Bacteria microarrays as sensitive tools for exploring pathogen surface epitopes and recognition by host receptors

We describe a protocol for the generation and validation of bacteria microarrays and their application to the study of specific features of the pathogen's surface and interactions with host receptors. Bacteria were directly printed on nitrocellulose-coated glass slides, using either manual or robotic arrayers, and printing quality, immobilization efficiency and stability of the arrays were rigorously controlled by incorporating a fluorescent dye into the bacteria. A panel of wild type and mutant strains of the human pathogen Klebsiella pneumoniae, responsible for nosocomial and community-acquired infections, was selected as model bacteria, and SYTO-13 was used as dye. Fluorescence signals of the printed bacteria were found to exhibit a linear concentration-dependence in the range of 1 x 10(8) to 1 x 10(9) bacteria per ml. Similar results were obtained with Pseudomonas aeruginosa and Acinetobacter baumannii, two other human pathogens. Successful validation of the quality and applicability of the established microarrays was accomplished by testing the capacity of the bacteria array to detect recognition by anti-Klebsiella antibodies and by the complement subcomponent C1q, which binds K. pneumoniae in an antibody-independent manner. The biotin/AlexaFluor-647-streptavidin system was used for monitoring binding, yielding strain-and dose-dependent signals, distinctive for each protein. Furthermore, the potential of the bacteria microarray for investigating specific features, e.g. glycosylation patterns, of the cell surface was confirmed by examining the binding behaviour of a panel of plant lectins with diverse carbohydrate-binding specificities. This and other possible applications of the newly developed arrays, as e.g. screening/evaluation of compounds to identify inhibitors of host-pathogen interactions, make bacteria microarrays a useful and sensitive tool for both basic and applied research in microbiology, biomedicine and biotechnology.

Structural analysis of glycans and development of microarrays from Helcobacter pylori cell surface glycome

Helicobacter pylori is a bacterial pathogen that affects more than half of the world’s population with gastro-intestinal diseases and is associated with gastric cancer. The cell surface of H. pylori is decorated with lipopolysaccharides (LPSs) composed of three distinct regions: a variable polysaccharide moiety (O-chain), a structurally conserved core oligosaccharide, and a lipid A region that anchors the LPS to the cell membrane. The O-chain of H. pylori LPS, exhibits unique oligosaccharide structures, such as Lewis (Le) antigens, similar to those present in the gastric mucosa and are involved in interactions with the host. Glucan, heptoglycan, and riban domains are present in the outer core region of some H. pylori LPSs. Amylose-like glycans and mannans are also constituents of some H. pylori strains, possibly co-expressed with LPSs. The complexity of H. pylori LPSs has hampered the establishment of accurate structure-function relationships in interactions with the host, and the design of carbohydrate-based therapeutics, such as vaccines. Carbohydrate microarrays are recent powerful and sensitive tools for studying carbohydrate antigens and, since their emergence, are providing insights into the function of carbohydrates and their involvement in pathogen-host interactions. The major goals of this thesis were the structural analysis of LPSs from H. pylori strains isolated from gastric biopsies of symptomatic Portuguese patients and the construction of a novel pathogen carbohydrate microarray of these LPSs (H. pylori LPS microarray) for interaction studies with proteins. LPSs were extracted from the cell surface of five H. pylori clinical isolates and one NCTC strain (26695) by phenol/water method, fractionated by size exclusion chromatography and analysed by gas chromatography coupled to mass spectrometry. The oligosaccharides released after mild acid treatment of the LPS were analysed by electrospray mass spectrometry. In addition to the conserved core oligosaccharide moieties, structural analyses revealed the presence of type-2 Lex and Ley antigens and N-acetyllactosamine (LacNAc) sequences, typically found in H. pylori strains. Also, the presence of O-6 linked glucose residues, particularly in LPSs from strains 2191 and NCTC 26695, pointed out to the expression of a 6-glucan. Other structural domains, namely ribans, composed of O-2 linked ribofuranose residues were observed in the LPS of most of H. pylori clinical isolates. For the LPS from strain 14382, large amounts of O-3 linked galactose units, pointing to the occurrence of a galactan, a domain recently identified in the LPS of another H. pylori strain. A particular feature to the LPSs from strains 2191 and CI-117 was the detection of large amounts of O-4 linked N-acetylglucosamine (GlcNAc) residues, suggesting the presence of chitin-like glycans, which to our knowledge have not been described for H. pylori strains. For the construction of the H. pylori LPS microarray, the structurally analysed LPSs, as well as LPS-derived oligosaccharide fractions, prepared as neoglycolipid (NGL) probes were noncovalently immobilized onto nitrocellulosecoated glass slides. These were printed together with NGLs of selected sequence defined oligosaccharides, bacterial LPSs and polysaccharides. The H. pylori LPS microarray was probed for recognition with carbohydratebinding proteins (CBPs) of known specificity. These included Le and blood group-related monoclonal antibodies (mAbs), plant lectins, a carbohydratebinding module (CBM) and the mammalian immune receptors DC-SIGN and Dectin-1. The analysis of these CBPs provided new information that complemented the structural analyses and was valuable in the quality control of the constructed microarray. Microarray analysis revealed the occurrence of type-2 Lex and Ley, but not type-1 Lea or Leb antigens, supporting the results obtained in the structural analysis. Furthermore, the H. pylori LPSs were recognised by DC-SIGN, a mammalian lectin known to interact with this bacterium through fucosylated Le epitopes expressed in its LPSs. The -fucose-specific lectin UEA-I, showed restricted binding to probes containing type-2 blood group H sequence and to the LPSs from strains CI-117 and 14382. The presence of H-type-2, as well Htype- 1 in the LPSs from these strains, was confirmed using specific mAbs. Although H-type-1 determinant has been reported for H. pylori LPSs, this is the first report of the presence of H-type-2 determinant. Microarray analysis also revealed that plant lectins known to bind 4-linked GlcNAc chitin oligosaccharide sequences bound H. pylori LPSs. STL, which exhibited restricted and strong binding to 4GlcNAc tri- and pentasaccharides, differentially recognised the LPS from the strain CI-117. The chitin sequences recognised in the LPS could be internal, as no binding was detected to this LPS with WGA, known to be specific for nonreducing terminal of 4GlcNAc sequence. Analyses of the H. pylori LPSs by SDS-PAGE and Western blot with STL provided further evidence for the presence of these novel domains in the O-chain region of this LPS. H. pylori LPS microarray was also applied to analysis of two human sera. The first was from a case infected with H. pylori (H. pylori+ CI-5) and the second was from a non-infected control.The analysis revealed a higher IgG-reactivity towards H. pylori LPSs in the H. pylori+ serum, than the control serum. A specific IgG response was observed to the LPS isolated from the CI-5 strain, which caused the infection. The present thesis has contributed to extension of current knowledge on chemical structures of LPS from H. pylori clinical isolates. Furthermore, the H. pylori LPS microarray constructed enabled the study of interactions with host proteins and showed promise as a tool in serological studies of H. pyloriinfected individuals. Thus, it is anticipated that the use of these complementary approaches may contribute to a better understanding of the molecular complexity of the LPSs and their role in pathogenesis.

Microarray-Based Study of Carbohydrate-Protein Binding by Gold Nanoparticle Probes

In order to develop a novel high-throughput tool for monitoring carbohydrate-protein interactions, we prepared carbohydrate or glycoprotein microarrays by immobilizing amino modified carbohydrates on aldehyde-derivatized glass slides or glycoprotein on epoxide-derivatized glass slides and carried out lectin binding experiments by using these microarrays, respectively. The interaction events are marked by attachment of gold nanoparticles followed by silver deposition for signal enhancement. The attachment of the gold nanoparticles is achieved by standard avidin-biotin chemistry.

Comparison of multiple and novel measures of dietary glycemic carbohydrate with insulin resistant status in older women

BACKGROUND:Previous epidemiological investigations of associations between dietary glycemic intake and insulin resistance have used average daily measures of glycemic index (GI) and glycemic load (GL). We explored multiple and novel measures of dietary glycemic intake to determine which was most predictive of an association with insulin resistance.METHODS:Usual dietary intakes were assessed by diet history interview in women aged 42-81 years participating in the Longitudinal Assessment of Ageing in Women. Daily measures of dietary glycemic intake (n = 329) were carbohydrate, GI, GL, and GL per megacalorie (GL/Mcal), while meal based measures (n = 200) were breakfast, lunch and dinner GL; and a new measure, GL peak score, to represent meal peaks. Insulin resistant status was defined as a homeostasis model assessment (HOMA) value of >3.99; HOMA as a continuous variable was also investigated.RESULTS:GL, GL/Mcal, carbohydrate (all P < 0.01), GL peak score (P = 0.04) and lunch GL (P = 0.04) were positively and independently associated with insulin resistant status. Daily measures were more predictive than meal-based measures, with minimal difference between GL/Mcal, GL and carbohydrate. No significant associations were observed with HOMA as a continuous variable.CONCLUSION:A dietary pattern with high peaks of GL above the individual's average intake was a significant independent predictor of insulin resistance in this population, however the contribution was less than daily GL and carbohydrate variables. Accounting for energy intake slightly increased the predictive ability of GL, which is potentially important when examining disease risk in more diverse populations with wider variations in energy requirements.

Differences in post-prandial responses to fat and carbohydrate loads in habitual high and low fat consumers (phenotypes).

The present study investigated metabolic responses to fat and carbohydrate ingestion in lean male individuals consuming an habitual diet high or low in fat. Twelve high-fat phenotypes (HF) and twelve low-fat phenotypes (LF) participated in the study. Energy intake and macronutrient intake variables were assessed using a food frequency questionnaire. Resting (RMR) and postprandial metabolic rate and substrate oxidation (respiratory quotient; RQ) were measured by indirect calorimetry. HF had a significantly higher RMR and higher resting heart rate than LF. These variables remained higher in HF following the macronutrient challenge. In all subjects the carbohydrate load increased metabolic rate and heart rate significantly more than the fat load. Fat oxidation (indicated by a low RQ) was significantly higher in HF than in LF following the fat load; the ability to oxidise a high carbohydrate load did not differ between the groups. Lean male subjects consuming a diet high in fat were associated with increased energy expenditure at rest and a relatively higher fat oxidation in response to a high fat load; these observations may be partly responsible for maintaining energy balance on a high-fat (high-energy) diet. In contrast, a low consumer of fat is associated with relatively lower energy expenditure at rest and lower fat oxidation, which has implications for weight gain if high-fat foods or meals are periodically introduced to the diet.

The Eat Smart Study : a randomised controlled trial of a reduced carbohydrate versus a low fat diet for weight loss in obese adolescents

Background Despite the recognition of obesity in young people as a key health issue, there is limited evidence to inform health professionals regarding the most appropriate treatment options. The Eat Smart study aims to contribute to the knowledge base of effective dietary strategies for the clinical management of the obese adolescent and examine the cardiometablic effects of a reduced carbohydrate diet versus a low fat diet. Methods and design Eat Smart is a randomised controlled trial and aims to recruit 100 adolescents over a 2½ year period. Families will be invited to participate following referral by their health professional who has recommended weight management. Participants will be overweight as defined by a body mass index (BMI) greater than the 90th percentile, using CDC 2000 growth charts. An accredited 6-week psychological life skills program ‘FRIENDS for Life’, which is designed to provide behaviour change and coping skills will be undertaken prior to volunteers being randomised to group. The intervention arms include a structured reduced carbohydrate or a structured low fat dietary program based on an individualised energy prescription. The intervention will involve a series of dietetic appointments over 24 weeks. The control group will commence the dietary program of their choice after a 12 week period. Outcome measures will be assessed at baseline, week 12 and week 24. The primary outcome measure will be change in BMI z-score. A range of secondary outcome measures including body composition, lipid fractions, inflammatory markers, social and psychological measures will be measured. Discussion The chronic and difficult nature of treating the obese adolescent is increasingly recognised by clinicians and has highlighted the need for research aimed at providing effective intervention strategies, particularly for use in the tertiary setting. A structured reduced carbohydrate approach may provide a dietary pattern that some families will find more sustainable and effective than the conventional low fat dietary approach currently advocated. This study aims to investigate the acceptability and effectiveness of a structured reduced dietary carbohydrate intervention and will compare the outcomes of this approach with a structured low fat eating plan. Trial Registration: The protocol for this study is registered with the International Clinical Trials Registry (ISRCTN49438757).

Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.

Cytokine responses to carbohydrate ingestion during recovery from exercise-induced muscle injury.

We investigated the effect of carbohydrate ingestion after maximal lengthening contractions of the knee extensors on circulating concentrations of myocellular proteins and cytokines, and cytokine mRNA expression in muscle. Using a cross-over design, 10 healthy males completed 5 sets of 10 lengthening (eccentric) contractions (unilateral leg press) at 120% 1 repetition-maximum. Subjects were randomized to consume a carbohydrate drink (15% weight per volume; 3 g/kg BM) for 3 h after exercise using one leg, or a placebo drink after exercise using the contralateral leg on another day. Blood samples (10 mL) were collected before exercise and after 0, 30, 60, 90, 120, 150, and 180 min of recovery. Muscle biopsies (vastus lateralis) were collected before exercise and after 3 h of recovery. Following carbohydrate ingestion, serum concentrations of glucose (30-90 min and at 150 min) and insulin (30-180 min) increased (P < 0.05) above pre-exercise values. Serum myoglobin concentration increased (similar to 250%; P < 0.05) after both trials. In contrast, serum cytokine concentrations were unchanged throughout recovery in both trials. Muscle mRNA expression for IL-8 (6.4-fold), MCP-1 (4.7-fold), and IL-6 (7.3-fold) increased substantially after carbohydrate ingestion. TNF-alpha mRNA expression did not change after either trial. Carbohydrate ingestion during early recovery from exercise-induced muscle injury may promote proinflammatory reactions within skeletal muscle.