Characterisation of the TaNF-Y family of transcription factors in wheat

Light plays a unique role for plants as it is both a source of energy for growth and a signal for development. Light captured by the pigments in the light harvesting complexes is used to drive the synthesis of the chemical energy required for carbon assimilation. The light perceived by photoreceptors activates effectors, such as transcription factors (TFs), which modulate the expression of light-responsive genes. Recently, it has been speculated that increasing the photosynthetic rate could further improve the yield potential of three carbon (C3) crops such as wheat. However, little is currently known about the transcriptional regulation of photosynthesis genes, particularly in crop species. Nuclear factor Y (NF-Y) TF is a functionally diverse regulator of growth and development in the model plant species, with demonstrated roles in embryo development, stress response, flowering time and chloroplast biogenesis. Furthermore, a light-responsive NF-Y binding site (CCAAT-box) is present in the promoter of a spinach photosynthesis gene. As photosynthesis genes are co-regulated by light and co-regulated genes typically have similar regulatory elements in their promoters, it seems likely that other photosynthesis genes would also have light-responsive CCAAT-boxes. This provided the impetus to investigate the NF-Y TF in bread wheat. This thesis is focussed on wheat NF-Y members that have roles in light-mediated gene regulation with an emphasis on their involvement in the regulation of photosynthesis genes. NF-Y is a heterotrimeric complex, comprised of the three subunits NF-YA, NF-YB and NF-YC. Unlike the mammalian and yeast counterparts, each of the three subunits is encoded by multiple genes in Arabidopsis. The initial step taken in this study was the identification of the wheat NF-Y family (Chapter 3). A search of the current wheat nucleotide sequence databases identified 37 NF-Y genes (10 NF-YA, 11 NF-YB, 14 NF-YC & 2 Dr1). Phylogenetic analysis revealed that each of the three wheat NF-Y (TaNF-Y) subunit families could be divided into 4-5 clades based on their conserved core regions. Outside of the core regions, eleven motifs were identified to be conserved between Arabidopsis, rice and wheat NF-Y subunit members. The expression profiles of TaNF-Y genes were constructed using quantitative real-time polymerase chain reaction (RT-PCR). Some TaNF-Y subunit members had little variation in their transcript levels among the organs, while others displayed organ-predominant expression profiles, including those expressed mainly in the photosynthetic organs. To investigate their potential role in light-mediated gene regulation, the light responsiveness of the TaNF-Y genes were examined (Chapters 4 and 5). Two TaNF-YB and five TaNF-YC members were markedly upregulated by light in both the wheat leaves and seedling shoots. To identify the potential target genes of the light-upregulated NF-Y subunit members, a gene expression correlation analysis was conducted using publically available Affymetrix Wheat Genome Array datasets. This analysis revealed that the transcript expression levels of TaNF-YB3 and TaNF-YC11 were significantly correlated with those of photosynthesis genes. These correlated express profiles were also observed in the quantitative RT-PCR dataset from wheat plants grown under light and dark conditions. Sequence analysis of the promoters of these wheat photosynthesis genes revealed that they were enriched with potential NF-Y binding sites (CCAAT-box). The potential role of TaNF-YB3 in the regulation of photosynthetic genes was further investigated using a transgenic approach (Chapter 5). Transgenic wheat lines constitutively expressing TaNF-YB3 were found to have significantly increased expression levels of photosynthesis genes, including those encoding light harvesting chlorophyll a/b-binding proteins, photosystem I reaction centre subunits, a chloroplast ATP synthase subunit and glutamyl-tRNA reductase (GluTR). GluTR is a rate-limiting enzyme in the chlorophyll biosynthesis pathway. In association with the increased expression of the photosynthesis genes, the transgenic lines had a higher leaf chlorophyll content, increased photosynthetic rate and had a more rapid early growth rate compared to the wild-type wheat. In addition to its role in the regulation of photosynthesis genes, TaNF-YB3 overexpression lines flower on average 2-days earlier than the wild-type (Chapter 6). Quantitative RT-PCR analysis showed that there was a 13-fold increase in the expression level of the floral integrator, TaFT. The transcript levels of other downstream genes (TaFT2 and TaVRN1) were also increased in the transgenic lines. Furthermore, the transcript levels of TaNF-YB3 were significantly correlated with those of constans (CO), constans-like (COL) and timing of chlorophyll a/b-binding (CAB) expression 1 [TOC1; (CCT)] domain-containing proteins known to be involved in the regulation of flowering time. To summarise the key findings of this study, 37 NF-Y genes were identified in the crop species wheat. An in depth analysis of TaNF-Y gene expression profiles revealed that the potential role of some light-upregulated members was in the regulation of photosynthetic genes. The involvement of TaNF-YB3 in the regulation of photosynthesis genes was supported by data obtained from transgenic wheat lines with increased constitutive expression of TaNF-YB3. The overexpression of TaNF-YB3 in the transgenic lines revealed this NF-YB member is also involved in the fine-tuning of flowering time. These data suggest that the NF-Y TF plays an important role in light-mediated gene regulation in wheat.

Practical stability of approximating discrete-time filters with respect to model mismatch

This paper establishes practical stability results for an important range of approximate discrete-time filtering problems involving mismatch between the true system and the approximating filter model. Practical stability is established in the sense of an asymptotic bound on the amount of bias introduced by the model approximation. Our analysis applies to a wide range of estimation problems and justifies the common practice of approximating intractable infinite dimensional nonlinear filters by simpler computationally tractable filters.

Retrieving information from microblog using pattern mining and relevance feedback

Retrieving information from Twitter is always challenging due to its large volume, inconsistent writing and noise. Most existing information retrieval (IR) and text mining methods focus on term-based approach, but suffers from the problems of terms variation such as polysemy and synonymy. This problem deteriorates when such methods are applied on Twitter due to the length limit. Over the years, people have held the hypothesis that pattern-based methods should perform better than term-based methods as it provides more context, but limited studies have been conducted to support such hypothesis especially in Twitter. This paper presents an innovative framework to address the issue of performing IR in microblog. The proposed framework discover patterns in tweets as higher level feature to assign weight for low-level features (i.e. terms) based on their distributions in higher level features. We present the experiment results based on TREC11 microblog dataset and shows that our proposed approach significantly outperforms term-based methods Okapi BM25, TF-IDF and pattern based methods, using precision, recall and F measures.

A two-stage approach for generating topic models

Topic modeling has been widely utilized in the fields of information retrieval, text mining, text classification etc. Most existing statistical topic modeling methods such as LDA and pLSA generate a term based representation to represent a topic by selecting single words from multinomial word distribution over this topic. There are two main shortcomings: firstly, popular or common words occur very often across different topics that bring ambiguity to understand topics; secondly, single words lack coherent semantic meaning to accurately represent topics. In order to overcome these problems, in this paper, we propose a two-stage model that combines text mining and pattern mining with statistical modeling to generate more discriminative and semantic rich topic representations. Experiments show that the optimized topic representations generated by the proposed methods outperform the typical statistical topic modeling method LDA in terms of accuracy and certainty.

In vitro functional characterisation of IGF-I : VN-induced breast cancer progression

Members of the insulin-like growth factor (IGF) family have been shown to play critical roles in normal growth and development, as well as in tumour biology. The IGF system is complex and the biological effects of the IGFs are determined by their diverse interactions between many molecules, including their interactions with extracellular matrix (ECM) proteins. Recent studies have demonstrated that IGFs associate with the ECM protein vitronectin (VN) through IGF-binding proteins (IGFBP) and that this interaction modulates IGF-stimulated biological functions, namely cell migration and cell survival through the cooperative involvement of the type-I IGF receptor (IGF-1R) and VN-binding integrins. Since IGFs play important roles in the transformation and progression of breast cancer and VN has been found to be over-expressed at the leading edge of breast tumours, this project aimed to describe the effects of IGF-I:VN interactions on breast cell function. This was undertaken to dissect the molecular mechanisms underlying IGF-I:VN-induced responses and to design inhibitors to block the effects of such interactions. The studies described herein demonstrate that the increase in migration of MCF-7 breast cancer cells in response to the IGF-I:IGFBP-5:VN complex is accompanied by differential expression of genes known to be involved in migration, invasion and/or survival, including Tissue-factor (TF), Stratifin (SFN), Ephrin-B2, Sharp-2 and PAI-1. This „migration gene signature‟ was confirmed using real-time PCR analysis. Substitution of the native IGF-I within the IGF-I:IGFBP:VN complex with the IGF-I analogue, \[L24]\[A31]-IGF-I, which has a reduced affinity for the IGF-1R, failed to stimulate cell migration and interestingly, also failed to induce the differential gene expression. This supports the involvement of the IGF-1R in mediating these changes in gene expression. Furthermore, lentiviral shRNA-mediated stable knockdown of TF and SFN completely abrogated the increased cell migration induced by IGF-I:IGFBP:VN complexes in MCF-7 cells. Indeed, when these cells were grown in 3D Matrigel™ cultures a decrease in the overall size of the 3D spheroids in response to the IGF-I:IGFBP:VN complexes was observed compared to the parental MCF-7 cells. This suggests that TF and SFN have a role in complex-stimulated cell survival. Moreover, signalling studies performed on cells with the reduced expression of either TF or SFN had a decreased IGF-1R activation, suggesting the involvement of signalling pathways downstream of IGF-1R in TF- and/or SFN-mediated cell migration and cell survival. Taken together, these studies provide evidence for a common mechanism activated downstream of the IGF-1R that induces the expression of the „migration gene signature‟ in response to the IGF-I:IGFBP:VN complex that confers breast cancer cells the propensity to migrate and survive. Given the functional significance of the interdependence of ECM and growth factor (GF) interactions in stimulating processes key to breast cancer progression, this project aimed at developing strategies to prevent such growth factor:ECM interactions in an effort to inhibit the downstream functional effects. This may result in the reduction in the levels of ECM-bound IGF-I present in close proximity to the cells, thereby leading to a reduction in the stimulation of IGF-1R present on the cell surface. Indeed, the inhibition of IGF-I-mediated effects through the disruption of its association with ECM would not alter the physiological levels of IGF-I and potentially only exert effects in situations where abnormal over expression of ECM proteins are found; namely carcinomas and hyperproliferative diseases. In summary, this PhD project has identified novel, innovative and realistic strategies that can be used in vitro to inhibit the functions exerted by the IGF-I:IGFBP:VN multiprotein complexes critical for cancer progression, with a potential to be translated into in vivo investigations. Furthermore, TF and SFN were found to mediate IGF-I:IGFBP:VN-induced effects, thereby revealing their potential to be used as therapeutic targets or as predictive biomarkers for the efficacy of IGF-1R targeting therapies in breast cancer patients. In addition to its therapeutic and clinical scope, this PhD project has significantly contributed to the understanding of the role of the IGF system in breast tumour biology by providing valuable new information on the mechanistic events underpinning IGF-I:VN-mediated effects on breast cell functions. Furthermore, this is the first instance where favourable binding sites for IGF-II, IGFBP-3 and IGFBP-5 on VN have been identified. Taken together, this study has functionally characterised the interactions between IGF-I and VN and through innovative strategies has provided a platform for the development of novel therapies targeting these interactions and their downstream effects.

Effectiveness of cysteine proteases on protein/pigment film removal

Objective: Theaflavin (TF) from the black tea can react to human salivary proline-rich proteins (PRPs) to form stains on exposed dental surfaces. Here, we employed a model of protein/pigment film using TF and dephosphorylated bovine b-casein (Db-CN), which has an extended conformation, similar to that of salivary PRPs, on a sensor surface to assess the efficacy of cysteine proteases (CPs) including papain, stem bromelain, and ficin, on removing TF bound to Db-CN and the control TF readsorption on the residual substrate surfaces was also measured. Methods: The protein/pigment complex film was built by using a quartz crystal microbalance with dissipation (QCM-D). The efficacies of CPs were assessed by Boltzman equation model. The surface details were detected by grazing angle infrared spectroscopy spectra, atomic force microscopy images, and contact angles. Results: The efficacy order of CPs on hydrolyzing protein/pigment complex film is ficin > papain > bromelain. The results from grazing angle infrared spectroscopy spectra, atomic force microscopy images, and contact angles demonstrated that TF bound on the Db- CN was effectively removed by the CPs, and the amount of TF readsorption on both the residual film of the Db-CN/TF and the Db-CN was markedly decreased after hydrolysis. Conclusion: This study indicates the potential application of the CPs for tooth stain removal and suggests that these enzymes are worthy of further investigation for use in oral healthcare.

Quantitative and qualitative changes in gene expression patterns characterize the activity of plaques in multiple sclerosis

Multiple sclerosis (MS) is a complex autoimmune disorder of the CNS with both genetic and environmental contributing factors. Clinical symptoms are broadly characterized by initial onset, and progressive debilitating neurological impairment. In this study, RNA from MS chronic active and MS acute lesions was extracted, and compared with patient matched normal white matter by fluorescent cDNA microarray hybridization analysis. This resulted in the identification of 139 genes that were differentially regulated in MS plaque tissue compared to normal tissue. Of these, 69 genes showed a common pattern of expression in the chronic active and acute plaque tissues investigated (Pvalue<0.0001, ρ=0.73, by Spearman's ρ analysis); while 70 transcripts were uniquely differentially expressed (≥1.5-fold) in either acute or chronic active tissues. These results included known markers of MS such as the myelin basic protein (MBP) and glutathione S-transferase (GST) M1, nerve growth factors, such as nerve injury-induced protein 1 (NINJ1), X-ray and excision DNA repair factors (XRCC9 and ERCC5) and X-linked genes such as the ribosomal protein, RPS4X. Primers were then designed for seven array-selected genes, including transferrin (TF), superoxide dismutase 1 (SOD1), glutathione peroxidase 1 (GPX1), GSTP1, crystallin, alpha-B (CRYAB), phosphomannomutase 1 (PMM1) and tubulin β-5 (TBB5), and real time quantitative (Q)-PCR analysis was performed. The results of comparative Q-PCR analysis correlated significantly with those obtained by array analysis (r=0.75, Pvalue<0.01, by Pearson's bivariate correlation). Both chronic active and acute plaques shared the majority of factors identified suggesting that quantitative, rather than gross qualitative differences in gene expression pattern may define the progression from acute to chronic active plaques in MS.

Personalized ontology learning for enhancing text mining effectiveness

Over the last decade, the majority of existing search techniques is either keyword- based or category-based, resulting in unsatisfactory effectiveness. Meanwhile, studies have illustrated that more than 80% of users preferred personalized search results. As a result, many studies paid a great deal of efforts (referred to as col- laborative filtering) investigating on personalized notions for enhancing retrieval performance. One of the fundamental yet most challenging steps is to capture precise user information needs. Most Web users are inexperienced or lack the capability to express their needs properly, whereas the existent retrieval systems are highly sensitive to vocabulary. Researchers have increasingly proposed the utilization of ontology-based tech- niques to improve current mining approaches. The related techniques are not only able to refine search intentions among specific generic domains, but also to access new knowledge by tracking semantic relations. In recent years, some researchers have attempted to build ontological user profiles according to discovered user background knowledge. The knowledge is considered to be both global and lo- cal analyses, which aim to produce tailored ontologies by a group of concepts. However, a key problem here that has not been addressed is: how to accurately match diverse local information to universal global knowledge. This research conducts a theoretical study on the use of personalized ontolo- gies to enhance text mining performance. The objective is to understand user information needs by a \bag-of-concepts" rather than \words". The concepts are gathered from a general world knowledge base named the Library of Congress Subject Headings. To return desirable search results, a novel ontology-based mining approach is introduced to discover accurate search intentions and learn personalized ontologies as user profiles. The approach can not only pinpoint users' individual intentions in a rough hierarchical structure, but can also in- terpret their needs by a set of acknowledged concepts. Along with global and local analyses, another solid concept matching approach is carried out to address about the mismatch between local information and world knowledge. Relevance features produced by the Relevance Feature Discovery model, are determined as representatives of local information. These features have been proven as the best alternative for user queries to avoid ambiguity and consistently outperform the features extracted by other filtering models. The two attempt-to-proposed ap- proaches are both evaluated by a scientific evaluation with the standard Reuters Corpus Volume 1 testing set. A comprehensive comparison is made with a num- ber of the state-of-the art baseline models, including TF-IDF, Rocchio, Okapi BM25, the deploying Pattern Taxonomy Model, and an ontology-based model. The gathered results indicate that the top precision can be improved remarkably with the proposed ontology mining approach, where the matching approach is successful and achieves significant improvements in most information filtering measurements. This research contributes to the fields of ontological filtering, user profiling, and knowledge representation. The related outputs are critical when systems are expected to return proper mining results and provide personalized services. The scientific findings have the potential to facilitate the design of advanced preference mining models, where impact on people's daily lives.

Enhanced n-gram extraction using relevance feature discovery

Guaranteeing the quality of extracted features that describe relevant knowledge to users or topics is a challenge because of the large number of extracted features. Most popular existing term-based feature selection methods suffer from noisy feature extraction, which is irrelevant to the user needs (noisy). One popular method is to extract phrases or n-grams to describe the relevant knowledge. However, extracted n-grams and phrases usually contain a lot of noise. This paper proposes a method for reducing the noise in n-grams. The method first extracts more specific features (terms) to remove noisy features. The method then uses an extended random set to accurately weight n-grams based on their distribution in the documents and their terms distribution in n-grams. The proposed approach not only reduces the number of extracted n-grams but also improves the performance. The experimental results on Reuters Corpus Volume 1 (RCV1) data collection and TREC topics show that the proposed method significantly outperforms the state-of-art methods underpinned by Okapi BM25, tf*idf and Rocchio.

Capital-C culture : the perfect American at the Brisbane Festival

A review of Philip Glass's opera The Perfect American. The Brisbane Festival’s production of Philip Glass’s opera The Perfect American is only the third production of the 2012 work ever to be staged. That’s quite a coup for the Brisbane Festival and Opera Queensland. The Perfect American was commissioned by Madrid’s Teatro Real and London’s English National Opera to mark the American composer’s 75th birthday. Glass’s telling of the Disney myth focuses on the final stages of Walt Disney’s life and career – a high art critique of a popular culture icon...

Injury narrative text classification : a preliminary study

Description of a patient's injuries is recorded in narrative text form by hospital emergency departments. For statistical reporting, this text data needs to be mapped to pre-defined codes. Existing research in this field uses the Naïve Bayes probabilistic method to build classifiers for mapping. In this paper, we focus on providing guidance on the selection of a classification method. We build a number of classifiers belonging to different classification families such as decision tree, probabilistic, neural networks, and instance-based, ensemble-based and kernel-based linear classifiers. An extensive pre-processing is carried out to ensure the quality of data and, in hence, the quality classification outcome. The records with a null entry in injury description are removed. The misspelling correction process is carried out by finding and replacing the misspelt word with a soundlike word. Meaningful phrases have been identified and kept, instead of removing the part of phrase as a stop word. The abbreviations appearing in many forms of entry are manually identified and only one form of abbreviations is used. Clustering is utilised to discriminate between non-frequent and frequent terms. This process reduced the number of text features dramatically from about 28,000 to 5000. The medical narrative text injury dataset, under consideration, is composed of many short documents. The data can be characterized as high-dimensional and sparse, i.e., few features are irrelevant but features are correlated with one another. Therefore, Matrix factorization techniques such as Singular Value Decomposition (SVD) and Non Negative Matrix Factorization (NNMF) have been used to map the processed feature space to a lower-dimensional feature space. Classifiers with these reduced feature space have been built. In experiments, a set of tests are conducted to reflect which classification method is best for the medical text classification. The Non Negative Matrix Factorization with Support Vector Machine method can achieve 93% precision which is higher than all the tested traditional classifiers. We also found that TF/IDF weighting which works well for long text classification is inferior to binary weighting in short document classification. Another finding is that the Top-n terms should be removed in consultation with medical experts, as it affects the classification performance.

Novel loci affecting iron homeostasis and their effects in individuals at risk for hemochromatosis

Variation in body iron is associated with or causes diseases, including anaemia and iron overload. Here, we analyse genetic association data on biochemical markers of iron status from 11 European-population studies, with replication in eight additional cohorts (total up to 48,972 subjects). We find 11 genome-wide-significant (P<5 × 10−8) loci, some including known iron-related genes (​HFE, ​SLC40A1, ​TF, ​TFR2, ​TFRC, ​TMPRSS6) and others novel (​ABO, ​ARNTL, ​FADS2, ​NAT2, ​TEX14). SNPs at ​ARNTL, ​TF, and ​TFR2 affect iron markers in ​HFE C282Y homozygotes at risk for hemochromatosis. There is substantial overlap between our iron loci and loci affecting erythrocyte and lipid phenotypes. These results will facilitate investigation of the roles of iron in disease.

Brain structure in healthy adults is related to serum transferrin and the H63D polymorphism in the HFE gene

Control of iron homeostasis is essential for healthy central nervous system function: iron deficiency is associated with cognitive impairment, yet iron overload is thought to promote neurodegenerative diseases. Specific genetic markers have been previously identified that influence levels of transferrin, the protein that transports iron throughout the body, in the blood and brain. Here, we discovered that transferrin levels are related to detectable differences in the macro- and microstructure of the living brain. We collected brain MRI scans from 615 healthy young adult twins and siblings, of whom 574 were also scanned with diffusion tensor imaging at 4 Tesla. Fiber integrity was assessed by using the diffusion tensor imaging-based measure of fractional anisotropy. In bivariate genetic models based on monozygotic and dizygotic twins, we discovered that partially overlapping additive genetic factors influenced transferrin levels and brain microstructure. We also examined common variants in genes associated with transferrin levels, TF and HFE, and found that a commonly carried polymorphism (H63D at rs1799945) in the hemochromatotic HFE gene was associated with white matter fiber integrity. This gene has a well documented association with iron overload. Our statistical maps reveal previously unknown influences of the same gene on brain microstructure and transferrin levels. This discovery may shed light on the neural mechanisms by which iron affects cognition, neurodevelopment, and neurodegeneration.

Nutrition intervention approaches to reduce malnutrition in oncology patients: A systematic review

Purpose Malnutrition is a very common problem in oncology patients and is associated with many negative consequences including poorer prognosis, quality of life and survival. However, malnutrition in oncology patients is often overlooked although there is growing evidence showing that it can be prevented or reduced through nutrition intervention. This paper aims to provide an updated review on the effectiveness of different nutrition intervention approaches on nutrition status outcomes in oncology patients. Methods Randomised controlled trials (RCTs) published between 1994 and 2014 which examined the effects of nutrition intervention approaches—in particular, nutrition counselling (NC), oral nutrition supplements (ONS) and tube feeding (TF)—on nutrition status outcomes of oncology patients were identified and reviewed. Results Thirteen papers from 11 RCTs with a total of 1077 participants were included. The intervention approaches included NC (four studies), NC + ONS (five studies), ONS (three studies) and TF (three studies). The various results suggest that NC with or without ONS was associated with consistent improvements in several nutrition status outcomes. On the other hand, ONS and TF were associated with inconsistent improvements in few aspects of nutrition status outcomes. Conclusions The referral of oncology patients for NC is recommended given the strong evidence of its beneficial effects on the prevention and reduction of malnutrition. Other forms of nutrition support including ONS and TF may then be included if deemed suitable and necessary for the individual.

Visual analytics for large-scale bioinformatic data sets

Rapid advances in sequencing technologies (Next Generation Sequencing or NGS) have led to a vast increase in the quantity of bioinformatics data available, with this increasing scale presenting enormous challenges to researchers seeking to identify complex interactions. This paper is concerned with the domain of transcriptional regulation, and the use of visualisation to identify relationships between specific regulatory proteins (the transcription factors or TFs) and their associated target genes (TGs). We present preliminary work from an ongoing study which aims to determine the effectiveness of different visual representations and large scale displays in supporting discovery. Following an iterative process of implementation and evaluation, representations were tested by potential users in the bioinformatics domain to determine their efficacy, and to understand better the range of ad hoc practices among bioinformatics literate users. Results from two rounds of small scale user studies are considered with initial findings suggesting that bioinformaticians require richly detailed views of TF data, features to compare TF layouts between organisms quickly, and ways to keep track of interesting data points.