Using emergent clustering methods to analyse short time series gene expression data from childhood leukemia treated with glucocorticoids

Acute lymphoblastic leukemia (ALL) causes the highest number of deaths from cancer in children aged between one and fourteen. The most common treatment for children with ALL is chemotherapy, a cancer treatment that uses drugs to kill cancer cells or stop cell division. The drug and dosage combinations may vary for each child. Unfortunately, chemotherapy treatments may cause serious side effects. Glucocorticoids (GCs) have been used as therapeutic agents for children with ALL for more than 50 years. Common and widely drugs in this class include prednisolone and dexamethasone. Childhood leukemia now has a survival rate of 80% (Pui, Robison, & Look, 2008). The key clinical question is identifying those children who will not respond well to established therapy strategies.GCs regulate diverse biological processes, for example, metabolism, development, differentiation, cell survival and immunity. GCs induce apoptosis and G1 cell cycle arrest in lymphoid cells. In fact, not much is known about the molecular mechanism of GCs sensitivity and resistance, and GCs-induced apoptotic signal transduction pathways and there are many controversial hypotheses about both genes regulated by GCs and potential molecular mechanism of GCs-induced apoptosis. Therefore, understanding the mechanism of this drug should lead to better prognostic factors (treatment response), more targeted therapies and prevention of side effects. GCs induced apoptosis have been studied by using microarray technology in vivo and in vitro on samples consisting of GCs treated ALL cell lines, mouse thymocytes and/or ALL patients. However, time series GCs treated childhood ALL datasets are currently extremely limited. DNA microarrays are essential tools for analysis of expression of many genes simultaneously. Gene expression data show the level of activity of several genes under experimental conditions. Genes with similar expression patterns could belong to the same pathway or have similar function. DNA microarray data analysis has been carried out using statistical analysis as well as machine learning and data mining approaches. There are many microarray analysis tools; this study aims to combine emergent clustering methods to get meaningful biological insights into mechanisms underlying GCs induced apoptosis. In this study, microarray data originated from prednisolone (glucocorticoids) treated childhood ALL samples (Schmidt et al., 2006) (B-linage and T-linage) and collected at 6 and 24 hours after treatment are analysed using four methods: Selforganizing maps (SOMs), Emergent self-organizing maps (ESOM) (Ultsch & Morchen, 2005), the Short Time series Expression Miner (STEM) (Ernst & Bar-Joseph, 2006) and Fuzzy clustering by Local Approximation of MEmbership (FLAME) (Fu & Medico, 2007). The results revealed intrinsic biological patterns underlying the GCs time series data: there are at least five different gene activities happening during the three time points; GCs-induced apoptotic genes were identified; and genes active at both time points or only at 6 hours or 24 hours were determined. Also, interesting gene clusters with membership in already known pathways were found thereby providing promising candidate gens for further inferring GCs induced apoptotic gene regulatory networks

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine

Identification of glucocorticoid-regulated genes and inferring their network focused on the glucocorticoid receptor in childhood leukaemia, based on microarray data and pathway databases

Acute lymphoblastic leukaemia (ALL) has the highest mortality rate in childhood cancer. Glucocorticoids (GCs) have been used as chemotherapeutic drugs for children with ALL for more than 50 years. GCs induce apoptosis in lymphoid cells. However, little is known about the molecular mechanism of GC-induced apoptosis and there are many controversial hypotheses about genes regulated by GCs and their gene networks. In particular, two main issues are investigated: (i) GC-regulated genes and (ii) the glucocorticoid receptor (GR) gene networks. Only few overlapping genes have been reported from previous studies. Moreover, GCs function by binding with their receptors. The underlying mechanisms of cell type specific GR gene networks are not well established. The goal of this thesis is to understand the mechanism of the GC-induced apoptosis mechanism. The first part of this thesis presents an identification of GC-regulated genes. This study uses secondary microarray data, originating from prednisolone (glucocorticoid) treated childhood ALL samples (Schmidt et al., 2006) (B-linage and T-linage) that were collected before treatment and at six and twenty four hours after treatment. We replicate the authors’ original study and discover more probe sets including all the probe sets from that original study. This result shows the robustness of this data. Then, we extend the data analysis and propose new criteria based on differences between T- and B-ALL patients. The results reveal the proposed GC-regulated genes. These candidate genes are grouped in order to find similar expression patterns which lead to possible co-regulated genes, or similar function and sharing networks and pathways. Four emergent clustering methods are used: Self organising maps (SOM), Emergent self organising maps (ESOM), the Short Time series Expression Miner (STEM) and Fuzzy clustering by Local Approximation of MEmbership (FLAME). These genes are used in the following gene expression analysis step. The second part of this thesis focuses on inferring gene networks of GC-regulated genes and GR. There are many tools available for inferring gene networks including mathematical modelling and statistical methods. Each tool has its own advantages and disadvantages. For a modelling method, how do we know that the model represents the true relationship or interaction among genes? The need to verify results from modelling still exists. Prior knowledge has been used for this purpose. In this study, we use literature knowledge-based network tools, mainly the Ingenuity Pathway Analysis software (IPA) to elucidate gene networks. First, we illustrated gene networks at three time intervals and identified the prominent genes during those time points. Second, we further elucidated GR gene networks using gene lists from STEM. Third, we investigated the behaviour of selected known genes from the apoptosis, p53 and NFB pathways and inferred gene networks from the selected genes. Fourth, we inferred GR gene networks using the same gene list from previous studies (Phillip et al., 2005). We also used another two network tools: the BiblioSphere Pathway Edition (BSPE), and Oncomine to enhance the reliability of the gene network. Finally, we propose a GR gene network. In summary, we undertook a gene to gene network of GC-induced apoptosis process based on childhood leukaemia patients. This study identified novel genes and their functions, and pinpointed possible gene networks which provide information for future research

Integrated analysis of gene network in childhood leukemia from microarray and pathway databases

Glucocorticoids (GCs) have been used as therapeutic agents for children with acute lymphoblastic leukaemia (ALL) for over 50 years. However, much remains to be understood about the molecular mechanism of GCs actions in ALL subtypes. In this study, we delineate differential responses of ALL subtypes, B- and T-ALL, to GCs treatment at systems level by identifying the differences among biological processes, molecular pathways, and interaction networks that emerge from the action of GCs through the use of a selected number of available bioinformatics methods and tools. We provide biological insight into GC-regulated genes, their related functions, and their networks specific to the ALL subtypes. We show that differentially expressed GC-regulated genes participate in distinct underlying biological processes affected by GCs in B-ALL and T-ALL with little to no overlap. These findings provide the opportunity towards identifying new therapeutic targets. © 2014 Amphun Chaiboonchoe et al

Molecular Mechanisms behind Safranal’s Toxicity to HepG2 Cells from Dual Omics

The spice saffron (Crocus sativus) has anticancer activity in several human tissues, but the molecular mechanisms underlying its potential therapeutic effects are poorly understood. We investigated the impact of safranal, a small molecule secondary metabolite from saffron, on the HCC cell line HepG2 using untargeted metabolomics (HPLC–MS) and transcriptomics (RNAseq). Increases in glutathione disulfide and other biomarkers for oxidative damage contrasted with lower levels of the antioxidants biliverdin IX (139-fold decrease, p = 5.3 × 105), the ubiquinol precursor 3-4-dihydroxy-5-all-trans-decaprenylbenzoate (3-fold decrease, p = 1.9 × 10−5), and resolvin E1 (−3282-fold decrease, p = 45), which indicates sensitization to reactive oxygen species. We observed a significant increase in intracellular hypoxanthine (538-fold increase, p = 7.7 × 10−6) that may be primarily responsible for oxidative damage in HCC after safranal treatment. The accumulation of free fatty acids and other biomarkers, such as S-methyl-5′-thioadenosine, are consistent with safranal-induced mitochondrial de-uncoupling and explains the sharp increase in hypoxanthine we observed. Overall, the dual omics datasets describe routes to widespread protein destabilization and DNA damage from safranal-induced oxidative stress in HCC cells

Algal Cell Factories: Approaches, Applications, and Potentials

With the advent of modern biotechnology, microorganisms from diverse lineages have been used to produce bio-based feedstocks and bioactive compounds. Many of these compounds are currently commodities of interest, in a variety of markets and their utility warrants investigation into improving their production through strain development. In this review, we address the issue of strain improvement in a group of organisms with strong potential to be productive “cell factories”: the photosynthetic microalgae. Microalgae are a diverse group of phytoplankton, involving polyphyletic lineage such as green algae and diatoms that are commonly used in the industry. The photosynthetic microalgae have been under intense investigation recently for their ability to produce commercial compounds using only light, CO2, and basic nutrients. However, their strain improvement is still a relatively recent area of work that is under development. Importantly, it is only through appropriate engineering methods that we may see the full biotechnological potential of microalgae come to fruition. Thus, in this review, we address past and present endeavors towards the aim of creating productive algal cell factories and describe possible advantageous future directions for the field

Tissue-Specific Transcriptomes Outline Halophyte Adaptive Strategies in the Gray Mangrove (Avicennia marina)

Avicennia marina forests fulfill essential blue carbon and ecosystem services, including halting coastal erosion and supporting fisheries. Genetic studies of A. marina tissues could yield insight into halophyte adaptive strategies, empowering saline agriculture research. We compare transcriptomes from A. marina pneumatophores, stems, leaves, flowers, seeds, and transcriptomes across four widely divergent environments in the Indo-Pacific (Red Sea, Arabian Gulf, Bay of Bengal, and Red River Delta) to decipher the shared and location-, tissue-, and condition-specific functions. On average, 4.8% of transcripts per tissue were uniquely expressed in that tissue, and 12.2% were shared in all five tissues. Flowers’ transcript expression was the most distinct, with domain-centric gene ontology analysis showing high enrichment for stimulus-responsive processes, as well as genes implicated in flowering (hydroxygeraniol dehydrogenase, TPM = 3687) and floral scent biosynthesis (e.g., benzoyl_coenzyme_A, 2497.2 TPM). Pneumatophores highly expressed antioxidant genes, such as glutathione S-transferase (GST, TPM = 4759) and thioredoxin (TRX, TPM = 936.2), as well as proteins in the GO term ‘Hydroquinone:oxygen oxidoreductase activity’ (enrichment Z = 7.69, FDR-corr. p = 0.000785). Tissue-specific metabolic pathway reconstruction revealed unique processes in the five tissues; for example, seeds showed the most complete expression of lipid biosynthetic and degradation pathways. The leaf transcriptome had the lowest functional diversity among the expressed genes in any tissue, but highly expressed a catalase (TPM = 4181) and was enriched for the GO term ‘transmembrane transporter activity’ (GO:0015238; Z = 11.83; FDR-corr. p = 1.58 × 10−9), underscoring the genes for salt exporters. Metallothioneins (MTs) were the highest-expressed genes in all tissues from the cultivars of all locations; the dominant expression of these metal-binding and oxidative-stress control genes indicates they are essential for A. marina in its natural habitats. Our study yields insight into how A. marina tissue-specific gene expression supports halotolerance and other coastal adaptative strategies in this halophytic angiosperm

Potential for Heightened Sulfur-Metabolic Capacity in Coastal Subtropical Microalgae

Summary: The activities of microalgae support nutrient cycling that helps to sustain aquatic and terrestrial ecosystems. Most microalgal species, especially those from the subtropics, are genomically uncharacterized. Here we report the isolation and genomic characterization of 22 microalgal species from subtropical coastal regions belonging to multiple clades and three from temperate areas. Halotolerant strains including Halamphora, Dunaliella, Nannochloris, and Chloroidium comprised the majority of these isolates. The subtropical-based microalgae contained arrays of methyltransferase, pyridine nucleotide-disulfide oxidoreductase, abhydrolase, cystathionine synthase, and small-molecule transporter domains present at high relative abundance. We found that genes for sulfate transport, sulfotransferase, and glutathione S-transferase activities were especially abundant in subtropical, coastal microalgal species and halophytic species in general. Our metabolomics analyses indicate lineage- and habitat-specific sets of biomolecules implicated in niche-specific biological processes. This work effectively expands the collection of available microalgal genomes by ∼50%, and the generated resources provide perspectives for studying halophyte adaptive traits. : Global Nutrient Cycle; Algology; Genomics; Metabolomics Subject Areas: Global Nutrient Cycle, Algology, Genomics, Metabolomic

Tissue-Specific Transcriptomes Outline Halophyte Adaptive Strategies in the Gray Mangrove (<i>Avicennia marina</i>)

Avicennia marina forests fulfill essential blue carbon and ecosystem services, including halting coastal erosion and supporting fisheries. Genetic studies of A. marina tissues could yield insight into halophyte adaptive strategies, empowering saline agriculture research. We compare transcriptomes from A. marina pneumatophores, stems, leaves, flowers, seeds, and transcriptomes across four widely divergent environments in the Indo-Pacific (Red Sea, Arabian Gulf, Bay of Bengal, and Red River Delta) to decipher the shared and location-, tissue-, and condition-specific functions. On average, 4.8% of transcripts per tissue were uniquely expressed in that tissue, and 12.2% were shared in all five tissues. Flowers’ transcript expression was the most distinct, with domain-centric gene ontology analysis showing high enrichment for stimulus-responsive processes, as well as genes implicated in flowering (hydroxygeraniol dehydrogenase, TPM = 3687) and floral scent biosynthesis (e.g., benzoyl_coenzyme_A, 2497.2 TPM). Pneumatophores highly expressed antioxidant genes, such as glutathione S-transferase (GST, TPM = 4759) and thioredoxin (TRX, TPM = 936.2), as well as proteins in the GO term ‘Hydroquinone:oxygen oxidoreductase activity’ (enrichment Z = 7.69, FDR-corr. p = 0.000785). Tissue-specific metabolic pathway reconstruction revealed unique processes in the five tissues; for example, seeds showed the most complete expression of lipid biosynthetic and degradation pathways. The leaf transcriptome had the lowest functional diversity among the expressed genes in any tissue, but highly expressed a catalase (TPM = 4181) and was enriched for the GO term ‘transmembrane transporter activity’ (GO:0015238; Z = 11.83; FDR-corr. p = 1.58 × 10−9), underscoring the genes for salt exporters. Metallothioneins (MTs) were the highest-expressed genes in all tissues from the cultivars of all locations; the dominant expression of these metal-binding and oxidative-stress control genes indicates they are essential for A. marina in its natural habitats. Our study yields insight into how A. marina tissue-specific gene expression supports halotolerance and other coastal adaptative strategies in this halophytic angiosperm