analyte to analyte. As a result, one generally needs to know the sequence of a peptide to identify its matching isotopic distributions in an automated fashion. In some experimental situations it would be necessary or desirable to match pairs of labeled and unlabeled peaks from peptides of unknown sequence. This article addresses this largely overlooked problem in the analysis of quantitative mass spectrometry data by presenting an algorithm that not only identifies isotopic distributions within a mass spectrum, but also annotates matches between natural abundance light isotopic distributions and their metabolically labeled counterparts. This algorithm is designed in two stages: first we annotate the isotopic peaks using a modified version of the IDM algorithm described last year; then we use a probabilistic classifier that is supplemented by dynamic programming to find the metabolically labeled matched isotopic pairs. Such a method is needed for high-throughput quantitative proteomic metabolomic experiments measured via mass spectrometry

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

Calculation of partial isotope incorporation into peptides measured by mass spectrometry

<p>Abstract</p> <p>Background</p> <p>Stable isotope probing (SIP) technique was developed to link function, structure and activity of microbial cultures metabolizing carbon and nitrogen containing substrates to synthesize their biomass. Currently, available methods are restricted solely to the estimation of fully saturated heavy stable isotope incorporation and convenient methods with sufficient accuracy are still missing. However in order to track carbon fluxes in microbial communities new methods are required that allow the calculation of partial incorporation into biomolecules.</p> <p>Results</p> <p>In this study, we use the characteristics of the so-called 'half decimal place rule' (HDPR) in order to accurately calculate the partial¹³C incorporation in peptides from enzymatic digested proteins. Due to the clade-crossing universality of proteins within bacteria, any available high-resolution mass spectrometry generated dataset consisting of tryptically-digested peptides can be used as reference.</p> <p>We used a freely available peptide mass dataset from <it>Mycobacterium tuberculosis </it>consisting of 315,579 entries. From this the error of estimated versus known heavy stable isotope incorporation from an increasing number of randomly drawn peptide sub-samples (100 times each; no repetition) was calculated. To acquire an estimated incorporation error of less than 5 atom %, about 100 peptide masses were needed. Finally, for testing the general applicability of our method, peptide masses of tryptically digested proteins from <it>Pseudomonas putida </it>ML2 grown on labeled substrate of various known concentrations were used and¹³C isotopic incorporation was successfully predicted. An easy-to-use script <abbrgrp><abbr bid="B1">1</abbr></abbrgrp> was further developed to guide users through the calculation procedure for their own data series.</p> <p>Conclusion</p> <p>Our method is valuable for estimating¹³C incorporation into peptides/proteins accurately and with high sensitivity. Generally, our method holds promise for wider applications in qualitative and especially quantitative proteomics.</p

Envelope: interactive software for modeling and fitting complex isotope distributions

<p>Abstract</p> <p>Background</p> <p>An important aspect of proteomic mass spectrometry involves quantifying and interpreting the isotope distributions arising from mixtures of macromolecules with different isotope labeling patterns. These patterns can be quite complex, in particular with <it>in vivo </it>metabolic labeling experiments producing fractional atomic labeling or fractional residue labeling of peptides or other macromolecules. In general, it can be difficult to distinguish the contributions of species with different labeling patterns to an experimental spectrum and difficult to calculate a theoretical isotope distribution to fit such data. There is a need for interactive and user-friendly software that can calculate and fit the entire isotope distribution of a complex mixture while comparing these calculations with experimental data and extracting the contributions from the differently labeled species.</p> <p>Results</p> <p>Envelope has been developed to be user-friendly while still being as flexible and powerful as possible. Envelope can simultaneously calculate the isotope distributions for any number of different labeling patterns for a given peptide or oligonucleotide, while automatically summing these into a single overall isotope distribution. Envelope can handle fractional or complete atom or residue-based labeling, and the contribution from each different user-defined labeling pattern is clearly illustrated in the interactive display and is individually adjustable. At present, Envelope supports labeling with ²H, ¹³C, and ¹⁵N, and supports adjustments for baseline correction, an instrument accuracy offset in the m/z domain, and peak width. Furthermore, Envelope can display experimental data superimposed on calculated isotope distributions, and calculate a least-squares goodness of fit between the two. All of this information is displayed on the screen in a single graphical user interface. Envelope supports high-quality output of experimental and calculated distributions in PNG or PDF format. Beyond simply comparing calculated distributions to experimental data, Envelope is useful for planning or designing metabolic labeling experiments, by visualizing hypothetical isotope distributions in order to evaluate the feasibility of a labeling strategy. Envelope is also useful as a teaching tool, with its real-time display capabilities providing a straightforward way to illustrate the key variable factors that contribute to an observed isotope distribution.</p> <p>Conclusion</p> <p>Envelope is a powerful tool for the interactive calculation and visualization of complex isotope distributions for comparison to experimental data. It is available under the GNU General Public License from <url>http://williamson.scripps.edu/envelope/</url>.</p

Molecular correlates of trait anxiety: expanding biomarker discovery from protein expression to turnover

Depression and anxiety disorders affect a great number of people in the world. Although remarkable efforts have been devoted to understanding the clinical and biological basis of these disorders, progress has been relatively slow. Furthermore, no laboratory test currently is available for diagnosis of anxiety and depression. These disorders are mainly diagnosed empirically on the basis of a doctor’s personal observations and experiences. Hence, discovery of biomarkers for these psychiatric disorders deserves much scientific attention. The animal models investigated in the present study represent high, low, and normal anxiety-like phenotypes (HAB, LAB, NAB) and were established by selective inbreeding. To compare the protein expression levels between different animal lines, living animals were metabolically labeled with the 15N stable isotope and then investigated by quantitative mass spectrometry. In addition, metabolomic studies were performed to shed light on pathways affected in the trait anxiety mouse model. A number of proteins and metabolites were found to be significantly altered in their expression levels between the three mouse lines. Both protein and metabolite information was used for in silico network analysis to find pathways pertinent to the pathobiology of anxiety disorders. Another focus of this thesis was the development of new methodologies for the metabolic labeling approach. This includes improved identification of labeled proteins and the analysis of protein turnover. The latter represents another important aspect in the field of proteomics and adds a dynamic dimension to the field. The method allows the detection of protein expression alterations at a much earlier stage. The newly developed ProTurnyer (Protein Turnover Analyzer) algorithm is able to calculate in a high throughput manner turnover for individual proteins

Determination of Peptide and Protein Ion Charge States by Fourier Transformation of Isotope-Resolved Mass Spectra

We report an automated method for determining charge states from high-resolution mass spectra. Fourier transforms of isotope packets from high-resolution mass spectra are compared to Fourier transforms of modeled isotopic peak packets for a range of charge states. The charge state for the experimental ion packet is determined by the model isotope packet that yields the best match in the comparison of the Fourier transforms. This strategy is demonstrated for determining peptide ion charge states from “zoom scan” data from a linear quadrupole ion trap mass spectrometer, enabling the subsequent automated identification of singly- through quadruply-charged peptide ions, while reducing the numbers of conflicting identifications from ambiguous charge state assignments. We also apply this technique to determine the charges of intact protein ions from LC-FTICR data, demonstrating that it is more sensitive under these experimental conditions than two existing algorithms. The strategy outlined in this paper should be generally applicable to mass spectra obtained from any instrument capable of isotopic resolution

Contribution of hatchery and natural origin Chinook salmon to the Lower Yuba River

Recovery of self-sustaining populations of wild salmon is a primary goal for many conservation programs. Connectivity patterns across time and space are key to understanding the demographic and genetic boundaries of a population. The impact of immigrants on local population dynamics and fitness are largely unknown, and straying rates remain largely unquantified. Here, we used otolith (“earstone”) Sr isotopes in adult Chinook salmon returning to the Yuba River in 2009 to determine the relative contributions of fish that were produced and returned to the Yuba Rivervs. produced in other rivers or hatcheries thatstrayed to the Yuba River to spawn. We observed considerable variation in otolith Sr profiles during early freshwater rearing, indicating that the surviving adults had used a diverse array of habitats and outmigration timings as juveniles. One “profile type” was characterized by a high and stable otolith core value, indicating egg development in isotopically heavy water, but which dropped to isotopically distinct values immediately after emergence, suggesting early movements and extended rearing in habitats isotopically distinct from the Yuba mainstem. This “step” was prevalent in the adult sample (38%), so had a significant impact on our natal assignments; however, we are confident that it is Yuba-diagnostic as the only plausible explanation is that egg development occurred in isotopically heavy water (of which the Yuba is the only conceivable option). Also,we have only ever seen this “profile type” in known-origin fish from the Yuba River and never from any other Central Valley tributaries or hatcheries. Otolith thermal mark analyses further strengthened our inferences, and water sampling revealed locations of potential rearing habitats in the watershed, based on isotopic values matching those observed in some of the otolith profiles. Our data indicated that the proportion of wild Yuba fish in the 2009 escapement was 57% (48-66%), with 43% (34-52%) comprised of strays from the Feather River and the Feather, Mokelumne and Merced River Hatcheries. Of the known phenotypic spring run fish in the 2009 sample, 50% had originated in and returned to the Yuba River

A system-wide stable isotope labeling approach for connecting natural products to their biosynthetic gene clusters

Although the first bacterial genome sequence was published almost 20 years ago, there is still no generalizable method for automatically assigning natural products to their cognate biosynthetic gene clusters (BGCs). This thesis describes the development of a mass spectrometry-based parallel stable isotope labeling (SIL) platform, termed IsoAnalyst, which automatically associates metabolite stable isotope labeling patterns with BGC structure prediction in order to connect natural products to their cognate BGCs. The parallel SIL experiments were optimized for small scale and a custom tool written in Python was developed for the untargeted detection and interpretation of SIL labeling patterns. This approach was validated in the industrial production strains Saccharopolyspora erythraea and Amycolatopsis mediterranei demonstrating that the compounds erythromycin A and rifamycin SV respectively, could be associated with the proper BGCs based on the distribution of isotopomer labeling patterns. The method was further validated by connecting known biosynthetic intermediates of these compounds to their associated BGCs and the identification of various siderophores through a combination of SIL labeling patterns and MS/MS fragmentation data. Extension to environmental organisms using a sequenced Micromonospora sp. from our Actinobacterial isolate library led to the discovery of lobosamide D, a new member of the lobosamide family of natural products, and an update to the lobosamide BGC to include relevant tailoring enzymes. This discovery illustrates the power of the IsoAnalyst platform for identifying new compounds, linking molecules to BGCs, and generating new knowledge about biosynthesis

Proteomics Reveals Novel Drosophila Seminal Fluid Proteins Transferred at Mating

Across diverse taxa, seminal fluid proteins (Sfps) transferred at mating affect the reproductive success of both sexes. Such reproductive proteins often evolve under positive selection between species; because of this rapid divergence, Sfps are hypothesized to play a role in speciation by contributing to reproductive isolation between populations. In Drosophila, individual Sfps have been characterized and are known to alter male sperm competitive ability and female post-mating behavior, but a proteomic-scale view of the transferred Sfps has been missing. Here we describe a novel proteomic method that uses whole-organism isotopic labeling to detect transferred Sfps in mated female D. melanogaster. We identified 63 proteins, which were previously unknown to function in reproduction, and confirmed the transfer of dozens of predicted Sfps. Relative quantification of protein abundance revealed that several of these novel Sfps are abundant in seminal fluid. Positive selection and tandem gene duplication are the prevailing forces of Sfp evolution, and comparative proteomics with additional species revealed lineage-specific changes in seminal fluid content. We also report a proteomic-based gene discovery method that uncovered 19 previously unannotated genes in D. melanogaster. Our results demonstrate an experimental method to identify transferred proteins in any system that is amenable to isotopic labeling, and they underscore the power of combining proteomic and evolutionary analyses to shed light on the complex process of Drosophila reproduction