Editorial: The next generation in (EuPA Open) Proteomics

Since the completion of the mapping of the human genome1,2, which allowed the identification of over 30,000 genes, continuous efforts have been made to associate the data acquired with DNA functions. New tools for analysing these data have been developed and new disciplines of study have been generated to explore the whole range of the potential applications of human genome-related information; the names of all these new fields of study end in omics. Genomics is the comprehensive analysis of DNA structure and function, whereas transcriptomics is the study of the mRNA pool found within a cell and describes gene expression3, proteomics instead studies the location, structure and function of proteins expressed in a biological system4. The genome is the total chromosome (hereditary) content of a biological system, while the proteome is all the proteins that the genome produces through biological transcription and translation5. The term “proteome” (PROTEins expressed by a genOME) was coined by Wilkins and colleagues in 19966. Initially the word proteomics referred to the techniques used to analyse a large number of proteins at the same time, but, at present, this word covers any approach that yields information on the abundance, properties, interactions, activities, or structures of proteins in a sample7. The name “protein”, derived from the Greek term proteios, meaning “the first rank”, was used for the first time by Berzelius in 1838 to emphasise the importance of these molecules8. The number of proteins produced by the 30,000–40,000 genes of the human genome is estimated to be three or four orders of magnitude higher9. The reasons for this numerical superiority and complexity are4,10: i) differential splicing of mRNA gene transcripts, which allows a single gene to produce multiple protein products; ii) the capability many proteins have of associating with other proteins to form complexes; iii) post-translational modifications, which are additional changes that proteins initially translated within a cell may undergo. These are covalent modifications that regulate protein functions, determining their activity state, cellular location and dynamic interactions with other proteins; the most important and best-studied post-translational modifications are phosphorylation and glycosylation, but many others are common (acetylation, methylation, lipid attachment, sulphation of tyrosine, ubiquitination and disulphide bond formation) among over 300 different known types. The genome, compared to the proteome, is stable4,5; the proteome, on the other hand, is dynamic and changes based on the type and functional state of a cell. The number of proteomes that can be defined within a biological system therefore increases as the complexity of the latter becomes greater7; indeed the number of proteomes within a cell is much lower than the number within a tissue and really much lower than the one within an organism. This makes proteomics a challenging field, largely due to the sheer size of the proteome and the volume of data that can be generated by it11. Transfusion medicine is a clinical discipline characterised by one of the most advanced quality management systems, which is structured so as to assure the production of blood components and raw materials, for biopharmaceutical fractionation, which are safe, efficient and effective12. In Italy, the collection and production procedures performed at blood banks are closely regulated by State laws and/or directives issued by government agencies. At present, proteomics seems to be the most promising tool for global quality assessment of the production process of blood components and blood derivatives. The potential role of proteomics in transfusion medicine has been addressed in several articles, since 20044,7,12–15. The objective of this review is to provide a brief overview of contemporary proteomics technologies and published studies on their applications in transfusion medicine, which are mainly the characterisation of blood product proteomes and their modifications caused by production or storage processes.

Objective To investigate the expression of serum lost goodwill target(LGT)proteome,and to analyze its clinical significance in evaluating prognosis of patient with critical illness on the basis of acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ)score. Methods The serum samples were collected from 96 patients with critical illness and 30 healthy volunteers as healthy control. The expression of serum LGT proteome was detected by surface-enhanced laser desorption/ionization-time of flight-mass spectrometry(SELDI-TOF-MS)protein-chip technology. The abundance value of LGT proteome in patients at admission was measured, and at the same time APACHE Ⅱ score was estimated, in order to analyze its clinical significance in patients with critical illness. Results The amount of LGT proteome in APACHE Ⅱ≥15 group[n= 35,(9.26 ± 7. 51)%]was significantly higher than that of APACHE Ⅱ＜ 15 group[n= 61,(4. 19 ± 4.07)%], and the LGT proteome amount in both groups was significantly higher than that of the healthy control group[(1.52± 0.47)%, both P＜0.01]. Spearman correlation analysis showed that there was significant positive correlation between the abundance of LGT proteome and the APACHE Ⅱ score (r=0. 317, P=0. 002). The abundance of LGT proteome in death group[n=23,(10. 14±9. 23)%]was significantly higher than that in survival group[n=73,(5. 83±3.57)%, P＜0. 01]. The fatality rate of the LGT proteome group with average abundance exceeding 5%[68.0%(17/25)]was significantly higher than that of the LGT proteome group with average abundance lower than 5%[8.5%(6/71), P＜0.01].According to the LGT proteome abundance to evaluate the prognosis of the patients, the positive predict rate was 68.0 %, the negative predict rate was 91.5 %, the false positive rate was 32. 0%, the false negative rate was 8.5%. Conclusion The LGT proteome was intimately correlated with the severity degree of disease condition and prognosis in patients with critical illness. The determination of LGT proteome combined with APACHE Ⅱ score evaluation can probably be an important indicator in evaluating the prognosis of patient with critical illness. Further research on LGT proteome is warranted to facilitate the prognostication and clinical decision-making. Key words: Critical illness ; Acute physiology and chronic health evaluation Ⅱ ; Lost goodwill target proteome ;

Cells within biological systems are constantly adjusting their protein synthesis in response to various environmental changes. To study the rapid cellular regulations in complex biological systems, global proteomic profiling provides important information on system-level regulations, yet physiological properties characteristic of individual cellular subpopulations could be hidden under the characterization. Instead, cell-selective proteomic profiling allows researchers to reveal the heterogeneities in biological systems with phenotypically and even genetically distinct subpopulations under different microenvironments. Chapter 1 describes the development of bioorthogonal noncanonical amino acid tagging (BONCAT) for proteomic profiling with resolution in both space and time: its initial role is protein labeling with temporal resolution via pulse-addition of noncanonical amino acid, which could be recognized by endogenous aminoacyl tRNA-synthetase (aaRS), into systems of interest; later on, mutant aaRSs are identified through mutant synthetase library screening, which allows for efficient incorporation of various types of noncanonical amino acids that could hardly be activated by endogenous machineries. The identification and exploitation of mutant aaRSs allow sensitive cellular selectivity during protein labeling. With unprecedented spatiotemporal resolution of BONCAT, and the advancement in high-resolution mass spectrometry and computational algorithms, BONCAT is a powerful technique for selective proteomic profiling to study physiological regulations in a wide range of complex biological systems. Chapter 2 describes the application of the BONCAT method in cell-selective proteomic profiling in Pseudomonas aeruginosa biofilms. In this work, we targeted an iron-starved subpopulation in biofilms and compared its proteomic profile with that of the entire system. Key gene and pathway regulations in the subpopulation are found through the analysis of the proteomic data, which suggest that iron-starved cells shift their priority towards housing keeping pathways, adapt an energy- and resources-saving mode to cope with their harsh local environmental conditions, and get prepared to disperse for better survival. Analysis of poorly studied proteins highly upregulated in the subpopulation led to the discovery of a previously uncharacterized protein (PA14_52000) that is potentially related to iron acquisition. The transposon insertion mutant PA14_52000::tn showed significantly enhanced pyoverdine production in rich medium and reduced biofilm formation. Chapter 3 describes the study of physiological regulations in Bacillus subtilis K-state subpopulation via BONCAT. A subset of B. subtilis cells, typically 10% - 20% of the entire population, enter K-state in a stochastic manner. With the low level of K-state entry rate and high randomness, we challenged BONCAT to specifically capture gene and pathway regulations in K-state cells and compared the proteomic profiling with that of the entire population. Regardless of the difficulties of selective protein labeling inherent in the system, our results indicate that BONCAT has high specificity and resolution in proteomic profiling for minor subpopulations and proteins with low overall absolute abundance. We found multiple pathways and genes characteristic of K-state regulated differentially from the entire population, either significantly up- or down-regulated. Proteins that are uncharacterized or previously known for functions irrelevant of K-state are highly abundant in the subpopulation, providing new insight toward their alternative functions critical for K-state cells and future investigation directions of K-state study.

Pituitary adenoma is a serious disease that affects human health through interfering hypothalamus-pituitary-target organ axis systems. Proteomics is an effective approach to elucidate molecular mechanisms of a pituitary adenoma and discover effective biomarkers for a pituitary adenoma. A great progress has been made in the field of pituitary adenoma proteomics in the past ten years: ⑴ the use of laser capture microdissection, ⑵ functional pituitary adenoma proteomics (such as prolactinoma), ⑶ proteomics analysis of invasive characteristics of nonfunctional pituitary adenoma, ⑷ protein post-translational modifications including phosphorylation and tyrosine nitration, ⑸ the use of protein antibody array, ⑹ proteomics analysis of hormone isoforms, ⑺ serum proteomics and peptidomics, ⑻ integration of proteomics and other omics data, and ⑼ proposal of multi-parameter systematic strategy for a pituitary adenoma. Key words: Pituitary neoplasms/ME; Proteome; Review

Single-cell functional proteomics assays can connect genomic information to biological function through quantitative and multiplex protein measurements. Tools for single-cell proteomics have developed rapidly over the past 5 years and are providing unique opportunities. This thesis describes an emerging microfluidics-based toolkit for single cell functional proteomics, focusing on the development of the single cell barcode chips (SCBCs) with applications in fundamental and translational cancer research. The microchip designed to simultaneously quantify a panel of secreted, cytoplasmic and membrane proteins from single cells will be discussed at the beginning, which is the prototype for subsequent proteomic microchips with more sophisticated design in preclinical cancer research or clinical applications. The SCBCs are a highly versatile and information rich tool for single-cell functional proteomics. They are based upon isolating individual cells, or defined number of cells, within microchambers, each of which is equipped with a large antibody microarray (the barcode), with between a few hundred to ten thousand microchambers included within a single microchip. Functional proteomics assays at single-cell resolution yield unique pieces of information that significantly shape the way of thinking on cancer research. An in-depth discussion about analysis and interpretation of the unique information such as functional protein fluctuations and protein-protein correlative interactions will follow. The SCBC is a powerful tool to resolve the functional heterogeneity of cancer cells. It has the capacity to extract a comprehensive picture of the signal transduction network from single tumor cells and thus provides insight into the effect of targeted therapies on protein signaling networks. We will demonstrate this point through applying the SCBCs to investigate three isogenic cell lines of glioblastoma multiforme (GBM). The cancer cell population is highly heterogeneous with high-amplitude fluctuation at the single cell level, which in turn grants the robustness of the entire population. The concept that a stable population existing in the presence of random fluctuations is reminiscent of many physical systems that are successfully understood using statistical physics. Thus, tools derived from that field can probably be applied to using fluctuations to determine the nature of signaling networks. In the second part of the thesis, we will focus on such a case to use thermodynamics-motivated principles to understand cancer cell hypoxia, where single cell proteomics assays coupled with a quantitative version of Le Chatelier's principle derived from statistical mechanics yield detailed and surprising predictions, which were found to be correct in both cell line and primary tumor model. The third part of the thesis demonstrates the application of this technology in the preclinical cancer research to study the GBM cancer cell resistance to molecular targeted therapy. Physical approaches to anticipate therapy resistance and to identify effective therapy combinations will be discussed in detail. Our approach is based upon elucidating the signaling coordination within the phosphoprotein signaling pathways that are hyperactivated in human GBMs, and interrogating how that coordination responds to the perturbation of targeted inhibitor. Strongly coupled protein-protein interactions constitute most signaling cascades. A physical analogy of such a system is the strongly coupled atom-atom interactions in a crystal lattice. Similar to decomposing the atomic interactions into a series of independent normal vibrational modes, a simplified picture of signaling network coordination can also be achieved by diagonalizing protein-protein correlation or covariance matrices to decompose the pairwise correlative interactions into a set of distinct linear combinations of signaling proteins (i.e. independent signaling modes). By doing so, two independent signaling modes – one associated with mTOR signaling and a second associated with ERK/Src signaling have been resolved, which in turn allow us to anticipate resistance, and to design combination therapies that are effective, as well as identify those therapies and therapy combinations that will be ineffective. We validated our predictions in mouse tumor models and all predictions were borne out. In the last part, some preliminary results about the clinical translation of single-cell proteomics chips will be presented. The successful demonstration of our work on human-derived xenografts provides the rationale to extend our current work into the clinic. It will enable us to interrogate GBM tumor samples in a way that could potentially yield a straightforward, rapid interpretation so that we can give therapeutic guidance to the attending physicians within a clinical relevant time scale. The technical challenges of the clinical translation will be presented and our solutions to address the challenges will be discussed as well. A clinical case study will then follow, where some preliminary data collected from a pediatric GBM patient bearing an EGFR amplified tumor will be presented to demonstrate the general protocol and the workflow of the proposed clinical studies.

Nearly all cellular functions are dictated by proteins, thus the proteome defines the functional capacity of a cell. Protein interactions form and dissociate in order to perform specific cellular functions. These dynamic interactions are subject to stochastic fluctuations causing cell-to-cell variability within a population. The investigation of dynamic and heterogeneous multimolecular protein complexes is a hallmark of experimental systems biology. However, this aim puts demanding requirements on analytical methods used since these should provide single molecule and single cell resolution. Due to the low copy number of proteins from individual cells, the lack of amplification techniques for proteins and the small sample volumes, single-cell proteomics still challenges existing biophysical and biochemical methods and requires novel and complementary approaches.
\n
\nThis thesis is part of a project that seeks for a novel approach to visual proteomics that aims for the qualitative and quantitative analysis of the entire proteome from a single eukaryotic cell by transmission electron microscopy (TEM), also termed as 'single-cell visual proteomics'. However, the identification of all the molecular components constituting the crude cell lysate is difficult and suggests to follow a targeted proteomics approach where isolation and separation techniques are applied in combination with TEM to make protein identification comprehensible.
\n
\nDuring the course of this thesis, a novel approach to targeted proteomics was developed and evaluated, combining microfluidic techniques with magnetic beads and photocleavable composites. The proposed method enables rapid and specific isolation of different protein complexes from a few thousand cells under close to physiological condition. The isolation method yields samples of high purity, in particular for a one-step purification method. Subsequent single particle analyses of negative stain TEM images provide averaged projection structures of the isolated target proteins. During the isolation process, the target protein complexes are immobilized and immuno-labelling techniques using electron-dense markers can be applied. This procedure allows protein-binding partners constituting a complex to be detected. Hence, the approach can provide initial information on structure and composition of weakly interacting protein complexes formed in vivo without applying time consuming traditional large-scale methods for protein expression and purification followed by complex hybrid techniques for analysis.
\n
\nIn addition, initial experiments showed that quantitative information on protein abundances can be extracted upon combining the isolation method with semi-automatic image acquisition and analysis procedures used for TEM investigations. Thereby the suitability of single particle TEM for protein quantification is reported for the first time. Consequently, the fundamentals of 'quantitative TEM' were elucidated and a method for reliable and efficient concentration measurements proposed. The results imply that picomolar to nanomolar ranged concentrations, typically hard to assess with traditional absorbance-based methods, can be reliably measured. Interestingly, this technique uses standard equipment readily available in many laboratories.

Introduction In recent years the proteome coverage using shotgun proteomics has steadily increased. Low complex proteomes, such as the E. coli or yeast proteomes, can be measured almost completely in a single LC-MS measurement. For relative quantitation, the label free approach has recently gained in popularity mainly due to its simplicity. However, this approach has been limited by the semi-stochastic nature of shotgun proteomics which leads to a large number of missing values, especially if many conditions are measured. Even though MS1 alignment attenuates the missing value problem, it is difficult to control the reliability of identification with this approach. Further, low intensity signals in MS1 often show interferences which lowers the precision of relative quantitation. Data independent acquisition (DIA) has promised to solve the missing value problem. By using wide precursor windows, DIA consistently measures all precursors that are above the limit of detection. For DIA data analysis we have developed the Spectronaut software. Using Spectronaut, we get more identifications in a single LC-MS measurement as compared to shotgun proteomics. Further, this approach resulted in quasi gap-free quantitation matrices without alignment and higher precision of quantitation as compared to shotgun proteomics.

Most current proteomics workflows, while highly developed and sophisticated, are usually not compatible with routine proteome analysis due to due heaviness of technical procedures and lack of reproducibility. Therefore, the development of reliable high-throughput proteomic platforms represents a crucial step for the advancement of proteomics research. In this thesis three examples of such platforms have been investigated. Every step of these pipelines was studied and subjected to further improvement, in order to enhance their efficiency and practicability. Many developments presented in this thesis can be helpful in different fields of proteomics. Engineering high-throughput proteomics pipelines will increase the role of proteomics in life science research and open new avenues for biomarkers discovery.

Editors Overview PART 1: TECHNOLOGIES Pre-analytical issues in clinical proteomic studies Protein separation by 2DE Protein separation : liquid chromatography HPLC in protein discovery IEF analysis of peptides for biomarkers analysis Capillary electrophoresis separations for clinical proteomics Quantitative proteomics using nanoLC with high accuracy mass spectrometry Antibody microarrays for protein and glycan detection Biomarker Identification: The Role of Experimental Design, Statistics, and Data Sharing PART 2: CANCER Applications of stable isotope tagging based quantitive proteomics in cancer research 2-D liquid seperations, protein microarrays and mass spectrometry in comprehensive analysis of PTM and biomarker discovery Development and Use of Reversed-Phase Protein Microarrays for Clinical Applications CDK1 and cancer: usefulness of proteomic approaches in assesment of the molecular mechanisms and efficacy of novel therapeutics PART 3: CARDIOVASCULAR DISEASE Diagnostic markers for monitoring heart transplant rejection The study of microheterogeneity in human plasma proteins: applications to acute myocardial infarction Discovery of biomarkers for cardiovascular diseases Development of biomarker Developfment Pipeline: Search for Myocardial Ischemia Biomarkers Albuminome as a tool for biomarker discovery PART 4: VASCULAR DISEASES Application of Metabolomics to Cardiovascular Biomarker and Pathway Discovery Urinary biomarkers in diabetic nephropathy and other glomerular diseases Pulmonary proteomics Proteomics providing insights into major psychiatric disorders PART 6: BIOMARKERS Redox Proteomics Analysis of Oxidative Modified Brain Proteins in Alzheimer's Disease and Mild Cognitive Impairment: Insights into the Progression of This Dementing Disorder Toxicoproteomics: correlating tissue and serum proteins in liver injuries Biomarkers for renal disease and uraemic toxins HIV and other viral screens PART 6: AUTOANTIBODIES AND SIGNATURE BIOMARKERS Application of Fungal Cyclic Peptides and Metabolites Microarray approaches to autoantibody profiling Identification of tumor antigen-directed autoantibodies PART 7: FUTURE Antibody and Reverse Capture Protein Microarrays Use of Antibody Microarrays in the Analysis of Inflammation, Autoimmunity, Viral Infection, and Cancer Metastases The Future: Translation from Discovery to the Clinic - Roles of HUPO and industry in biomarker discovery

With the completion of many genome sequencing projects of organisms, the post-genomic era has arrived with the application of proteomics in the overall level of complexity of life. Protein is the executor of biological growth, differentiation, metabolism and regulation, and thus it becomes a very important technical method in examination of the differential protein expression of organism under different conditions. Differential proteomics is mainly used for selection and identification of the differences and changes between the proteins at the different types and state of samples, looking for any significant factors that cause the difference in protein spectrum of the sample, thus to understand the development and change of individual physiology. The proteome of some parasites has developed rapidly as a branch of proteomics. In this paper, we reviewed the differential proteomics applied in parasite research. Key words: Proteomics; Differential proteomics; Parasite

The concept of vulnerable plaques has been long described since some atherosclerotic lesions rupture suddenly causing myocardial infarctions and strokes while others remain quiescent or stable for many years. Two potentially feasible approaches were used here in an attempt to identify these vulnerable plaques: imaging and urine proteomics. Imaging: Several intravascular imaging techniques have been investigated to identify vulnerable plaques without definitive success yet, demanding better understanding of pathophysiology of these lesions and more reliable imaging methods. We described the near infrared range (NIR) intrinsic fluorescent activity to be a property unique to the unstable plaques, using a well-established mouse model of tandem stenosis as well as human carotid endarterectomy samples. The source of NIR autofluorescence is shown to be intraplaque haemorrhage where haem degradation products were intermingled with various chemicals of the necrotic core. We also demonstrated that changes in the plaque burden were reflected by the changes in NIR fluorescent intensity using haem oxygenase enzyme modulation which played a complex role in the pathophysiology of plaque progression and vulnerability. NIR autofluorescence in the areas of intraplaque haemorrhage, a critical element of plaque vulnerability, provides a much needed new foundation in the field of intravascular imaging for unstable plaques. Although NIR fluorescent imaging is still in the pre-clinical stage, it should be further explored with the aim of developing intravascular probe to be applied in clinical studies. Urine proteomics: A pilot proteomic study aimed at the identification of secreted urinary peptide biomarkers and the modelling of a prognostic classifier for acute coronary syndrome (ACS) is reported in this thesis. The urinary proteome profile data of 126 individuals who had suffered from ACS up to 5 years post urine sampling and proteome data of 126 controls without ACS were analysed. The initial statistical comparison of proteome profile data of 84 individuals with an ACS and 84 matched controls resulted in the discovery of 75 potential ACS-specific prognostic peptide biomarkers. Based on these peptide biomarkers we established the support vector machine-modelled prognostic ACS classifier ACSP75. The performance of the classifier was assessed using sensitivity, specificity and discrimination (c-statistics) and was compared the performance of the Framingham risk score (FHS), and to an algorithm combining the classifier, age and BMI. In the validation data set, the classifier identified individuals with an ACS with a sensitivity of 73.8% and demonstrated reasonable discrimination (c statistic=0.664). The classifier showed similar performance compared to FHS (C-statistics: 0.664 vs 0.644 [p=0.692] for classifier and FHS, respectively). In a model where we combined the classifier with other traditional risk factors (BMI and age), the algorithm showed good discrimination (c-statistic=0.707), but was not significantly better than the classifier itself (p=0.213). The sensitivity (83.3 %) and specificity (78.6 %) of the composite classifier was better than the classifier on its own. We demonstrate that the proteomic classifier ACSP75 based on urinary peptide biomarkers has the potential to predict future ACS events. The classifier and the composite classifier should be validated in a large cohort.

Objective To analyze the serum related proteomic fingerprints when Lost Goodwill Target (LGT) proteomic fingerprints drifting from negative to positive in the advanced lung adenocarcinoma patients. Methods The serum proteomic fingerprints of 31 advanced lung adenocarcinoma patients whose LGT fingerprints drifted from negative to positive were detected by SELDI and CM10 protein chip. More than 10 % cluster and M/Z values from 11,000+H to 12,000+H was regarded as LGT positive, otherwise as negative. Different fingerprints were screened by Biomarker Wizard 3.1 and Biomarker Wizard software and the decision tree model was established. Results There were 16 statistically different protein peaks when LGT fingerprints drifting from negative to positive, including 10 up-regulation proteomic fingerprints (M/Z:11531, 11483, 11686, 11394, 11822, 11323, 11911, 12450, 5811 and 5709) and 6 down regulation proteomic fingerprints( M/Z: 4126, 13927, 13784, 7001, 1959 and 2741). Conclusion By SELDI and CM10 protein chip detection, up-regulating fingerprints of M/Z 11531, 11483, 11686, 11394, 11822 and 11323 were regarded as the subtype of LGT when it drifting from negative to positive, while up-regulation of M/Z 11911,12450, 5811 and 5709 and down-regulating of M/Z 4126, 13927, 13784, 7001 and 1959 were regarded as the related fingerprints when LGT drifting from negative to positive. The above different fingerprints are constituted of the fingerprints library when LGT fingerprints drifting from negative to positive and it will provide a platform for studying the LGT proteins. Key words: Lung neoplasms; Adenocarcinoma; Spectrometry,mass,matrix-assisted laser desorptim-ionization; Proteomics; Peptide maping

Armillaria mellea is a major plant pathogen of timber and agronomic crops. Yet, no large-scale “-omics” data are available to enable new studies, and limited experimental models are available to investigate basidiomycete pathogenicity. Herein it is revealed that the A. mellea genome comprises 58.35 Mb, contains 14473 gene models, of average length 1575 bp (4.72 introns/gene). A novel, large-scale proteomic shotgun analysis method, was developed for high-throughput proteomics, applicable to other Armillaria spp. and basidiomycete studies. Tandem mass spectrometry identified 951 mycelial (n = 629 unique) and secreted (n = 183 unique) proteins from A. mellea. Almost 100 mycelial proteins were either species-specific or previously unidentified at the protein level. Signal sequence occurrence was 4-fold greater for secreted (50.2%) compared to mycelial (12%) proteins. Analyses revealed a rich reservoir of carbohydrate degrading enzymes, laccases, lignin peroxidases and large cytochrome P450ome in the A. mellea proteome, reminiscent of both basidiomycete and ascomycete glycodegradative arsenals. Under oxidative stress, A. mellea underwent extensive proteome remodelling within 3 hours and comparative proteomics detected differentially regulated proteins (n = 14). Expression of proteins involved in methionine and polyamine biosynthesis (n = 2) and proteins (n = 2) involved maintenance of cellular homeostasis by regulation of homocysteine levels were significantly upregulated. This response may mitigate against oxidative cellular damage. Proteins (n = 2) putatively involved in the regulation of biosynthesis of specific polyamines were also identified. Remarkably, A. mellea exhibits a specific and significant killing effect against Candida albicans during co-culture. Proteomic investigation of this interaction revealed 205 secreted proteins, with the unique expression of defensive and potentially offensive A. mellea proteins (n = 30). Four proteins expressed in co-cultures were specific to A. mellea, or previously unidentified either by homology or at the protein level. Overall, the data reveal new insights into the origin of basidiomycete virulence, and a new model system for further studies aimed at deciphering fungal pathogenic mechanisms is presented.

Phenotyping of human muscle based on its profile of myosin heavy chain isoforms is commonly used to help understand changes in muscle function. However, in many instances, measureable changes in force output or contractility occur in the absence of any change in myosin heavy chain profile. Therefore, more sophisticated analysis is required. Proteomic techniques including 2-dimensional gel electrophoresis, high- performance liquid chromatography and peptide mass spectrometry can be used to investigate changes in the abundance of hundreds of proteins simultaneously. To date, such techniques have not been used to specifically characterise the human myofibrillar proteome, or study how the myofibrillar proteome relates to muscle outputs such as peak isometric force or the velocity of contraction. This thesis presents a series of studies that develop proteomic techniques for the analysis of myofibrillar proteins as well as optimisation of techniques for measuring the range of muscle output from isometric through to velocity maximum of the human knee extensor muscles in vivo. After optimisation, the proteomic and muscle function measurement were employed to study diurnal variation. Time-of-day differences in sports performance and muscle function are widely reported, and typically, performance is ~10 % greater in the evening compared to the morning. This is consistent with our result in Chapter 3; we investigated this chapter by conducting a battery of muscle performance tests in a population of well-familiarised participants. Our data show that RFD exhibits the greatest diurnal variation (18 %) followed by isometric force (10.2 %). The diurnal variation in IKD data was less robust (range 8.1 - 9.8 %), which may have been due to the lesser precision of this technique compared to MVC and RFD. Therefore MVC and RFD were used in the final study. In final study, this thesis reports significantly (P<0.05) greater peak isometric force (11 %) and rate of force development (16 %) of knee extensor muscles of young strength-trained males in the evening compared to morning. Proteomic analysis of biopsy samples of the vastus lateralis profiled more than 100 myofibrillar protein species and detected 8 significant differences in protein abundance between morning and evening samples. The greatest difference was in the abundance of the slow isoform of myosin binding protein C (MyBPC1), which is known to modulate the activity of actin-bound myosin ATPases. MyBPC1 was resolved to 6 species; therefore the difference in abundance of one species reported here likely represents a change in post-translational modification. Therefore, this thesis provides associational evidence that post-translational modification of MyBPC1 contributes to the diurnal variation in muscle function.

Mammalian skeletal muscles are composed of a variety of muscle fibers with specialized functional properties. Slow fibers are suited for long lasting and low intensity contractile activity, while various subtypes of fast fibers are optimized to produce high force and power even with a significant fatigue. The functional specialization of muscle fibers is based on selective gene expression regulation, which provides each fiber with a specific protein complement. The recent refinement of small-scale sample preparation, combined with the development of mass spectrometers characterized by high sensitivity, sequencing speed and mass accuracy, has allowed the characterization of the proteome of single muscle fibers with an unprecedented resolution. In the last few years, the first studies on the global proteomics of individual fibers of different types have been published. In this short review we discuss the methodological advancements which have opened the way to single fiber proteomics and the discovery power of this approach. We provide examples of how specific features of single fibers can be overlooked when whole muscle or multi-fiber samples are analyzed and can only be detected when a single fiber proteome is analyzed. Thus, novel subtype-specific metabolic features, most prominently mitochondrial specialization of fiber types have been revealed by single fiber proteomics. In the same way, specific adaptive responses of single fibers to aging or loss of neural input have been detected when single fibers were individually analyzed. We conclude that the fiber type-resolved proteomes represent a powerful tool which can be applied to a variety of physiological and pathological conditions.