A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Improved sub-cellular resolution via simultaneous analysis of organelle proteomics data across varied experimental conditions

Spatial organisation of proteins according to their function plays an important role in the specificity of their molecular interactions. Emerging proteomics methods seek to assign proteins to sub-cellular locations by partial separation of organelles and computational analysis of protein abundance distributions among partially separated fractions. Such methods permit simultaneous analysis of unpurified organelles and promise proteome-wide localisation in scenarios wherein perturbation may prompt dynamic re-distribution. Resolving organelles that display similar behavior during a protocol designed to provide partial enrichment represents a possible shortcoming. We employ the Localisation of Organelle Proteins by Isotope Tagging (LOPIT) organelle proteomics platform to demonstrate that combining information from distinct separations of the same material can improve organelle resolution and assignment of proteins to sub-cellular locations. Two previously published experiments, whose distinct gradients are alone unable to fully resolve six known protein-organelle groupings, are subjected to a rigorous analysis to assess protein-organelle association via a contemporary pattern recognition algorithm. Upon straightforward combination of single-gradient data, we observe significant improvement in protein-organelle association via both a non-linear support vector machine algorithm and partial least-squares discriminant analysis. The outcome yields suggestions for further improvements to present organelle proteomics platforms, and a robust analytical methodology via which to associate proteins with sub-cellular organelles.

Comparison of five popular normalization methods using a spike-in proteomics data set

Mass spectrometry (MS)-based proteomics has seen significant technical advances during the past two decades and mass spectrometry has become a central tool in many biosciences. Despite the popularity of MS-based methods, the handling of the systematic non-biological variation in the data remains a common problem. This biasing variation can result from several sources ranging from sample handling to differences caused by the instrumentation. Normalization is the procedure which aims to account for this biasing variation and make samples comparable. Many normalization methods commonly used in proteomics have been adapted from the DNA-microarray world. Studies comparing normalization methods with proteomics data sets using some variability measures exist. However, a more thorough comparison looking at the quantitative and qualitative differences of the performance of the different normalization methods and at their ability in preserving the true differential expression signal of proteins, is lacking. In this thesis, several popular and widely used normalization methods (the Linear regression normalization, Local regression normalization, Variance stabilizing normalization, Quantile-normalization, Median central tendency normalization and also variants of some of the forementioned methods), representing different strategies in normalization are being compared and evaluated with a benchmark spike-in proteomics data set. The normalization methods are evaluated in several ways. The performance of the normalization methods is evaluated qualitatively and quantitatively on a global scale and in pairwise comparisons of sample groups. In addition, it is investigated, whether performing the normalization globally on the whole data or pairwise for the comparison pairs examined, affects the performance of the normalization method in normalizing the data and preserving the true differential expression signal. In this thesis, both major and minor differences in the performance of the different normalization methods were found. Also, the way in which the normalization was performed (global normalization of the whole data or pairwise normalization of the comparison pair) affected the performance of some of the methods in pairwise comparisons. Differences among variants of the same methods were also observed.

The integration of proteomics and systems approaches to map regulatory mechanisms underpinning platelet function.

Platelets in the circulation are triggered by vascular damage to activate, aggregate and form a thrombus that prevents excessive blood loss. Platelet activation is stringently regulated by intracellular signalling cascades, which when activated inappropriately lead to myocardial infarction and stroke. Strategies to address platelet dysfunction have included proteomics approaches which have lead to the discovery of a number of novel regulatory proteins of potential therapeutic value. Global analysis of platelet proteomes may enhance the outcome of these studies by arranging this information in a contextual manner that recapitulates established signalling complexes and predicts novel regulatory processes. Platelet signalling networks have already begun to be exploited with interrogation of protein datasets using in silico methodologies that locate functionally feasible protein clusters for subsequent biochemical validation. Characterization of these biological systems through analysis of spatial and temporal organization of component proteins is developing alongside advances in the proteomics field. This focused review highlights advances in platelet proteomics data mining approaches that complement the emerging systems biology field. We have also highlighted nucleated cell types as key examples that can inform platelet research. Therapeutic translation of these modern approaches to understanding platelet regulatory mechanisms will enable the development of novel anti-thrombotic strategies.

Unravelling the Neospora caninum secretome through the secreted fraction (ESA) and quantification of the discharged tachyzoite using high-resolution mass spectrometry-based proteomics

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Shotgun proteomics reveals physiological response to ocean acidification in Crassostrea gigas

Background. Ocean acidification as a result of increased anthropogenic CO2 emissions is occurring in marine and estuarine environments worldwide. The coastal ocean experiences additional daily and seasonal fluctuations in pH that can be lower than projected end of century open ocean pH reductions. Projected and current ocean acidification have wide-ranging effects on many aquatic organisms, however the exact mechanisms of the impacts of ocean acidification on many of these animals remains to be characterized. Methods. In order to assess the impact of ocean acidification on marine invertebrates, Pacific oysters (Crassostrea gigas) were exposed to one of four different pCO2 levels for four weeks: 400 µatm (pH 8.0), 800 µatm (pH 7.7), 1000 µatm (pH 7.6), or 2800 µatm (pH 7.3). At the end of 4 weeks a variety of physiological parameters were measured to assess the impacts of ocean acidification: tissue glycogen content and fatty acid profile, shell micromechanical properties, and response to acute heat shock. To determine the effects of ocean acidification on the underlying molecular physiology of oysters and their stress response, some of the oysters from 400 µatm and 2800 µatm were exposed to an additional mechanical stress and shotgun proteomics were done on oysters from high and low pCO2 and from with and without mechanical stress. Results. At the end of the four week exposure period, oysters in all four pCO2 environments deposited new shell, but growth rate was not different among the treatments. However, micromechanical properties of the new shell were compromised by elevated pCO2. Elevated pCO2 affected neither whole body fatty acid composition, nor glycogen content, nor mortality rate associated with acute heat shock. Shotgun proteomics revealed that several physiological pathways were significantly affected by ocean acidification, including antioxidant response, carbohydrate metabolism, and transcription and translation. Additionally, the proteomic response to a second stress differed with pCO2, with numerous processes significantly affected by mechanical stimulation at high versus low pCO2 (all proteomics data are available in the ProteomeXchange under the identifier PXD000835). Discussion. Oyster physiology is significantly altered by exposure to elevated pCO2, indicating changes in energy resource use. This is especially apparent in the assessment of the effects of pCO2 on the proteomic response to a second stress. The altered stress response illustrates that ocean acidification may impact how oysters respond to other changes in their environment. These data contribute to an integrative view of the effects of ocean acidification on oysters as well as physiological trade-offs during environmental stress.

Functional proteomics to dissect tyrosine kinase signalling pathways in cancer

Advances in the generation and interpretation of proteomics data have spurred a transition from focusing on protein identification to functional analysis. Here we review recent proteomics results that have elucidated new aspects of the roles and regulation of signal transduction pathways in cancer using the epidermal growth factor receptor (EGFR), ERK and breakpoint cluster region (BCR)-ABL1 networks as examples. The emerging theme is to understand cancer signalling as networks of multiprotein machines which process information in a highly dynamic environment that is shaped by changing protein interactions and post-translational modifications (PTMs). Cancerous genetic mutations derange these protein networks in complex ways that are tractable by proteomics.

Mutations in the gene encoding IFT dynein complex component WDR34 cause jeune asphyxiating thoracic dystrophy

Bidirectional (anterograde and retrograde) motor-based intraflagellar transport (IFT) governs cargo transport and delivery processes that are essential for primary cilia growth and maintenance and for hedgehog signaling functions. The IFT dynein-2 motor complex that regulates ciliary retrograde protein transport contains a heavy chain dynein ATPase/motor subunit, DYNC2H1, along with other less well functionally defined subunits. Deficiency of IFT proteins, including DYNC2H1, underlies a spectrum of skeletal ciliopathies. Here, by using exome sequencing and a targeted next-generation sequencing panel, we identified a total of 11 mutations in WDR34 in 9 families with the clinical diagnosis of Jeune syndrome (asphyxiating thoracic dystrophy). WDR34 encodes a WD40 repeat-containing protein orthologous to Chlamydomonas FAP133, a dynein intermediate chain associated with the retrograde intraflagellar transport motor. Three-dimensional protein modeling suggests that the identified mutations all affect residues critical for WDR34 protein-protein interactions. We find that WDR34 concentrates around the centrioles and basal bodies in mammalian cells, also showing axonemal staining. WDR34 coimmunoprecipitates with the dynein-1 light chain DYNLL1 in vitro, and mining of proteomics data suggests that WDR34 could represent a previously unrecognized link between the cytoplasmic dynein-1 and IFT dynein-2 motors. Together, these data show that WDR34 is critical for ciliary functions essential to normal development and survival, most probably as a previously unrecognized component of the mammalian dynein-IFT machinery.

Cholesterol Disturbances in Inherited Neurodegenerative Diseases : Studies on Npc1, Ppt1 and Ctsd deficient mice

The central nervous system (CNS) is the most cholesterol-rich organ in the body. Cholesterol is essential to CNS functions such as synaptogenesis and formation of myelin. Significant differences exist in cholesterol metabolism between the CNS and the peripheral organs. However, the regulation of cholesterol metabolism in the CNS is poorly understood compared to our knowledge of the regulation of cholesterol homeostasis in organs reached by cholesterol-carrying lipoprotein particles in the circulation. Defects in CNS cholesterol homeostasis have been linked to a variety of neurodegenerative diseases, including common diseases with complex pathogenetic mechanisms such as Alzheimer s disease. In spite of intense effort, the mechanisms which link disturbed cholesterol homeostasis to these diseases remain elusive. We used three inherited recessive neurodegenerative disorders as models in the studies included in this thesis: Niemann-Pick type C (NPC), infantile neuronal ceroid lipofuscinosis and cathepsin D deficiency. Of these three, NPC has previously been linked to disturbed intracellular cholesterol metabolism. Elucidating the mechanisms with which disturbances of cholesterol homeostasis link to neurodegeneration in recessive inherited disorders with known genetic lesions should shed light on how cholesterol is handled in the healthy CNS and help to understand how these and more complex diseases develop. In the first study we analyzed the synthesis of sterols and the assembly and secretion of lipoprotein particles in Npc1 deficient primary astrocytes. We found that both wild type and Npc1 deficient astrocytes retain significant amounts of desmosterol and other cholesterol precursor sterols as membrane constituents. No difference was observed in the synthesis of sterols and the secretion of newly synthesized sterols between Npc1 wild type, heterozygote or knockout astrocytes. We found that the incorporation of newly synthesized sterols into secreted lipoprotein particles was not inhibited by Npc1 mutation, and the lipoprotein particles were similar to those excreted by wild type astrocytes in shape and size. The bulk of cholesterol was found to be secreted independently of secreted NPC2. These observations demonstrate the ability of Npc1 deficient astrocytes to handle de novo sterols, and highlight the unique sterol composition in the developing brain. Infantile neuronal ceroid lipofuscinosis is caused by the deficiency of a functional Ppt1 enzyme in the cells. In the second study, global gene expression studies of approximately 14000 mouse genes showed significant changes in the expression of 135 genes in Ppt1 deficient neurons compared to wild type. Several genes encoding for enzymes of the mevalonate pathway of cholesterol biosynthesis showed increased expression. As predicted by the expression data, sterol biosynthesis was found to be upregulated in the knockout neurons. These data link Ppt1 deficiency to disturbed cholesterol metabolism in CNS neurons. In the third study we investigated the effect of cathepsin D deficiency on the structure of myelin and lipid homeostasis in the brain. Our proteomics data, immunohistochemistry and western blotting data showed altered levels of the myelin protein components myelin basic protein, proteolipid protein and 2 , 3 -cyclic nucleotide 3 phosphodiesterase in the brains of cathepsin D deficient mice. Electron microscopy revealed altered myelin structure in cathepsin D deficient brains. Additionally, plasmalogen-derived alkenyl chains and 20- and 24-carbon saturated and monounsaturated fatty acids typical for glycosphingolipids were found to be significantly reduced, but polyunsaturated species were significantly increased in the knockout brains, pointing to a decrease in white matter. The levels of ApoE and ABCA1 proteins linked to cholesterol efflux in the CNS were found to be altered in the brains of cathepsin D deficient mice, along with an accumulation of cholesteryl esters and a decrease in triglycerols. Together these data demonstrate altered myelin architecture in cathepsin D deficient mice and link cathepsin D deficiency to aberrant cholesterol metabolism and trafficking. Basic research into rare monogenic diseases sheds light on the underlying biological processes which are perturbed in these conditions and contributes to our understanding of the physiological function of healthy cells. Eventually, understanding gained from the study of disease models may contribute towards establishing treatment for these disorders and further our understanding of the pathogenesis of other, more complex and common diseases.

A proteomic view of probiotic Lactobacillus rhamnosus GG

Lactobacillus rhamnosus GG is a probiotic bacterium that is known worldwide. Since its discovery in 1985, the health effects and biology of this health-promoting strain have been researched at an increasing rate. However, knowledge of the molecular biology responsible for these health effects is limited, even though research in this area has continued to grow since the publication of the whole genome sequence of L. rhamnosus GG in 2009. In this thesis, the molecular biology of L. rhamnosus GG was explored by mapping the changes in protein levels in response to diverse stress factors and environmental conditions. The proteomics data were supplemented with transcriptome level mapping of gene expression. The harsh conditions of the gastro-intestinal tract, which involve acidic conditions and detergent-like bile acids, are a notable challenge to the survival of probiotic bacteria. To simulate these conditions, L. rhamnosus GG was exposed to a sudden bile stress, and several stress response mechanisms were revealed, among others various changes in the cell envelope properties. L. rhamnosus GG also responded in various ways to mild acid stress, which probiotic bacteria may face in dairy fermentations and product formulations. The acid stress response of L. rhamnosus GG included changes in central metabolism and specific responses related to the control of intracellular pH. Altogether, L. rhamnosus GG was shown to possess a large repertoire of mechanisms for responding to stress conditions, which is a beneficial character of a probiotic organism. Adaptation to different growth conditions was studied by comparing the proteome level responses of L. rhamnosus GG to divergent growth media and to different phases of growth. Comparing different growth phases revealed that the metabolism of L. rhamnosus GG is modified markedly during shift from the exponential to the stationary phase of growth. These changes were seen both at proteome and transcriptome levels and in various different cellular functions. When the growth of L. rhamnosus GG in a rich laboratory medium and in an industrial whey-based medium was compared, various differences in metabolism and in factors affecting the cell surface properties could be seen. These results led us to recommend that the industrial-type media should be used in laboratory studies of L. rhamnosus GG and other probiotic bacteria to achieve a similar physiological state for the bacteria as that found in industrial products, which would thus yield more relevant information about the bacteria. In addition, an interesting phenomenon of protein phosphorylation was observed in L. rhamnosus GG. Phosphorylation of several proteins of L. rhamnosus GG was detected, and there were hints that the degree of phosphorylation may be dependent on the growth pH.

A proteomic analysis of thioacetamide-induced hepatotoxicity and cirrhosis in rat livers

Thioacetamide (TAA) administration is an established technique for generating rat models of liver fibrosis and cirrhosis. Oxidative stress is believed to be involved as TAA-induced liver fibrosis is initiated by thioacetamide S-oxide, which is derived from the biotransformation of TAA by the microsomal flavine-adenine dinucleotide (FAD)-containing monooxygense (FMO) and cytochrome P450 systems. A two-dimensional gel electrophoresis-mass spectrometry approach was applied to analyze the protein profiles of livers of rats administered with sublethal doses of TAA for 3, 6 and 10 weeks respectively. With this approach, 59 protein spots whose expression levels changed significantly upon TAA administration were identified, including three novel proteins. These proteins were then sorted according to their common biochemical properties and functions, so that pathways involved in the pathogenesis of rat liver fibrosis due to TAA-induced toxicity could be elucidated. As a result, it was found that TAA-administration down-regulated the enzymes of the primary metabolic pathways such as fatty acid beta-oxidation, branched chain amino acids and methionine breakdown. This phenomenon is suggestive of the depletion of succinyl-CoA which affects heme and iron metabolism. Up-regulated proteins, on the other hand, are related to oxidative stress and lipid peroxidation. Finally, these proteomics data and the data obtained from the scientific literature were integrated into an

The extracellular vesicles of the helminth pathogen, Fasciola hepatica: biogenesis pathways and cargo molecules involved in parasite pathogenesis.

Extracellular vesicles (EVs) released by parasites have important roles in establishing and maintaining infection. Analysis of the soluble and vesicular secretions of adult Fasciola hepatica has established a definitive characterisation of the total secretome of this zoonotic parasite. Fasciola secretes at least two sub-populations of EVs that differ according to size, cargo molecules and site of release from the parasite. The larger EVs are released from the specialised cells that line the parasite gastrodermus and contain the zymogen of the 37 kDa cathepsin L peptidase that performs a digestive function. The smaller exosome-like vesicle population originate from multivesicular bodies within the tegumental syncytium and carry many previously described immunomodulatory molecules that could be delivered into host cells. By integrating our proteomics data with recently available transcriptomic datasets we have detailed the pathways involved with EV biogenesis in F. hepatica and propose that the small exosome biogenesis occurs via ESCRT-dependent MVB formation in the tegumental syncytium before being shed from the apical plasma membrane. Furthermore, we found that the molecular machinery required for EV biogenesis is constitutively expressed across the intra-mammalian development stages of the parasite. By contrast, the cargo molecules packaged within the EVs are developmentally regulated, most likely to facilitate the parasites migration through host tissue and to counteract host immune attack.