Horizontal gene transfer contributed to the evolution of extracellular surface structures

The single-cell layered ectoderm of the fresh water polyp Hydra fulfills the function of an epidermis by protecting the animals from the surrounding medium. Its outer surface is covered by a fibrous structure termed the cuticle layer, with similarity to the extracellular surface coats of mammalian epithelia. In this paper we have identified molecular components of the cuticle. We show that its outermost layer contains glycoproteins and glycosaminoglycans and we have identified chondroitin and chondroitin-6-sulfate chains. In a search for proteins that could be involved in organising this structure we found PPOD proteins and several members of a protein family containing only SWT (sweet tooth) domains. Structural analyses indicate that PPODs consist of two tandem β-trefoil domains with similarity to carbohydrate-binding sites found in lectins. Experimental evidence confirmed that PPODs can bind sulfated glycans and are secreted into the cuticle layer from granules localized under the apical surface of the ectodermal epithelial cells. PPODs are taxon-specific proteins which appear to have entered the Hydra genome by horizontal gene transfer from bacteria. Their acquisition at the time Hydra evolved from a marine ancestor may have been critical for the transition to the freshwater environment

Identification of Ligand-Receptor Interactions Between Saccharomyces cerevisiae α-factor Pheromone Receptor (Ste2p) and its Tridecapeptide Ligand

G protein-coupled receptors (GPCRs) are a class of integral membrane receptor proteins that are characterized by a signature seven-transmembrane (7TM) configuration. These receptors comprise a large and diverse gene family found in fungi, plants, and the animal kingdom. Recent studies with GPCRs have begun to elucidate their importance in many physiological processes, thus various human diseases are associated with GPCR pathology. Although the overall 3D structure of these receptors carry similar features, binding of an extraordinarily diverse array of ligands trigger many different biological pathways. The α-factor receptor (Ste2p) of Saccharomyces cerevisiae belongs to the GPCR family. Upon the α-factor binding to Ste2p, a signal is transduced via an associated guanine-nucleotide binding protein initiating a cascade of events that leads to the mating of haploid yeast cells. As only two GPCRs and two G proteins are encoded in the S. cerevisiae genome, this yeast presents a relatively simple system to study GPCR signal transduction in comparison to mammalian cells that possess hundreds of GPCRs and tens of G proteins. Part I of this dissertation is an overview of GPCRs in general with specific emphasis on the peptide pheromone α-factor and its receptorSte2p. Part II of this dissertation details the design and characterization of a number ofiodinatable α-factor pheromone analogs containing the photo-cross-linkable 4-benzoyl-Lphenylalanine (Bpa) group. One of these analogs [Bpa1, Y3, R7, Nle12, F13] was radioiodinated for detection and used as a probe for cross-linking studies with Ste2p. Chemical (with CNBr & BNPS-skatole) and enzymatic (with Trypsin) cleavage of the receptor/analog complex after the cross-linking was examined to determine the interaction between the α-factor probe and a fragment of the receptor. Data from these digestions indicated that the position one of the α-factor interacts with residues 251 to 294 in the receptor. Similarly Part III of this dissertation describes the design and synthesis of five photoactivatable α-factor analogs that carry Bpa at positions one, three, five, eight, or thirteen. All of these analogs were biotinylated at the ε-amine of the Lys7 for detection and purification purposes. The biological activity (growth arrest assay) and binding affinities of all analogs for Ste2p were determined. Two of the analogs tested, Bpa1 and Bpa5, showed three- to four-fold lower affinity compared to α-factor, whereas Bpa3 and Bpa13 had seven- to twelve-fold lower affinities, respectively. Bpa8 competed poorly with [3H]α-factor for Ste2p. All of the analogs tested had detectable halos in the growth arrest assay indicating that these analogs are α-factor agonists. Cross-linking studies demonstrated that [Bpa1]α-factor, [Bpa3]α-factor, [Bpa5]α-factor and [Bpa13]α-factor were cross-linked to Ste2p; the biotin tag on the pheromone was detected by a NeutrAvidin-HRP conjugate on Western blots. Digestion of Bpa1, Bpa3, and Bpa13 cross-linked receptors with chemical and enzymatic reagents suggested that the N-terminus of the pheromone interacts with a binding domain consisting of residues from the extracellular ends of TM5, TM6, and TM7 and portions of EL2 and EL3 close to these TMs. Additionally it was concluded that there is a direct interaction between the position 13 side chain and a region of Ste2p (F55-R58) at the extracellular end of TM1. Parts II and III of this dissertation indicate that Bpa-containing α-factor probes are useful in determining contacts between α-factor and Ste2p and initiating mapping of the ligand binding site of the GPCR for its peptide ligand. This dissertation (Part IV) also presents the application of different purification methods and the use of two mass spectrometry instruments for identification of ligandreceptor interactions at the molecular level. Results presented in this part showed that although a single step purification was enough for western blot analyses of the cross-linked receptor fragments, at least a two-step purification and enrichment of the biotinylated peptide fragments were necessary for mass spectrometric studies. MALDITOF experiments showed that the affinity purification of the biotinylated fragments by monomeric avidin beads was successful. Data obtained from CNBr fragments of Bpa1 cross-linked membranes were in agreement with the previous results discussed in Parts II and III of this dissertation suggesting the cross-linking between position one of α-factor and a region of Ste2p covering residues 251 to 294. This part also illustrated that the analyses of the MS/MS data from the cross-linked fragments were more complex than the fragmentation data obtained from biotinylated α-factor; the presence of multiple charge states of fragment ions and unusual fragmentation of branched peptides indicated the necessity of using an instrument with higher resolution. In addition, analyses of the MS/MS data with a customized algorithm would be required to deconvolute the sequence of the cross-linked fragment(s) to identify the cross-linked residue(s) on Ste2p. The final part of this dissertation reviews the overall conclusions and discussion. This part also contains suggestions for future experiments that could help identification of contact points between Ste2p and its peptide ligand α-factor. Additional studies on this GPCR system employing high-resolution mass spectral characterization of fragments should allow identification of residue-to-residue interactions between the analogs used in this study and Ste2p. Such information will aid the mapping of the ligand-binding site of the pheromone receptor and has the potential to provide key insights into peptide ligand mediated activation of GPCRs. This and similar studies may ultimately lead to the discovery of how peptide ligands initiate signal transduction through GPCRs

Novel Mass Spectrometry-based Epitope Mapping Procedures of Autoantigens

Preventive medicine for patients is to be realized from monitoring and management of early stage disease rather than from late stage treatment. Among the strategies of preventive medicine is the detection of prognostic and diagnostic protein signatures, in particular epitopes against which autoantibodies in blood plasma are directed during the progression of the disease. Recognizing such diseasespecific antibodies in patient screenings requires knowledge of the respective epitopes. In this work, novel mass spectrometry-based methodologies for epitope mapping of autoantigens are described

Metabolic profiling of human saliva before and after induced physiological stress by ultra-high performance liquid chromatography-ion mobility-mass spectrometry

A method has been developed for metabolite profiling of the salivary metabolome based on protein precipitation and ultra-high performance liquid chromatography coupled with ion mobility-mass spectrometry (UHPLC–IM–MS). The developed method requires 0.5 mL of human saliva, which is easily obtainable by passive drool. Standard protocols have been established for the collection, storage and pre-treatment of saliva. The use of UHPLC allows rapid global metabolic profiling for biomarker discovery with a cycle time of 15 min. Mass spectrometry imparts the ability to analyse a diverse number of species reproducibly over a wide dynamic range, which is essential for profiling of biofluids. The combination of UHPLC with IM–MS provides an added dimension enabling complex metabolic samples to be separated on the basis of retention time, ion mobility and mass-to-charge ratio in a single chromatographic run. The developed method has been applied to targeted metabolite identification and untargeted metabolite profiling of saliva samples collected before and after exercise-induced physiological stress. δ-Valerolactam has been identified as a potential biomarker on the basis of retention time, MS/MS spectrum and ion mobility drift time

Status of complete proteome analysis by mass spectrometry: SILAC labeled yeast as a model system

BACKGROUND: Mass spectrometry has become a powerful tool for the analysis of large numbers of proteins in complex samples, enabling much of proteomics. Due to various analytical challenges, so far no proteome has been sequenced completely. O'Shea, Weissman and co-workers have recently determined the copy number of yeast proteins, making this proteome an excellent model system to study factors affecting coverage. RESULTS: To probe the yeast proteome in depth and determine factors currently preventing complete analysis, we grew yeast cells, extracted proteins and separated them by one-dimensional gel electrophoresis. Peptides resulting from trypsin digestion were analyzed by liquid chromatography mass spectrometry on a linear ion trap-Fourier transform mass spectrometer with very high mass accuracy and sequencing speed. We achieved unambiguous identification of more than 2,000 proteins, including very low abundant ones. Effective dynamic range was limited to about 1,000 and effective sensitivity to about 500 femtomoles, far from the subfemtomole sensitivity possible with single proteins. We used SILAC (stable isotope labeling by amino acids in cell culture) to generate one-to-one pairs of true peptide signals and investigated if sensitivity, sequencing speed or dynamic range were limiting the analysis. CONCLUSION: Advanced mass spectrometry methods can unambiguously identify more than 2,000 proteins in a single proteome. Complex mixture analysis is not limited by sensitivity but by a combination of dynamic range (high abundance peptides preventing sequencing of low abundance ones) and by effective sequencing speed. Substantially increased coverage of the yeast proteome appears feasible with further development in software and instrumentation

Algorithms for integrated analysis of glycomics and glycoproteomics by LC-MS/MS

The glycoproteome is an intricate and diverse component of a cell, and it plays a key role in the definition of the interface between that cell and the rest of its world. Methods for studying the glycoproteome have been developed for released glycan glycomics and site-localized bottom-up glycoproteomics using liquid chromatography-coupled mass spectrometry and tandem mass spectrometry (LC-MS/MS), which is itself a complex problem. Algorithms for interpreting these data are necessary to be able to extract biologically meaningful information in a high throughput, automated context. Several existing solutions have been proposed but may be found lacking for larger glycopeptides, for complex samples, different experimental conditions, different instrument vendors, or even because they simply ignore fundamentals of glycobiology. I present a series of open algorithms that approach the problem from an instrument vendor neutral, cross-platform fashion to address these challenges, and integrate key concepts from the underlying biochemical context into the interpretation process. In this work, I created a suite of deisotoping and charge state deconvolution algorithms for processing raw mass spectra at an LC scale from a variety of instrument types. These tools performed better than previously published algorithms by enforcing the underlying chemical model more strictly, while maintaining a higher degree of signal fidelity. From this summarized, vendor-normalized data, I composed a set of algorithms for interpreting glycan profiling experiments that can be used to quantify glycan expression. From this I constructed a graphical method to model the active biosynthetic pathways of the sample glycome and dig deeper into those signals than would be possible from the raw data alone. Lastly, I created a glycopeptide database search engine from these components which is capable of identifying the widest array of glycosylation types available, and demonstrate a learning algorithm which can be used to tune the model to better understand the process of glycopeptide fragmentation under specific experimental conditions to outperform a simpler model by between 10% and 15%. This approach can be further augmented with sample-wide or site-specific glycome models to increase depth-of-coverage for glycoforms consistent with prior beliefs

Computational Framework for Data-Independent Acquisition Proteomics.

Mass spectrometry (MS) is one of the main techniques for high throughput discovery- and targeted-based proteomics experiments. The most popular method for MS data acquisition has been data dependent acquisition (DDA) strategy which primarily selects high abundance peptides for MS/MS sequencing. DDA incorporates stochastic data acquisitions to avoid repetitive sequencing of same peptide, resulting in relatively irreproducible results for low abundance peptides between experiments. Data independent acquisition (DIA), in which peptide fragment signals are systematically acquired, is emerging as a promising alternative to address the DDA's stochasticity. DIA results in more complex signals, posing computational challenges for complex sample and high-throughput analysis. As a result, targeted extraction which requires pre-existing spectral libraries has been the most commonly used approach for automated DIA data analysis. However, building spectral libraries requires additional amount of analysis time and sample materials which are the major barriers for most research groups. In my dissertation, I develop a computational tool called DIA-Umpire, which includes computational and signal processing algorithms to enable untargeted DIA identification and quantification analysis without any prior spectral library. In the first study, a signal feature detection algorithm is developed to extract and assemble peptide precursor and fragment signals into pseudo MS/MS spectra which can be analyzed by the existing DDA untargeted analysis tools. This novel step enables direct and untargeted (spectral library-free) DIA identification analysis and we show the performance using complex samples including human cell lysate and glycoproteomics datasets. In the second study, a hybrid approach is developed to further improve the DIA quantification sensitivity and reproducibility. The performance of DIA-Umpire quantification approach is demonstrated using an affinity-purification mass spectrometry experiment for protein-protein interaction analysis. Lastly, in the third study, I improve the DIA-Umpire pipeline for data obtained from the Orbitrap family of mass spectrometers. Using public datasets, I show that the improved version of DIA-Umpire is capable of highly sensitive, untargeted analysis of DIA data for the data generated using Orbitrap family of mass spectrometers. The dissertation work addresses the barriers of DIA analysis and should facilitate the adoption of DIA strategy for a broad range of discovery proteomics applications.PhDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120699/1/tsouc_1.pd