Metabolic and ecological study of environmental pentose utilizing bacteria (E-PUB)

Lignocellulosic materials, such as sugar cane bagasse, a waste product of the sugarcane processing industry, agricultural residues and herbaceous crops, may serve as an abundant and comparatively cheap feedstock for largescale industrial fermentation, resulting in the production of marketable end-products. However, the complex structure of lignocellulosic materials, the presence of various hexose and pentose sugars in the hemicellulose component, and the presence of various compounds that inhibit the organisms selected for the fermentation process, all constitute barriers that add to the production costs and make full scale industrial production economically less feasible. The work presented in this thesis was conducted in order to screen microorganisms for ability to utilize pentose sugars derived from the sugar mill industrial waste. A large number of individual bacterial strains were investigated from hemi-cellulose rich material collected at the Proserpine and Maryborough sugar mills, notably soil samples from the mill sites. The research conducted to isolation of six pentose-capable Gram-positive organisms from the actinomycetes group by using pentose as a sole carbon source in the cultivation process. The isolates were identified as Corynebacterium glutamicum, Actinomyces odontolyticus, Nocardia elegans, and Propionibacterium freudenreichii all of which were isolated from the hemicellulose-enriched soil. Pentose degrading microbes are very rare in the environment, so this was a significant discovery. Previous research indicated that microbes could degrade pentose after genetic modification but the microbes discovered in this research were able to naturally utilize pentose. Six isolates, identified as four different genera, were investigated for their ability to utilize single sugars as substrates (glucose, xylose, arabinose or ribose), and also dual sugars as substrates (a hexose plus a pentose). The results demonstrated that C. glutamicum, A. odontolyticus, N. elegans, and P. freudenreichii were pentose-capable (able to grow using xylose or other pentose sugar), and also showed diauxie growth characteristics during the dual-sugar (glucose, in combination with xylose, arabinose or ribose) carbon source tests. In addition, it was shown that the isolates displayed very small differences in growth rates when grown on dual sugars as compared to single sugars, whether pentose or hexose in nature. The anabolic characteristics of C. glutamicum, A. odontolyticus, N. elegans and P. freudenreichii were subsequently investigated by qualitative analysis of their end-products, using high performance liquid chromatography (HPLC). All of the organisms produced arginine and cysteine after utilization of the pentose substrates alone. In addition, P. freudenreichii produced alanine and glycine. The end-product profile arising from culture with dual carbon sources was also tested. Interestingly, this time the product was different. All of them produced the amino acid glycine, when grown on a combination substrate-mix of glucose with xylose, and also glucose with arabinose. Only N. elegans was able to break down ribose, either singly or in combination with glucose, and the end-product of metabolism of the glucose plus ribose substrate combination was glutamic acid. The ecological analysis of microbial abundance in sugar mill waste was performed using denaturing gradient gel electrophoresis (DGGE) and also the metagenomic microarray PhyloChip method. Eleven solid samples and seven liquid samples were investigated. A very complex bacterial ecosystem was demonstrated in the seven liquid samples after testing with the PhyloChip method. It was also shown that bagasse leachate was the most different, compared to all of the other samples, by virtue of its richness in variety of taxa and the complexity of its bacterial community. The bacterial community in solid samples from Proserpine, Mackay and Maryborough sugar mills showed huge diversity. The information found from 16S rDNA sequencing results was that the bacterial genera Brevibacillus, Rhodospirillaceae, Bacillus, Vibrio and Pseudomonas were present in greatest abundance. In addition, Corynebacterium was also found in the soil samples. The metagenomic studies of the sugar mill samples demonstrate two important outcomes: firstly that the bagasse leachate, as potentially the most pentose-rich sample tested, had the most complex and diverse bacterial community; and secondly that the pentose-capable isolates that were initially discovered at the beginning of this study, were not amongst the most abundant taxonomic groups discovered in the sugar mill samples, and in fact were, as suspected, very rare. As a bioprospecting exercise, therefore, the study has discovered organisms that are naturally present, but in very small numbers, in the appropriate natural environment. This has implications for the industrial application of E-PUB, in that a seeding process using a starter culture will be necessary for industrial purposes, rather than simply assuming that natural fermentation might occur.

Profiling event logs to configure risk indicators for process delays

Risk identification is one of the most challenging stages in the risk management process. Conventional risk management approaches provide little guidance and companies often rely on the knowledge of experts for risk identification. In this paper we demonstrate how risk indicators can be used to predict process delays via a method for configuring so-called Process Risk Indicators(PRIs). The method learns suitable configurations from past process behaviour recorded in event logs. To validate the approach we have implemented it as a plug-in of the ProM process mining framework and have conducted experiments using various data sets from a major insurance company.

A data mining driven crash risk profiling method for road asset management

This thesis takes a new data mining approach for analyzing road/crash data by developing models for the whole road network and generating a crash risk profile. Roads with an elevated crash risk due to road surface friction deficit are identified. The regression tree model, predicting road segment crash rate, is applied in a novel deployment coined regression tree extrapolation that produces a skid resistance/crash rate curve. Using extrapolation allows the method to be applied across the network and cope with the high proportion of missing road surface friction values. This risk profiling method can be applied in other domains.

Controlling buoyancy-driven profiling floats for applications in ocean observation

Establishing a persistent presence in the ocean with an autonomous underwater vehicle (AUV) capable of observing temporal variability of large-scale ocean processes requires a unique sensor platform. In this paper, we examine the utility of vehicles that can only control their depth in the water column for such extended deployments. We present a strategy that utilizes ocean model predictions to facilitate a basic level of autonomy and enables general control for these profiling floats. The proposed method is based on experimentally validated techniques for utilizing ocean current models to control autonomous gliders. With the appropriate vertical actuation, and utilizing spatio–temporal variations in water speed and direction, we show that general controllability results can be met. First, we apply an A* planner to a local controllability map generated from predictions of ocean currents. This computes a path between start and goal waypoints that has the highest likelihood of successful execution. A computed depth plan is generated with a model-predictive controller (MPC), and selects the depths for the vehicle so that ambient currents guide it toward the goal. Mission constraints are included to simulate and motivate a practical data collection mission. Results are presented in simulation for a mission off the coast of Los Angeles, CA, USA, that show encouraging results in the ability of a drifting vehicle to reach a desired location.

A data mining driven risk profiling method for road asset management

Road surface skid resistance has been shown to have a strong relationship to road crash risk, however, applying the current method of using investigatory levels to identify crash prone roads is problematic as they may fail in identifying risky roads outside of the norm. The proposed method analyses a complex and formerly impenetrable volume of data from roads and crashes using data mining. This method rapidly identifies roads with elevated crash-rate, potentially due to skid resistance deficit, for investigation. A hypothetical skid resistance/crash risk curve is developed for each road segment, driven by the model deployed in a novel regression tree extrapolation method. The method potentially solves the problem of missing skid resistance values which occurs during network-wide crash analysis, and allows risk assessment of the major proportion of roads without skid resistance values.

Development and validation of a ultra performance LC-ESI/MS method for analysis of metabolic phenotypes of healthy men in day and night urine samples

Ultra-performance LC coupled to quadrupole TOF/MS (UPLC-QTOF/MS) in positive and negative ESI was developed and validated to analyze metabolite profiles for urine from healthy men during the day and at night. Data analysis using principal components analysis (PCA) revealed differences between metabolic phenotypes of urine in healthy men during the day and at night. Positive ions with mass-to-charge ratio (m/z) 310.24 (5.35 min), 286.24 (4.74 min) and 310.24 (5.63 min) were elevated in the urine from healthy men at night compared to that during the day. Negative ions elevated in day urine samples of healthy men included m/z 167.02 (0.66 min), 263.12 (2.55 min) and 191.03 (0.73 min), whilst ions m/z 212.01 (4.77 min) were at a lower concentration in urine of healthy men during the day compared to that at night. The ions m/z 212.01 (4.77 min), 191.03 (0.73 min) and 310.24 (5.35 min) preliminarily correspond to indoxyl sulfate, citric acid and N-acetylneuraminic acid, providing further support for an involvement of phenotypic difference in urine of healthy men in day and night samples, which may be associated with notably different activities of gut microbiota, velocity of tricarboxylic acid cycle and activity of sialic acid biosynthesis in healthy men as regulated by circadian rhythm of the mammalian bioclock.

Integrative genomic profiling reveals conserved genetic mechanisms for tumorigenesis in common entities of non-Hodgkin's lymphoma

Recent developments in genomic technologies have resulted in increased understanding of pathogenic mechanisms and emphasized the importance of central survival pathways. Here, we use a novel bioinformatic based integrative genomic profiling approach to elucidate conserved mechanisms of lymphomagenesis in the three commonest non-Hodgkin's lymphoma (NHL) entities: diffuse large B-cell lymphoma, follicular lymphoma, and B-cell chronic lymphocytic leukemia. By integrating genome-wide DNA copy number analysis and transcriptome profiling of tumor cohorts, we identified genetic lesions present in each entity and highlighted their likely target genes. This revealed a significant enrichment of components of both the apoptosis pathway and the mitogen activated protein kinase pathway, including amplification of the MAP3K12 locus in all three entities, within the set of genes targeted by genetic alterations in these diseases. Furthermore, amplification of 12p13.33 was identified in all three entities and found to target the FOXM1 oncogene. Amplification of FOXM1 was subsequently found to be associated with an increased MYC oncogenic signaling signature, and siRNA-mediated knock-down of FOXM1 resulted in decreased MYC expression and induced G2 arrest. Together, these findings underscore genetic alteration of the MAPK and apoptosis pathways, and genetic amplification of FOXM1 as conserved mechanisms of lymphomagenesis in common NHL entities. Integrative genomic profiling identifies common central survival mechanisms and highlights them as attractive targets for directed therapy.

Gene expression studies in multiple sclerosis

Multiple sclerosis (MS) is a serious neurological disorder affecting young Caucasian individuals, usually with an age of onset at 18 to 40 years old. Females account for approximately 60x of MS cases and the manifestation and course of the disease is highly variable from patient to patient. The disorder is characterised by the development of plaques within the central nervous system (CNS). Many gene expression studies have been undertaken to look at the specific patterns of gene transcript levels in MS. Human tissues and experimental mice were used in these gene-profiling studies and a very valuable and interesting set of data has resulted from these various expression studies. In general, genes showing variable expression include mainly immunological and inflammatory genes, stress and antioxidant genes, as well as metabolic and central nervous system markers. Of particular interest are a number of genes localised to susceptible loci previously shown to be in linkage with MS. However due to the clinical complexity of the disease, the heterogeneity of the tissues used in expression studies, as well as the variable DNA chips/membranes used for the gene profiling, it is difficult to interpret the available information. Although this information is essential for the understanding of the pathogenesis of MS, it is difficult to decipher and define the gene pathways involved in the disorder. Experiments in gene expression profiling in MS have been numerous and lists of candidates are now available for analysis. Researchers have investigated gene expression in peripheral mononuclear white blood cells (PBMCs), in MS animal models Experimental Allergic Encephalomyelitis (EAE) and post mortem MS brain tissues. This review will focus on the results of these studies.

A genetic analysis of serotonergic biosynthetic and metabolic enzymes in migraine using a DNA pooling approach

Migraine is a common debilitating primary headache disorder with significant mental, physical and social health implications. The brain neurotransmitter 5-hydroxytryptamine (5-HT; serotonin) is involved in nociceptive pathways and has been implicated in the pathophysiology of migraine. With few genetic studies investigating biosynthetic and metabolic enzymes governing the rate of 5-HT activity and their relationship to migraine, it was the objective of this study to assess genetic variants within the human tryptophan hydroxylase (TPH), amino acid decarboxylase (AADC) and monoamine oxidase A (MAOA) genes in migraine susceptibility. This objective was undertaken using a high-throughput DNA pooling experimental design, which proved to be a very accurate, sensitive and specific method of estimating allele frequencies for single nucleotide polymorphism, insertion deletion and variable number tandem repeat loci. Application of DNA pooling to a wide array of genetic loci provides greater scope in the assessment of population-based genetic association study designs. Despite the application of this high-throughput genotyping method, negative results from the two-stage DNA pooling design used to screen loci within the TPH, AADC and MAOA genes did not support their role in migraine susceptibility.

Gene expression profiling in human breast cancer – toward personalised therapeutics?

The most integrated approach toward understanding the multiple molecular events and mechanisms by which cancer may develop is the application of gene expression profiling using microarray technologies. As molecular alterations in breast cancer are complex and involve cross-talk between multiple cellular signalling pathways, microarray technology provides a means of capturing and comparing the expression patterns of the entire genome across multiple samples in a high throughput manner. Since the development of microarray technologies, together with the advances in RNA extraction methodologies, gene expression studies have revolutionised the means by which genes suitable as targets for drug development and individualised cancer treatment can be identified. As of the mid-1990s, expression microarrays have been extensively applied to the study of cancer and no cancer type has seen as much genomic attention as breast cancer. The most abundant area of breast cancer genomics has been the clarification and interpretation of gene expression patterns that unite both biological and clinical aspects of tumours. It is hoped that one day molecular profiling will transform diagnosis and therapeutic selection in human breast cancer toward more individualised regimes. Here, we review a number of prominent microarray profiling studies focussed on human breast cancer and examine their strengths, their limitations, clinical implications including prognostic relevance and gene signature significance along with potential improvements for the next generation of microarray studies.

'Sarcobesity' : a metabolic conundrum

Two independent but inter-related conditions that have a growing impact on healthy life expectancy and health care costs in developed nations are an age-related loss of muscle mass (i.e., sarcopenia) and obesity. Sarcopenia is commonly exacerbated in overweight and obese individuals. Progression towards obesity promotes an increase in fat mass and a concomitant decrease in muscle mass, producing an unfavourable ratio of fat to muscle. The coexistence of diminished muscle mass and increased fat mass (so-called 'sarcobesity') is ultimately manifested by impaired mobility and/or development of life-style-related diseases. Accordingly, the critical health issue for a large proportion of adults in developed nations is how to lose fat mass while preserving muscle mass. Lifestyle interventions to prevent or treat sarcobesity include energy-restricted diets and exercise. The optimal energy deficit to reduce body mass is controversial. While energy restriction in isolation is an effective short-term strategy for rapid and substantial weight loss, it results in a reduction of both fat and muscle mass and therefore ultimately predisposes one to an unfavourable body composition. Aerobic exercise promotes beneficial changes in whole-body metabolism and reduces fat mass, while resistance exercise preserves lean (muscle) mass. Current evidence strongly supports the inclusion of resistance and aerobic exercise to complement mild energy-restricted high-protein diets for healthy weight loss as a primary intervention for sarcobesity.

Targeting histone deacetylases for the treatment of immune, endocrine and metabolic disorders

The 'histone code' is a well-established hypothesis describing the idea that specific patterns of post-translational modifications to histones act like a molecular "code" recognised and used by non-histone proteins to regulate specific chromatin functions. One modification which has received significant attention is that of histone acetylation. The enzymes which regulate this modification are described as histone acetyltransferases or HATs, and histone deacetylases or HDACs. Due to their conserved catalytic domain HDACs have been actively targeted as a therapeutic target. The proinflammatory environment is increasingly being recognised as a critical element for both degenerative diseases and cancer. The present review will discuss the current knowledge surrounding the clinical potential & current development of histone deacetylases for the treatment of diseases for which a proinflammatory environment plays important roles, and the molecular mechanisms by which such inhibitors may play important functions in modulating the proinflammatory environment. © 2009 Bentham Science Publishers Ltd.

Secretome and degradome profiling shows that Kallikrein-related peptidases 4, 5, 6, and 7 induce TGFβ-1 signaling in ovarian cancer cells

Kallikrein-related peptidases, in particular KLK4, 5, 6 and 7 (4-7), often have elevated expression levels in ovarian cancer. In OV-MZ-6 ovarian cancer cells, combined expression of KLK4-7 reduces cell adhesion and increases cell invasion and resistance to paclitaxel. The present work investigates how KLK4-7 shape the secreted proteome ("secretome") and proteolytic profile ("degradome") of ovarian cancer cells. The secretome comparison consistently identified >900 proteins in three replicate analyses. Expression of KLK4-7 predominantly affected the abundance of proteins involved in cell-cell communication. Among others, this includes increased levels of transforming growth factor β-1 (TGFβ-1). KLK4-7 co-transfected OV-MZ-6 cells share prominent features of elevated TGFβ-1 signaling, including increased abundance of neural cell adhesion molecule L1 (L1CAM). Augmented levels of TGFβ-1 and L1CAM upon expression of KLK4-7 were corroborated in vivo by an ovarian cancer xenograft model. The degradomic analysis showed that KLK4-7 expression mostly affected cleavage sites C-terminal to arginine, corresponding to the preference of kallikreins 4, 5 and 6. Putative kallikrein substrates include chemokines, such as growth differentiation factor 15 (GDF 15) and macrophage migration inhibitory factor (MIF). Proteolytic maturation of TGFβ-1 was also elevated. KLK4-7 have a pronounced, yet non-degrading impact on the secreted proteome, with a strong association between these proteases and TGFβ-1 signaling in tumor biology. © 2013 Federation of European Biochemical Societies.

The adaptive metabolic response to exercise-induced weight loss influences both energy expenditure and energy intake

BACKGROUND/OBJECTIVEs A decline in resting energy expenditure (REE) beyond that predicted from changes in body composition has been noted following dietary-induced weight loss. However, it is unknown whether a compensatory downregulation in REE also accompanies exercise (EX)-induced weight loss, or whether this adaptive metabolic response influences energy intake (EI). SUBJECTS/METHODS Thirty overweight and obese women (body mass index (BMI)=30.6±3.6 kg/m2) completed 12 weeks of supervised aerobic EX. Body composition, metabolism, EI and metabolic-related hormones were measured at baseline, week 6 and post intervention. The metabolic adaptation (MA), that is, difference between predicted and measured REE was also calculated post intervention (MApost), with REE predicted using a regression equation generated in an independent sample of 66 overweight and obese women (BMI=31.0±3.9 kg/m2). RESULTS Although mean predicted and measured REE did not differ post intervention, 43% of participants experienced a greater-than-expected decline in REE (−102.9±77.5 kcal per day). MApost was associated with the change in leptin (r=0.47; P=0.04), and the change in resting fat (r=0.52; P=0.01) and carbohydrate oxidation (r=−0.44; P=0.02). Furthermore, MApost was also associated with the change in EI following EX (r=−0.44; P=0.01). CONCLUSIONS Marked variability existed in the adaptive metabolic response to EX. Importantly, those who experienced a downregulation in REE also experienced an upregulation in EI, indicating that the adaptive metabolic response to EX influences both physiological and behavioural components of energy balance.

A preliminary study of the effects of Tai Chi and Qigong medical exercise on indicators of metabolic syndrome, glycaemic control, health-related quality of life, and psychological health in adults with elevated blood glucose

Objectives To evaluate the feasibility, acceptability and effects of a Tai Chi and Qigong exercise programme in adults with elevated blood glucose. Design, Setting, and Participants A single group pre–post feasibility trial with 11 participants (3 male and 8 female; aged 42–65 years) with elevated blood glucose. Intervention Participants attended Tai Chi and Qigong exercise training for 1 to 1.5 h, 3 times per week for 12 weeks, and were encouraged to practise the exercises at home. Main Outcome Measures Indicators of metabolic syndrome (body mass index (BMI), waist circumference, blood pressure, fasting blood glucose, triglycerides, HDL-cholesterol); glucose control (HbA1c, fasting insulin and insulin resistance (HOMA)); health-related quality of life; stress and depressive symptoms. Results There was good adherence and high acceptability. There were significant improvements in four of the seven indicators of metabolic syndrome including BMI (mean difference −1.05, p<0.001), waist circumference (−2.80 cm, p<0.05), and systolic (−11.64 mm Hg, p<0.01) and diastolic blood pressure (−9.73 mm Hg, p<0.001), as well as in HbA1c (−0.32%, p<0.01), insulin resistance (−0.53, p<0.05), stress (−2.27, p<0.05), depressive symptoms (−3.60, p<0.05), and the SF-36 mental health summary score (5.13, p<0.05) and subscales for general health (19.00, p<0.01), mental health (10.55, p<0.01) and vitality (23.18, p<0.05). Conclusions The programme was feasible and acceptable and participants showed improvements in metabolic and psychological variables. A larger controlled trial is now needed to confirm these promising preliminary results.