Deciphering the signaling mechanisms of the plant cell wall degradation machinery in Aspergillus oryzae

published_or_final_versio

Mass spectrometry-based proteomic analysis to characterise barley breeding lines

Barley is a key ingredient in the malting and brewing industry, and it is the fourth most important crop being cultivated worldwide. The protein content of the barley grain is one of the main components determining the quality and nutritive value of the food and beverages prepared from barley. Mass spectrometry-based proteomic analysis is a valuable tool that can guide and inform plant breeding strategies and crop improvement programs. Understanding the proteome changes in barley grain under different growing locations, the impact of different environmental conditions and its relationship with malting characteristics have the potential to inform breeding programs to achieve high-quality malt. Moreover, hordeins, the major barley storage proteins, are among the known triggers of coeliac disease (CD). Therefore, investigating the changes in the overall grain proteome, especially hordeins provides valuable insight from a food safety perspective. This thesis focuses on the proteomic investigation of barley grain to understand differences due to genetic and environmental factors and how these differences impact end use application after food processing steps such as malting. In Chapter 2 of this thesis, the proteome and malting characteristics of three different barley genotypes grown in three different locations in Western Australia were measured by applying a bottom-up proteomics workflow. First, using discovery proteomics, 1,571 proteins were detected and in the next step, by applying a global proteome quantitation workflow, 920 proteins were quantified in barley samples. Data analysis revealed that growing location outweighed the impact of genetic background, and samples were clustered into two major groupings of northern and southern growing locations. Also, a relationship between proteome measurements and malting characteristics using weighted gene co-expression network analysis (WGCNA) were investigated. The statistical analysis showed that both the genotypes and the growing locations strongly correlate with changes in the proteomes and desirable traits such as malt yield. Finally, linking meteorological data with proteomic measurements revealed how high-temperature stress in northern regions affects the seed temperature tolerance during malting, resulting in a higher malt yield. In Chapter 3, a targeted proteomics approach was used to investigate the changes of hordein peptides after malting in grain samples of previously developed hordein-reduced barley lines, including a triple-hordein-reduced ultra-low gluten (ULG) barley line and their corresponding malt samples. Peptides representing hordein-like proteins, including B-, D- and γ-hordeins and avenin-like proteins (ALPs), were tracked using relative quantitation across single-, double-, and triple-hordein-reduced barley grain and malt samples. Further analysis showed that malting further reduced the quantity of B-, D- and -hordeins and ALPs in the ULG malt sample compared to the unmalted grain. Moreover, the detection and quantitation of globulin proteins in the experimental samples indicated a compensation mechanism of storage proteins leading to the biosynthesis of seed storage globulins (vicilin-like globulins) in the ULG-line derived grain and malt sample compared to the wild type. Taken together, these results suggest that the compensation effect enables the hordein-reduced ULG line to maintain the balance of overall N-rich reservoir accumulation. In Chapter 4, the impact of malting of barley grain was investigated by unbiased proteome comparison of the grain and malt. Using discovery proteomics, 2,688 proteins were detected in the barley grain and 3,034 proteins in the malt samples of which 807 proteins were unique to malt samples. Next, Gene Ontology (GO) enrichment analysis was performed on the unique proteins and revealed that “hydrolysing activity” was the most significant GO term enriched in malt over barley. By conducting quantitative proteomics using SWATH-MS, 2,654 proteins were quantified in the barley grain and malt samples. Based on their proteome level quantitation, the unsupervised clustering analysis showed two distinct clusters representing grain flour and malt samples. Moreover, a relationship between hordein-reduced backgrounds and proteome data was established. The results showed that the inclusion of C-hordein-reduced lines significantly impacted the proteome level changes in the grain and malt samples, more so than the inclusion of the B- and D-hordein-reduced lines. Furthermore, univariate analyses were performed to identify the differentially abundant proteins in each hordein-reduced background by comparing barley grain to malt samples. Finally, GO enrichment analysis was performed on the up- and down-regulated proteins detected from the pair-wise comparisons. GO enrichment analyses revealed that the up-regulated proteins in C-hordein-reduced lines were primarily involved in the small molecule metabolic process and provided more energy during malting to facilitate seed germination. Advancements in mass spectrometry-based proteomics approaches and cutting-edge bioinformatics tools have revolutionised protein detection and quantitation from model and non-model species, enabling us to obtain unprecedented views on changes in the barley grain proteomes at the molecular level. The results generated from this PhD project have further illustrated the underlying complex regulatory mechanisms controlling storage protein accumulation upon malting in barley grains. The approaches used and the insights gleaned have the potential to accelerate the development of new varieties with desired traits of interest. Specifically, the foundational knowledge and workflow developed from this thesis can be applied in the selection of unique germplasm by barley breeders for barley food and beverage applications

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Applications of bioinformatics and machine learning in the analysis of proteomics data

In chapter one, a general introduction to the basic principles and techniques of MS-based proteomics, quantification strategies, and a generalized shotgun proteomics workflow are given. Moreover, I also outline how to analyze proteomics data from a bioinformatics perspective including normalization, dealing with missing values, differential analysis, functional annotation, as well as how to reveal the biology from post-translational modification data. Furthermore, I generalized the basics of machine learning algorithms from the perspective of supervised and unsupervised machine learning, along with that the application of machine learning algorithms to the identification of protein complexes. In chapter two, we are seeking to explore the drug addiction mechanism in melanoma cells that carry BRAF mutation. We present a proteomics and phosphoproteomics study of BRAFi-addicted melanoma cells (i.e., 451Lu cell line) in response to BRAFi withdrawal, in which ERK1, ERK2, and JUNB were genetically silenced separately using CRISPR-Cas9. We show that inactivation of ERK2 and, to a lesser extent, JUNB prevents drug addiction in these melanoma cells, while, conversely, knockout of ERK1 fails to reverse this phenotype, showing a response similar to that of control cells. Our data indicate that ERK2 and JUNB share comparable proteome responses dominated by the reactivation of cell division. Importantly, we find that EMT activation in drug-addicted melanoma cells upon drug withdrawal is affected by silencing ERK2 but not ERK1. Moreover, we reveal that PIR acts as an effector of ERK2, and phosphoproteome analysis reveals that silencing of ERK2 but not ERK1 leads to the amplification of GSK3 kinase activity. Our results depict possible mechanisms of drug addiction in melanoma, which may provide a guide for therapeutic strategies in drug-resistant melanoma. In chapter three, we are dedicated to exploring the role of PD-1 in T cell activation by comparing the proteome and phosphoproteome profiles in resting and activated CD8+ T cells, in which PD-1 was silenced using CRISPR–Cas9. Our data reveal that the activated T cells reprogrammed their proteome and phosphoproteome marked by activating of mTORC1 pathway. Moreover, we find that silencing of PD-1 altered the expression of E3 ubiquitin-- protein ligases, and increased glucose and lactate transporters. On the phosphoproteomics level, it evokes phosphorylation events in the mTORC1 pathway and activates the epidermal growth factor and its downstream MAPK pathway. Therefore, the data presented in this chapter depicts mechanisms of PD-1 in response to TCR stimulation in CD8+ T cells, which may provide a guide in immune homeostasis and immune checkpoint therapy. In chapter four, we construct a comprehensive map of human protein complexes through the integration of protein-protein interactions and protein abundance features. A deep learning framework was built to predict protein-protein interactions (PPIs), followed by a two-stage clustering to identify protein complexes. Our deep learning technique-based classifier significantly outperformed recently published machine learning prediction models with an F1-measure of 0.68 and captured in the process 5,010 complexes containing over 9,000 unique proteins. Moreover, this deep learning model enables us to capture poorly characterized interactions and the co-expressed protein involved interactions

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field