Automated work-flow for processing high-resolution direct infusion electrospray ionization mass spectral fingerprints

The use of mass spectrometry (MS) is pivotal in analyses of the metabolome and presents a major challenge for subsequent data processing. While the last few years have given new high performance instruments, there has not been a comparable development in data processing. In this paper we discuss an automated data processing pipeline to compare large numbers of fingerprint spectra from direct infusion experiments analyzed by high resolution MS. We describe some of the intriguing problems that have to be addressed, starting with the conversion and pre-processing of the raw data to the final data analysis. Illustrated on the direct infusion analysis (ESI-TOF-MS) of complex mixtures the method exploits the full quality of the high-resolution present in the mass spectra. Although the method is illustrated as a new library search method for high resolution MS, we demonstrate that the output of the preprocessing is applicable to cluster-, discriminant analysis, and related multivariate methods applied directly to mass spectra from direct infusion analysis of crude extracts. This is done to find the relationship between several terverticillate Penicillium species and identify the ions responsible for the segregation.     

Characterizing the electrospray-ionization mass spectral fragmentation pattern of enzymatically derived hyaluronic acid oligomers

Oligosaccharides derived from hyaluronic acid by action of bovine testicular hyaluronidase (BTH) (hyaluronate 4-glycanohydrolase, E.C. 3.2.1.35) were characterized by mass spectrometry (MS) with electrospray-ionization mass spectrometry (ESIMS) and compared with results obtained by matrix-assisted laser desorption/ionization. Both oligomers with an odd number and even number of sugar units with molecular masses up to 8 kDa were observed in the ESI spectra. However, the generation of odd-numbered oligomers is not consistent with the regiospecificity of the enzyme and with the MALDI results, which indicated even-numbered oligomers exclusively. In addition, a third method of characterization, high-performance anion-exchange chromatography (HPAEC), showed only even-numbered oligomers. Relative intensities of the odd-numbered oligomers demonstrated in ESIMS a cone-voltage dependence suggesting the odd-numbered oligomers resulted from collisional activation. In order to achieve results by ESI that mirror results from other techniques, the cone voltage must be kept low and precisely controlled. This study displays the usefulness and possible vulnerabilities of ESIMS when utilized for carbohydrate analysis without corroborating data from other methods of analysis.     

DNA fingerprints of sheep using an M13 probe

The bacteriophage M13 DNA was used to detect hypervariable minisatellites in several families of Booroola sheep as well as Merino and Suffolk sheep. Digestion of sheep DNA gave rise to three to eight fragments with different restriction enzymes demonstrating considerable polymorphism between the different breeds. The length of informative DNA fragments varied in size from 6 to 20kb. The DNA fingerprints generated were individual specific and allowed for differentiation between closely related animals. The pattern obtained with sheep DNA was different from that observed with humans and other vertebrates in the proportion of high molecular weight DNA fragments present. Pedigree analysis of DNA patterns of dams and their offspring for several sets of twins and triplets showed a clear distinction between individuals and failed to reveal the presence of monozygosity.     

A new method for taking fingerprints using photographic film

A new method for obtaining dermatoglyphic prints is described. Photographic film is used enabling prints to be obtained directly, and at any enlargement. The sweat-pores can be shown in detail, and counted.     

Characterizing phosphoproteins and phosphoproteomes using mass spectrometry.

The reversible phosphorylation of proteins plays a major role in many vital cellular processes by modulating protein function and transmitting signals within cellular pathways and networks. Because phosphorylation is dynamic and the sites of modification cannot be predicted by an organism's genome, proteomic measurements are required to identify sites of and changes in the phosphorylation state of proteins. The low stoichiometry of phosphorylation sites that accompany the multifarious nature of protein phosphorylation in biological systems continues to challenge the dynamic range of present mass spectrometry (MS) technologies and proteomic measurements, despite the preponderance of research and analytical methods devoted to this area. This review addresses some of the strategies and limitations involving the use of MS to map and quantify changes in protein phosphorylation sites for samples that range from a single protein to an entire proteome, and presents several compelling reasons as to why comprehensive phosphorylation site analysis has proven to be so elusive without a hypothesis-driven experimental approach to elicit more meaningful and confident results.     

Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints

Background

Drug-induced liver injury (DILI) is a critical issue in drug development because DILI causes failures in clinical trials and the withdrawal of approved drugs from the market. There have been many attempts to predict the risk of DILI based on in vivo and in silico identification of hepatotoxic compounds. In the current study, we propose the in silico prediction model predicting DILI using weighted molecular fingerprints.

Results

In this study, we used 881 bits of molecular fingerprint and used as features describing presence or absence of each substructure of compounds. Then, the Bayesian probability of each substructure was calculated and labeled (positive or negative for DILI), and a weighted fingerprint was determined from the ratio of DILI-positive to DILI-negative probability values. Using weighted fingerprint features, the prediction models were trained and evaluated with the Random Forest (RF) and Support Vector Machine (SVM) algorithms. The constructed models yielded accuracies of 73.8% and 72.6%, AUCs of 0.791 and 0.768 in cross-validation. In independent tests, models achieved accuracies of 60.1% and 61.1% for RF and SVM, respectively. The results validated that weighted features helped increase overall performance of prediction models. The constructed models were further applied to the prediction of natural compounds in herbs to identify DILI potential, and 13,996 unique herbal compounds were predicted as DILI-positive with the SVM model.

Conclusions

The prediction models with weighted features increased the performance compared to non-weighted models. Moreover, we predicted the DILI potential of herbs with the best performed model, and the prediction results suggest that many herbal compounds could have potential to be DILI. We can thus infer that taking natural products without detailed references about the relevant pathways may be dangerous. Considering the frequency of use of compounds in natural herbs and their increased application in drug development, DILI labeling would be very important.

     

Characterizing peptides in individual mammalian cells using mass spectrometry

Cell-to-cell chemical signaling plays multiple roles in coordinating the activity of the functional elements of an organism, with these elements ranging from a three-neuron reflex circuit to the entire animal. In recent years, single-cell mass spectrometry (MS) has enabled the discovery of cell-to-cell signaling molecules from the nervous system of a number of invertebrates. We describe a protocol for analyzing individual cells from rat pituitary using matrix-assisted laser desorption/ionization MS. Each step in the sample preparation process, including cell stabilization, isolation, sample preparation, signal acquisition and data interpretation, is detailed here. Although we employ this method to investigate peptides in individual pituitary cells, it can be adapted to other cell types and even subcellular sections from a range of animals. This protocol allows one to obtain 20-30 individual cell samples and acquire mass spectra from them in a single day.     

Discrimination and identification of coastal Douglas-fir clones using needle flavonoid fingerprints

This paper describes a method for discriminating and identifying 10 successful Douglas-fir (Pseudotsuga menziesii var. menziesii) clones using foliar flavonoids. All the 101 individuals analyzed by high performance liquid chromatography contained two proanthocyanidins: prodelphinidin and procyanidin and six flavonols: myricetin, quercetin, larycitrin, kaempferol, isorhamnetin and syringetin, but in different proportions. The experimental protocol used was very reproducible since the variation coefficients for each flavonoid did not exceed 9%. Submission of the flavonoid data to multivariate discriminant analysis allowed excellent discrimination of the 10 clones with 89% of the individuals being well-grouped. Then a clonal bank was established in which the fingerprint of each clone is defined by its position in the multidimensional space of the discriminant analysis. The clonal identity of several unknown individuals was determined with success by projecting their flavonoid data in a subsequent discriminant analysis.     

Characterizing proteolytic cleavage site activity using bio-basis function neural networks

MOTIVATION: In protein chemistry, proteomics and biopharmaceutical development, there is a desire to know not only where a protein is cleaved by a protease, but also the susceptibility of its cleavage sites. The current tools for proteolytic cleavage prediction have often relied purely on regular expressions, or involve models that do not represent biological data well. RESULTS: A novel methodology for characterizing proteolytic cleavage site activities has been developed, which incorporates two fundamental features: activity class prediction and the use of an amino acid similarity matrix for (non-parametric) neural learning. The first solved the problem of predicting proteolytic efficiency. The second significantly improved the robustness in prediction and reduced the time complexity for learning. This study shows that activity class prediction is successful when applying this methodology to the prediction and characterization of Trypsin cleavage sites and the prediction of HIV protease cleavage sites. AVAILABILITY: Requests for software and data should be made respectively to Dr Zheng Rong Yang and Miss Rebecca Thomson.     

Characterizing the evolution of genetic variance using genetic covariance tensors

Determining how genetic variance changes under selection in natural populations has proved to be a very resilient problem in evolutionary genetics. In the same way that understanding the availability of genetic variance within populations requires the simultaneous consideration of genetic variance in sets of functionally related traits, determining how genetic variance changes under selection in natural populations will require ascertaining how genetic variance–covariance (G) matrices evolve. Here, we develop a geometric framework using higher-order tensors, which enables the empirical characterization of how G matrices have diverged among populations. We then show how divergence among populations in genetic covariance structure can then be associated with divergence in selection acting on those traits using key equations from evolutionary theory. Using estimates of G matrices of eight male sexually selected traits from nine geographical populations of Drosophila serrata, we show that much of the divergence in genetic variance occurred in a single trait combination, a conclusion that could not have been reached by examining variation among the individual elements of the nine G matrices. Divergence in G was primarily in the direction of the major axes of genetic variance within populations, suggesting that genetic drift may be a major cause of divergence in genetic variance among these populations.     

Significance test for comparing complex microbial community fingerprints using pairwise similarity measures

Modern electrophoresis techniques are often applied to investigate complex microbial communities. Analysis systems like GelCompare transform the optical pattern of the electrophoresis lanes into high-dimensional observation vectors and calculate measures of difference or similarity between pairs of lanes. Usually, these measures are applied in cluster analyses. Here, we apply permutation tests for the comparison of groups of lanes based on these pairwise measures together with some extensions for a combined analysis of several electrophoresis gels. The procedures are available as a computer program. An example is given for the comparison of bacterial soil communities, testing the effect of different crop plants. Each community was represented by amplified ribosomal gene fragments separated in a denaturing gradient gel.     

Characterizing genetic diversity of contemporary pacific chickens using mitochondrial DNA analyses

Background

Mitochondrial DNA (mtDNA) hypervariable region (HVR) sequences of prehistoric Polynesian chicken samples reflect dispersal of two haplogroups—D and E—by the settlers of the Pacific. The distribution of these chicken haplogroups has been used as an indicator of human movement. Recent analyses suggested similarities between prehistoric Pacific and South American chicken samples, perhaps reflecting prehistoric Polynesian introduction of the chicken into South America. These analyses have been heavily debated. The current distribution of the D and E lineages among contemporary chicken populations in the Western Pacific is unclear, but might ultimately help to inform debates about the movements of humans that carried them.

Objectives

We sought to characterize contemporary mtDNA diversity among chickens in two of the earliest settled archipelagoes of Remote Oceania, the Marianas and Vanuatu.

Methods

We generated HVR sequences for 43 chickens from four islands in Vanuatu, and for 5 chickens from Guam in the Marianas.

Results

Forty samples from Vanuatu and three from Guam were assigned to haplogroup D, supporting this as a Pacific chicken haplogroup that persists in the Western Pacific. Two haplogroup E lineages were observed in Guam and two in Vanuatu. Of the E lineages in Vanuatu, one was identical to prehistoric Vanuatu and Polynesian samples and the other differed by one polymorphism. Contrary to our expectations, we observed few globally distributed domesticate lineages not associated with Pacific chicken dispersal. This might suggest less European introgression of chickens into Vanuatu than expected. If so, the E lineages might represent lineages maintained from ancient Pacific chicken introductions. The Vanuatu sample might thus provide an opportunity to distinguish between maintained ancestral Pacific chicken lineages and replacement by global domesticates through genomic analyses, which could resolve questions of contemporary haplogroup E chicken relationships and inform interpretations of debated sequences from archaeological samples.