preVIEW: from a fast prototype towards a sustainable semantic search system for central access to COVID-19 preprints

The current COVID-19 pandemic emphasizes the use of so-called preprints - a type of publication that is not subject to peer review. Due to its global relevance, there is an immense number of COVID-19-related preprints every day. To help researchers find relevant information, we have developed the semantic search engine preVIEW, it integrates preprints from currently seven different preprint servers. For semantic indexing, we implemented various text mining components to tag, for example, diseases or SARS-CoV-2 specific proteins. While the service initially served as a prototype developed together with users, we present a re-engineering towards a sustainable semantic search system, which was inevitable due to the continuously growing number of preprint publications. This enables easy reuse of the components and allows rapid adaptation of the service to further user needs

A Simple Standard for Sharing Ontological Mappings (SSSOM).

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec

A Simple Standard for Sharing Ontological Mappings (SSSOM)

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec

A Global and Targeted Proteomic Investigation of Aspergillus fumigatus

Aspergillus fumigatus is an opportunistic pathogen that can cause invasive disease in immunocompromised individuals and, less frequently, in immunocompetent hosts. Proteomic investigation of A. fumigatus has the potential to enable global analysis of protein expression, identify potential targets for vaccine or diagnostic tool development, and characterise system-wide responses to external stimuli. Implementation of a large-scale proteomic strategy lead to the identification of non-redundant proteins from mycelia (n = 390) and culture supernatants (n = 42) of A. fumigatus. Utilisation of MS-based proteomics facilitated the identification of proteins typically under-represented in 2D-PAGE proteome maps, including proteins with multiple transmembrane regions, hydrophobic proteins and proteins with extremes of molecular mass and pI. Pre-fractionation of complex protein samples, by gel-filtration or gold nanoparticle pre-incubation, demonstrated potential for reduction of sample complexity. Indirect identification of secondary metabolite cluster expression was achieved using a global MS-based proteomic approach, with proteins (n = 20) from LaeA-regulated clusters detected. Targeted immunoproteomics resulted in the identification of antigenic proteins (n = 25) from A. fumigatus, reactive with sera from healthy individuals, and characterisation of these proteins may shed light on the pathobiology of A. fumigatus. Mechanisms involved in the interaction of A. fumigatus with gliotoxin were also examined, using phenotypic analysis, comparative proteomics and metabolomics. Gliotoxin was observed to relieve H2O2-induced stress, in a dose-dependent manner (0 - 10 μg/ml) and this correlated with a significant increase in expression of the gliotoxin oxidoreductase GliT (p < 0.05). This indicates a role for gliotoxin, and potentially GliT, in relief of oxidative stress in A. fumigatus. Correspondingly, proteins associated with response to stress were observed to significantly decrease in expression in the co-addition condition, relative to H2O2 alone (p < 0.05). Comparative proteomic profiling of the gliotoxin-sensitive mutant, A. fumigatus ΔgliK, revealed perturbation of translation, the methyl cycle and the endoplasmic reticulum in response to gliotoxin. This informs on the mechanisms involved in gliotoxin-mediated toxicity and may apply to other gliotoxin-sensitive species. Loss of gliotoxin production in A. fumigatus ΔgliK correlated with significant elevation in intracellular ergothioneine levels (p < 0.001). This study describes the first identification of ergothioneine in A. fumigatus and represents a target for future redox investigations

Quantitative pharmacoproteomics investigation of anti-cancer drugs in mouse. Development and optimisation of proteomics workflows for evaluating the effect of anti-cancer drugs on mouse liver

Minimizing anti-cancer drug toxicity is a major challenge for the pharmaceutical industry. Toxicity is most frequently due to either the direct interaction of the drug on previously unidentified targets or its conversion to metabolites by drug metabolizing enzymes (e.g. CYP450 enzymes) that cause cellular, tissue or organ damage. Pharmacoproteomics is beginning to take a central role in studying changes in protein expression corresponding to drug administration, the results of which, inform about the mode of action, toxicity, and resistance in pre-clinical and clinical stages of drug development. The main aim of this research is to apply comparative proteomics studies on livers from male and female mice xenograft models treated with major anti-cancer drugs (5-flourouracil, paclitaxel, cisplatin, and doxorubicin) and CYP inducer, TCPOBOP, to investigate their effect on protein expression profiles (proteome). Within this thesis, an attention is paid to optimise a highly validated proteomics workflow for biomarker identification. Proteins were extracted from liver microsomes of mice treated in two separate sets; Set A – male (5-fluoruracil, doxorubicin, cisplatin and untreated) or Set B – female (5-fluoruracil, paclitaxel, TCPOBOP and untreated) using cryo-pulverization and sonication method. The extracts were digested with trypsin ii and the resulting peptides labelled with 4-plex iTRAQ reagents. The labelled peptides were subjected for separation in two-dimensions by iso-electric focusing (IEF) and RP-HPLC techniques before analysis by mass spectrometry and database searching for protein identification. Set A and Set B resulted in identification and quantification of 1146 and 1743 proteins, respectively. Moreover, Set A and Set B recovered 26 and 34 cytochrome P450 isoforms, respectively. The microsomal changes after drug treatments were quite similar. However, more changes were observed in the male set. Up-regulation of MUPs showed the greatest distinction in the protein expression patterns in the treated samples comparing to the untreated controls. In Set A, 5-fluoruracil and cisplatin increased the expression of three isoforms (MUP1, 2, and 6), whereas doxorubicin has increased the expression of four isoforms (MUP1, 2, 3, and 6). On the other side, only TCPOBOP in Set B has increased the expression of two isoforms (MUP1 and 6). Our findings showed that the expression of MUP, normally involved in binding and excretion of pheromones, have drug- and sex-specific differences. The mechanism and significance of MUP up-regulation are ambiguous. Therefore, the impact of each therapeutic agent on MUP and xenobiotic enzymes will be discussed

Application of mass spectrometry-based proteomics to barley research

Barley (Hordeum vulgare) is the fourth most cultivated crop in the world in terms of production volume, and it is also the most important raw material of the malting and brewing industries. Barley belongs to the grass (Poaceae) family and plays an important role in food security and food safety for both humans and livestock. With the global population set to reach 9.7 billion by 2050, but with less available and/or suitable land for agriculture, the use of biotechnology tools in breeding programs are of considerable importance in the quest to meet the growing food gap. Proteomics as a member of the “omics” technologies has become popular for the investigation of proteins in cereal crops and particularly barley and its related products such as malt and beer. This technology has been applied to study how proteins in barley respond to adverse environmental conditions including abiotic and/or biotic stresses, how they are impacted during food processing including malting and brewing, and the presence of proteins implicated in celiac disease. Moreover, proteomics can be used in the future to inform breeding programs that aim to enhance the nutritional value and broaden the application of this crop in new food and beverage products. Mass spectrometry analysis is a valuable tool that, along with genomics and transcriptomics, can inform plant breeding strategies that aim to produce superior barley varieties. In this review, recent studies employing both qualitative and quantitative mass spectrometry approaches are explored with a focus on their application in cultivation, manufacturing, processing, quality, and the safety of barley and its related products

A novel metadata analysis approaches for analyzing and understanding wood-decaying mechanisms exhibited by fungi

Fuel has become an essential commodity in our day to day life. Increase in global population and decreasing fuel reserves have forced mankind to look for other fuel alternatives. Forest biomass serves as a potential renewable resource for substituting the conventional fossil-based fuels. In the last few decades, several chemical, physical and microbial based methods were being developed for the breakdown and conversion of lignocellulosic components to commercially valuable products including bioethanol and other platform chemicals. The separation of lignocellulosic biomass plays a significant role in conversion of lignocellulosic biomass to ethanol and other valuable products respectively. Naturally, lignocellulosic components are arranged in intricate networks leading to its high recalcitrance nature. Over years research groups around the world have isolated and characterized several lignocellulose degrading microorganims. Naturally, fungi play a crucial role in maintaining the geo-carbon cycle by decaying all the dead organic matter on the earth’s surface. Majority of the wood-decaying fungi are grouped under Basidiomycota division. Based on their decay patterns the basidiomycetous fungi were classified into white-rot, brown-rot and soft-rot fungi. Understanding these natural fungal decaying mechanisms will benefit the growing biofuel, biorefining and bioremediation industries

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

Responses of Plants to Environmental Stresses

Environmental abiotic stresses, such as extreme temperatures, drought, excess light, salinity, and nutrient deficiency, have detrimental effects on plant growth, development, and yield. Plants are equipped with various adaptation mechanisms to cope with such unfavorable conditions. Our understanding of plants’ abiotic stress responses is crucial to maintaining efficient plant productivity. This book on the responses of plants to environmental stresses is an attempt to find answers to several basic questions related to their adaptation and protective mechanisms against abiotic stresses. The following chapters of the book describe examples of plants’ protective strategies, which cover physiological, cellular, biochemical, and genomic mechanisms. This book is aimed for use by advanced students and researchers in the area of stress biology, plant molecular biology and physiology, agriculture, biochemistry, as well as environmental sciences